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 .
DETAILED ACTION
This action is in response to papers filed on 10/14/2025.
Claims 1, 15, and 19 have been amended.
Claims 16 and 17 have been cancelled.
Claim 21 has been added.
Claims 1-15 and 17-21 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 1-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claims are directed to a system (Claim 1), a process (method as introduced in Claim 15), and/or a storage medium comprising machine-readable instructions (Claim 19), thus Claims 1-20 fall within one of the four statutory categories. See MPEP 2106.03.
Step 2A, Prong 1:
The claimed invention recites an abstract idea according to MPEP §2106.04. The independent claims which recite the following claim limitations as an abstract idea, are underlined below.
Claims 1, 15 ,and 19 recite (as represented by the language of Claim 1):
at least one processor;
a non-transitory, processor-readable medium storing machine-readable instructions that cause the at least one processor to:
access from different data sources in different formats, indicator data of at least one index including data pertaining to multiple indicators and a plurality of sub- indicators of the multiple indicators,
wherein the at least one index is indicative of an attribute of a supply chain for a commodity, wherein the indicator data includes a hierarchical structure of entities wherein at least one of the entities includes a supplier entity with a plurality of suppliers of the commodity;
convert the indicator data accessed from the different data sources into a uniform format;
store within a master file the indicator data converted into a uniform format;
build at least one index model with the at least one index as a target variable and one or more of the multiple indicators as explanatory variables;
train the at least one index model on [a selected subset of] the indicator data included in the master file, wherein the at least one index model is trained for predicting values for the attribute;
obtain from the at least one trained index model, attribute value predictions for the attribute of the supply chain, wherein the attribute value predictions include the values predicted for at least the supplier entity;
wherein obtaining the predictions further comprises:
obtaining the attribute value predictions for the at least one index for a specified time period;
splitting the attribute value predictions of the specified time period into training data and test data;
training an auto regression integrated moving average (ARIMA) model on the training data;
validating the ARIMA model using the test data and obtaining forecasts for the attribute of the supply chain from the validated ARIMA model;
automatically generate a filtered list of suppliers by omitting one or more of the plurality of suppliers that have the attribute value predictions less than a predetermined threshold attribute value;
transmit to a receiver, the filtered supplier list for procurement of the commodity.
The underlined claim limitations as emphasized above, as drafted, recite a process that, under its broadest reasonable interpretation, covers the performance of commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) in the form of predicting performance of a supply chain based on data collected from the supply chain. Other than reciting a computer implementation, nothing in the claim elements precludes the step from encompassing the performance of commercial or legal interactions which represents the abstract idea of Certain Methods of Organizing Human Activity. But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for analyzing index, attribute, and indicator data of a supply chain to provide predictions based on the analysis. For example, a user can merely use collected supply chain data to predict which suppliers would be the best choice and make a ranked list of those suppliers from which to choose.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite additional elements such as:
at least one processor;
a non-transitory, processor-readable medium storing machine-readable instructions executable by the at least one processor;
access [data] from different data sources in different formats, convert the data into a uniform format, and store the converted/uniform data within a master file;
build at least one index model with the at least one index as a target variable and one or more of the multiple indicators as explanatory variables;
train the at least one index model on [a selected subset of] the indicator data included in the master file, wherein the at least one index model is trained for predicting values for the attribute;
obtain [value predictions] from the at least one trained index model;
training an auto regression integrated moving average (ARIMA) model using the training data subset, validating the ARIMA model using the test data subset, and obtaining forecasts from the validated ARIMA model;
automatically generate a filtered list; and
transmit the filtered supplier list to a receiver.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
Accordingly, since the specification describes the additional elements in general terms, without describing the particulars, the additional elements may be broadly but reasonably construed as generic computing components being used to perform the judicial exception (see specification at Fig. 11; [0053], generic computer recitations1; [0033], “master file 252 e.g., a flat file such as a spreadsheet or a database file, etc.” and generic receivers (see also [0034]; [0039]; [0040]; etc.; Fig. 1, data sources displayed as generic databases (150), this is the only reference to what may be represent data sources). Additionally, the use of the ARIMA model is described at a high level of generality and merely specifying this specific type of model (including training and verifying on different subsets of data) does not clearly integrate the abstract idea into a practical application or provide an inventive concept. These claimed additional elements merely recite the words “apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
1 Additionally, the index model is described throughout the specification in general terms and is only loosely connected to the computing system. No specific type of modeling is recited (such as machine learning, artificial intelligence, neural networks, and the like), including the training, and is therefore interpreted as a generic model performed using general-purpose technology. The only reference to any modeling technology is in the background section (machine learning), however, this is described at a very high level of generality and not clearly related to the models used in the claimed invention.
Step 2B:
The claims do not include additional elements, individually or in combination, 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 amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept at Step 2B. Thus, the claim is not patent eligible.
Dependent Claims:
Claims 2-14, 18, 20, and 21 recite further elements related to the supply chain data analysis and prediction determinations steps of the parent claims. These activities fail to differentiate the claims from the related activities in the parent claims and fail to provide any material to render the claimed invention to be significantly more than the identified abstract ideas, as outlined below.
Claim 2 recites “wherein building the at least one index model further causes the at least one processor to: select the one or more indicators based on correlations of the multiple indicators with the at least one index”, which further specifies additional types of data to be used for training the model, but does not lead toward eligibility. The additional types of selected indicator data are part of the abstract idea and merely adding that data to the training data does not integrate the abstract idea into a practical application or provide an inventive concept.
Claim 3 recites “wherein building the at least one index model further causes the at least one processor to: select, for each corresponding indicator of the one or more indicators, a subset of the plurality of sub-indicators based on correlations of the subset of plurality of sub-indicators with the corresponding indicator of the one or more indicators, wherein the plurality of sub-indicators contribute to each of the multiple indicators”, which further specifies additional types of data to be used for training the model, but does not lead toward eligibility. The additional types of selected indicator data are part of the abstract idea and merely adding that data to the training data does not integrate the abstract idea into a practical application or provide an inventive concept.
Claim 4 recites “wherein the correlation of the each of the one or more indicators with the at least one index is obtained using Pearson's Correlation coefficient”; Claim 5 recites “wherein the correlations of the subsets of the plurality of sub-indicators for the one or more indicators are obtained using Pearson's Correlation coefficient”; Claim 7 recites “wherein the at least one index model includes a multiple linear regression model”; Claim 13 recites “wherein the non-transitory, processor- readable medium stores machine-readable instructions that further cause the at least one processor to: quantify effects of the important features on the at least one index using quantile regression”. These claims recite specific types of data analysis, which specifies further steps related to the prediction determination of the parent claims, but does not make the claims any less abstract.
Claim 6 recites “wherein building the at least one index model further causes the at least one processor to: build a respective index model for the at least one index for each country included in the master file”, however, specifying that the model is repeated for each country not integrate the abstract idea into a practical application or provide an inventive concept, as each model is built in the same manner for each country.
Claim 8 recites “provide reasons for deleting the one or more suppliers with the predicted attribute values less than the predetermined threshold attribute value”, which specifies further steps related to the prediction determination of the parent claims and merely recites additional output data, but does not make the claims any less abstract.
Claim 9 recites “wherein providing reasons for the deletion further causes the at least one processor to: compute feature importance scores for the multiple indicators; identify as important features, one or more of the multiple indicators based on a descending order of the feature importance scores; and provide the important features as the reasons for the deletion”; Claim 10 recites “wherein computing the feature importance scores further causes the at least one processor to: compute the feature importance scores using a random forest model with the attribute as a target variable and the multiple indicators and the plurality of sub- indicators as features; and identify important features from a descending order of the feature importance scores”. These claims specify further steps related to the prediction determination of the parent claims. The additional types of analysis and data output is part of the abstract idea and merely using a processor to compute it does not integrate the abstract idea into a practical application or provide an inventive concept.
Claim 11 recites “identify top interacting features from the important features, which together affect the attribute values of the at least one index”; Claim 12 recites “calculate partial dependencies of the top interacting features on the at least one index”; Claim 14 recites “update a supplier database based at least on the filtered supplier list”; Claim 18 recites “wherein the hierarchical structure of entities includes commodity entity at a highest level, country entity at a mid-level, and the supplier entity at a lowest level of the hierarchical structure”; Claim 20 recites “receive a user request related to the attribute values predictions of the at least one index; and responsive to the user request, display a heat map showing a distribution of the attribute value predictions for the commodity across the globe”. These claims specify further steps related to the prediction determination of the parent claims, but does not make the claims any less abstract.
Claim 21 recites “splitting the attribute value predictions based on data recency, wherein the training data includes the attribute value predictions of an earlier portion of the specified time period and the testing data includes the attribute value predictions of a later portion of the specified time period”, which specifies further steps related to the data training steps of the parent claims and merely recites additional training steps, but does not make the claims any less abstract.
The claims do not provide any new additional limitations or meaningful limits beyond abstract idea that are not addressed above in the independent claims therefore, they do not integrate the abstract idea into a practical application nor do they provide significantly more to the abstract idea. Thus, after considering all claim elements, both individually and as a whole, it has been determined that the claims do not integrate the judicial exception into a practical application or provide an inventive concept. Therefore, Claims 2-14, 18, 20, and 21 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 8, 14, 15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Purtell (WO 2009/011915 A2) in view of McDonald et al. (Patent No. US 11,645,617 B1) in further view of Kirchenbauer et al. (Pub. No. US 2014/0316940 A1) in further view of McNair (Patent No. US 9,152,918 B1).
In regards to Claims 1, 15, and 19, Purtell discloses:
An index modeling system/method, comprising:
at least one processor; a non-transitory, processor-readable medium storing machine-readable instructions that cause the at least one processor to: (page 8, lines 2628)
access from different data sources, indicator data of at least one index including data pertaining to multiple indicators and a plurality of sub-indicators of the multiple indicators, wherein the at least one index is indicative of an attribute of a supply chain for a commodity, wherein the indicator data includes a hierarchical structure of entities wherein at least one of the entities includes a supplier entity with a plurality of suppliers of the commodity; (Claim 1; data is obtained from multiple business partners (sources) including multiple indicators and sub-indicators that are associated with at least one index (in this case two indexes, supplier and country); page 4, lines 23-24, centralized location of data; Claim 2; Claim 3, Example of indicator is “measure of the severity and level of anti-Western terrorism exposures associated with the country”, and examples of sub-indicators are “number of terrorist groups operating within a country, the number of terrorist groups with anti-Western ideology, and the historical success of attempts by the groups to disrupt commerce” (see also page 7, lines 13-20 for examples of indicators and sub-indicators for “supplier risk index”), country risk index and supplier risk index are indicative of an attribute of a supply chain for a commodity (see also Claim 5; Claim 6); page 1, line 26; page 3, lines 2-10 and 19-21; etc., invention is used for evaluation of supply chains and members of the supply chain; page 7, line 26, the data includes data indicating “supply chain structure” (hierarchy, see also page 1, lines 11-26))
obtain attribute value predictions for the attribute of the supply chain, wherein the attribute value predictions include the values predicted for at least the supplier entity; (page 6, line 20-page 7, line 32; etc., the module uses the data to determine risk assessments/scores for attributes associated with suppliers (supplier risk score, country risk score, etc.),examples of attributes with value predictions used for risk indexes include “Foreign manufacturer…US Customs Broker…Highway…Rail Carrier…Sea Carrier…US Importer…”, other examples of various attributes used to determine the risk indexes are described throughout the reference in varying levels of detail (see also, page 7, lines 13-20; Claim 2; Claim 3; Claim 5; Claim 6; etc.))
Purtell discloses data retrieved from multiple sources and a module for performing the determinations and predictions (as described above). Purtell does not explicitly disclose the following steps for training a model to perform predictions, however, McDonald teaches:
access from different data sources in different formats; and convert the indicator data accessed from the different data sources into a uniform format; (column 6, line 55-column 7, line 12, “…data processing module 202 receives data from…one or more supply chain entities…Data processing module 202 prepares received data for use in training and prediction by checking received data for errors and transforming the received data…transforms the received data to normalize, aggregate, and/or rescale the data…”, normalizing represents transformation to a uniform format)
store within a master file the indicator data converted into a uniform format; (column 6, line 55-column 7, line 12, preparation of the data for use in training the model includes aggregating the received data, the aggregated data used for training would represent a master file)
build at least one index model with the at least one index as a target variable and one or more of the multiple indicators as explanatory variables; (column 6, line 55-column 7, line 12, “…data processing module 202 receives data from…one or more supply chain entities…Data processing module 202 prepares received data for use in training and prediction by checking received data for errors and transforming the received data…transforms the received data to normalize, aggregate, and/or rescale the data…”, normalizing represents transformation to a uniform format; column 12, lines 5-36, the model makes predictions based on a set of analyzed “casual factors”, the target index would represent predictions such as “predicted retail volume” based on variables/indicators that can be used to explain the prediction/index such as “…transaction data 280, supply chain data 282, product data 284, inventory data 286, store data 290, customer data 292, demand forecasts 294, and other data…”, these variables include supply chain information (see also column 12, lines 48-55), along with other business related data that affects a supply chain (see also column 12, line 37-column 14, line 41))
train the at least one index model on at least a selected subset of the indicator data included in the master file, wherein the at least one index model is trained for predicting values for the attribute; and obtain from the at least one trained index model, value predictions; (column 6, line 55-column 7, line 12, “…prepares the received data for use in training the machine learning model and/or one or more trained models…for use in training and prediction…”); column 7, lines 13-40, the trained models are used to forecast demand (predictions); column 12, lines 5-36, the model makes predictions based on a set of analyzed “casual factors”, the target index would represent predictions such as “predicted retail volume” based on variables/indicators that can be used to explain the prediction/index such as “…transaction data 280, supply chain data 282, product data 284, inventory data 286, store data 290, customer data 292, demand forecasts 294, and other data…” (also representing a subset of indicators) [underlined claim material appears in Claim 15 only])
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell so as to have included the above steps for training a model to provide predictions/determinations, as taught by McDonald in order to provide the entity with data that is useful for determining supply partners (McDonald, Abstract; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.).
Additionally, McDonald teaches:
wherein obtaining the predictions further comprises:
obtaining the attribute value predictions for the at least one index for a specified time period; (column 7, lines 13-21, training data includes historical time series, see also column 9, lines 25-34; column 11, lines 24-39)
splitting the attribute value predictions of the specified time period into training data and test data; (column 15, lines 4-23, shows historical data split into multiple sets, some used for training, some used for testing (“…historical data may consist of several independent data sets…used for independent training, prediction, and test runs…”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell so as to have included obtaining the predicted attribute values for the at least one index for a specified time period and splitting the predicted attribute values of the specified time period into training data and test data, as taught by McDonald in order to provide the training data with relevant historical data for determining correlations for forecasting, such as historic patterns (McDonald, column 7, lines 13-21; column 9, lines 25-34; column 11, lines 24-39; column 15, lines 4-23)
Purtell/McDonald discloses the scoring and ranking of supply chain partners (as described above, see also Purtell, page 6, lines 15-23, discusses ranking of partners based on scores and eligibility). Purtell/McDonald does not explicitly disclose generating a filtered list, but Kirchenbauer teaches:
automatically generate a filtered list of suppliers by omitting one or more of the plurality of suppliers that have the attribute value predictions less than a predetermined threshold attribute value; ([0114]; [0121]- [0123]; Claim 12, supplier are removed from a list if their rating is below a threshold (as described throughout the reference, rated attributes are related to risks associated with the supplier (as comparable to the ratings of Purtell), see [0007] as one example))
transmit to a receiver, the filtered supplier list for procurement of the commodity ([0011], the results are displayed in a filtered manner that allows the user to suppliers with the most potential).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/McDonald so as to have included automatically generate a filtered list of suppliers by omitting one or more of the plurality of suppliers that have the attribute value predictions less than a predetermined threshold attribute value and transmit to a receiver, the filtered supplier list for procurement of the commodity, as taught by Kirchenbauer in order to provide the entity with data that is useful for determining supply partners (Kirchenbauer , [0011]; [0035]; [0052]; [0063]; [0114]; [0121]; Claim 12; etc.; McDonald, Abstract; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.).
Purtell/Mcdonald/Kirchenbauer discloses the above system/method for training an index model for forecasting attributes of a supply chain. Purtell/Mcdonald/Kirchenbauer does not explicitly disclose, but McNair teaches:
training an Auto Regressive Integrated Moving Average (ARIMA) model on the training data; validating the ARIMA model using the test data; and obtaining forecasts from the validated ARIMA model (column 4, line 37-column 4, line 5, shows an ARIMA model that is used to produce forecasts and is validated)
It would have been obvious to one of ordinary still in the art to include in the forecasting and rating system of Purtell/Mcdonald/Kirchenbauer, the ability to apply the ARIMA model as taught by McNair 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. One of ordinary skill in the art would understand how to apply the forecasting (ARIMA) model to the system of Purtell/Mcdonald/Kirchenbauer regardless of the type of data being forecasted and the prior art references demonstrate the level of skill required to do so.
In regards to Claim 2, Purtell/McDonald discloses the above system/method for building an index model using multiple indicators. Additionally, Purtell discloses:
select the one or more indicators based on correlations of the multiple indicators with the at least one index (Claim 2; Claim 3, Example of indicator is “measure of the severity and level of anti-Western terrorism exposures associated with the country”, and examples of sub-indicators are “number of terrorist groups operating within a country, the number of terrorist groups with anti-Western ideology, and the historical success of attempts by the groups to disrupt commerce” (see also page 7, lines 13-20 for examples of indicators and sub-indicators for “supplier risk index”), country risk index and supplier risk index are “indicative of an attribute of a supply chain for a commodity” (see also Claim 5; Claim 6), indicators are correlated to the index for which they are used to determine scores/rankings)
In regards to Claim 3, Purtell/McDonald discloses the above system/method for building an index model using multiple indicators. Additionally, Purtell discloses:
select, for each corresponding indicator of the one or more indicators, a subset of the plurality of sub-indicators based on correlations of the subset of plurality of sub-indicators with the corresponding indicator of the one or more indicators, wherein the plurality of sub-indicators contribute to each of the multiple indicators (Claim 2; Claim 3, Example of indicator is “measure of the severity and level of anti-Western terrorism exposures associated with the country”, and examples of sub-indicators corresponding to the indicators are “number of terrorist groups operating within a country, the number of terrorist groups with anti-Western ideology, and the historical success of attempts by the groups to disrupt commerce” (see also page 7, lines 13-20 for examples of indicators and sub-indicators for “supplier risk index”), each index has different indicators and sub-indicators associated with it)
In regards to Claim 8, Purtell/McDonald/Kirchenbauer discloses the above system/method for training an index model for forecasting attributes of a supply chain that includes using a threshold to determine what suppliers should be removed from a list. Purtell additionally discloses providing reports and other information to users regarding suppliers/partners (page 8, lines 2-16, shows the requestor being provided information that outlines supplier/partner risk scores, assessments, etc. so they can make informed decisions, “…a variety of reporting tools to help the requestor efficiently track and assess business partner information… module can prepare a Business Partner Risk Report giving the "big picture" risk level for each business partner, while the Business Partner Full Report allows deeper investigation into the information provided…”, see also Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.;). Purtell/McDonald does not explicitly disclose providing reasons for deleting the one or more suppliers with the predicted attribute values less than the predetermined threshold attribute value.
However, Kirchenbauer additionally teaches providing reasons for deleting the one or more suppliers with the predicted attribute values less than the predetermined threshold attribute value ([0123], rating information can additionally include reasons for the supplier ratings and exclusions)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/McDonald so as to have included provide reasons for deleting the one or more suppliers with the predicted attribute values less than the predetermined threshold attribute value, as taught by Kirchenbauer in order to provide the entity with data that is useful for determining what cause risk among suppliers/partners (Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 2-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.).
In regards to Claim 14, Purtell/McDonald discloses the scoring and ranking of supply chain partners (as described above, see also Purtell, page 6, lines 15-23, discusses ranking of partners based on scores and eligibility). Additionally, Kirchenbauer teaches the determination of a supplier list (as described above). Purtell/McDonald does not explicitly disclose updating a database based on a generated list, however Kirchenbauer teaches:
update a supplier database based at least on the filtered supplier list; ([0120], the ratings for suppliers can be accessed from stored data (supplier database), indicating that the database was updated with the supplier rating after the ratings are determined))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/McDonald so as to have included update a supplier database based at least on the filtered supplier list, as taught by Kirchenbauer in order to provide the entity with data that is useful for determining supply partners (Kirchenbauer , [0011]; [0035]; [0052]; [0063]; [0114]; [0121]; Claim 12; etc.; McDonald, Abstract; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.).
In regards to Claim 18, Purtell/McDonald/Kirchenbauer discloses the above system/method for training an index model for forecasting attributes of a supply chain. Additionally, Purtell discloses data related to suppliers, commodities, and countries.
Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose wherein the hierarchical structure of entities includes commodity entity at a highest level, country entity at a mid-level, and the supplier entity at a lowest level of the hierarchical structure.
However, it would have been obvious to one of ordinary skill in the art to have included a hierarchical structure (or any other particular structure) that includes these specific levels because the specific order of entities does not significantly affect the performance of the claimed invention. The claims provide no material indicating how the particular order of entities in the hierarchy would affect the functioning or processing of the claimed invention.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included wherein the hierarchical structure of entities includes commodity entity at a highest level, country entity at a mid-level, and the supplier entity at a lowest level of the hierarchical structure (or any other entity order), because, as claimed, the process for performing the data prediction (including data collection, building a model, determining a list of suppliers, etc.) would perform in the same manner and produce the same results regardless of the order of entities in the hierarchy.
In regards to Claim 20, Purtell additionally discloses:
receive a user request related to the attribute values predictions of the at least one index; and responsive to the user request, display a heat map showing a distribution of the attribute value predictions for the commodity across the globe (page 8, lines 12-24, requestors can obtain reports that include plotting and mapping of what countries partners are located in)
Claim(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Purtell in view of McDonald in further view of Kirchenbauer in further view of McNair in further view of Zhang et al. (CN 118898411 A).
In regards to Claim 4, Purtell/Mcdonald/Kirchenbauer/McNair
discloses the above system/method for training an index model for forecasting attributes of a supply chain. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Zhang teaches:
wherein the correlation of the each of the one or more indicators with the at least one index is obtained using Pearson's Correlation coefficient (page 22, lines 27-35, “…The correlation coefficient matrix in the disclosure is a mathematical tool for quantifying the linear relationship strength and direction between a plurality of indicators …”, describes a Pearson’s correlation coefficient calculations (linear strength between two variables) applied to index indicators)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included wherein the correlation of the each of the one or more indicators with the at least one index is obtained using Pearson's Correlation coefficient, as taught by Zhang in order to ensure the accuracy and validity of the evaluation model and reduce error caused by the sample selection (Zhang, page 22, lines 21-26). One of ordinary skill in the art would understand how to apply the correlation coefficient calculations to the indicator data of Purtell/Mcdonald/Kirchenbauer/McNair regardless of the type of data being forecasted and the prior art references demonstrate the level of skill required to do so (additionally it is noted that the reference and instant application are classified in the same area, G06Q 10/067).
In regards to Claim 5, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Zhang teaches:
wherein the correlations of the subsets of the plurality of sub-indicators for the one or more indicators are obtained using Pearson's Correlation coefficient (page 22, lines 27-35, “…The correlation coefficient matrix in the disclosure is a mathematical tool for quantifying the linear relationship strength and direction between a plurality of indicators …”, describes a Pearson’s correlation coefficient calculations (linear strength between two variables) applied to index indicators)
Examiner’s Note: The use of the correlation coefficient calculation would perform in the same manner regardless of the specific attributes being compared. For example, applying the correlation coefficient calculations to indicators and indexes would be performed in the same manner as applying it to indicators and sub-indicators and one of ordinary skill in the art would understand how to apply it to both sets of data.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included wherein the correlations of the subsets of the plurality of sub-indicators for the one or more indicators are obtained using Pearson's Correlation coefficient, as taught by Zhang in order to ensure the accuracy and validity of the evaluation model and reduce error caused by the sample selection (Zhang, page 22, lines 21-26). One of ordinary skill in the art would understand how to apply the correlation coefficient calculations to the indicator data of Purtell/Mcdonald/Kirchenbauer/McNair regardless of the type of data being forecasted and the prior art references demonstrate the level of skill required to do so (additionally it is noted that the reference and instant application are classified in the same area, G06Q 10/067).
In regards to Claim 6, Purtell/Mcdonald/Kirchenbauer/McNair/Zhang discloses the above system/method for building an index model including use of a master file. Additionally, Purtell discloses:
build a respective index model for the at least one index for each country included in the [collected data] (page 7, lines 29-32; page 8, lines 12-24)
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Purtell in view of McDonald in further view of Kirchenbauer in further view of McNair in further view of Singh et al. (Pub. No. US 2002/0169657 A1).
In regards to Claim 7, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Singh teaches:
wherein the at least one model includes a multiple linear regression model ([0020], modeling includes the use of multiple linear regression; [0016]; [0024]; [0026]; etc., forecasting is related to supply chain activities)
It would have been obvious to one of ordinary still in the art to include in the forecasting and rating system of Purtell/Mcdonald/Kirchenbauer/McNair, the ability to apply the multiple linear regression model as taught by Singh 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 (Singh, [0020], “…framework that allows multiple alternative forecasting algorithms, including well known statistical algorithms such as Fourier and multiple linear regression ("MLR") algorithms and proprietary algorithms useful for modeling…”)
Claim(s) 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Purtell in view of McDonald in further view of Kirchenbauer in further view of McNair in further view of Sharma et al. (WO 2020034044 A1).
In regards to Claim 9, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain, including providing reasons for deletion of a supplier from a list. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Sharma teaches:
compute feature importance scores for the multiple indicators; identify as important features, one or more of the multiple indicators based on a descending order of the feature importance scores; ([00113], random forest classification is used on features in order to determine importance of features, feature importance can be used to interpret results and gain insights from results (representing “reasons”), the important features are provided for this insight and interpretation; Fig. 11A-Fig. 15A; [0030]-[0037], shows feature importance data for multiple features displayed in descending order; Abstract and throughout reference, drawn to predictions for supply chain activities)
provide the important features as the reasons for the [classification] ([00132]-[00148], demonstrate multiple process for determining reasons for occurrences in the supply chain based on determined feature importance, this demonstrates the data that provides insights/interpretations (reasons))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included compute feature importance scores for the multiple indicators; identify as important features, one or more of the multiple indicators based on a descending order of the feature importance scores; and provide the important features as the reasons for the [classification], as taught by Sharma in order to provide the entity with data that is useful for understanding the predictions made by the system (Sharma, [00113]; ([00132]-[00148]; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.)
In regards to Claim 10, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain, including providing reasons for deletion of a supplier from a list. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Sharma teaches:
compute the feature importance scores using a random forest model with the attribute as a target variable and the multiple indicators and the plurality of sub-indicators as features; ([0078], predictions are made for success or failure of a supply chain in delivery based on multiple steps in the chain; [0085]-[0087], random forest modeling is used, multiple factors related to the success or failure of a supply chain delivery (indicators, sub-indicators) are used to determine a success/failure predictions (attribute being modeled))
identify important features from a descending order of the feature importance
scores. ([00113], random forest classification is used on features in order to determine importance of features, feature importance can be used to interpret results and gain insights from results (representing “reasons”) , the important features are provided for this insight and interpretation; Fig. 11A-Fig. 15A; [0030]-[0037], shows feature importance data for multiple features displayed in descending order)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included compute the feature importance scores using a random forest model with the attribute as a target variable and the multiple indicators and the plurality of sub-indicators as features and identify important features from a descending order of the feature importance scores , as taught by Sharma in order to provide the entity with data that is useful for understanding the predictions made by the system (Sharma, [00113]; ([00132]-[00148]; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.)
In regards to Claim 11, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain, including providing reasons for deletion of a supplier from a list. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Sharma teaches:
identify top interacting features from the important features, which together affect the attribute values of the at least one index; ([00142]; Fig. 15A, demonstrate most important features in descending order, since multiple important features are used in calculation, they would be understood to “work together” to affect the attribute values)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included identify top interacting features from the important features, which together affect the attribute values of the at least one index, as taught by Sharma in order to provide the entity with data that is useful for understanding the predictions made by the system (Sharma, [00113]; ([00132]-[00148]; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.)
In regards to Claim 12, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain, including providing reasons for deletion of a supplier from a list. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Sharma teaches:
calculate partial dependencies of the top interacting features on the at least
one index; ([0096], featured may be correlated/interdependent; [0148], demonstrates an example of a relationship between features used for the prediction analysis; [00113], shows that correlations (dependencies) can have an effect on the importance of the related features (independencies and correlations are not presented as completely dependent/correlated, demonstrating partial dependence/correlations, as they may not necessarily be 100% corelated/dependent))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have included modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have calculate partial dependencies of the top interacting features on the at least one index, as taught by Sharma in order to provide the entity with data that is useful for understanding the predictions made by the system (Sharma, [00113]; ([00132]-[00148]; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.)
In regards to Claim 13, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training an index model for forecasting attributes of a supply chain, including providing reasons for deletion of a supplier from a list. Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose, but Sharma teaches:
quantify effects of the important features on the at least one index using quantile regression; ([0080]; Fig. 8B, shows the predictions being made for multiple segments (success/failure attribute), representing a quantile regression analysis (predicts the relationship between the indicators and variables for multiple segments); [0078], predictions are made for success or failure of a supply chain in delivery based on multiple steps in the chain; [0085]-[0087], random forest modeling is used, multiple factors related to the success or failure of a supply chain delivery (indicators, sub-indicators) are used to determine a success/failure predictions (attribute being modeled); [00113], random forest classification is used on features in order to determine importance of features, feature importance can be used to interpret results and gain insights from results (representing “reasons”) , the important features are provided for this insight and interpretation)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included quantify effects of the important features on the at least one index using quantile regression, as taught by Sharma in order to provide the entity with data that is useful for understanding the predictions made by the system (Sharma, [00113]; ([00132]-[00148]; Purtell, page 3, lines 2-10; page 6, lines 16-19; page 8, lines 18-24; etc.; McDonald, Abstract; Kirchenbauer , [0011]; [0114]; [0121]; Claim 12; etc.).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Purtell in view of McDonald in further view of Kirchenbauer in further view of McNair in further view of アチン， ジェレミー et al. (JP 7107926 B2, hereafter referred to as Achin).
In regards to Claim 21, Purtell/Mcdonald/Kirchenbauer/McNair discloses the above system/method for training model, including separating attribute value predictions data into training and testing sets. Additionally, McDonald teaches the historical training data can include time series data (column 7, lines 13-40). Purtell/Mcdonald/Kirchenbauer/McNair does not explicitly disclose the following steps for splitting the data, however, Achin teaches:
wherein splitting the attribute value predictions of the specified time period into training data and test data further comprises:
splitting the [data] based on data recency, wherein the training data includes the attribute value predictions of an earlier portion of the specified time period and the testing data includes the attribute value predictions of a later portion of the specified time period (page 3, ¶ 2, “A predicted variable may be referred to as a "target," "response," or "dependent variable." The remaining variables that can be used to make predictions may be referred to as "features," "predictors," or “independent variables." Observations are generally divided into at least one "training “data set and at least one "testing" data set. A data analyst then selects a statistical learning procedure and runs that procedure on the training data set to generate a predictive model. The analyst then tests the model generated on the test data set to determine the predictive goodness of the model's target value against the actual observation of the target.”; page 4, ¶ 5, “…the time period covered by the training data, the recency of the time period covered by the training data…”; page 25, “(Item 36)”, “(Item 37)”, “…wherein the second test time range does not overlap any portion of the first training time range and does not overlap any portion of the second training time range.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Purtell/Mcdonald/Kirchenbauer/McNair so as to have included splitting the [data] based on data recency, wherein the training data includes the attribute value predictions of an earlier portion of the specified time period and the testing data includes the attribute value predictions of a later portion of the specified time period, as taught by Achin in order to provide additional steps for determining the suitability of a predictive model (Achin, page 26, (Item 50); McNair, column 4, line 65-column 5, line 2; McDonald, column 7, lines 13-21; column 9, lines 25-34; column 11, lines 24-39; column 15, lines 4-23).
Additional Prior art Identified but not Relied Upon
Edwards et al. (CA 2239602 A1). Discloses Auto Regressive Integrated Moving Average (ARIMA), validating ARIMAs and time series (see at least page 1, (57); page 18, line 4; page 16, line 18-page 17 line 3). The cited page numbers refer to the page number on the bottom, right corner of the page (Page x of 42).
Gilpin et al. (Pub. No. US 2007/0156508 A1). Discloses supply forecasting system/method, including indicators weighted by correlations and significance (importance) contributions (see at least Claim 8; Claim 12).
Iyer et al. (Pub. No. US 2021/0256318 A1). Discloses Auto Regressive Integrated Moving Average (ARIMA), including training, testing, and validating ARIMAs (see at least [0021]; [0044]).
Postel et al. (Patent No. US 8,244,736 B2). Discloses determination of an optimized supplier list and the removal of suppliers based on a threshold (see at least Claim 1; Claim 10).
ÜSTÜNDAĞ et al. (WO 2023113729 A1). Discloses prediction system/method, including training and test data sets, validated ARIMA (Auto Regressive Integrated Moving Average), and training models to perform the predictions (see at least page 1, paragraph 3; page 20, paragraph 2).
Zimmerman et al. (Pub. No. US 2008/0103874 A1). Discloses supply chain forecasting system/method, including multiple indicators, historical time data, and multiple linear regression algorithm (see at least [0004]).
Response to Arguments
Applicant’s arguments filed 10/14/2025 have been fully considered but they are not persuasive.
I. Rejection of Claims under 35 U.S.C. §101:
Applicant asserts that the claimed invention provides an improved system, integrates into a practical application, and solves a technical problem associated with existing index modeling systems. However, Applicant only provides assertions and allegations regarding current systems and does not provide any evidence or background to support these assertions/allegations. Applicant provides no evidence demonstrating these alleged deficiencies in the current state of the art, and/or how the Applicants’ asserted solutions address these deficiencies in a meaningful manner. For example, Applicant does not explain why current systems could not or would not be able to perform these activities, how/why do the features of the claimed invention reduce complexity over current or prior systems, etc.
See MPEP 2106.05(a), Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field (“If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”).
In regards to Applicant’s additional remarks on page 14, the cited portions of the specification ([0042]-[0049]) merely describe processes used in the claims, but fail to provide any explanations regarding how the alleged improvement or practical application is achieved and/or how it addresses the alleged deficiencies/problems in the art in a meaningful manner.
Additionally, Applicant fails to provide any evidence or background (including cited specification [0023] and [0024]) to clearly demonstrate that the claimed invention is not conventional. It is also noted that the above rejections do not currently rely on conventionality as part of the grounds for rejection.
II. Rejection of Claims under 35 U.S.C. §103:
Applicant’s remarks are drawn to the newly provided claim material and are therefore moot in view of the newly provided prior art rejections, citations, and/or explanations, provided above.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.D.S/Examiner, Art Unit 3629 February 14, 2026
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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