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 .
This action is in response to the application filed 5/16/2023. Claims 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement filed 1/31/2024 fails 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. It has been placed in the application file, but the information referred to therein has not been considered.
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.
Regarding Claim 1:
Step 1:
The claim recites an apparatus, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation transform the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least: for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value which is a mathematical concept.
Step 2A prong 2:
The additional element of at least one processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of at least one non-transitory memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of receive raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute does not integrate the abstract idea into practical application because receiving raw historical data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
The additional element of train the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
The additional element of generate output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of at least one processor is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does amount to significantly more MPEP 2106.05(f).
The additional element of at least one non-transitory memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does amount to significantly more MPEP 2106.05(f).
The additional element of receive raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
The additional element of train the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
The additional element of generate output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
Regarding Claim 2:
Claim 2 which incorporates the rejection of Claim 1, recites a further abstract idea cause performance of at least one enterprise management operations based at least in part on the generated output data which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 3:
Claim 3 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element cause rendering of a results interface that presents the generated output data which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 4:
Claim 4 which incorporates the rejection of Claim 3, recites a further abstract idea a determined mismatch between a current parameter value associated with the particular object corresponding to the selected attribute and a corresponding predicted value associated with the particular object in the output data corresponding to the selected attribute which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element the results interface presents the output data with respect to a plurality of objects which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 5:
Claim 5 which incorporates the rejection of Claim 3, recites a further abstract idea a level of confidence associate with an instance of the parameter expanded data corresponding to the particular object which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element the results interface presents the output data with respect to a plurality of objects which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 6:
Claim 6 which incorporates the rejection of Claim 1, recites a further abstract idea generating a pseudo-curve model that represents the parameter expanded data which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training the at least one machine learning model based at least in part on the pseudo-curve model which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 7:
Claim 7 which incorporates the rejection of Claim 6, recites a further abstract idea generating… based at least in part on the pseudo-curve model, an elasticity model expressing elasticity output values with respect to input values corresponding to the selected attribute, wherein the output data comprises the elasticity output values which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training… based at least in part on the pseudo-curve model, an elasticity model expressing elasticity output values with respect to input values corresponding to the selected attribute, wherein the output data comprises the elasticity output values which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 8:
Claim 8 which incorporates the rejection of Claim 6, recites a further abstract idea generating… based at least in part on the pseudo-curve model, a shifter model expressing shifted output values with respect to input values corresponding to the selected attribute and with respect to at least one additional variable, wherein the output data comprises the shifted output values which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training… based at least in part on the pseudo-curve model, a shifter model expressing shifted output values with respect to input values corresponding to the selected attribute and with respect to at least one additional variable, wherein the output data comprises the shifted output values which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 9:
Claim 9 which incorporates the rejection of Claim 1, recites a further abstract idea performing at least one optimization operation using at least one optimization algorithm with respect to at least a portion of the output data generated by the trained at least one machine learning model, wherein the output data comprises results of the at least one optimization operations which amounts to a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 10:
Claim 10 incorporates the rejection of Claim 1. The claim recites a description of the selected attribute, the at least one historical value, the parameter expanded, and the output data of Claim 1 and is ineligible for the same reasons as set forth in Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 11:
Step 1:
The claim recites a computer-implemented method, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation transforming the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least: for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value which is a mathematical concept.
Step 2A prong 2:
The additional element of receiving raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute does not integrate the abstract idea into practical application because receiving raw historical data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
The additional element of training the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
The additional element of generating output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of receiving raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
The additional element of training the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
The additional element of generating output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
Regarding Claim 12:
Claim 12 which incorporates the rejection of Claim 11, recites a further abstract idea causing performance of at least one enterprise management operations based at least in part on the generated output data which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 13:
Claim 13 incorporates the rejection of Claim 11. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element causing rendering of a results interface that presents the generated output data which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 14:
Claim 14 which incorporates the rejection of Claim 13, recites a further abstract idea a determined mismatch between a current parameter value associated with the particular object corresponding to the selected attribute and a corresponding predicted value associated with the particular object in the output data corresponding to the selected attribute which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element the results interface presents the output data with respect to a plurality of objects which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 15:
Claim 15 which incorporates the rejection of Claim 13, recites a further abstract idea a level of confidence associate with an instance of the parameter expanded data corresponding to the particular object which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element the results interface presents the output data with respect to a plurality of objects which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 16:
Claim 16 which incorporates the rejection of Claim 11, recites a further abstract idea generating a pseudo-curve model that represents the parameter expanded data which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training the at least one machine learning model based at least in part on the pseudo-curve model which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 17:
Claim 17 which incorporates the rejection of Claim 16, recites a further abstract idea generating… based at least in part on the pseudo-curve model, an elasticity model expressing elasticity output values with respect to input values corresponding to the selected attribute, wherein the output data comprises the elasticity output values which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training… based at least in part on the pseudo-curve model, an elasticity model expressing elasticity output values with respect to input values corresponding to the selected attribute, wherein the output data comprises the elasticity output values which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 18:
Claim 18 which incorporates the rejection of Claim 16, recites a further abstract idea generating… based at least in part on the pseudo-curve model, a shifter model expressing shifted output values with respect to input values corresponding to the selected attribute and with respect to at least one additional variable, wherein the output data comprises the shifted output values which is a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites an additional element training… based at least in part on the pseudo-curve model, a shifter model expressing shifted output values with respect to input values corresponding to the selected attribute and with respect to at least one additional variable, wherein the output data comprises the shifted output values which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 19:
Claim 19 which incorporates the rejection of Claim 11, recites a further abstract idea performing at least one optimization operation using at least one optimization algorithm with respect to at least a portion of the output data generated by the trained at least one machine learning model, wherein the output data comprises results of the at least one optimization operations which amounts to a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 20:
Step 1:
The claim recites a computer program product, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation transform the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least: for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value which is a mathematical concept.
Step 2A prong 2:
The additional element of at least one non-transitory memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of receive raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute does not integrate the abstract idea into practical application because receiving raw historical data is considered an insignificant extra solution activity of “mere data gathering” MPEP 2106.05(g).
The additional element of train the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
The additional element of generate output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(h).
Step 2B:
The additional element of at least one non-transitory memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does amount to significantly more MPEP 2106.05(f).
The additional element of receive raw historical data identifying at least one historical value corresponding to at least one attribute value of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
The additional element of train the at least one machine learning model based at least in part on the parameter expanded data is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
The additional element of generate output data corresponding to the prediction task using the trained at least one machine learning model is generally linked to the abstract idea, therefore does amount to significantly more MPEP 2106.05(h).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6-13, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wick, (US Patent Publication No. US 20210312488 A1), hereinafter “Wick”.
Regarding Claim 1, Wick teaches:
An apparatus comprising at least one processor (Abstract, “computer comprising a processor”) and at least one non-transitory memory comprising program code stored thereon, wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to at least (paragraph 23, “tangible non-transitory computer-readable media that have computer-readable instructions encoded thereon, and the instructions may comprise instructions to perform functions of the methods described herein”):
receive raw historical data identifying at least one historical value corresponding to at least one attribute value (paragraph 7, “forecasting demand using historical data and one or more price-demand elasticity causal factors”) of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute (product prices are ordered attribute values, paragraph 3, “causal factors, such as… product prices”);
transform the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least:
for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value (historical values for each attribute value of price is aggregated including a historical value that corresponds to an attribute value and historical values greater than the attribute value, paragraph 44, “historical supply chain data 252 aggregated or disaggregated at various levels of granularity… historic time series data, such as sales patterns, prices… influencing future demand of a particular item sold in a given store on a specific day” paragraph 66, “aggregated or un-aggregated, such as, for example, sales per week, sales per week per location, sales per day, sales per day per season”);
train the at least one machine learning model based at least in part on the parameter expanded data (paragraph 44, “Input data 220 of model training system 110 database 114 comprises a selection of one or more periods of historical supply chain data 252 aggregated or disaggregated at various levels of granularity”, paragraph 77, “training module 206 trains one or more price-demand elasticity models using training data”); and
generate output data corresponding to the prediction task using the trained at least one machine learning model (paragraph 40, “prediction module 208 applies samples of current data 230 to trained models 228 to generate demand forecasting predictions stored as predictions data”).
Regarding Claim 2, Wick teaches the apparatus of Claim 1. Wick further teaches:
wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: cause performance of at least one enterprise management operations based at least in part on the generated output data (paragraph 25, “transmit instructions to one or more supply chain entities 140 based on one or more predictions generated by one or more trained models… instructions may comprise: an instruction to increase available production capacity at one or more supply chain entities 140, an instruction to alter product supply levels at one or more supply chain entities 140, an instruction to adjust product mix ratios at one or more supply chain entities 140, and/or an instruction to alter the configuration of packaging of one or more products sold by one or more supply chain entities 140”).
Regarding Claim 3, Wick teaches the apparatus of Claim 1. Wick further teaches:
wherein the at least one non-transitory memory and the program code are configured to, with the at least one processor, further cause the apparatus to at least: cause rendering of a results interface that presents the generated output data (paragraph 41, “model training system 110 generates and displays a user interface (UI), such as, for example, a graphical user interface (GUI), that displays data stored in database 114, 124, and/or 134, including but not limited to one or more interactive visualizations of predictions”).
Regarding Claim 6, Wick teaches the apparatus of Claim 1. Wick further teaches:
wherein training the at least one machine learning model based at least in part on the parameter expanded data comprises generating a pseudo-curve model that represents the parameter expanded data and training the at least one machine learning model based at least in part on the pseudo-curve model (curve parameters are pseudo-curve model because they parametrize an estimated price demand curve e.g. a pseudo-curve model, paragraph 96, “Model training system 110 may also permit the prediction of individual price-demand elasticity curve parameters and the use of curve parameters as a sub-estimator in a larger demand forecasting model”).
Regarding Claim 7, Wick teaches the apparatus of Claim 6. Wick further teaches:
wherein training the at least one machine learning model based at least in part on the parameter expanded data comprises generating and training, based at least in part on the pseudo-curve model (paragraph 96, “Model training system 110 may also permit the prediction of individual price-demand elasticity curve parameters and the use of curve parameters as a sub-estimator in a larger demand forecasting model”), an elasticity model expressing elasticity output values with respect to input values corresponding to the selected attribute, wherein the output data comprises the elasticity output values (paragraph 77, “training module 206 trains one or more price-demand elasticity models using training data”, paragraph 88, “prediction module 208 uses the trained model to predict the individual causal effect of a demand-shaping action, such as a price change, on the target variable, i.e. the demand”, paragraph 96, “Model training system 110 may also permit the prediction of individual price-demand elasticity curve parameters and the use of curve parameters as a sub-estimator in a larger demand forecasting model”).
Regarding Claim 8, Wick teaches the apparatus of Claim 6. Wick further teaches:
wherein training the at least one machine learning model based at least in part on the parameter expanded data comprises generating and training, based at least in part on the pseudo-curve model (paragraph 96, “Model training system 110 may also permit the prediction of individual price-demand elasticity curve parameters and the use of curve parameters as a sub-estimator in a larger demand forecasting model”), a shifter model expressing shifted output values with respect to input values corresponding to the selected attribute and with respect to at least one additional variable, wherein the output data comprises the shifted output values (second machine learning model is shifter model because it shifts predicted demand outputs based on additional variable of effects of markdown sales, paragraph 42, “markdown correction module 212 adjusts data of training data 224 or current data 230 to correct for the effects of markdown sales on recorded sales data as it pertains to predicting demand”, Abstract, “using a second machine learning model, a corrected demand target based on total sales and markdown sales”).
Regarding Claim 9, Wick teaches the apparatus of Claim 1. Wick further teaches:
wherein generating the output data corresponding to the prediction task comprises performing at least one optimization operation using at least one optimization algorithm with respect to at least a portion of the output data generated by the trained at least one machine learning model, wherein the output data comprises results of the at least one optimization operations (cyclic boosting uses optimization algorithm relating to output data generated, paragraph 36, “specification of a feature sequence in combination with a coordinate descent optimization of the machine learning algorithm, such as the case in cyclic boosting”).
Regarding Claim 10, Wick teaches the apparatus of Claim 1. Wick further teaches:
wherein the selected attribute represents a price attribute (paragraph 3, “causal factors, such as… product prices”), each historical value of the at least one historical value representing a determined demand value corresponding to a particular price value represented by the corresponding attribute value (paragraph 46, “price-demand elasticity data may comprise data recording the effect that setting the price of a particular product has on the sales of that product over a specified period of time”), the parameter expanded data comprises an aggregation of demand at a given price and all prices greater than the given price, and the output data represents predicted demand, predicted elasticity (parameter expanded data has aggregated price data and corresponding demand predicted, paragraph 44, “historical supply chain data 252 aggregated or disaggregated at various levels of granularity… historic time series data, such as sales patterns, prices… influencing future demand of a particular item sold in a given store on a specific day”, output data contains predicted demand and predicted elasticity, paragraph 40, “generate demand forecasting predictions”, paragraph 49, “forecast demand and to subsequently predict individual causal effects (such as, for example, increases or decreases in product sales based on the selection of product prices)”), and/or determined optimal pricing based on a pseudo-demand model determined based at least in part on the parameter expanded data.
Regarding Claim 11, Wick teaches:
A computer-implemented method comprising:
receiving raw historical data identifying at least one historical value corresponding to at least one attribute value (paragraph 7, “forecasting demand using historical data and one or more price-demand elasticity causal factors”) of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute (product prices are ordered attribute values, paragraph 3, “causal factors, such as… product prices”);
transforming the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least:
for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value (historical values for each attribute value of price is aggregated including a historical value that corresponds to an attribute value and historical values greater than the attribute value, paragraph 44, “historical supply chain data 252 aggregated or disaggregated at various levels of granularity… historic time series data, such as sales patterns, prices… influencing future demand of a particular item sold in a given store on a specific day” paragraph 66, “aggregated or un-aggregated, such as, for example, sales per week, sales per week per location, sales per day, sales per day per season”);
training the at least one machine learning model based at least in part on the parameter expanded data (paragraph 44, “Input data 220 of model training system 110 database 114 comprises a selection of one or more periods of historical supply chain data 252 aggregated or disaggregated at various levels of granularity”, paragraph 77, “training module 206 trains one or more price-demand elasticity models using training data”); and
generating output data corresponding to the prediction task using the trained at least one machine learning model (paragraph 40, “prediction module 208 applies samples of current data 230 to trained models 228 to generate demand forecasting predictions stored as predictions data”).
Regarding Claim 12, the rejection of Claim 11 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 2.
Regarding Claim 13, the rejection of Claim 11 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 3.
Regarding Claim 16, the rejection of Claim 11 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 6.
Regarding Claim 17, the rejection of Claim 16 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 7.
Regarding Claim 18, the rejection of Claim 16 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 8.
Regarding Claim 19, the rejection of Claim 11 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 9.
Regarding Claim 20, Wick teaches:
A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to (paragraph 23, “tangible non-transitory computer-readable media that have computer-readable instructions encoded thereon, and the instructions may comprise instructions to perform functions of the methods described herein”):
receive raw historical data identifying at least one historical value corresponding to at least one attribute value (paragraph 7, “forecasting demand using historical data and one or more price-demand elasticity causal factors”) of a set of ordered attribute values, wherein the set of ordered attribute values corresponds to a selected attribute (product prices are ordered attribute values, paragraph 3, “causal factors, such as… product prices”);
transform the raw historical data into parameter expanded data corresponding to the selected attribute, wherein the parameter expanded data is associated with a prediction task to be performed via at least one machine learning model and comprises aggregated data associated with at least a subset of the set of ordered attribute values, by at least:
for each particular attribute value of the set of ordered attribute values, aggregating (i) each historical value that corresponds to the particular attribute value from the at least one historical value, and (ii) each historical value that corresponds to an attribute value that is greater than the particular attribute value from the at least one historical value (historical values for each attribute value of price is aggregated including a historical value that corresponds to an attribute value and historical values greater than the attribute value, paragraph 44, “historical supply chain data 252 aggregated or disaggregated at various levels of granularity… historic time series data, such as sales patterns, prices… influencing future demand of a particular item sold in a given store on a specific day” paragraph 66, “aggregated or un-aggregated, such as, for example, sales per week, sales per week per location, sales per day, sales per day per season”);
train the at least one machine learning model based at least in part on the parameter expanded data (paragraph 44, “Input data 220 of model training system 110 database 114 comprises a selection of one or more periods of historical supply chain data 252 aggregated or disaggregated at various levels of granularity”, paragraph 77, “training module 206 trains one or more price-demand elasticity models using training data”); and
generate output data corresponding to the prediction task using the trained at least one machine learning model (paragraph 40, “prediction module 208 applies samples of current data 230 to trained models 228 to generate demand forecasting predictions stored as predictions data”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kovachev et al. (US Patent Publication No. US 20230315763 A1), hereinafter Kovachev.
Regarding Claim 4, Wick teaches the apparatus of Claim 3. Wick does not teach, but Kovachev teaches:
wherein the results interface presents the output data with respect to a plurality of objects based at least in part on, for each particular object of the plurality of objects, a determined mismatch between a current parameter value associated with the particular object corresponding to the selected attribute and a corresponding predicted value associated with the particular object in the output data corresponding to the selected attribute (residual shows mismatch between predicted value and current value, Kovachev, paragraph 33, “graphical user interface of the computer display device, one or more line charts comprising one or more graph lines… residual values”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kovachev’s interface display of a line chart of residual values with Wick’s predicted values and current values. The motivation to do so would be to provide a visualization of the results of the model (Kovachev, paragraph 15, “graphical user interface showing visualizations of time series analysis results”, Figure 5A)
Regarding Claim 5, Wick teaches the apparatus of Claim 3. Wick does not teach, but Kovachev teaches:
wherein the results interface presents the output data with respect to a plurality of objects based at least in part on, for each particular object of the plurality of objects, a level of confidence associated with an instance of the parameter expanded data corresponding to the particular object (Kovachev, paragraph 40, “graphical user interface of the computer display device, one or more area charts comprising one or more areas corresponding to forecasted event data with upper confidence intervals, forecasted event data, and forecasted event data with lower confidence intervals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Kovachev’s interface display of an area chart of data with confidence intervals with Wick’s data. The motivation to do so would be to provide a visualization of the results of the model (Kovachev, paragraph 17, “showing a portion of a graphical user interface showing visualizations of time series forecasting results”, Figure 7B)
Regarding Claim 14, the rejection of Claim 13 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 4.
Regarding Claim 15, the rejection of Claim 13 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSE CHEN COULSON whose telephone number is (571)272-4716. The examiner can normally be reached Monday-Friday 8:30-5:30.
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, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSE C COULSON/
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122