Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/193,330, filed on March 20, 2023. In response to Examiner’s Non-Final Office Action of December 1, 2025, Applicant, on March 2, 2026, amended claims 1, 2, 5, 9, 10, 13, 14 and 17, and canceled claims 8 and 16. 1, 2, 5-6, 9, 10, 13, 14, and 17 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are withdrawn.
Response to Arguments
Applicant’s arguments filed March 2, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed March 2, 2026.
On pages 8-11 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims are not directed to an abstract idea specifically nor do the claims fall into an abstract idea grouping. In response , Examiner finds the amended claim language recites several abstract ideas including Methods of Organizing Human Activities- Commercial interactions. . The claims recite performing the training using a backpropagation algorithm. When given their broadest reasonable interpretation in light of the disclosure, the backpropagation and loss function minimization algorithm are mathematical calculations. The plain meaning of this term is optimization using a series of mathematical calculations. Limitation is also recited as being performed by a predicting. The recited device is recited at a high level of generality.. Please see 101 analysis below for additional detail.
On pages 11-13 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims are integrated into a practical application. In response, regarding the 35 U.S.C. § 101 rejection, Examiner finds the present claims improve an existing business process of supply chain demand analysis and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to collect and analyze data and are all, both individually and in combination, computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II). Examiner asserts, regardless of the complexity of the data analysis and/or processing, without recitation of improvements to the functioning of the technology, technological field and/or computer-related technology (i.e. software), the steps outlined in the claimed invention to collect input vectors and predict demand amount to no more than mere instructions to implement the idea on a general purpose computer. Applicant has not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves.
On pages 13-14 of the Remarks regarding 35 U.S.C. § 101, Applicant states the similar to Desjardins the claimed limitations demonstrate a technical improvement integrated practical application. In response, Examiner finds Applicants arguments in relation to this matter are not persuasive. Specifically, in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflect the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of demand forecasting in supply chain management in which computer components are used as a tool to perform the instructions of the scheduling process. Applicant has not presented an argument that alters this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter..
On page 14 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims recite significantly more than an abstract idea In addition to being eligible under prongs 1 and 2 of Step 2A, Applicant's claims are also independently eligible under Step 2B for providing sufficient inventive concept.. In response, Examiner asserts when performing the § 101 analysis, Examiner did consider each claim and every limitation, both individually and in combination as according to the PTO's guidelines for § 101 eligibility. Please see 101 analysis below for additional detail.
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, 2, 5-6, 9, 10, 13, 14, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 2, 5-6, 9, 10, 13, 14, and 17 are directed to predicting demand for supply chain.
Claim 1 recites a method for demand forecasting for supply chain management, Claim 9 recites a system for demand forecasting for supply chain management and Claim 17 recites an article of manufacture for demand forecasting for supply chain management, which include for a future time-period, feeding input vectors wherein the input vectors comprise: an intensity vector corresponding to an intensity of a possible disruption-event at each point of time within the future time-period; a duration vector corresponding to a duration of the possible disruption-event; and one or more extrinsic data vectors corresponding to one or more possible extrinsic data parameters associated with each point of time within the future time-period, wherein one or more extrinsic data parameters are used to identify variations in demand specific to a target industry, and wherein the one or more extrinsic data parameters are used to sense deviations from normal trends and adjust demand forecasts wherein generating training data vectors for the trained ML model comprises: receiving a disruption data for a reference time- period; processing the disruption data for the reference time-period to analyze a disruption-event within the reference time-period; determining the intensity of the disruption-event at each point of time within the reference time-period and the duration of the disruption- event generating a sparse multivariate time series for a reference time-period by collating historical demand data, the intensity vector corresponding to an intensity of a possible disruption-event at each point of time within the reference time- period, the duration vector corresponding to a duration of the possible disruption- event, and the one or more extrinsic data parameters corresponding to the one or more extrinsic data parameters at each point of time within the reference time- period;
specifying a plurality of loss function corresponding to respective time points in the reference time-period, wherein the loss function that compares predicted demand (Dpredicted) with actual demand (D actual demand,),mapping each input matrix (IN1, IN2,... INi) derived from the sparse multivariate time series to a corresponding loss function (L1, L2,... Li), wherein mapping pair (INi, Li) of each input matrix with an associated loss function may be represented as follows:
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obtaining a predicted demand for a target product in the future time-period from the trained ML model based on the input vectors and a sparse multivariate time series, wherein the sparse multivariate time series is generated based on the training data vectors for the reference time-period; comparing the predicted demand with an actual demand; determining a magnitude of error of prediction based on the comparing; for each mapped pair, displaying the predicted demand for the target product and the magnitude of error of prediction; and providing one or more actionable insights based on the predicted demand for the target product, wherein the one or more actionable insights provide guidance for inventory planning, pricing of the target product, and sales strategy.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activities- commercial interactions”. The recitation of “device”, ”system”; “memory”, “processor”, “user interface” and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activities- commercial interactions” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “device”, ”system”; “memory”, “processor”, “user interface” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 9 and claim 17 recite using one or more machine learning analysis techniques (a). backpropagating the corresponding loss function to adjust a weighted matrix of the ML model until the corresponding loss function for each point of time in the reference time-period is minimized ;(b) and training the ML model until the loss function for each point of time in the reference time-period is minimized, wherein upon completion of backpropagation for all mapped pairs and achieving minimization of the corresponding loss functions, using the ML model to predict demand for a future time-period and (c) retraining, by the predicting device, the trained ML model based on the magnitude of error of prediction; rendering, by the predicting device, at least one of historical demand data, the disruption data, the one or more extrinsic data parameters, the sparse multivariate time series, the training data vectors, and a loss function. The claim recites performing the training using a backpropagation algorithm. When given their broadest reasonable interpretation in light of the disclosure, the backpropagation and loss function minimization algorithm are mathematical calculations. The plain meaning of this term is optimization using a series of mathematical calculations. Limitation is also recited as being performed by a predicting. The recited device is recited at a high level of generality.
The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in forecast analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “device”, ”system”; “memory”, “processor”, “user interface” and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). With regards to the “backpropagation/ training/retraining of the ML model” and Step 2B- the machine learning is solely used a tool to perform the instructions of the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2, 5, 6, 10, 13, and 14 recite training the ML model using the training data vectors for the reference time-period, wherein the trained ML model corresponds to a forward-looking model; wherein training the ML model further comprises generating the training data vectors based on the sparse multivariate time series, wherein the training data vectors comprise: a historical data vector corresponding to the historical demand data at each point of time within the reference time-period; wherein the one or more extrinsic data parameters comprise: competitors and market data parameters, macroeconomic data parameters, socio-economic data parameters, and consumer-specific data parameters associated with the target industry, wherein the target product is associated with the target industry. and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 9 and 17. Regarding Claims 10, 13-14, and the additional elements of “processor” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 2, 5, 10, 13, and the additional element of training and retraining machine learning model -the machine learning is solely used a tool to perform the instructions of the abstract idea. Examiner recommends reviewing Example 47 of the recent USPTO guidance for an eligible example.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1, 2, 5-6, 9, 10, 13, 14, and 17 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 9 and Claim 17), the prior art does not teach or fairly suggest:
“… generating a sparse multivariate time series for a reference time-period by collating historical demand data, the intensity vector corresponding to an intensity of a possible disruption-event at each point of time within the reference time- period, the duration vector corresponding to a duration of the possible disruption- event, and the one or more extrinsic data parameters corresponding to the one or more extrinsic data parameters at each point of time within the reference time- period; specifying a plurality of loss function corresponding to respective time points in the reference time-period, wherein the loss function that compares predicted demand (Dpredicted) with actual demand (Dactual,),mapping each input matrix (IN1, IN2,... INi) derived from the sparse multivariate time series to a corresponding loss function (L1, L2,... Li), wherein mapping pair (INi, Li) of each input matrix with an associated loss function may be represented as follows:
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for each mapped pair, backpropagating the corresponding loss function to adjust a weighted matrix () of the ML model until the corresponding loss function for each point of time in the reference time-period is minimized; and training the ML model until the loss function for each point of time in the reference time-period is minimized, wherein upon completion of backpropagation for all mapped pairs (INi, Li) and achieving minimization of the corresponding loss functions, using the ML model to predict demand for a future time-period. ”.
Examiner finds that Recasens et al., US Publication No. 20220318711A1 teaches a receiving training data representing historic consumer demand for products, detecting changepoints in that data that may be associated with disruptive events, identifying relevant data for modeling, performing clustering, processing configuration information, training one or more machine learning models that are capable of evaluating other received data more accurately, and outputting results to a user display device. (see Abstract). In particular, Recasens discloses a distributed computer system 100 identifies relevant data for modeling. The entire training data set may be modeled, or a subset of the training data set may be modeled. In an embodiment, step 330 comprises distributed computer system 100 calculating or retrieving a baseline forecast for one or more products to determine if any of those products are currently being, or will be in the future, impacted by a disruptive event. In an embodiment, a baseline forecast is retrieved which was calculated at a time prior to a time value detected to be a changepoint in one or more products at step 320(par. 0063). (see par. 0060-0067).
Wang et al. , US Publication No. 20210224700A1 teaches techniques to forecast financial data using deep learning. These techniques are operative to transform time series data in a financial context into a machine learning model configured to predict future financial data. The machine learning model may implement a deep learning structure to account for a sequence-sequence prediction where a movement/distribution of the time series data is non-linear. The machine learning model may incorporate features related to one or more external factors affecting the future financial data. Other embodiments are described and claimed. (see Abstract). In particular, Wang discloses converting feature set into coefficients of a function (e.g., a polynomial function) for approximating the external factor(s) and their impact on the above-mentioned prediction. The parameters may be dynamic to account for differences in terms of business domains. (see par. 0005, 0064).
Andelman, US Publication No. 20150025933A1 teaches risk mitigation calculating on at least one processor, a likelihood of occurrence for a risk event, a degree of disruption for the risk event and a disruption time for the risk event. Each risk event is in a list of risk events. The likelihood of occurrence is based on a frequency of risk of the risk event. The degree of disruption is based on a severity of risk of the risk event. The disruption time is based on a duration of restoring an asset to a condition prior to the risk event. A value at risk based on the likelihood of occurrence, the degree of disruption and the disruption time is determined for the risk event. A mitigation action is determined to reduce the value at risk. An indication of the mitigating action is presented on a user interface. (see Abstract). In particular, Andelman discloses calculating a likelihood of occurrence for one of the risk events in the list of risk events and for one of the stores in the plurality of stores, the likelihood of occurrence based on the frequency of risk; calculating a degree of disruption for one of the risk events in the list of risk events and for one of the stores in the plurality of stores, the degree of disruption based on the severity of risk (see Claim 1).
Although Recasens, Wang and Andelman teach the analysis elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the algorithm / equations of the claim.
Therefore, for at least these reasons, Claim 1 (similarly Claim 9 and Claim 17) is eligible over the prior art.
The dependent claims 2, 5-6, 10, and 13-14 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1( claim 9 ) that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. 10783442 B1 to Torkkola et al.- Abstract-“ Techniques described herein include a method and system for item demand forecasting that utilizes machine learning techniques to generate a set of quantiles. In some embodiments, several item features may be identified as being relevant to an item forecast and may be provided as inputs to a regression module, which may calculate a set of quantiles for each item. A set of quantiles may comprise a number of confidence levels or probabilities associated with calculated demand values for an item. In some embodiments, costs associated with the item may be used to select an appropriate quantile associated (e.g., based on a corresponding confidence level). In some embodiments, an item demand forecast may be generated based on the calculated demand value associated with the selected quantile. In some embodiments, one or more of the item may be automatically ordered based on that item demand forecast.”
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624