DETAILED ACTION
Status of the Application
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Final Action on the merits in response to the application filed on 03/24/2026.
Claims 1, 4, 7, 10, 13 and 16 have been amended.
Claim 19 has been added
Claims 1-19 remain pending in this application.
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 1-6, 19 are directed towards a computing apparatus, claims 7-12 are directed towards a directed towards computer-readable storage medium, and claims 13-18 are directed towards a method, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including training data. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-19, the independent claims (claims 1, 7, and 13 ) are directed to managing of forecast data, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
receive, by the processor, historical data comprising data compiled over a first time interval;
generate, by the processor, a plurality of features based on the historical data;
generate, by the processor, forecast data for a forecast window;
collect, by the processor, real-time data over a second time interval, the second time interval less than the forecast window.
these steps fall and recite an abstract ideas because they are directed to Certain Methods of Organizing Human Activities” as recited, described or set forth above, could be argued as implementable through computer-aided mental processes, when tested per MPEP 2106.04(a) ¶3, 3), and MPEP 2106.04(a)(2) III C, such as by computer-aided evaluation, judgement and observation.
If a claim limitation, under its broadest reasonable interpretation observation and evaluations, then it falls within the ”mental processes”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of apparatus, processor, memory, machine learning, model (Claim 7 computer readable storage medium, computer, apparatus, processor, memory, machine learning, model; Claim 13 processor, machine learning, model). The claims recite the steps are performed by the apparatus, processor, memory, machine learning, model.
The limitations of
1. A computing apparatus, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
clean, by the processor, the historical data in preparation for feature generation;
train, by the processor, a machine-learning model using the plurality of features;
determine, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and
form, by the processor, an adjusted forecast data by removing the error from the forecast data,
wherein the apparatus is configured to execute the collecting, determining, and forming by an intervening subroutine executed after generating the forecast data without the need to execute the machine learning to generate features and train or retrain another forecast model on computationally large training data.
are mere data processing and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by apparatus, processor, memory, machine learning, model. The apparatus, processor, memory, machine learning, model are recited at a high level of generality. In limitation (a), apparatus, processor, memory, machine learning, model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The apparatus, processor, memory, machine learning, model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning model does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the apparatus, processor, memory, machine learning, model. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0014 “the machine learning model can be a deep-learning model, a statistical model, or a tree-based model. Where the machine learning model is a tree-based model, the method can further comprise: joining, by the processor, clean data obtained from a plurality of sources, into a single source; and tuning, by the processor, one or more hyperparameters.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
A computing apparatus, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
clean, by the processor, the historical data in preparation for feature generation;
train, by the processor, a machine-learning model using the plurality of features;
determine, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and
form, by the processor, an adjusted forecast data by removing the error from the forecast data,
wherein the apparatus is configured to execute the collecting, determining, and forming by an intervening subroutine executed after generating the forecast data without the need to execute the machine learning to generate features and train or retrain another forecast model on computationally large training data.
are recited at a high level of generality. These elements amount to processing and transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-6, 8-12, and 14-19 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 3, 8, 14 recite models; claims 3-5, 9-11, 15-17, 19 recite processors. The dependent claims 2-6, 8-12, and 14-19 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-6, 8-12, and 14-19 recites apparatus, processor, memory, machine learning, model which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-6, 8-12, and 14-19 recites apparatus, processor, memory, machine learning, model, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-6, 8-12, and 14-19 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 7, and 13. Therefore claims 2-6, 8-12, and 14-19 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Reasons for Removing the Prior Art Rejection
The rejections under 35 U.S.C. 102 as to claim 1-11 are removed in light of Applicant's claims and remarks of 03/24/2026, which are deemed persuasive as to independent claim 1. The reasons for withdrawal of the rejections under 35 U.S.C. 103 can be found at the following claim limitations of 03/24/2026 at claim 1 as follows:
Claim 1
A computing apparatus, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
receive, by the processor, historical data comprising data compiled over a first time interval;
clean, by the processor, the historical data in preparation for feature generation;
generate, by the processor, a plurality of features based on the historical data;
train, by the processor, a machine-learning model using the plurality of features;
generate, by the processor, forecast data for a forecast window;
collect, by the processor, real-time data over a second time interval, the second time interval less than the forecast window;
determine, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and
form, by the processor, an adjusted forecast data by removing the error from the forecast data,
wherein the apparatus is configured to execute the collecting, determining, and forming by an intervening subroutine executed after generating the forecast data without the need to execute the machine learning to generate features and train or retrain another forecast model on computationally large training data.
Applicant’s Remarks of 03/24/2026 at pg. 31-33 as follows:
“Independent claim 1 recites: "determining, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and forming, by the processor, an adjusted forecast data by removing the error from the forecast data."
At pages 8-9, Office Action alleges that paragraphs [0006]-[0007], and [0081] of Ouellet discloses these features. Applicant respectfully disagrees. At paragraph [0081], Ouellet merely describes data remediation for an external data store that can be used to train an ML model. It fails to describe any adjustment to forecast data to form an adjusted forecast by removing that error:..
Ouelette thus fails to teach (or suggest) "determining, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and forming, by the processor, and adjusted forecast data by removing the error from the forecast data."”
This applies to independent claims 7 and 13 as these claims includes the same feature of claim 1.
Page 9 of 13
Response to Arguments
Applicant’s arguments filed 03/24/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 03/24/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 7-10 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
A computing apparatus, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
receive, by the processor, historical data comprising data compiled over a first time interval;
clean, by the processor, the historical data in preparation for feature generation;
generate, by the processor, a plurality of features based on the historical data;
train, by the processor, a machine-learning model using the plurality of features;
generate, by the processor, forecast data for a forecast window;
collect, by the processor, real-time data over a second time interval, the second time interval less than the forecast window;
determine, by the processor, an error in the forecast data, based on a difference between the forecast data and the real-time data; and
form, by the processor, an adjusted forecast data by removing the error from the forecast data,
wherein the apparatus is configured to execute the collecting, determining, and forming by an intervening subroutine executed after generating the forecast data without the need to execute the machine learning to generate features and train or retrain another forecast model on computationally large training data.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for managing of forecast data, which constitutes methods related to ”mental processes”; “method of organizing human activity” which are still considered an abstract idea under the 2019 PEG. The computer networks are comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 17 that Applicant points to as improvements are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps of:
pg. 17 “Claim 1 recites an improvement to a conventional machine learning model that employed in a system that regularly and periodically trains new machine learning models on large data sets. The claim recites a technical improvement to the system employing the machine learning model, which is clearly an improved technique for the technical field of artificial intelligence, as opposed to mere data gathering, organizing human activity, or mental processes.”
pg. 18 “Claim 1 recites language directed to a specific improvement to machine learning training and execution effected by producing "adjusted forecast data without the need to generate features and train another forecast model on the computationally large training data; transmitting the first adjusted forecast data to the user."”
These arguments at pg. 17, 18 seems to describe a “particular way” of managing of forecast data of the abstract idea. “ Also, the claims do not recite “adjusted forecast data without the need to generate features and train another forecast model on the computationally large training data; transmitting the first adjusted forecast data to the user.”
Furthermore, at pg. 26 the Applicant recites that “ Nor are these recitations mere extra solution activity, as they are integral to improving computer performance by executing the improvement to machine learning model forcasting.” the Examiner wants to point out that claims do not show or break down how this is actually executed, at this point it’s just an aspirational statement. There is no technological problem or solution here. Thus, making it more prevalent for the Examiner to direct the Applicant to Leapfrog Enters.
Then at pg. 23, 24 the Applicant admission that the application is directed to improving the user’s experience and not the computer itself
(at pg.23, 24 “However, the
real-time sales data on Days 1-6 is only used to
forecast demand after Day 6. A user would like to
use the daily sales data between Days 1-6, to
adjust the demand during that period. The
machine learning pipeline, however, is set to re- train every week, in this example. Below, is
described how the real-time daily sales data can be incorporated to adjust the forecast provided
by the machine learning model.”)
these arguments the Applicant is admitting that the application is directed to improving the user’s experience and not the system software or any type of computer or structure.
In regards to Ex Parte Desjardins, the instant claims are not similar to Ex Parte Desjardins, Examiner finds the Board determined the improvements in Desjardins to be directed to addressing problems arising in the context of a technical improvements to machine learning systems, which overcome a problem specifically arising in the realm of AI and machine learning inventions. There is no similar technological problem or solution here.
Regarding, automation when determining whether a computer-implemented functional claim would have been obvious, examiners should note that broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which managing of forecast data will fall under. The 2019 PEG which states:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lei et al. (US 2020/0130425) disclose forecast demand of an item by receiving historical sales data for the item for a plurality of past time periods including a plurality of features that define one or more feature sets.
Joseph et al. (US 2020/0210920) disclose a machine learning system with date alignment features for improved demand forecasting for products and/or services.
Adato et al. (US 2020/0074402) disclose a system for processing images captured in a retail store and automatically identifying a product shortage.
Ananthapur Bache et al. (US 2020/0065424) disclose devices that filter a subset of items from a database according to a model which predicts sales of items in the database with social media data and the subset of items filtered according to a highest predicted sales. The model comprises a machine learning model trained with sales history of items from the database and social media data history.
T. (US 2020/0005340) disclose Tree (CDT) for an entity in accordance with an attribute value (AV) based demand transfer estimation for a product category using machine learning.
Smith et al. (US 2019/0272557) disclose dynamically generating discounted product digital notifications based on remaining product shelf life.
Scarpati et al. (US 2018/0374109) disclose methods for estimating multiple types of retail business volume based on multiple types of data are described. Historical volume data, prior recorded business volume, characteristics of the store including departments, and geographical location are used. Historical data is transformed into multiple features that capture seasonality, trends, the effects of special events and other business characteristics.
Nemati et al. (US 2018/0308051) disclose providing e-commerce suppliers an alternative shipping and distribution system based on real-time sales and demand being coupled with iterative machine learning processes.
Etzioni et al. (US 2012/0303412) disclose Data relating to products sold across a plurality of merchants may be gathered from a variety of sources and processed, including with machine learning components.
Chen et al. (WO 2019015631) disclose the use of machine learning for making prediction.
Bapat et al., User-sensitive Scheduling of Home Appliances, http://conferences.sigcomm.org/sigcomm/2011/papers/greennet/p43.pdf, Proceedings of the 2nd ACM SIGCOMM workshop on Green networking, 2011 (discussing the monitoring of forecasting home appliances regarding power consumption).
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 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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