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 .
Status of Claim
This action is in reply in response to application filed on 22 of November 2024.
Claims 1-20 are currently pending and are rejected as described below.
Claim Rejections - 35 USC § 101
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 therefore, 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machines, article of manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014). See MPEP 2106.03(II).
The claims are then analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
With respect to 2A Prong 1, claim 15 recites “a memory for storing a computer program for collaborative human-machine learning for demand planning; and a processor connected to the memory, wherein the processor is configured to execute program instructions of the computer program comprising: receiving a forecast for the demand planning from a machine; receiving an indication of a particular event using private information from a user; estimating an effect of the particular event; receiving lagged demand; and adjusting the forecast for the demand planning using the estimated effect of the particular event and the lagged demand”. Claims 1 and 8 disclose similar limitations as Claim 15 as disclosed, and therefore recites an abstract idea.
More specifically, claims 1, 8, and 15 are directed to “Mental Process” in particular “concepts performed in the human mind (including an observation, evaluation, judgment, opinion)” and “Certain Methods of Organizing Human Activity in particular “commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)” as discussed in MPEP §2106.04(a)(2), and in the 2019-01-08 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claims recite an abstract idea.
Dependent claims 2-7, 9-14, and 16-20 further recite abstract idea(s) contained within the independent claims, and do not contribute to significant more or enable practical application. Thus, the dependent claims are rejected under 101 based on the same rationale as the independent claims.
Under Prong Two of Step 2A of the Alice/Mayo test, the examiner acknowledges that Claims 1, 8, and 15 recite additional elements yet the additional elements do not integrate the abstract idea into a practical application. In order for the judicial exception to be “integrated into a practical application”, an additional element or a combination of additional elements in the claim “will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” PEG, 84 Fed. Reg. 54 (Jan. 7, 2019). The courts have identified examples in which a judicial exception has not been integrated into a practical application when “an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.” PEG, 84 Fed. Reg. 55 (Jan. 7, 2019); MPEP § 2106.05(h). The claims are directed to an abstract idea.
In particular, claims 1, 8, and 15 recite additional elements boldened and underlined above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process. Further, the remaining additional element(s) italicized above reflect insignificant extra solution activities to the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
With respect to step 2B, claims 1, 8, and 15 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. The claim recites the additional elements described above. These are generic computer components recited as performing generic computer functions that are mere instructions to apply an exception, because it does no more than merely invoke computers or machinery as a tool to perform an existing process, as evidenced by at least ¶29-30 “Computing environment 200 contains an example of an environment for the execution of at least some of the computer code (stored in block 201) involved in performing the disclosed methods, such as improving the accuracy for demand planning by using collaborative human-machine learning. In addition to block 201, computing environment 200 includes, for example, demand planning system 103, wide area network (WAN) 224, end user device (EUD) 202, remote server 203, public cloud 204, and private cloud 205. In this embodiment, demand planning system 103 includes processor set 206 (including processing circuitry 207 and cache 208), communication fabric 209, volatile memory 210, persistent storage 211 (including operating system 212 and block 201, as identified above), peripheral device set 213 (including user interface (UI) device set 214, storage 215, and Internet of Things (IoT) sensor set 216), and network module 217. Remote server 203 includes remote database 218. Public cloud 204 includes gateway 219, cloud orchestration module 220, host physical machine set 221, virtual machine set 222, and container set 223. Demand planning system 103 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 218. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 200, detailed discussion is focused on a single computer, specifically demand planning system 103, to keep the presentation as simple as possible. Demand planning system 103 may be located in a cloud, even though it is not shown in a cloud in FIG. 2. On the other hand, demand planning system 103 is not required to be in a cloud except to any extent as may be affirmatively indicated”.
As a result, claims 1, 8, and 15 do not include additional elements, when recited alone or in combination, that amount to significantly more than the above-identified judicial exception (the abstract idea). 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.
Claims 2-7, 9-14, and 16-20 do not disclose additional elements, further narrowing the abstract ideas of the independent claims and thus not practically integrated under prong 2A as part of a practical application or under 2B not significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being obvious by the combination of US 20020169657 to Singh et. al. (hereinafter referred to as “Singh”) in view of US 20240420026 to Breeding-Allison (hereinafter referred to as “Allison”) and in further view of US 20200257943 to Huber et. al. (hereinafter referred to as “Huber”).
(A) As per Claims 1, 8, and 15:
Singh expressly discloses:
receiving lagged demand; (Singh ¶56 the task of creating and maintaining a statistical model to be associated with a particular demand forecasting unit (DFU) must take into account two basic elements in order to provide realistic statistical forecasts; it must take into account historical data to generate the statistical algorithm, and must project historical demand patterns into the future).
adjusting the forecast for the demand planning using the estimated effect of the particular event, and the lagged demand; (Singh ¶25, 56 the present invention also provides intelligent event modeling capabilities and allows for management overrides and forecast adjustments given newly received data regarding changes in actual demand. As such, event data is incorporated into forecast models on an ongoing basis to help adjust forecast decisions on the fly and refine the forecasting process for future endeavors. By including both market planning and demand planning capabilities, the present invention links product mix, promotion, and price analyses with traditional demand forecasting. The task of creating and maintaining a statistical model to be associated with a particular demand forecasting unit (DFU) must take into account two basic elements in order to provide realistic statistical forecasts; it must take into account historical data to generate the statistical algorithm, and must project historical demand patterns into the future).
Although Singh teaches systems and methods for demand forecasting that enable multiple-scenario comparisons, it doesn’t expressly disclose receiving forecast demand from a machine, however Allison teaches:
receiving a forecast for the demand planning from a machine; (Allison ¶34 the adaptive forecasting system 101 (e.g. the machine) may aggregate and clean data from various sources, handle missing values, normalize data, and perform feature engineering to prepare high-quality input for the prediction models (i.e., forecasting models)).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Singh’s intelligent event modeling capabilities that allows for management overrides and forecast adjustments and have the adaptive forecasting system 101 aggregate and clean data from various sources of Allison as both are analogous art which teach solutions that include both market planning and demand planning capabilities, linking product mix, promotion, and price analyses with traditional demand forecasting as taught in Singh and handle missing values, normalize data, and perform feature engineering to prepare high-quality input for the prediction models as taught in Allison.
Although Singh in view of Allison teaches systems and methods for demand forecasting that enable multiple-scenario comparisons, it doesn’t expressly disclose receiving event data using private information from a user and estimating the effect of the event, however Huber teaches:
receiving an indication of a particular event using private information from a user;
estimating an effect of the particular event; (Huber ¶79 aspects of embodiments of the present invention are directed to systems and methods for performing forecasting, prediction, and collaboration (among human forecasters and between human and machine forecasters) by combining inputs from human forecasters (e.g., through crowdsourcing or aggregating multiple human opinions) with inputs from machine learning based forecasters).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Singh in view of Allison’s intelligent event modeling capabilities that allows for management overrides and forecast adjustments and performing forecasting, prediction, and collaboration between humans and machine of Huber as both are analogous art which teach solutions that include both market planning and demand planning capabilities, linking product mix, promotion, and price analyses with traditional demand forecasting as taught in Singh in view of Allison and have human forecasters through crowdsourcing provide inputs to the machine learning based forecaster as taught in Huber.
Singh teaches methods for demand forecasting that enable multiple-scenario comparisons in the Abstract. Meanwhile, Allison teaches a computer readable medium in ¶6-7.
(B) As per Claims 2, 9, and 16:
Singh expressly discloses:
wherein the machine comprises at least one of a statistical model, a machine learning model, or an algorithm that uses public information to produce the forecast for the demand planning; (Singh ¶48 as will be readily appreciated by one of ordinary skill in the art, there are various well known and proprietary statistical algorithms that can be used alternatively in particular circumstances to predict future demand based upon historical data. The MMF of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as “models”) so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market).
(C) As per Claims 3, 10, and 17:
Although Singh in view of Allison and in further view of Huber teaches systems and methods for demand forecasting that enable multiple-scenario comparisons, it doesn’t expressly disclose estimating the effect of the event based in weighing the event’s effect based on historical estimates, however Huber additionally teaches:
wherein the estimate of the effect of the particular event is based, at least in part, on weighing the particular event’s effect based on a prior history of estimates of the particular event’s effect; (Huber ¶114-116 updated models are added to an ensemble of models (discussed in more detail below) and may contribute to the final forecast computed in operation 321, where the weight of the user's modified model in the final output is determined by factors such as their historical accuracy and the historical accuracy of the models they produce).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Singh in view of Allison and in further view of Huber’s intelligent event modeling capabilities that allows for management overrides and have updated models added to an ensemble of models of Huber as both are analogous art which teach solutions that include both market planning and demand planning capabilities, linking product mix, promotion, and price analyses with traditional demand forecasting as taught in Singh in view of Allison and in further view of Huber and have the weight of the user's modified model in the final output be determined by factors such as their historical accuracy and the historical accuracy of the models they produce as taught additionally in Huber.
(D) As per Claims 4, 11, and 18:
Singh expressly discloses:
utilizing a performance metric to compare prior machine forecasts and human judgement to the adjusted forecast for the demand planning; (Singh ¶44-45 due to the multiple model framework (“MMF”) employed by the present invention, DFU model variants can have multiple types of history and multiple forecast algorithms combined to form several alternative models for a given product. In this manner, forecasts for DFUs which are model variants of one another can thereby be created for comparison and eventual adoption of the best model as described below).
(E) As per Claims 5, 12, and 19:
Although Singh in view of Allison and in further view of Huber teaches systems and methods for demand forecasting that enable multiple-scenario comparisons, it doesn’t expressly disclose continuing to adjust the forecast until a threshold is met, however Allison additionally teaches:
continuing to adjust the forecast for the demand planning until the performance metric is improved to exceed a threshold value; (Allison ¶49 in block 203, in forecasting tasks, exogenous features play a crucial role in enhancing the predictive capabilities of time series models. These features, often external to the primary datasets are forecasted independently using appropriate methods, such as machine-learning algorithms or statistical models. Subsequently, a performance threshold is established to assess the quality of these forecasts, ensuring they meet predefined criteria for accuracy and reliability. Feature whose forecasts surpass this threshold are deemed suitable candidates for inclusion as inputs to the time series model. By incorporating these exogenous factors, which may capture external influences such as economic indicators or market trends, the time series model gains additional explanatory power and can generate more robust and accurate predictions).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Singh in view of Allison and in further view of Huber’s intelligent event modeling capabilities that allows for management overrides and have a performance threshold is established to assess the quality of these forecasts, ensuring they meet predefined criteria for accuracy and reliability of Allison as both are analogous art which teach solutions that include both market planning and demand planning capabilities, linking product mix, promotion, and price analyses with traditional demand forecasting as taught in Singh in view of Allison and in further view of Huber and feature whose forecasts surpass this threshold are deemed suitable candidates for inclusion as inputs to the time series model as taught additionally in Allison.
(E) As per Claims 6, 13, and 20:
Singh expressly discloses:
receiving lagged judgements; (Singh ¶81 demand history data often includes data points (such as spikes or valleys in demand) that can cause problems if they are included within calculations to predict future demand. Examples of such problematic data include variation in demand caused by unusual market conditions, decreases in demand due to obsolete or superseded products, general data errors, and the inability of the user to distinguish between base and non-base data. In order to account for such problematic data in base or non-base history, at step 701, the user is provided the opportunity to adjust history to overcome such problems).
adjusting the forecast for the demand planning using at least one of the estimated effect of the particular event, the lagged demand, and the lagged judgement; (Singh ¶84-85 At step 704, the system proceeds to adjust the statistical forecast produced by a particular model such that the forecast takes into account planned marketing strategies, competitive events, judgment-based forecast effects, or occasional one-time occurrences).
(E) As per Claims 7 and 14:
Although Singh in view of Allison and in further view of Huber teaches systems and methods for demand forecasting that enable multiple-scenario comparisons, it doesn’t expressly disclose wherein the private information is not taken into account by the algorithm, however Huber additionally teaches:
wherein the private information corresponds to information with predictive value that an algorithm does not take into account; (Huber ¶109 a human predictor might be able to easily detect and assign meaning to such outliers in the data. The human predictor may then discount the outliers in the data when making their prediction. A machine model may produce more reliable results by ignoring the portion of the data corresponding to the outlier even).
It would be obvious to one of ordinary skill in the art at the time of the claimed invention was filed to have modified Singh in view of Allison and in further view of Huber’s intelligent event modeling capabilities that allows for management overrides and have a human predictor detect and assign meaning to such outliers in the data of Huber as both are analogous art which teach solutions that include both market planning and demand planning capabilities, linking product mix, promotion, and price analyses with traditional demand forecasting as taught in Singh in view of Allison and in further view of Huber and have a machine model produce more reliable results by ignoring the portion of the data corresponding to the outlier even as taught additionally in Huber.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Ganesan, A., Paul, A., Nagabushnam, G., & Gul, M. J. J. (2021). Human‐in‐the‐Loop Predictive Analytics Using Statistical Learning. Journal of Healthcare Engineering, 2021
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATHEUS R STIVALETTI whose telephone number is (571)272-5758. The examiner can normally be reached on M-F 8:30-5:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571)272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1822.
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/MATHEUS RIBEIRO STIVALETTI/Primary Examiner, Art Unit 3623 2/19/2026