DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks dated 9/18/2025 have been considered. Claims 7 and 14 are cancelled and claims 21-22 are added. Claims 1-6, 8-13, and 15-22 are pending.
Response to Arguments
On page 9 of Applicant’s 9/18/2025 Amendment and remarks, Applicant asserts that support for the claim amendments can be found in former claims 7 and 14, Figs. 1 and 2A, and the paras. on p. 3, line 13, page 8, line 12 of the instant specification. Support for new claims 21-22 is at Fig. 2A and the paragraph beginning on pages 7, line 19 of the instant specification.
The examiner agrees that such portions of the disclosure, together with page 3, lines 18-20 and page 8, lines 12-22, provide sufficient written description support for the amendments to the claims and new claims 21-22.
In particular, the “the correlation algorithm takes the median of the changes (variations) that occurred for the FGA forecast and the base mod forecast, and changes the percentage in the CTO forecast accordingly” disclosure (see p. 8, lines 12-22) supports the amendments to the independent claims relating to “deriving a percentage change” and “applying the derived percentage change” limitations, as one of ordinary skill would understand that the inventors possessed deriving such a percentage change in order to “change the percentage in the CTO forecast accordingly.”
On page 11 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2A, Prong 1, Applicant first argues that the claims do not recite a mathematical concept.
PNG
media_image1.png
388
640
media_image1.png
Greyscale
The examiner respectfully disagrees. With respect to the independent claims, the only limitation that was identified as a mathematical calculation (as an alternative to the mental process finding), was the “correlate the predicted quantity results of the machine learning forecasting processes using a correlation algorithm, ...” limitation. In this action, this limitation has been amended to recite “correlate the predicted quantity results of the machine learning forecasting processes using a correlation algorithm, the correlation algorithm modifying the forecasting results associated with the predicted quantity of the components used to customize the customizable base system by deriving a percentage change from a median of variations in one or more of the forecasting results associated with the predicted quantity of the non-customizable system and the predicted quantity of the customizable base system, and applying the derived percentage change to the forecasting results associated with the predicted quantity of the components.”
The examiner specifically identified this limitation as a mathematical calculation (and not just a mathematical “concept”). This claim limitation, as amended, recites using a “correlation algorithm” by “deriving a percentage change from a median of variations in one or more of the forecasting results associated with the predicted quantity of the non-customizable system and the predicted quantity of the customizable base system” and then “applying the derived percentage change to the forecasting results.” These are mathematical calculations used to perform a “correlation algorithm.” Indeed, at page 11, Applicant admits that “the amended claims use calculations as part of a specific, multi-step process.”
Applicant appears to argue that the claim limitation merely “involves” mathematical calculations and does not “recite” mathematical calculations. However, the examiner respectfully disagrees because specific types of calculations, used to carry out a “correlation algorithm”, are claimed, which is a mathematical calculation (in addition to being a mental process).
On page 11 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2A, Prong 1, Applicant next argues that the mental processes of claim 1 cannot “practically” be performed in the human mind.
PNG
media_image2.png
290
650
media_image2.png
Greyscale
PNG
media_image3.png
274
662
media_image3.png
Greyscale
The examiner respectfully disagrees. Applicant first identifies the “performing three separate machine learning forecasts” group of limitations as not being practically performed in the human mind. While the examiner agrees that “machine learning forecasts” cannot practically be performed in the human mind, the rejection specifies that it is the “perform the ... forecasting processes respectively...” limitation that is a mental process, and a human, such as a supply chain professional, can review the first, second, and third datasets mentally and predict the recited quantities mentally. The “machine learning” limitation is addressed under Step 2A, Prong 2 and Step 2B.
Second, Applicant argues that the “correlating the results by deriving a percentage change from a median of variations in two forecasts to modify the third” is not a mental process. The examiner respectfully disagrees, as correlating data by “deriving a percentage change” is both a mathematical calculation (explained above) and a mental step because such calculation can be carried out mentally by a human, or using pencil and paper.
Third, Applicant argues that the “automatically causing a separate purchasing system to issue electronic purchase orders” is not a mental process. The examiner agrees with this, and therefore this newly-added limitation is analyzed under Step 2A, prong 2 and Step 2B.
Finally, Applicant appears to argue that the human mind cannot perform the first and second limitations because “the human mind is not equipped to process the volumes of historical data required, or perform the “specific multi-stage correlation.” The examiner respectfully disagrees. The claims do not specific any minimum volume of historical data to be analyzed, and regardless, a human can process a lot of data, although it may take a good amount of time. And the “multi-stage correlation” is a few straightforward calculations that could easily be performed mentally (or using pencil and paper) by a human. Indeed, prior to the availability of computers, supply chain professionals were able to make forecasting predictions, and mathematicians were able to do complicated correlation algorithms, so the fact that computers make these limitations more efficient does not change the fact that they are indeed mental processes.
On page 12 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2A, Prong 2, Applicant next argues that the claims reflect a specific technical solution to a problem.
PNG
media_image4.png
398
654
media_image4.png
Greyscale
The examiner respectfully disagrees. First, the claims do not recite “a specific computational architecture of three distinct forecasts for different product types” as argued by Applicant. Instead, the claims generically recite inputting 3 types of data into a generic “machine learning algorithm” without providing any details about the structure of the machine learning model carrying out the algorithm.
Second, the claims do not “employ[] a specific, unconventional correlation algorithm to refine the component forecast.” As explained above, the application of a correlation algorithm is merely a mental process, and therefore such limitation is not available under Step 2A, Prong 2, to integrate the judicial exception into a practical application. Moreover, Applicant does not explain why such algorithm is allegedly “unconventional”, and regardless, the examiner has not invoked the Berkheimer doctrine with respect to this limitation.
Third, while the examines agrees that “causes a separate purchasing system to automatically issue electronic purchase orders” is not a mental step, the examiner respectfully submits that such limitation is merely a field-of-use limitation, where the mental processes of deriving a forecast (and associated needed components) are limited to the field of purchasing systems that can issue electronic purchase orders.
On page 13 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2A, Prong 2, Applicant next argues that the claims reflect a “particular technical solution.”
PNG
media_image5.png
134
660
media_image5.png
Greyscale
The examiner respectfully disagrees. The claim, at most, relates to improving the mental processes of determining forecasted demand from historical data, and there are no technological improvements claimed. The recited “machine learning forecasting processes” are claimed at a high-level of generality, and there are no limitations relating to the structure of the model implementing such processes, let alone any improvements to such structure.
On page 13 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2A, Prong 2, Applicant next argues that the Federal Circuit’s decision in Recentive v. Fox supports its patent eligibility arguments.
PNG
media_image6.png
162
660
media_image6.png
Greyscale
The examiner respectfully disagrees. First, the examiner notes that Applicant has not provided sufficient facts to explain why the present claims are distinguishable from the claims in Recentive. Second, even if the claims are distinguishable, that does not require a finding that the claims are eligible, as the court in Recentive found the claims not to be subject matter eligible as noted by Applicant.
On page 13 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2B, Applicant argues that the claims recite “a combination of specific, non-conventional features that provide a technical improvement beyond any alleged abstract idea.”
PNG
media_image7.png
442
668
media_image7.png
Greyscale
The examiner respectfully disagrees. First, the examiner respectfully disagrees that the claims “provide a technical improvement beyond any alleged abstract idea” for the same reasons explained above with respect to Step 2A, Prong 2.
Second, the examiner disagrees with Applicant’s unsupported assertion that the claims recite “non-conventional features” and notes that Applicant has not provided any evidence supporting such assertion.
Third, as explained above, performing forecasting and applying a correlation algorithm are mental processes. The generic “machine learning” limitation that is used to perform the applying does not provide any improvements to the functioning of a computer or to any other technology or technical field. See MPEP 2106.05(a).
Finally, as explained above with respect to Step 2A, Prong 2, the “automated downstream action where the resulting supply plan causes a separate purchasing system to automatically issue electronic purchase orders to vendor systems” is merely a field-of-use limitation. See MPEP 2106.05(h).
On page 14 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2B, Applicant argues that the specification describes a particular technical benefit.
PNG
media_image8.png
84
652
media_image8.png
Greyscale
The examiner respectfully disagrees. The examiner respectfully submits that this is merely a conclusory statement that does not provide one of ordinary skill with the detail necessary to recognize an actual improvement to the functioning of a computer or to any other technology or technical field. See MPEP 2106.05(a).
On page 14 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2B, Applicant argues that if eligibility is a “close call”, a rejection should be made under 101 only when it is more likely than not that a claim is ineligible.
The examiner respectfully submits that this is not a “close call.” The claims recite numerous mental processes, and in general, relate to using generic machine learning techniques to automate a mental process (of demand forecasting) that humans were performing long before computers and machine learning techniques became available.
On page 14 of Applicant’s 9/18/2025 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, and with respect to Step 2B, Applicant argues that new dependent claims 21 and 22 are eligible because these new claims ground “the forecasting models in these specific, tangible data sources.”
The examiner respectfully disagrees. Simply identifying more specific data used in the machine learning training is merely data gathering, and does not provide a technical improvement.
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 Step 1 of the Alice/Mayo framework, Claims 1-17 are directed to an apparatus (a machine), Claims 8-14 are directed to a method (a process), and Claims 15-20 are directed to a computer program product comprising a non-transitory processor-readable storage medium (an article of manufacture), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components (e.g., “hardware processor”, “machine-learning model”).
pre-process at least portions of the first data set, the second data set and the third data set (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can pre-process at least portions of the first, second, and third data sets by reviewing the data sets for typos or other minor errors)
perform the ... forecasting processes respectively on the pre-processed portions of the first data set, the second data set and the third data set to determine predicted quantity results, the predicted quantity results comprising at least a predicted quantity of the non-customizable system, a predicted quantity of the customizable base system, and a predicted quantity of the components used to customize the customizable base system to supply (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a supply chain professional can review the first, second, and third datasets mentally and predict the recited quantities recited in this claim mentally)
correlate the predicted quantity results of the machine learning forecasting processes using a correlation algorithm, the correlation algorithm modifying the forecasting results associated with the predicted quantity of the components used to customize the customizable base system by deriving a percentage change from a median of variations in one or more of the forecasting results associated with the predicted quantity of the non-customizable system and the predicted quantity of the customizable base system, and applying the derived percentage change to the forecasting results associated with the predicted quantity of the components (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can correlate the predicted results using a correlation algorithm such as a variation of Pearson’s correlation coefficient, and mathematically deriving a percentage change from a median of variations in one or more of the forecasting results associated with the predicted quantity of the non-customizable system and the predicted quantity of the customizable base system, and then applying the mathematically derived percentage change to the forecasting results; the examiner further notes that mathematically determining correlations between variables, and deriving a percentage change from data, are each a mathematical calculation, which is another type of abstract idea)
generate a supply plan for the components used to customize the customizable base system based on the modified forecasting results associated with the predicted quantity of the components used to customize the customizable base system third data set (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a supply chain professional can mentally formulate a supply plan (and write such plan on paper) based on the modified results)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “computer-implemented” and “graph neural network”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “An apparatus comprising: at least one processing platform comprising at least one processor coupled to at least one memory, the at least one processing platform, when executing program code, is configured to” limitation, such limitations are recited at a high-level of generality and amount to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional elements of processing platforms, memory, processors, program code. These additional elements are recited at a high-level of generality and amount to no more than mere instructions to apply the exception using generic computer components (processing platforms, memory, processors, program code). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “input to one or more machine learning algorithms a total system distribution history data set for a manufacturer in a given region over a given time period, to obtain: (i) a first data set representing historical data associated with a non-customizable system, ... (ii) a second data set representing historical data associated with a customizable base system, ... and (iii) a third data set representing historical data associated with components used to customize the customizable base system ... wherein the third data set is determined based on the total system distribution history data set, the first data set and the second data set” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “the non-customizable system comprising a base structure and a manufacturer selected set of one or more components configured to attach to the base structure, ... the customizable base system comprising the base structure, ... the components used to customize the customizable base system comprising a consumer selected set of the one or more components configured to attach to the base structure” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (customizable products having a base structure + optional components). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “for use in respective machine learning forecasting processes, wherein the pre-processing comprises using the one or more machine learning algorithms” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “automatically transmit the generated supply plan to at least one purchasing system operatively coupled to the at least one processing platform” limitation, such limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). Moreover, such additional element of data transmission is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. post-solution activity of transmitting data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “wherein the generated supply plan causes the purchasing system to automatically generate and issue one or more electronic purchase orders to specific component vendor systems based on the generated supply plan” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (purchasing systems that can issue electronic purchase orders). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “computer-implemented” and “graph neural network”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “An apparatus comprising: at least one processing platform comprising at least one processor coupled to at least one memory, the at least one processing platform, when executing program code, is configured to” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “input to one or more machine learning algorithms a total system distribution history data set for a manufacturer in a given region over a given time period, to obtain: (i) a first data set representing historical data associated with a non-customizable system, ... (ii) a second data set representing historical data associated with a customizable base system, ... and (iii) a third data set representing historical data associated with components used to customize the customizable base system ... wherein the third data set is determined based on the total system distribution history data set, the first data set and the second data set” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “the non-customizable system comprising a base structure and a manufacturer selected set of one or more components configured to attach to the base structure, ... the customizable base system comprising the base structure, ... the components used to customize the customizable base system comprising a consumer selected set of the one or more components configured to attach to the base structure” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding the “for use in respective machine learning forecasting processes, wherein the pre-processing comprises using the one or more machine learning algorithms” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “automatically transmit the generated supply plan to at least one purchasing system operatively coupled to the at least one processing platform” limitation, this limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”). Moreover, as discussed above, the additional element of data transmission is recited at a high level of generality and amounts to extra-solution activity of transmitting data, i.e. post-solution activity of transmitting data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “wherein the generated supply plan causes the purchasing system to automatically generate and issue one or more electronic purchase orders to specific component vendor systems based on the generated supply plan” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding Claim 2
Step 2A, Prong 1
wherein pre-processing at least portions of the first data set, the second data set and the third data set further comprises performing a ... classification process on each of the portions of the first data set, the second data set and the third data set. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can perform a classification process on the first, second, and third data sets, such as to classify particular product lines)
Step 2A, Prong 2
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 3
Step 2A, Prong 1
wherein the obtaining the third data set, comprises ... analyzing a bill of materials for the non-customizable system to identify a subset of the total system distribution history data set not included in the first data set for use in the machine learning forecasting processes (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can mentally review a bill of materials (which is just a document) to identify a subset of the total system distribution history data set not included in the first data set, e.g., the human can underline the portions of the BOM using a pencil to determine portions not included in the subset of the total system distribution history data set not included in the first data set)
Step 2A, Prong 2
Regarding the “digitally” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation generically recites an effect of the judicial exception, or claims every mode of accomplishing that effect (e.g., all “digital” modes). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “digitally” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation generically recites an effect of the judicial exception, or claims every mode of accomplishing that effect. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 4
Step 2A, Prong 1
perform a smoothing process on respective results of the machine learning forecasting processes of the first data set and the second data set (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can mentally apply a smoothing function to the forecasted data, such as a simple moving average; the examiner notes that such a smoothing process is also a mathematical calculation, which is another type of abstract idea)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 5
Step 2A, Prong 1
wherein the smoothing process comprises a linear regression algorithm. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can perform smoothing using a linear regression algorithm; the examiner further notes that such a smoothing process is also a mathematical calculation, which is another type of abstract idea)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
wherein one or more of the ... forecasting processes comprises a Bayesian network-based process. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using physical aids such as pencil and paper), for example, a human can perform a Bayesian network-based process, which is a probabilistic graphical model, mentally or using pencil and paper)
Step 2A, Prong 2
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “machine learning” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 8
Step 2A, Prong 1
Claim 8 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“processor”, “memory”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 8 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“processor”, “memory”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 8 recites a method that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“processor”, “memory”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2B.
Claims 9-13 depend from claim 8, and claim a method that corresponds to the apparatuses of claims 2-6, respectively, and therefore claims 9-13 are rejected for the same reasons explained above with respect to claim 8 and claims 2-6, respectively.
Regarding Claim 15
Step 2A, Prong 1
Claim 15 recites a computer program product comprising a non-transitory processor-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“computer program product comprising a non-transitory processor-readable storage medium”, “processing platform”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 15 recites a computer program product comprising a non-transitory processor-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“computer program product comprising a non-transitory processor-readable storage medium”, “processing platform”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 15 recites a computer program product comprising a non-transitory processor-readable storage medium that corresponds to the apparatus of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“computer program product comprising a non-transitory processor-readable storage medium”, “processing platform”, “program code”, “machine learning”), such additional generic computing components do not change the analysis under Step 2B.
Claims 16-20 depend from claim 15, and claim a computer program product comprising a non-transitory processor-readable storage medium that corresponds to the apparatuses of claims 2-6, respectively, and therefore claims 16-20 are rejected for the same reasons explained above with respect to claim 15 and claims 2-6, respectively.
Regarding Claim 21
Step 2A, Prong 2
Regarding the “inputting order sales data and seasonal sales data to the machine learning forecasting process for the non-customizable system” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of inputting data into a machine learning forecasting process. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (inputting data into a machine learning forecasting process). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “inputting backlog data, seasonality data, and safety stock data to the machine learning forecasting process for the components used to customize the customizable base system” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of inputting data into a machine learning forecasting process. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (inputting data into a machine learning forecasting process). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Step 2B
Regarding the “inputting order sales data and seasonal sales data to the machine learning forecasting process for the non-customizable system” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “inputting backlog data, seasonality data, and safety stock data to the machine learning forecasting process for the components used to customize the customizable base system” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Moreover, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Claim 22 depends from claim 15 and claims a method that corresponds to the apparatus of claim 21 and is therefore rejected for the same reasons explained above with respect to claims 15 and 21.
Allowable Subject Matter
Claims 1-6, 8-13, and 15-22 would be allowed over the prior art if the rejections under 35 U.S.C. 101 are overcome.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Independent Claims 1, 8, and 15
Independent claims 1, 8, and 15 would be considered allowable because none of the references of record, either alone or in combination, fairly disclose or suggest the combination of limitations specified in the independent claims, including at least:
input to one or more machine learning algorithms a total system distribution history data set for a manufacturer in a given region over a given time period, to obtain: (i) a first data set representing historical data associated with a non-customizable system, the non-customizable system comprising a base structure and a manufacturer selected set of one or more components configured to attach to the base structure; (ii) a second data set representing historical data associated with a customizable base system, the customizable base system comprising the base structure; and (iii) a third data set representing historical data associated with components used to customize the customizable base system, the components used to customize the customizable base system comprising a consumer selected set of the one or more components configured to attach to the base structure, wherein the third data set is determined based on the total system distribution history data set, the first data set and the second data set;
perform the machine learning forecasting processes respectively on the pre-processed portions of the first data set, the second data set and the third data set to determine predicted quantity results, the predicted quantity results comprising at least a predicted quantity of the non- customizable system, a predicted quantity of the customizable base system, and a predicted quantity of the components used to customize the customizable base system to supply;
correlate the predicted quantity results of the machine learning forecasting processes using a correlation algorithm, the correlation algorithm modifying the forecasting results associated with the predicted quantity of the components used to customize the customizable base system by deriving a percentage change from a median of variations in one or more of the forecasting results associated with the predicted quantity of the non-customizable system and the predicted quantity of the customizable base system, and applying the derived percentage change to the forecasting results associated with the predicted quantity of the components;
The closest prior art of record discloses:
US 20200143313 A1 (OHLSSON) discloses “systems and methods for improved inventory management and optimization using machine learning techniques, which may be applied to improve inventory management by more accurately determining optimal inventory levels.” (para. 0002). “In some embodiments, the system can compute the present, incoming or expected demand requirement using forecasted demand for finished products and a bill of materials (BOM) for the finished products. In some embodiments, the BOM can comprise a dynamic hierarchical graph.” (para. 0018).
US 20130191185 A1 (GALVIN) discloses techniques for “automated analysis of complex customer care processes.” (para. 0003). “For example, a after running a training set through a machine learning module 1110, a set of patterns might be deduced and expressed in terms of a set of adaptive parameters 1120. Then, using these parameters to "tune" a prediction algorithm, an initial subset of a data test set is passed through the prediction algorithm, and a predicted set of attributes for a likely subsequent data set is generated. ... Either continuously or after a required level of forecast accuracy is achieved, adaptive parameters 1120 may be used to make predictions based on sets of incoming data retrieved from data layer 1102, and in some embodiments adaptive parameters 1120 are fed back into data layer 1102 as shown in FIG. 11.” (para. 0122).
US 20170213176 A1 (CHEN) discloses “workflow systems.” (para. 0006). “The storage engine 104 can divide or otherwise organize the storage medium to separate the parts of the workflow that are customizable and the parts of the workflow that are non-customizable. ... For example, the business rules can be organized in the form of a file system and the customizable rules can be maintained in a database. The restricted parts and the customizable parts can be kept in a similar or the same data structure to facilitate combining the restricted parts and the customizable parts into a single workflow.” (para. 0011).
US 20220277263 A1 (ESMALIFALAK) discloses “computerized systems for predictively managing inventory.” (para. 0001). “Some embodiments described herein utilize a machine learning model which may learn, based on historical work data, how much work a user/organization/entity may be doing in the future, and the type and quantity of parts that future work will require. This may allow for improved accuracy of forecasts, because forecasts are not based on any assumptions around potential demand, and are instead based on direct demand for individual parts (or sets of correlated parts).” (para. 0022).
US 20190205833 A1 (BIRO) explains that “Demand planning in supply chains is an increasingly complex, and resource intensive task. There are multiple changes that are taking place in modern supply chains, such as an increase of the number of products offered by manufacturing companies, shorter product lifecycles, and an increased demand for more personalized products. In demand planning, demand for new products is forecast based on demand of predecessor products, and/or similar products from the past.” (para. 0001). Discloses using a “weighted combined history” to generate a forecast. (para. 0035).
US 20120059734 A1 (CHISM) discloses “Automated materials ordering, again upon cost and order approval, interfaces the data compiled in the fabric and frame material "B.O.M." with the vendors' corresponding catalogue codes, automatically creating and emailing an electronic purchase order to the appropriate vendor for bulk materials processing and shipment.” (para. 0054)
US 20170140319 A1 (GOTTEMUKKALA) discloses “ Additionally, the user can elect to also apply that percentage change to other remaining forecast values (e.g., the forecast for Mass Market distribution of Cosmo Cola in M07-2013). As the user manipulates the value of the forecast (or other measures of the underlying plan model), the effect of this change can be illustrated in one or more (automatically updated) views of the plan model presented to the user, including the tower view.” (para. 0123).
Kim, Myungsoo, et al. "Demand forecasting based on machine learning for mass customization in smart manufacturing." Proceedings of the 2019 International Conference on Data Mining and Machine Learning. 2019. “In this study, we survey why mass customization is needed in Smart Manufacturing and look for appropriate demand prediction techniques by comparing the traditional linear analysis method ARIMA time series analysis with the nonlinear analysis method LSTM neural network model.” (p. 5, section 5).
Li, Jiahua, et al. "Machine learning algorithm generated sales prediction for inventory optimization in cross-border E-commerce." International Journal of Frontiers in Engineering Technology 1.1 (2019): pp. 62-74. “On the cross-border e-commerce ERP platform, the product sales forecast and inventory optimization strategy realized by machine learning algorithm can effectively summarize the key factors, use the sales record big data, so that the forecast value, the expectations fit the actual value of the basic trend.” (Abstract.)
However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in independent claims 1, 8, and 15. Moreover, the examiner finds that one of ordinary skill would not have been motivated to combine the prior art of record in the specific manner claimed in independent claims 1, 8, and 15 without the hindsight aid of Applicant’s disclosure. Therefore, to the extent that these features are not found in the prior art cited by the examiner, claims 1, 8, and 15 would be allowed over the prior art of record if the rejections under 35 U.S.C. 101 are overcome.
Dependent claims 2-6, 9-13, and 16-22 would similarly be allowed for depending from an allowed independent base claim, if the rejections under 35 U.S.C. 101 are overcome.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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, Omar Fernandez Rivas can be reached at 571-272-2589. 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.
/MICHAEL C. LEE/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
Prosecution Insights