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 .
DETAILED ACTION
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 24, 2026 has been entered.
2. Claims 1, 7-8, 14-15, and 21 are pending in this application.
Claim Rejections - 35 USC § 112
3. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
4. Claims 1, 7-8, 14-15, and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The independent claims 1, 8, and 15 recite the limitations “wherein the training data is computationally large training data” and “…adjust the first forecast value…without the need to generate features and train another forecast model on the computationally large training data” were not described in the specification in such a way as to reasonably convey to one skilled in the relevant art.
The Specification para [0122] described “In some embodiments, the amount of data in the testing portion may be too large for timely execution, in which case, an absolute time frame of data is chosen; para [0031], This configuration (e.g. features and hyperparameters) is saved to the datastore and the selected model is trained on a larger training set and its performance is measured on a testing set that corresponds to the most recent data acquired about the product sales at the Kanata store.” Therefore, the Specification do not describe “wherein the training data is computationally large training data.”
The Specification para [0107] described “If the time threshold is not surpassed, monitor module 112 proceeds to instruct forecasting module 114 to forecast using the current model at block 1018, without any retraining”; para ‘[0110], “Features engineering may be automated in the sense that the system can generate features more amenable to machine learning without having a user define one or more transformations of the features engineering process”; para [0116], “Where retraining of the selected ML model occurs without model selection process 1102 (i.e. retraining only), the selected ML model is retrained on an expanded engineered data set comprising data corresponding to the training and validation portions of the dataset (at block 1114).” Therefore, the Specification do not describe “…adjust the first forecast value…without the need to generate features and train another forecast model on the computationally large training data.”
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1, 7-8, 14-15, and 21 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Independent claim 1, which is illustrative of the all independent claims and analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method for using a forecast model for providing forecast 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.
The claim recites the steps of: generating first forecast data for a first forecast time interval…; determining an error in the first forecast data comprises: determining a difference between each of the first forecast values of the first forecast data…; determining a percentage error of the difference for each of the differences of the forecast values…; determining an average of the percentage errors as an average percentage error…, adjusting the forecast values of the first forecast data…without the need to generate features and train another forecast model…; generating second feature data based on the second forecast data, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps of: generate first feature data to train a first machine learning forecast model, wherein the training data is computationally large training data, which are directed to mathematical relationships, falls within “Mathematical Concepts” grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (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 “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data”, “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval.” The claim also recites that the steps are performed by a processor and a memory.
The additional elements “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data” are mere data gathering, transmitting and receiving 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 transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and transmitting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the user interface, they are just merely used as general means for collecting and transmitting data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
The additional elements “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” are used to generally apply the abstract idea without placing any limits on how the machine learning models/algorithms function. Rather, these limitations only recite the outcome of “to form a first/second forecast model” and “generating first/second forecast data for a first/second forecast time interval” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements of “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” also merely indicate a field of use or technological environment in which the judicial exceptions are performed. Although the additional elements “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” limit the identified judicial exceptions “to form a first/second forecast model” and “generating first/second forecast data for a first/second forecast time interval”, this type of limitations merely confine the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
The claim recites the additional element of “processor” and “a memory” for performing the recited steps. The processor and the memory are recited at a high level of generality. In the limitations “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data”, the processor and the memory are used as tools for performing the function of collecting and transmitting data. In the limitations, “generating first forecast data for a first forecast time interval…; determining an error in the first forecast data comprises: determining a difference between each of the first forecast values of the first forecast data…; determining a percentage error of the difference for each of the differences of the forecast values…; determining an average of the percentage errors as an average percentage error…, adjusting the forecast values of the first forecast data…without the need to generate features and train another forecast model…; generating second feature data based on the second forecast data”, the processor and the memory are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processor, the memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as 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).
Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to memory, improvement to the machine learning models, they are just merely used as general means for collecting and retrieving data, and performing the abstract ideas.
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, Prong One: 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 of “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and transmitting. 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 “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
As discussed in Step 2A, Prong Two above, the recitation of the processor to perform the recited steps, 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. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 8 and 15, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 8 directed to a medium, independent claim 15 directed to a system, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Dependent claims 7, 14, and 21, have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent- ineligible under 35 U.S.C. 101.
Regarding dependent claims 7, 14, and 21, the claims simply refine the abstract idea by further reciting determining an error in the first forecast data…, adjusting the first forecast data based on the error…, that fall under the category of mental process grouping of abstract ideas as described above in the independent claim 1. Moreover, the claims recite the additional elements “receiving subsequent sales data” and “transmitting the subsequent adjusted forecast data to the user” are mere data transmitting and receiving 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 transmitting and receiving. See MPEP 2106.05 (see claim 1 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea. Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1, 7-8, 14-15, and 21 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Novelty and Non-Obviousness
7. No prior arts were applied to the claims because the Examiner is unaware of any prior arts, alone or in combination, which disclose at least the limitations of “determining an error in the first forecast data, wherein determining the error comprises: determining a difference between each of the first forecast values of the first forecast data and the corresponding first sales values of the first sales data for each of the plurality of store locations for the at least one time period in the second time interval; determining a percentage error of the difference for each of the differences of the first forecast values of the first forecast data and the corresponding first sales values for each of the plurality of store locations for the at least one time period in the second interval; and determining an average of the percentage errors as an average percentage error; for each of the plurality of store locations, adjusting the first forecast values of the first forecast data corresponding to the remainder of the first forecast time interval after the second time interval by the average percentage error to form first adjusted forecast data” recited in the independent claims 1, 8, and 15.
Response to Arguments/Amendment
8. Applicant's arguments with respect to claims 1, 7-8, 14-15, and 21 have been fully considered but are not persuasive.
Claim Rejections - 35 USC § 101
Claims 1, 7-8, 14-15, and 21 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more (See details above).
In response to the Applicant’s arguments that “the amended claims are not
directed to Mental Process”, the Examiner respectfully disagrees and submits that:
The claim recites the steps of: generating first forecast data for a first forecast time interval…; determining an error in the first forecast data comprises: determining a difference between each of the first forecast values of the first forecast data…; determining a percentage error of the difference for each of the differences of the forecast values…; determining an average of the percentage errors as an average percentage error…, adjusting the forecast values of the first forecast data…without the need to generate features and train another forecast model…; generating second feature data based on the second forecast data, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Moreover, the claim recites the steps of: generate first feature data to train a first machine learning forecast model, wherein the training data is computationally large training data, which are directed to mathematical relationships, falls within “Mathematical Concepts” grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
The new features added to the claims ““generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” are additional elements and are analyzing in Step 2A, Prong Two and Step 2B explained above.
In response to the Applicant’s arguments that the new features added to the claims “adjusting the first forecast values of the first forecast data corresponding to the remainder of the first forecast time interval after the second time interval by the average percentage error to form first adjusted forecast data without the need to generate features and train another forecast model on the computationally large training data”, provide improvement to the technology, the Examiner respectfully disagrees and submits that these new features added to the claims do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, the machine learning model or other technology. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing. Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application.
The additional elements “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” are used to generally apply the abstract idea without placing any limits on how the machine learning models/algorithms function. Rather, these limitations only recite the outcome of “to form a first/second forecast model” and “generating first/second forecast data for a first/second forecast time interval” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements of “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” also merely indicate a field of use or technological environment in which the judicial exceptions are performed. Although the additional elements “generate first feature data to train a first machine learning forecast model”, “processing the first feature data for training a machine learning algorithm to form the first forecast model”, “training the first machine learning forecast model on the first feature data”, “generating first forecast data for a first forecast time interval using the first machine learning forecast model”, ”generate second feature data to train a second machine learning forecast model”, “processing the second feature data for training a machine learning algorithm to form a second forecast model”, “using the second forecast model for providing second forecast data for a second forecast time interval” limit the identified judicial exceptions “to form a first/second forecast model” and “generating first/second forecast data for a first/second forecast time interval”, this type of limitations merely confine the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Moreover, the additional elements of “collecting historical data for a first forecast time interval”, “saving the historical data collected in the first time interval to a training data store”, “transmitting the first forecast data to a user”, “receiving first sales data for the plurality of store locations for a second time interval”, “transmitting the first adjusted forecast data to the user”, “collecting second forecast data”, “saving the second forecast data to the training data store”, “transmitting the second forecast data to the user, wherein the determining an error in the first forecast data and transmitting the first adjusted forecast data to the user is executed prior to transmitting the second forecast data” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
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. Therefore, the claims are not patent eligible.
According, the 101 rejection is maintained.
Conclusion
9. Claims 1, 7-8, 14-15, and 21 are rejected.
10. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Arroyo et al. (US 2020/0302455) disclose a system and method for using machine learning to improve forecasting of market behavior which includes identifying market forecast data associated with forecasting intervals; retrieving the forecast data and actual market behavior data corresponding to a historical forecasting interval.
Morgan et al. (US 2020/0134642) disclose the demand forecasting system and methods for predicting demand of products in a retail context. Forecast models are built and used to score incoming sales data to predict future demand for items.
Popscu et al. (US 2020/0074485) disclose select demand forecast parameters for a demand model for one or more items, receive historical sales data for the items on a per store basis and receive a plurality of seasonality curves for a first item.
Johnson et al. (US 2017/0213227) disclose apparatuses and methods for wide access to numerous different previously compiled forecast modeling. In some embodiments, the system enables wide access to forecasting, comprising: a forecast model database that maintains numerous different forecast models that when run produce resulting forecast data relevant to making business decisions.
Chien et al. (US 2008/0255924) disclose computer-implemented systems and methods to perform accuracy analysis with respect to forecasting models, wherein the forecasting models provide predictions based upon a pool of production data.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 April 16, 2026
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