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 .
This action is in response to the communication filed on January 14, 2026.
Response to Amendment
Applicant’s amendment filed on January 14, 2026 with respect to claims 1-20 has been received, entered into the record and considered.
As a result of the amendment, claims 1, 8 and 15 has been amended.
Claims 1-20 remain pending in this office action.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 14, 2026 has been entered.
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, 12 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 This part of the eligibility analysis evaluates whether the claim falls within any statutory category MPEP 2106.03.
Step 2A Prong One This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(II) and the October 2019 Update, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step 2A Prong 2 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. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG.
Step 2B This part of the eligibility analysis evaluates whether the claim as a whole amount 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. MPEP 2106.05.
Step 1 Statutory Category:
Claims 1-7 are recited as being directed to “a method comprising …”. Claims 8-14 is recited as being directed to “a system comprising …”.
Claims 15-20 is recited as being directed to a “non-transitory computer readable storage medium storing instruction …” Thus, claims 1, 8 and 15 have been identified to be directed towards the appropriate statutory category. Below is further analysis related to step 2.
a). In analyzing under step 2A Prong One, Does the claim recite an abstract idea law of nature or natural phenomenon? Yes.
Claims 1, 8 and 15 recites, receiving a data query comprising a reference to an input data set of a database; generating, by a processing device, a plurality of hyperparameter sets based on characteristics of the input data set; training a plurality of machine learning models by varying the plurality of hyperparameter sets to obtain an output accuracy of at least one of the plurality of machine learning models that satisfies a threshold; selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model satisfying the threshold; and returning the output of the first machine learning model..
As claim texts drafted by a set of very minimal limitations (or elements) of each of the four claim categories, receiving a query referencing to set of data in a database; generating hyperparameter set; training machine learning models; selecting a model based on the accuracy of output; and return the output of the model, are merely a process that, under its broadest reasonable interpretation, covers mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of processing device, memory and a computer readable medium which are explicitly generic computing components, including:
“receiving a data query comprising a reference to an input data set of a database”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a user can query a row or column with different values of market or business data stored in a database, using his/her mind by observation and opinion. Therefore, the receiving limitation is a mental process (including an observation, evaluation, judgment, opinion).
Similarly, generating, by a processing device, a plurality of hyperparameter sets based on characteristics of the input data set; as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a user can generate a set of parameters such as which item or goods are more popular in different season, or different time of the year, trend, pattern, etc., (i.e., hyperparameter based on characteristics), using his or her mind or with the aid of pen and paper, by simply observing and judgement. Therefore, the generating limitation is a mental process (including an observation, evaluation, judgment, opinion).
Similarly, selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model satisfying the threshold, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, a user can select the best accurate model using his/her mind, by evaluation and judgement. Therefore, the selecting limitation is a mental process (including an observation, evaluation, judgment, opinion).
The claim recites three additional; elements: “receiving a query referencing to set of data in a database”; “training a plurality of machine learning models by varying the plurality of hyperparameter sets to obtain an output accuracy of at least one of the plurality of machine learning models that satisfies a threshold” and “returning the output of the first machine learning model”.
The receiving step as recited amounts to mere data gathering for use in the detection step, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Further receiving input step as recited also amounts to mere data gathering which is a form of insignificant extra-solution activity. The training step as recited is merely a generic computer component taking an off the shelves model and train with different values, compare the output and select the one which gives the best accurate result and return the output. Hence, training step and output step is an insignificant extra-solution activity. Accordingly, even in combination, 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. The claim is directed to the abstract idea.
b) In analyzing under step 2A Prong Two, Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “system comprising one or more computers”, “one or more non-transitory computer readable storage medium”, and “training machine learning model”. The additional components are generic computer components even being recited as additional limitations, however, do not preclude the claims from reciting an abstract idea. For instance, as the above detailed analysis on the minimal limitations as abstract ideas that can be performed mentally in mind by human, without reciting any “additional element” to integrate the judicial exception into a practical application.
The processes of receiving necessities for performing an action and providing indication of completed such that it amounts no more than mere instructions to apply the exception using a generic computer component, processing unit(s), memory and computer readable medium for the processes. That is, the limitations represent well-understood, routine, conventional activity (See MPEP 2106.05(g) or 2106.05(d) for receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp). Accordingly, even considering all the elements as additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As such, the claim is directed to an abstract idea.
c) In analyzing under step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception? NO
The claims 1, 8 and 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, there is simply no additional elements adding to the already analyzed very few minimal steps of performing action. The steps, represent well-understood, routine, conventional activity previously known to the industry and are specified at a high level of generality, and in the context of the limitations reciting performing action that can be practically performed in the human mind and may be considered to fall within the mental process and mathematical concepts groupings.
As such, the limitations represent well-understood, routine, conventional activity (See MPEP 2106.05(g) or 2106.05(d) for receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec; Storing and retrieving information in memory: Versata; Analyzing data: Genetic Techs; Determining: OIP Techs; Electronic recordkeeping: Alice Corp). The claims are not patent eligible.
Further the limitations in the dependent claims 2-7, 9-14 and 16-20 are an extension of the abstract idea of claim 1, 8 and 15 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Joseph et al (US 2020/0184494 A1), in view of Flukert et al (US 10,936,947 B1).
As per claim 1, Joseph discloses:
- a method comprising (Para [0007], line 1-5, “methods and computer-readable media for computing demand forecasts based on machine learning techniques”),
- generating, by a processing device, a plurality of hyperparameter sets based on characteristics of the input data set (Fig. 1, 3, Para [0034], [0038], set of parameters generating time series data for a demand forecast (i.e., plurality of hyperparameter set based on characteristic, Para [0057]),
- training a plurality of machine learning models using the plurality of hyperparameter sets to obtain an output accuracy of at least one of the plurality of machine learning models that satisfies a threshold (Fig. 4, item 410-418, Para [0008] [0009], [0037], [0049]- [0052], training or re-raining a machine learning model using different timeseries data to obtain an accurate output that satisfy the user criteria or threshold),
- selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model (Para [0009], [0019], [0051], Fig. 4A-4C, selecting based on accuracy of the output a machine learning model that satisfy the user configurable criteria,
- returning the output of the first machine learning model (Para [0036], [0045], receive output from the model, Fig. 4B),
Joseph does not explicitly disclose receiving a data query comprising a reference to an input data set of a database. However, in the same field of endeavor Flunkert in an analogous art disclose receiving a data query comprising a reference to an input data set of a database (column 17, line 30-40, column 18, line 1-10, Fig. 8, receiving a timeseries forecast query).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the data query referring to a set of data in a database taught by Flunkert as the means to generate hyperparameter based on characteristics of the input data and training and selecting a machine learning model based on accuracy of output of different mode of Joseph. (Joseph, Para [008] [ 009], Fig. 3-4, Flunkert, column 17, line 30-40, column 18, line 1-10, Fig. 8). Joseph and Flunkert are analogous prior art since they both deal with training machine learning model using generated hyperparameter from an input query. A person of the ordinary skill in the art would have been motivated to make aforementioned modification to accurately forecasting of future demand of a goods or time series data. This is because one aspect of Joseph invention is to forecasting demand for a dataset based on the most accurate results for the particular dataset and/or other criteria which can be configured by users, as described in Para [0002] [ 0007]. Query input data referencing to a dataset in a database is part of this process. However, Joseph doesn’t specify any particular manner that input data referencing to a dataset in a database are queried. This would have lead one of the ordinary skill in the art to seek and recognize input data referencing to a dataset in a database are queried as taught by Flunkert. Flunkert teaches query data set from a database to provide accurate forecast, as described in column 1, line 5-10, as desired by Joseph.
As per claim 2, rejection of claim 1 is incorporated, and further Flunkert discloses:
- wherein generating the plurality of hyperparameter sets comprises varying a value of each hyperparameter of the plurality of hyperparameter sets based on at least one of a volatility or a range of the input data set (hyperparameter based on range of input, column 11, line 10-20, column 14, line 30-40).
As per claim 3, rejection of claim 2 is incorporated and further Flunkert discloses:
- wherein the plurality of hyperparameter sets comprises one or more of a trend factor, a seasonality factor, or holiday factors (hyperparameter with seasonal and holiday factor, column 3, line 15-35, column 15, line 25-35).
As per claim 4, rejection of claim 1 is incorporated, and further Joseph discloses:
- wherein the training of the plurality of machine learning models is performed concurrently on a plurality of compute nodes (Para [0035], training performed in concurrently).
As per claim 5, rejection of claim 1 is incorporated, and further Joseph discloses:
- wherein each of the plurality of machine learning models are trained to perform a time series forecasting operation on the input data set (Para [0038], [0057], time series forecast).
As per claim 6, rejection of claim 1 is incorporated, and further Joseph discloses:
- wherein selecting the first machine learning model of the plurality of machine learning models based on the accuracy of the output of the first machine learning model comprises comparing accuracy values of respective output data sets of each of the plurality of machine learning models to select the first machine learning model having a highest accuracy value (Para [0029], [0048], matching or comparing the evaluation result with another model).
As per claim 7, rejection of claim 6 is incorporated, and further Flunkert discloses:
- wherein respective ones of the accuracy values comprise a confidence interval of the output data sets (column 17, line 10-20, column 18, line 20-30, associated confidence interval of the dataset).
As per claims 8-14,
Claims 8 -14 are system claims corresponding to method claims 1-7 respectively and rejected under the same reason set forth to the rejection of claims 1-7 above.
As per claims 15-20,
Claims 15-20 are computer readable medium claims corresponding to method claims 1 -6 respectively and rejected under the same reason set forth to the rejection of claims 1-6 above.
Response to Arguments
Applicant's arguments filed on January 14, 2026, with respect to claims 1-20 have been fully considered but they moot because of the new ground of rejection necessitated by the amendment to the claims.
In response to applicant’s argument in page 1-3, applicants argued that, cited references, alone or in combination, fail to suggest the features “generating, by a processing device, a plurality of hyperparameter sets based on characteristics of the input data set training; a plurality of machine learning models by varying the plurality of hyperparameter sets to obtain an output accuracy of at least one of the plurality of machine learning models that satisfies a threshold," as recited in claim 1.
Examiner respectfully response that, newly found reference Joseph teaches - generating, by a processing device, a plurality of hyperparameter sets based on characteristics of the input data set in Fig. 1, 3, Para [0034], [0038], set of parameters generating time series data for a demand forecast (i.e., plurality of hyperparameter set based on characteristic, Para [0057]). Joseph also teaches training a plurality of machine learning models using the plurality of hyperparameter sets to obtain an output accuracy of at least one of the plurality of machine learning models that satisfies a threshold in Fig. 4, item 410-418, Para [0008] - [0009], [0037], [0049]- [0052], training or re-raining a machine learning model using different timeseries data to obtain an accurate output that satisfy the user criteria or threshold), and Joseph teaches selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model in Para [0009], [0019], [0051], Fig. 4A-4C, selecting based on accuracy of the output a machine learning model that satisfy the user configurable criteria,
Therefore, examiner firmly believe that, Joseph and Flunkert alone or in combination reasonably teaches the amended limitation and other applicant’s argued limitation, as claimed.
Contact Information
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED R UDDIN whose telephone number is (571)270-3138. The examiner can normally be reached M-F: 9:00 AM-5:00 PM.
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, Apu Mofiz can be reached on 571-272-4080. 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.
/MOHAMMED R UDDIN/Primary Examiner, Art Unit 2161