DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed June 29th, 2026, in which claims 1, 4, 8, 11, 15, and 18 have been amended and claims 2-3, 5, 9-10, 12, 16-17, and 19 have been cancelled. No claims have been added. The amendments have been entered, and claims 1, 4, 6-8, 11, 13-15, 18, and 20 are currently pending in the case. Claims 1, 8, and 15 are independent claims.
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 claim 1:
Step 1: Claim 1 is directed to [a] method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“partitioning the time series into a plurality of partitions, each partition of the plurality of partitions comprising a time window, wherein a size of the time window captures a pattern in the time window” this encompasses the mental portioning of observed time series into a plurality of partitions according to observed patterns. Further, this limitation is a mathematical concept.
“determine one or more relevant patterns captured within each partition of the plurality of partitions” this encompasses the mental determination of observed patterns within observed partitions.
“generating…a plurality of tokens, wherein for the each partition of the plurality of partitions… output[s] a corresponding token, wherein the each corresponding token includes a multidimensional vector space including a position encoding that indicates the each partition of the plurality partitions with respect to other partitions of the plurality of partitions” this encompasses the mental creation of a plurality of tokens based on observed time series.
“determine relationships between and correlations among the plurality of tokens and relevant impacts that determined relationships and correlations among the plurality of tokens have on the plurality of tokens as a whole” this encompasses the mental determination of relationships among observed tokens.
“generating…a transformer vector, wherein the transformer vector includes a summary of the determined relationships and the correlations made…among the plurality of tokens” this encompasses the mental creation of a transformer vector based on observed relationships among observed tokens.
“assigning…a classification to the transformer vector, wherein …one of three sign classes [is assigned], including up, down, and flat, as a prediction for a time period that is of the same duration as the time window and that is an immediate next time period outside of the time series, based on a probability distribution among the three sign classes.” this encompasses the mental assignment of a classification of one of a number of signs for a future time period based to an observed transformer vector.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…a time series” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns”, “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…a time series” limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to 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). as well as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claim 4, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“a number of dimensions in the multidimensional vector space corresponds to a number of patterns” a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the convolutional neural network machine learning model is trained to predict” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 6, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the classification is a sign prediction” a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 7, the rejection of claim 6 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the sign prediction is an upward indication” a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 8:
Step 1: Claim 8 is directed to [a] system, therefore it falls under the statuary category of a machine.
Step 2A Prong 1: The claim recites, in part:
“partition the time series into a plurality of partitions, each partition of the plurality of partitions comprising a time window, wherein a size of the time window captures a pattern in the time window” this encompasses the mental portioning of observed time series into a plurality of partitions according to observed patterns. Further, this limitation is a mathematical concept.
“determine one or more relevant patterns captured within each partition of the plurality of partitions” this encompasses the mental determination of observed patterns within observed partitions.
“generate…a plurality of tokens, wherein for the each partition of the plurality of partitions… output[s] a corresponding token, wherein the each corresponding token includes a multidimensional vector space including a position encoding that indicates the each partition of the plurality partitions with respect to other partitions of the plurality of partitions” this encompasses the mental creation of a plurality of tokens based on observed time series.
“determine relationships between and correlations among the plurality of tokens and relevant impacts that determined relationships and correlations among the plurality of tokens have on the plurality of tokens as a whole” this encompasses the mental determination of relationships among observed tokens.
“generate…a transformer vector, wherein the transformer vector includes a summary of the determined relationships and the correlations made…among the plurality of tokens” this encompasses the mental creation of a transformer vector based on observed relationships among observed tokens.
“assign…a classification to the transformer vector, wherein …one of three sign classes [is assigned], including up, down, and flat, as a prediction for a time period that is of the same duration as the time window and that is an immediate next time period outside of the time series, based on a probability distribution among the three sign classes.” this encompasses the mental assignment of a classification of one of a number of signs for a future time period based to an observed transformer vector.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…a time series” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns”, “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…a time series” limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to 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). as well as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claims 11 and 13-14:
The rejection of claim 8 is further incorporated, the rejection of claims 4 and 6-7 are applicable to claims 11 and 13-14, respectively.
Regarding claim 15:
Step 1: Claim 15 is directed to [a] non-transitory computer readable storage medium, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“partitioning the time series into a plurality of partitions, each partition of the plurality of partitions comprising a time window, wherein a size of the time window captures a pattern in the time window” this encompasses the mental portioning of observed time series into a plurality of partitions according to observed patterns. Further, this limitation is a mathematical concept.
“determine one or more relevant patterns captured within each partition of the plurality of partitions” this encompasses the mental determination of observed patterns within observed partitions.
“generating…a plurality of tokens, wherein for the each partition of the plurality of partitions… output[s] a corresponding token, wherein the each corresponding token includes a multidimensional vector space including a position encoding that indicates the each partition of the plurality partitions with respect to other partitions of the plurality of partitions” this encompasses the mental creation of a plurality of tokens based on observed time series.
“determine relationships between and correlations among the plurality of tokens and relevant impacts that determined relationships and correlations among the plurality of tokens have on the plurality of tokens as a whole” this encompasses the mental determination of relationships among observed tokens.
“generating…a transformer vector, wherein the transformer vector includes a summary of the determined relationships and the correlations made…among the plurality of tokens” this encompasses the mental creation of a transformer vector based on observed relationships among observed tokens.
“assigning…a classification to the transformer vector, wherein …one of three sign classes [is assigned], including up, down, and flat, as a prediction for a time period that is of the same duration as the time window and that is an immediate next time period outside of the time series, based on a probability distribution among the three sign classes.” this encompasses the mental assignment of a classification of one of a number of signs for a future time period based to an observed transformer vector.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…a time series” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “at a forecasting platform”, “the convolutional neural network machine learning model is trained on historical data”, “made by the transformer machine learning model”, “the multilayer perceptron classifier assigns”, “processing the time series with a convolutional neural network machine learning model”, “by the convolutional neural network machine learning model”, “the convolutional neural network machine learning model outputs”, “processing the plurality of tokens with a transformer machine learning model”, “by the transformer machine learning model”, “determined by the transformer machine learning model”, “by a multilayer perceptron classifier”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…a time series” limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to 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). as well as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claims 18 and 20:
The rejection of claim 15 is further incorporated, the rejection of claims 4 and 6 are applicable to claims 18 and 20, respectively.
Allowable Subject Matter
Claims 1, 4, 6-8, 11, 13-15, 18, and 20 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101 set forth in this Office action.
After detailed search, the cited arts, neither alone nor in combination, teach the claimed subject matter of claim 1, “the multilayer perceptron classifier assigns one of three sign classes, including up, down, and flat, as a prediction for a time period that is of the same duration as the time window and that is an immediate next time period outside of the time series, based on a probability distribution among the three sign classes”.
Pertinent art Zhang et al. ("DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", Zhang et al., 23 Jan 2020) discloses using an machine learning model to forecast signs including “At each time-step, our model generates a signal from the network outputs (−1, 0, +1) to indicate the price movements in k steps.” but does not specifically disclose a multi-layer perceptron to perform the classification, and the same immediate window duration as well as the other claimed subject matter of claim 1.
Response to Arguments
Applicant's arguments filed June 29th, 2026 (hereinafter “Remarks”) have been fully considered but they are not persuasive.
Rejections under 35 U.S.C. § 101:
Argument 1:
“The Office Action characterizes claim 1 as reciting mental partitioning of an observed time series, mental creation of tokens, mental creation of a transformer vector, and mental assignment of a classification. Office Action, Claim Rejections - 35 U.S.C. § 101, Step 2A Prong 1. That characterization is no longer a reasonable characterization of claim 1 as amended. The claim does not merely recite observing time-series information and thinking about partitions, tokens, relationships, or classifications.” (Remarks, page 8).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” See MPEP § 2106.04(a)(2)(III). While the limitations have been narrowed, they can be considered mental steps that can be performed in the human mind, or by a human using a pen and paper.
Argument 2:
“The USPTO's eligibility guidance explains that the mental-process grouping is limited to concepts that can practically be performed in the human mind, including observations, evaluations, judgments, and opinions, and that a claim does not recite a mental process when the human mind is not equipped to perform the claim limitations…Here, the claimed operations are not human mental evaluations, judgments, or opinions. They require trained machine-learning components that generate and process partition-corresponding multidimensional token representations and a transformer vector. A human mind is not practically equipped to execute a trained CNN model to identify partition-level pattern activations in historical time-series data, output corresponding tokens with multidimensional vector spaces and position encodings, process those tokens with a transformer model to determine inter-token relationships, correlations, and impacts on the token set as a whole, and assign a probability-distribution-based sign classification using an MLP classifier.” (Remarks, pages 9-10).
Examiners Response:
Examiner respectfully disagrees, as identified in the 35 U.S.C. § 101 rejections, the use of the trained CNN constitutes an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). The MPEP states “In Affinity Labs, the claim recited a broadcast system in which a cellular telephone located outside the range of a regional broadcaster (1) requests and receives network-based content from the broadcaster via a streaming signal, (2) is configured to wirelessly download an application for performing those functions, and (3) contains a display that allows the user to select particular content. 838 F.3d at 1255-56, 120 USPQ2d at 1202. The court identified the claimed concept of providing out-of-region access to regional broadcast content as an abstract idea, and noted that the additional elements limited the wireless delivery of regional broadcast content to cellular telephones (as opposed to any and all electronic devices such as televisions, cable boxes, computers, or the like). 838 F.3d at 1258-59, 120 USPQ2d at 1204. Although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment (cellular telephones) and thus fails to add an inventive concept to the claims. 838 F.3d at 1259, 120 USPQ2d at 1204.” See MPEP § 2106.05(h).
Argument 3:
“The Office Action also characterizes several claim limitations as mathematical concepts. But the amended claim does not merely claim mathematical operations in the abstract.” (Remarks, page 10).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).” See MPEP § 2106.04(a)(2)(I). While the claims do not recite explicit mathematical formulae, they do express mathematical concepts through operations on data, for instance the partitioning of time series data.
Argument 4:
“The present claim is therefore unlike claims that merely collect information, analyze it at a high level, and display a result. The claim does not invoke a computer merely as a tool for a preexisting mental process, nor does it simply append generic machine-learning terminology to an otherwise abstract result. Rather, the claim recites a particular way of achieving time-series forecasting through a defined sequence of machine-learning components and intermediate data structure” (Remarks, pages 12-13).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.” See MPEP § 2106.05(a)(II). Here, the use of computing components to perform the claims amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process or is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.05(h), 2106.05(f)(2).
Argument 5:
“The amended claim recites an ordered combination that is significantly more than any alleged abstract idea. Even if individual concepts such as partitioning, vector representations, or classification are viewed in isolation as involving mathematics, the ordered combination is not conventional data gathering followed by generic analysis.” (Remarks, pages 13-14).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “[An] example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.” See MPEP § 2106.04(a)(2)(III)(C). Further, “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.” See MPEP § 2106.04(a)(2)(III). Here, the combination of mental processes with additional elements does not amount to significantly more. Further, the additional elements are recited at a high level of generality, and even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB Z SUSSMAN MOSS whose telephone number is (571) 272-1579. The examiner can normally be reached Monday - Friday, 9 a.m. - 5 p.m. ET.
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/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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