DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/764,104 filed on 07/03/2024. Claims 1, 3-7, and 9-16 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/764,104 filed 07/03/2024 claims foreign priority to Chinese Application No. 202310805726.1, filed 07/03/2023.
Response to Amendment
4. In the response filed November 19, 2025, Applicant amended claims 1, 3, 7, and 9, and cancelled claims 2 and 8. New claims 15 and 16 were presented for examination.
5. Applicant's amendments to claims 1, 3, 7, and 9 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
6. Applicant's arguments filed November 19, 2025, have been fully considered.
7. Applicant submits “In particular, under Step 2A, the claims are not directed to an abstract idea.” More specifically Applicant submits “that utilizing a Graph Convolutional Network to aggregate neighbor node information through complex matrix operations (such as the graph Laplacian operator) is an advanced machine learning technique that far exceeds the cognitive capabilities of ordinary humans or conventional mathematical operations.” [Applicant’s Remarks, 11/19/2025, pages 15-16]
The Examiner respectfully disagrees. The fact that the claim uses a Graph Convolutional Network and complex matrix operations does not remove the claim from reciting an abstract idea. MPEP 2106 explains that mathematical concepts and data processing algorithms remain abstract, regardless of their complexity or whether they exceed human cognitive capability. The claim limitations, including correlations calculation, matrix transformations, and neural-network process, are still mathematical data analyses, which fall withing the “Mathematical Concepts” abstract idea grouping. Additionally, the claims does not improve computer technology or provide any specific technological solution, the Graph Convolutional Network is only used as a tool to process data and predict copper prices, and the remainder of the claim applies he resulting data to a production-planning mode, which is itself a mathematical construct. Accordingly, this argument is found unpersuasive.
8. Applicant submits “In particular, when compared to Example 39 of Subject Matter Eligibility Examples: Abstract Ideas issued on January 7, 2019, which also recites a method for training a neural network, the same analysis precisely applies to amended claim 1 because the steps of the amended claim 1 are not practically performed in the human mind. Similar to claim 1 of Example 39, the current claim 1 does not recite a mental process. Thus, amended claim 1 is eligible because it does not recite a judicial exception.” [Applicant’s Remarks, 11/19/2025, page 17]
Applicant argues against the §101 rejection by seeking to analogize to the claims in Example 39. In response to Applicant’s suggestion that the claims are eligible for the same reasons as set forth in the analysis of Example 39 (training a neural network for facial detection) from the “Subject Matter Eligibility Examples” (published 01/07/2019), the Examiner respectfully disagrees. The Examiner emphasizes that the eligibility analysis provided for claim 1 of Example 39 does not indicate that eligibility hinges on “training a neural network.” The Examiner does acknowledge that training a neural network, machine learning, and the like may be evaluated as additional elements if a judicial exception if recited in the claims during evaluation under Step 2A Prong 1. However, with respect to Example 39, the analysis under Step 2A Prong 1 plainly found that the claim “does not recite any of the judicial exceptions….the claim does not recite any mathematical relationships, formulas or equations….the claim does not recite a mental process…the claim does not recite any method of organizing human activity…Thus, the claim is eligible because it does not recite a judicial exception.” In contrast, when evaluated under Step 2A Prong 1, Applicant’s claims plainly set forth steps for managing commercial interactions (e.g., marketing or sales activities or behaviors; business relations), and managing interactions between people including by following rules or instructions, which falls under the “Certain Methods of Organizing Human Activity” abstract idea grouping, and “Mathematical Concepts” abstract idea grouping as discussed below in the §101 rejection.
Moreover, while claim 1 of Example 39 does involve training a neural network, this technique is performed for improvement of the technological process of digital facial image detection. Applicant’s claims do not involve training a neural network for improvement of the technological process of digital facial image detection, but instead seek to “constructing a production plan mathematical model” and “obtaining a copper mine production plan,” which directly pertains to the abstract idea itself, which are results devoid of technical improvement comparable to the digital facial image detection of Example 39. Accordingly, Applicant’s reliance on the eligibility rationale of Example 39 is not persuasive.
9. Applicant submits “that even if the independent claim 1 is directed to a judicial exception, the judicial exception is integrated into a practical application.” [Applicant’s Remarks, 11/19/2025, page 17]
In response to Applicant’s argument that “even if the independent claim 1 is directed to a judicial exception, the judicial exception is integrated into a practical application,” it is noted that the additional elements in exemplary claim 1 are: a processor, a Graph Convolutional Network (GCN) model, an encoder, a decoder, a feedforward module in the encoder, and a feedforward module in the decoder, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
It is also noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no computing system, device, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to a copper mine production plan, which is not a technical result or improvement thereof.
Furthermore, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For the reasons above, this argument is found unpersuasive.
10. Applicant submits “Amended claim 1 recites a method for optimizing an open-pit mine production plan based on time-series prediction of copper price, specifically, by employing an encoder and a decoder for data processing, amended claim 1 achieves “breaking bottlenecks in information utilization” and promotes more efficient connectivity and better information aggregation. These improvements objectively enhance the functioning of a computer, enabling amended claim 1 to process and analyze complex multi-factor time-series data more efficiently and accurately, thereby providing high- precision copper price predictions for optimizing open-pit mine production plans. This enhancement of the computer's own performance is strong evidence of the judicial exception being integrated into a practical application. Therefore, the claimed invention improves the existing technology and is integrated into a practical application.” [Applicant’s Remarks, 11/19/2025, pages 18-19]
The Examiner respectfully disagrees. In response to Applicant’s argument it is noted that the claim’s use of an encoder, decoder does not constitute an improvement to the functioning of a computer or any other technology. These elements are used solely as part of a data processing pipeline to analyze information and generate predicted copper prices. The assertion that the model “breaks bottlenecks” or “promotes more efficient connectivity and better information aggregation” describes improvement in the quality of the prediction not improvements to computer architecture, or any technological process. The claim does not recite any specific modification to computer operation. The claim simply applies known computational models to economic data. Producing a more accurate prediction or production plan is an improvement to the abstract idea itself, not the computer. Therefore, the claim does not integrate the abstract idea into a practical application. Accordingly, this argument is found unpersuasive.
11. Applicant submits “The claimed invention holds meaningful applications in optimizing the open-pit mine production plan. Accordingly, amended claim 1 is eligible because it is not directed to an abstract idea.” [Applicant’s Remarks, 11/19/2025, page 20]
In response to Applicant’s argument, the Examiner respectfully disagrees. Although the method is applied to optimizing open-pit mine production, the claim is still directed to mathematical operations, data analysis, and price prediction, which are considered abstract ideas. Applying an abstract idea in a particular industry does not, by itself, make it a practical application. The claim does not recite any improvement to computer technology. The claim simply uses a processor to perform standard computational steps. Therefore, the abstract idea is not meaningfully integrated unto a practical application. Accordingly, this argument is found unpersuasive.
12. Applicant submits that “the claimed invention meets the improvements to another technology, and thus qualifies as "significantly more" under M.P.E.P. § 2106.05. For example, as a whole, amended claim 1 constructs a time-series prediction model, which is not a well-understood, routine, or conventional activity or step in the field. By introducing specific technologies such as Graph Convolutional Networks (GCN), encoder-decoder architecture, weighted autocorrelation operations, and multi-stage progressive decomposition, amended claim 1 achieves prediction accuracy and optimization effects that are difficult to attain with traditional methods.” [Applicant’s Remarks, 11/19/2025, pages 20-21]
Applicant alludes to Step 2B of the eligibility inquiry by suggesting that “the claimed invention meets the improvements to another technology, and thus qualifies as "significantly more.” The Examiner respectfully disagrees and notes that the claims merely produce a result in the form of a “copper mine production plan,” which is not an improvement to the processor, encoder, decoder, or any other system or technology. The claims have not been shown to modify, reconfigure, manipulate, or transform the processor, encoder, decoder, or any technology in any discernible manner, much less yield an improvement thereto. There is no indication that any of the additional elements or the combination of elements amount to an improvement to the computer or to any technology. Their individual and collective functions merely provide generic computer implementation. Therefore, these additional claim elements do not amount to significantly more than the abstract idea itself. Lastly, it is noted that the claim’s use of a Graph Convolutional Network, encoder, decoder, weighted autocorrelation represent mathematical and data processing techniques, not improvements to computer technology. Achieving more accurate prediction addresses the abstract idea itself, not the functioning of a computer. Accordingly, this argument is found unpersuasive.
13. Applicant submits “The rejection is factually flawed because the Office fails to offer a reasoned explanation or provide the necessary facts to show why Applicant’s claims are “well- understood, routine, and conventional,” as required by the recent U.S. Patent and Trademark Office Memorandum dated April 19, 2018 discussing Berkheimer v. HP, Inc., 881 F.3d 1360 (Fed. Cir., 2018) (the “Berkheimer Memorandum’).” [Applicant’s Remarks, 11/19/2025, page 22]
Specifically, regarding the rejection under 35 U.S.C. § 101, Applicant submits that “The rejection is factually flawed because the Office fails to offer a reasoned explanation or provide the necessary facts to show why Applicant’s claims are “well- understood, routine, and conventional,” as required by the recent U.S. Patent and Trademark Office Memorandum dated April 19, 2018 discussing Berkheimer v. HP, Inc., 881 F.3d 1360 (Fed. Cir., 2018).” As best understood by the Examiner, Applicant’s reliance on the Berkheimer Memo is based on Applicant’s misunderstanding of the Berkheimer decision, which is germane only to Step 2B eligibility inquiry into whether certain additional claim limitations are well-understood, routine, and conventional and the evidentiary requirements to support factual findings related thereto. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
Accordingly, the Examiner emphasizes that a §101 rejection, including one based on a judicial exception, does not hinge on whether or not any particular limitation or the entire claimed subject matter is directed to “well-understood, routine, and conventional activities.” Notably, a §101 rejection may be proper even none of the claim limitations are deemed well-understood, routine, and conventional. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
Moreover, the Examiner notes that the additional elements, which are primarily directed to a general purpose computer, satisfy the requirement of Berkheimer by citation to Applicant’s Specification and court decisions that the generic computing elements analyzed under Step 2B are well-understood, routine, and conventional. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The computer is broadly applied at a high level of generality to implement these abstract ideas. Paragraph 0020 of Applicant’s specification explain that the invention may be carried out with a central processing unit (CPU). Applicant’s invention focuses on orchestrating a deployment plan. The additional elements are broadly applied to the abstract idea(s) at a high level of generality (“similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,” as explained in MPEP § 2106.05(f)) and they operate in well-understood, routine, and conventional manners.
As described above, the additional elements are broadly applied at a high level of generality to carry out the claim functions. The additional elements are broadly applied to the abstract idea(s) at a high level of generality and they operate in well-understood, routine, and conventional manners. It is noted that the standard of review under 35 USC §101 is different from that of 35 USC §102/103. Under 101 the standard for determining if the claimed invention was well-understood, routine, and conventional to a skilled artisan at the time of the patent is a factual determination akin to the analysis under 35 U.S.C. § 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. “Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.)” Memorandum, dated 04/19/2018.
Additionally, MPEP 2106.05(d)(lI) provides a non-exhaustive list of elements the courts have found to be well-understood, routine, and conventional. Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec. Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life. Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank. Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. Electronically scanning or extracting data from a physical document, e.g. see Content Extraction and Transmission, LLC v. Wells Fargo Bank. A web browser’s back and forward button functionality, e.g. see Internet Patent Corp. v. Active Network, Inc. Recording a customer’s order, e.g. see Apple, Inc. v. Ameranth. Presenting offers and gathering statistics, e.g. see OIP Techs, v. Amazon, Inc. Determining an estimated outcome and setting a price, e.g. see OIP Techs, v. Amazon, Inc.
Moreover, it is noted that the addition of non-conventional components to an abstract idea does not necessarily turn an abstraction into something concrete. Further, the Examiner points out that limitations that were found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception also include: adding the words “apply it” or equivalent with the judicial exceptions, or mere instruction to implement an abstract idea on a computer, simply appending well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, of the judicial exception. As described below, the claims of the instant application are drawn to an abstract idea. It is noted that for the role of a computer in a computer-implemented invention to be deemed meaningful, it must involve more than performance of "well-understood, routine and conventional activities previously known in the industry.” Claim 1 is directed to performing the method steps, but these limitations add nothing of substance to the underlying abstract idea. Accordingly, this argument is found unpersuasive.
Furthermore, in response to Applicant’s argument that “the rejection is factually flawed because the Office fails to offer a reasoned explanation or provide the necessary facts to show why Applicant’s claims are “well-understood, routine, and conventional,” it is noted that only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f), no evidence is needed. Citations for conventionality to MPEP 2106.05 were already provided. Arguing abstract elements for Berkheimer is not persuasive. See BSG Tech, LLC v. Buyseasons, Inc., 899 F.3d 1281,1290 (Fed. Cir. 2018) states “Our precedent has consistently employed this same approach. If a claim’s only “inventive concept” is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea. See, e.g., Berkheimer, 881 F.3d at 1370 (holding claims lacked an inventive concept because they “amount to no more than performing the abstract idea of parsing and comparing data with conventional computer components”). The Office Action did provide factual evidence and met the burden elaborated by the Berkheimer court of by the April PTO Guidance. See Office Action, dated 09/17/2025, pages 6-9. The Examiner notes that the additional elements, satisfy the requirement of Berkheimer by citation to Applicant’s Specification and court decisions that the generic computing elements analyzed under Step 2B are well-understood, routine, and conventional. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). The various additional elements of the claims also receive and process data. All of these functions have been recognized by the courts as generic and well-understood computer functionality. Applicant's novelty lies in the details of the abstract ideas and computer functions that are well-understood, routine, and conventional. The additional elements are broadly applied to the abstract idea(s) at a high level of generality (“similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,” as explained in MPEP § 2106.05(f)) and they operate in well-understood, routine, and conventional manners. Accordingly, this argument is found unpersuasive.
14. Applicant submits “ Without conceding the merits of the rejection and solely to expedite prosecution of the present application, independent claim 1 has been amended to recite all features of original claim 2. Applicant respectfully submits that Khoshalan, Magagula, and Shishvan, either alone or in combination, do not teach or suggest the claimed combination including the newly added elements and they thus do not render the claimed invention obvious. Indeed, the Office has acknowledged that claim 2 contains allowable subject matter. The Office Action, page 23. Accordingly, Applicant respectfully submits that amended claim 1 is patentable under 35 U.S.C. § 103.” [Applicant’s Remarks, 11/19/2025, page 24]
In response to Applicant’s argument, it is noted that Applicant's amendments to claim 1 are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 103. Accordingly, this rejection has been withdrawn. Reasons for allowance (over the prior art) are provided below.
15. Applicant submits “Claim 15 further recites “executing the copper mine production plan, comprising implementing a schedule for mining a particular block area during a certain period of time as defined by the copper mine production plan.” This limitation connects the “copper mine production plan” with the physical mining operations of an open-pit mine, adding a meaningful restriction, specifically the use of information from the judicial exception (copper mine production plan) to carry out physical mining activities for specific periods and specific block areas of the mine. On the one hand, this limitation employs the judicial exception in conjunction with the actual physical operations of mining equipment, rather than remaining at a theoretical or abstract level.” [Applicant’s Remarks, 11/19/2025, page 26]
The Examiner respectfully disagrees. Claim 15 merely recites “executing the copper mine production plan” by implementing a schedule, but it does not recite any specific control of mining machinery, modification of equipment, or technological steps for carrying out the mining. The limitation describes the use of information from the plan in general, theoretical sense, without providing a concrete, technological implementation. The claim remains at the level of planning and data processing, and does not integrate the abstract idea into a practical application. Accordingly, this argument is found unpersuasive.
16. Applicant submits “Similar to Example 45, claim 15 also connects the judicial exception to a technical field and improves the prior art by applying it to a specific technical process. Therefore, claim 15, as a whole, integrates the judicial exception into a practical application, thereby not being directed to a judicial exception.” [Applicant’s Remarks, 11/19/2025, page 26]
In response to the Applicant’s arguments that claim 15 is similar to Example 45, the Examiner respectfully disagrees. With respect to Applicant's comparison to Example 45, Examiner points out that the claims in Example 45 involved product claims reciting a machine or manufacture (a controller) for controlling the injection molding of a hypothetical chemical (polyurethane polymer X46). As stated in Example 45, claim 1 is ineligible because it is directed to judicial exceptions, and the claim as a whole does not integrate the exceptions into a practical application or amount to significantly more than the exceptions. Claim 2 recites the same judicial exceptions as claim 1, but is eligible because it recites other meaningful limitations that use the abstract ideas to improve the previous molding technology, such that the claim integrates the exceptions into a practical application. Claim 3 recites the same judicial exceptions as claims 1 and 2, and lacks any additional elements that integrate the exceptions into a practical application, but nonetheless is eligible in Step 2B because it recites a specific and unconventional tool in the data gathering steps that amounts to significantly more than the exceptions. Claim 4 recites a different judicial exception, and is eligible because it recites other meaningful limitations that use the abstract idea to improve the previous molding technology, such that the claim as a whole integrates the exception into a practical application. The claim at issue are far different from the claims in Example 45. The claims of the present case involves a method for optimizing an open-pit mine production plan based on time-series prediction of copper price. The claim of the instant application do not recite other meaningful limitations that use the abstract ideas to improve the previous molding technology, such that the claim integrates the exceptions into a practical application. Furthermore, the claims of the instant application do not recite a specific and unconventional tool that amounts to significantly more than the exceptions. Finally, the claim as a whole does not integrate the exception into a practical application. Accordingly, this argument is found unpersuasive.
17. Applicant's remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 112
18. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
19. Claims 1, 3-7, and 9-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
20. Claim 1 was amended to recite “obtaining a focused multi-factor dataset T by focusing the data correlation matrix using a Graph Convolutional Network (GCN) model, and inputting the focused multi-factor dataset to an encoder: the encoder and a decoder corresponding to autocorrelation mechanism.” The remaining limitations refer to “the focused multi-factor dataset T”. It is unclear whether “the focused multi-factor dataset” refers to the previously introduced “focused multi-factor dataset T” or to a different multi-factor dataset. This ambiguity renders the claim indefinite. As a result, the claim does not particularly point out and distinctly claim the subject matter regarded as the invention. Independent claim 7 recites similar limitations as claim 1 and is therefore determined to be indefinite for the same reason as claim 1. Appropriate correction/clarification is required.
21. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
22. 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.
23. Claims 1, 3-7, and 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
24. Claims 1, 3-7, and 9-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1, 3-6, 15), system (claims 7, 9-12, 16), device (claim 13), and computer program product (claim 14) are directed to at least one potentially eligible category of subject matter (i.e., machine and process, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1, 3-7, and 9-16 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite abstract ideas that fall into the (1) “Certain Methods of Organizing Human Activity” by setting forth steps for managing commercial interactions (e.g., marketing or sales activities or behaviors; business relations), and managing interactions between people including by following rules or instructions; and (2) “Mathematical Concepts” such as mathematical relationships, formulas and calculations, as set forth in the enumerated groupings of abstract ideas set forth in MPEP 2106. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below:
obtaining a raw dataset, including: obtaining a dataset of historical copper price and a dataset of other relevant factors correlating with the copper price, the dataset of other relevant factors comprising at least one of a copper ore price, an iron ore price, a nickel ore price, a national consumer index, a national producer index, a copper product consumer index, and corresponding time; and integrating the dataset of historical copper price and the dataset of other relevant factors into the raw dataset on a monthly basis; data preprocessing, including: performing a linear transformation on the raw dataset using a linear normalization manner, mapping data in the raw dataset to a range of [0,1] respectively; and performing data enhancement on factors in the normalized raw dataset using a linear interpolation manner, and performing time interpolation on each of the normalized data using the linear interpolation manner to obtain a multi-factor dataset T on a daily basis; wherein the multi-factor dataset T includes at least one of a historical copper price factor, a copper ore price factor, an iron ore price factor, a nickel ore price factor, a national consumer index factor, a national producer index factor, a copper product consumer index factor, and the corresponding time; generating a data correlation matrix, including: calculating Kendall's correlation coefficient in the multi-factor dataset T to obtain the data correlation matrix; constructing a time-series prediction model, including: obtaining a focused multi-factor dataset T by focusing the data correlation matrix using a Graph Convolutional Network (GCN) model, and inputting the focused multi-factor dataset to an encoder; the encoder and a decoder corresponding to autocorrelation mechanism; performing a first autocorrelation operation on data in the focused multi- factor dataset T, wherein the first autocorrelation operation comprises calculating by using the Kendall's correlation coefficient within a range of [0, 1] in the data correlation matrix as a weight; obtaining a first trend term sequence and a seasonal term sequence by performing a first sequence decomposition on a plurality of factors in the multi- factor dataset T, respectively, and assigning the first trend term sequence to an initialized trend term model; inputting the seasonal term sequence output by a feedforward module in the encoder into the decoder and performing a second autocorrelation operation in the decoder, and performing a second sequence decomposition to obtain a second trend term sequence, and assigning the second trend term sequence to the trend term model; and outputting data from a feedforward module in the decoder, and performing a third sequence decomposition of the output data from the feedforward module in the decoder, and adding an independent trend term sequence to the seasonal term sequence to obtain the copper price prediction data: wherein the independent trend term sequence is the trend term model; predicting the copper price, including: inputting the multi-factor dataset T into the time-series prediction model to obtain copper price prediction data; constructing a production plan mathematical model and importing the copper price prediction data as parameters into the production plan mathematical model; and obtaining a copper mine production plan by solving the production plan mathematical model using an ant colony algorithm. These steps encompass mathematical concepts since the step recites mathematical concepts, relationships, formulas or equations, or calculations, and are also organizing human activity by reciting steps for managing commercial interactions (e.g., marketing or sales activities or behaviors; business relations), and managing interactions between people including by following rules or instructions. Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mathematical Concepts” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claims 7, 13, and 14 recite similar limitations as claim 1 and is therefore determined to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are: a processor, a Graph Convolutional Network (GCN) model, an encoder, a decoder, a feedforward module in the encoder, and a feedforward module in the decoder (claim 1), at least one storage device storing a set of instructions, at least one processor, the system, a Graph Convolutional Network (GCN) model, an encoder, a decoder, a feedforward module in the encoder, a feedforward module in the decoder (claim 7), a processor (claim 13), and a set of computer instructions, the storage medium, a computer (claim 14). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “obtaining” step is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution data gathering activity, which is not sufficient to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are: a processor, a Graph Convolutional Network (GCN) model, an encoder, a decoder, a feedforward module in the encoder, and a feedforward module in the decoder (claim 1), at least one storage device storing a set of instructions, at least one processor, the system, a Graph Convolutional Network (GCN) model, an encoder, a decoder, a feedforward module in the encoder, a feedforward module in the decoder (claim 7), a processor (claim 13), and a set of computer instructions, the storage medium, a computer (claim 14). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification describes generic computing devices that may be used to implement the invention, which cover virtually any computing device under the sun (See, e.g., Spec. at paragraph 0020). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
Even if the “obtaining” step is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution data gathering activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) i.- 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); iv. 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 if the encoder and decoder were evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of an encoder and decoder is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Wang et al., US 2024/0365137 A1 (paragraph 0024: “using conventional encoders/decoders”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 3-6, 9-12, and 15-16 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 3-6 and 15 recite “wherein the autocorrelation mechanism is a series-wise connection autocorrelation mechanism, wherein the series-wise connection autocorrelation mechanism comprises at least period-based dependencies and time delay aggregation,” “wherein the constructing a production plan mathematical model and importing the copper price prediction data as parameters into the production plan mathematical model comprises: importing the production plan mathematical model into a max-min ant colony algorithm, setting a maximum profit function considering copper price fluctuation and constraints that need to be solved, and setting a block model, an economic parameter, and process parameters of the max-min ant colony algorithm,” “wherein the constructing the production plan mathematical model comprises: establishing a formula for maximizing a total profit derived from mining at an objective of the production plan:
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where B is a set of all block numbers, gb is a copper content of block b, wherein the copper content is obtained based on a weight of the block b and a copper grade, ct is the copper price during a period t and ct ε C, C is a set of the copper price prediction data, T is a set of the period t, vt is a mining cost during the period t considering a cash discount rate, xbt denotes whether the block b is mined during the period t, and xbt is a variable between 0 and 1; determining mining priority geometry constraints:
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where Bb is a set of antecedent block numbers of the block b, and b' is the antecedent block of the block b;
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determining resource capacity constraints:
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where R is a set of operable resource r, qbr is an amount of the resource r consumed by mining the block b,
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is a maximum limit on the amount of available resource r during the period t, and
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is a minimum limit on the amount of available resource r during the period t,” “wherein the obtaining a copper mine production plan by solving the production plan mathematical model using an ant colony algorithm comprises: determining an initial plan; initializing pheromones, and assigning higher pheromone values to blocks that construct the initial plan; performing one or more rounds of iterations, wherein any one of which includes: constructing a production plan, including: generating a plurality of stochastic production plans based on existing pheromone trajectories; evaporating the pheromones, including: reducing the pheromone values of all blocks; and depositing the pheromones, including: assigning new pheromone values to blocks in the stochastic production plans, and proceeding to a next round of iterations in response to that a maximum number of iterations is not reached; and obtaining an optimized mining final boundary and an optimized production plan,” “executing the copper mine production plan, comprising: implementing a schedule for mining a particular block area during a certain period of time as defined by the copper mine production plan,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” abstract idea groupings. The other dependent claims have been evaluated as well, however, similar to claims 2-6 and 15 these claims also recite steps/details that are part of the abstract idea itself when analyzed under Step 2A Prong One of the eligibility inquiry and thus fall within the scope of the same “Certain methods of organizing human activity” and “Mathematical Concepts.” Dependent claims recite additional elements of encoder (claims 3, 9). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible. Even if the encoder and decoder were evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of an encoder and decoder is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Wang et al., US 2024/0365137 A1 (paragraph 0024: “using conventional encoders/decoders”).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Allowable Subject Matter
25. With respect to independent claim 1, Khoshalan, Magagula, and Shishvan collectively teach features for obtaining a raw dataset, including: obtaining a dataset of historical copper price and a dataset of other relevant factors correlating with the copper price, the dataset of other relevant factors comprising at least one of a copper ore price, an iron ore price, a nickel ore price, a national consumer index, a national producer index, a copper product consumer index, and corresponding time; and integrating the dataset of historical copper price and the dataset of other relevant factors into the raw dataset on a monthly basis; data preprocessing, including: performing a linear transformation on the raw dataset using a linear normalization manner, mapping data in the raw dataset to a range of [0, 1] respectively; and performing data enhancement on factors in the normalized raw dataset using a linear interpolation manner, and performing time interpolation on each of the normalized data using the linear interpolation manner to obtain a multi-factor dataset T on a daily basis; wherein the multi-factor dataset T includes at least one of a historical copper price factor, a copper ore price factor, an iron ore price factor, a nickel ore price factor, a national consumer index factor, a national producer index factor, a copper product consumer index factor, and the corresponding time; generating a data correlation matrix, including: calculating Kendall's correlation coefficient in the multi-factor dataset T to obtain the data correlation matrix; constructing a time-series prediction model; predicting the copper price, including: inputting the multi-factor dataset T into the time-series prediction model to obtain copper price prediction data; constructing a production plan mathematical model and importing the copper price prediction data as parameters into the production plan mathematical model; and obtaining a copper mine production plan by solving the production plan mathematical model using an ant colony algorithm [See Non-Final Rejection mailed 09/17/2025 for detailed prior art citations corresponding to the above-noted subject matter].
However, with respect to independent claim 1, Khoshalan, Magagula, and Shishvan, and the other prior art of record does not teach obtaining a focused multi-factor dataset T by focusing the data correlation matrix using a Graph Convolutional Network (GCN) model, and inputting the focused multi-factor dataset to an encoder; the encoder and a decoder corresponding to autocorrelation mechanism: performing a first autocorrelation operation on data in the focused multi- factor dataset T, wherein the first autocorrelation operation comprises calculating by using the Kendall's correlation coefficient within a range of [0, 1] in the data correlation matrix as a weight; obtaining a first trend term sequence and a seasonal term sequence by performing a first sequence decomposition on a plurality of factors in the multi- factor dataset T, respectively, and assigning the first trend term sequence to an initialized trend term model; inputting the seasonal term sequence output by a feedforward module in the encoder into the decoder and performing a second autocorrelation operation in the decoder, and performing a second sequence decomposition to obtain a second trend term sequence, and assigning the second trend term sequence to the trend term model; and outputting data from a feedforward module in the decoder, and performing a third sequence decomposition of the output data from the feedforward module in the decoder, and adding an independent trend term sequence to the seasonal term sequence to obtain the copper price prediction data: wherein the independent trend term sequence is the trend term model, as recited and arranged in combination with the other limitations of independent claim 1.
While Khoshalan teaches performing a first autocorrelation (page 6: “This statistical test was applied to measure the linear correlation between two data sets”) and outputting data from a feedforward module (page 7: “a feedforward backpropagation multi-layer perceptron neural network was utilized to predict the copper price.”) and Magagula teaches Kendall's correlation coefficient (page 22: “Kendall Tau correlation is a nonparametric correlation method that tests rank correlation coefficients. Kendall Tau is usually used for discrete circumstances where the tested variable pairs are ranked and their ranking is used to decide whether they are concordant or discordant.”; page 25: “The Kendall-Tau correlation method can be implemented instead). Khoshalan does mention the use of feedforward neural network and focuses on forecasting copper prices, but it does not teach or suggest the specific limitations outlined in amended claim 1. In particular, it does not involve the use of a Graph Convolutional Network to focus a data correlation matrix, nor does it incorporate autocorrelation operations weighted by Kendall’s correlation coefficient. Additionally, Khoshalan does not employ an encoder-decoder framework, nor does it perform multi-stage trend and seasonal sequence decomposition or implement dedicated trend term model as part of its forecasting pipeline. As discussed above, it mentioned general feedforward modeling, but does not describe the specific sequence of steps required by the steps in claim 2.The Magagula reference discusses Kendall’s correlation coefficient at a high-level, highlighting its usefulness for measuring ran-based relationships in complex, uncertain data environment. However, it does not describe “wherein the first autocorrelation operation comprises calculating by using the Kendall's correlation coefficient within a range of [0, 1] in the data correlation matrix as a weight.” It also does not integrate this statistical measure with an encoder-decoder framework or combine it with a Graph Convolutional Network and multi-stage decomposition as required by claim 2. Lastly, Chen et al., Patent No.: US 11,537,852 B2 describes the use of Graph Convolutional Networks (col. 1, lines 52-67 & col. 2, lines 1-6), particularly in the context of dynamic graph structures where the model parameters evolve over time. However, the prior art of record either individually or in combination does not teach “obtaining a focused multi-factor dataset T by focusing the data correlation matrix using a Graph Convolutional Network (GCN) model, and inputting the focused multi-factor dataset to an encoder; the encoder and a decoder corresponding to autocorrelation mechanism: performing a first autocorrelation operation on data in the focused multi- factor dataset T, wherein the first autocorrelation operation comprises calculating by using the Kendall's correlation coefficient within a range of [0, 1] in the data correlation matrix as a weight; obtaining a first trend term sequence and a seasonal term sequence by performing a first sequence decomposition on a plurality of factors in the multi- factor dataset T, respectively, and assigning the first trend term sequence to an initialized trend term model; inputting the seasonal term sequence output by a feedforward module in the encoder into the decoder and performing a second autocorrelation operation in the decoder, and performing a second sequence decomposition to obtain a second trend term sequence, and assigning the second trend term sequence to the trend term model; and outputting data from a feedforward module in the decoder, and performing a third sequence decomposition of the output data from the feedforward module in the decoder, and adding an independent trend term sequence to the seasonal term sequence to obtain the copper price prediction data: wherein the independent trend term sequence is the trend term mode, as recited in claims 1/7. The prior art of record neither teaches nor suggests all particulars of the limitations as recited in independent claim 1 (and similarly encompassed in independent claim 7). While individual features may be known per se, there is no teaching or suggestion absent Applicant’s own disclosure to combine these features other than with impermissible hindsight. Furthermore, neither the prior art, the nature of the problem, nor knowledge of a person having ordinary skill in the art provides for any predictable or reasonable rationale to combine prior art teachings to make the entire claim obvious. Accordingly, claims 1, 3-7, and 9-16 are allowable over the prior art. Claims 1, 3-7, and 9-16 are not allowable, however, because claims 1, 3-7, and 9-16 remain rejected under 35 U.S.C. 112(b) and 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Megannon et al., Pub. No.: US 2024/0249226 A1 – describes a computer-assisted system and a method suitable for automatically creating all possible extraction plans for mining a given natural resource and reserve.
Sattarvand, Javad, and Christian Niemann-Delius. "A new metaheuristic algorithm for long-term open-pit production planning." Archives of Mining Sciences 58.1 (2013) – describes a metaheuristic algorithm which has been developed based on the Ant Colony Optimisation (ACO) and its efficiency. To apply the ACO process on mine planning problem, a series of variables are considered for each block as the pheromone trails that represent the desirability of the block for being the deepest point of the mine in that column for the given mining period.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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