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 .
Response to Amendment
This Office action is responsive to amendment filed on 10/15/2025. Claims 1-3, 5-11, 13-17, and 19-20 are pending.
Response to Arguments
In response to applicant’s argument regarding rejection under 35 U.S.C. 101 on page 13 of Remarks, “In the accompanying amendment, claim 1 has been amended to expressly recite that the claimed method is a computer-implemented method...using a graph neural network executed by one or more hardware processors (emphases added)… These amendments make explicit that the claimed method is implemented using specific computer hardware executing a particular neural network architecture, and is not a mere mathematical abstraction.”
Examiner respectfully disagrees because merely reciting a computer implemented method and one or more hardware processor to execute mathematical models at a high level of generality does not integrate the judicial exception into a practical application under step 2A prong two or provide significantly more under step 2B. Furthermore, the claim does not recite a specific computer hardware, but rather recites a general hardware processor (e.g., one or more hardware processors) to execute functions, MPEP 2106.05(f) recites “simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”. Accordingly, simply adding “one or more hardware processor” and “computer implemented method” fails to integrate the judicial exception into a practical application because such limitations amount to no more than mere instructions to apply the judicial exception using computer components.
Applicant further asserted on page 14, “Here, the GNN structure, comprising interdependent NE, ENN, and NNN components operating in an encoder-decoder framework, represents a non-generic configuration that improves computer functionality by enabling the system to model and infer temporal-causal dependencies among multiple time-series variables more effectively than conventional neural network architectures. Thus, claim 1 is directed to a practical, technological improvement in machine learning architectures, not to a mere abstract mathematical concept.”
Examiner respectfully disagrees because GNN comprises NE, ENN, NNN components operating in an encoder-decoder framework are not hardware components, but are mathematical models/equations or abstract idea, see at least [0041-0043] describing NE, ENN, NNN are corresponding to equations 4-6. Thus, even when NE, ENN, and NNN are represented in a non-generic configuration, such configuration is the mathematical expressions as described in [0041-0043] rather than hardware configuration. Accordingly, any arguably improvements, such as enabling the system to model and infer temporal-causal dependencies among multiple time series variable more effectively, are a direct consequence of performing the abstract idea as recited in the claims using the non-generic mathematical configuration of NE, ENN, and NNN and MPEP 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
Applicant further asserted on page 14-15, “The claim recites specific technical features that meaningfully limit any alleged abstract idea. Execution by one or more hardware processors ensures implementation in a concrete computing environment. The encoder-decoder structure of the GNN, including defined submodules (NE, ENN, NNN), specifically constrains how data is processed, e.g., encoding causal and temporal dependencies into edge features and transmitting them through a node aggregation function. These components interact in a structured, technological manner to achieve improved causal discovery accuracy and temporal prediction performance, which are computer-implemented outcomes that cannot be performed mentally or by pen-and-paper.”
Examiner respectfully disagrees because as explained above, the NE, ENN, and NNN are not considered as hardware components interacting in a structured and technological manner to achieve an improvement, but rather the NE, ENN, and NNN are interpreted in light of the specification (e.g., [0041-0043]) as mathematical models or expressions to process data that achieves the arguably improvements, and according to MPEP 2106.05(a), the mathematical models or abstract idea alone cannot provide the improvement.
Applicant further asserted on page 15, “Specifically, the recitation of “one or more hardware processors executing the encoder, decoder, NE, ENN, and NNN” ties the claim to a particular computing machine performing concrete operations, satisfying MPEP §2106.05(b) (integration with a particular machine). Moreover, the combination of NE, ENN, and NNN within both the encoder and decoder is a non-routine, non-conventional structure in the field of machine learning. It goes beyond standard feedforward or recurrent neural networks to encode causal and temporal dependencies, thereby improving the functioning of the computer in the specific context of time series modeling. The method results in enhanced causal interpretability and prediction accuracy for multivariable time series, a recognized technical improvement in artificial intelligence and signal processing systems.”
Examiner respectfully disagrees because the recitation of one or more hardware processors executing the encoder, decoder, NE, ENN, and NNN does not tie the claim to a particular machine, but such limitation merely recites one or more hardware processors at a high level of generality, wherein the one or more hardware processors execute mathematical models to perform the abstract idea as recited in the claim 1 because such encoder, decoder, NE, ENN, and NNN are not hardware components when interpreted in light of the specification (see at least [0041-0043] describing the mathematical expressions corresponding to the NE, ENN, and NNN within the encoder and decoder. Accordingly, even though the combination of NE, ENN, and NNN within both the encoder and decoder is a non-routine, non-conventional structure in the field of machine learning, such combination is still an abstract idea. MPEP 2106.04(I) “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions” and MPEP 2106.05(I) “a claim for a new abstract idea is still an abstract idea”.
Applicant further responded to Examiner’s assertion on page 15-16, “The Examiner’s assertion that the improvement “is a direct consequence of performing the mathematical concept” (see Office action, page 4, second paragraph) mischaracterizes the invention. Nearly all computer-implemented technologies—from encryption to image compression—are based on mathematical operations (emphasis added). What distinguishes patent-eligible computer-implemented inventions is their specific, practical application that enhances computer performance. For example, cryptographic algorithms such as RSA rely on mathematical number theory, yet are patent eligible because their claimed implementations improve the security functionality of computing systems. Similarly, the claimed GNN architecture improves how computers process, represent, and infer causal relationships in multivariable temporal data, thereby producing an improvement in computational technology itself.”
Examiner respectfully disagrees because 101 analysis is performed on a case by case and analyzed using the two part framework according to MPEP. As explained above, the claimed GNN including encoder, decoder, EN, ENN, and NNN are interpreted, under broadest reasonable interpretation in light of the specification, as mathematical models and any arguably improvements provided by the models are not considered as a technological improvement under 101 analysis because MPEP 2106.05(a) recites “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
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 1-3, 5-11, 13-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The apparatus claims 8, 10-11, and 13-15 are addressed before the method claims 1-3, 5-7 and product claims 9, 16-17, and 19-20.
Claim 8 recite an electronic device
Under Prong One of Step 2A of the USPTO current eligibility guidance (MPEP 2106), the claim recites limitations cover mathematical calculations, relationship, and/or formula, such as performing a method of multivariable time series processing using a graphical neural network including an encoder, a decoder, each of the encoder and decoder including a node embedding (NE) module, an edge neural network (ENN), and a node neural network (NNN), the method comprising obtaining a time series set comprises a plurality of first time series segments, each of the plurality of first time series segments having a same length and being a multivariable time series (figure 3a [0030-0031] describes the step of obtaining data, dividing data to obtain a plurality of segments, such that each segment having the same length and is multivariable, or figure 3b [0032] describes an alternative way by using sliding the observation window to divide and obtain a plurality of segments. Thus, such step of obtaining data comprising a plurality of segments are characterized as mathematical concept of dividing data. also see specification [0040-0046] describe the equations 4-6 corresponding to the NE, ENN, and NNN to implement encoder and decoder of GNN, which are mathematical models. Accordingly, the graph neural network including encoder and decoder having EN, ENN, and NNN are mathematical models to perform the mathematical algorithm); for each of the first time series segments in the time series set, inputting the first time series segment into the graph neural network to predict a multivariable reference value corresponding to a first time point that is a next time point adjacent to a latest time point in the first time series segment; inputting the first time series segment and a multivariable series tag corresponding to the first time point into the encoder of the graph neural network to obtain noise features corresponding to respective variables and predicted features of respective variable of the first time point; and inputting the predicted features of the respective variables of the first time point and the noise features corresponding to the respective variables into the decoder of the graph neural network to obtain the multivariable reference value corresponding to the first time point ([0039-0047] describes the step of predicting multivariable reference values by utilizing equations 4-6 to generate the output X’t that indicates a predicted reference value of each variable at the first time point based on Zt and G(X), wherein the equations 4-6 corresponds to the NE, ENN, and NNN to implement encoder and decoder of the GNN, which are mathematical model for modeling neural network, and data are inputted into the equation for operations); wherein the NE module of each of the encoder and the decoder maps node information to a feature space using a multilayer perceptron (MLP); the ENN of each of the encoder and the decoder encodes a causal relationship represented by edges and time lag information as edge features, and updates the edge features based on a causal matrix; and the NNN transmits the edge features to a next node through an aggregation function ([0040-0046] describes the mathematical equations 4-6 that corresponding to the NE, ENN, and NNN to perform the mathematical operations); determining an optimization function based on multivariable reference values corresponding to a plurality of the first time points and multivariable series tags corresponding to the plurality of the first time points, the optimization function comprising a loss function and a causal matrix ([0048-0051] describes the step of determining an optimization function utilizing equation 7); determining values of respective parameters in the causal matrix with an objective of minimizing the optimization function ([0052-0054] describes the step of determining values of respective parameters utilizing equation 8); and determining, based on the values of the respective parameters in the causal matrix, a causal relationship between multiple variables in the multivariable time series ([0055-0057] describes the step of determining a causal relationship between multiple variables using the comparison between the calculated parameters and zero or a preset threshold) . Therefore, the claim includes limitations that fall within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong Two of Step 2A, this judicial exception is not integrated into a practical application. The claim additionally recites an electronic device comprising a memory for storing instructions or a computer program, one or more hardware processors for executing the instructions or the computer program in the memory to cause the electronic device to perform a method and using the one or more hardware processors to execute GNN, encoder, decoder, NE module, ENN and NNN. However, the additional elements are recited at a high level of generality, i.e., as computer components performing computer functions of storing, executing, and processing data. Furthermore, the claim recites step of inputting and transmitting data can also be considered as insignificant extra solution activity that amounts to mere data gathering. Such element fails to provide a meaningful limitation on the judicial exception, and amount to no more than mere instructions to apply the judicial exception using computer components. Thus, the claim is directed to an abstract idea.
Under Step 2B, as discussed with respect to Prong Two of Step 2A, the additional elements in the claim amount no more than mere instructions to apply the exception using a generic component. The same conclusion is reached in step 2B, i.e., mere instruction to apply an exception on a computer component cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept that is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception. The steps of inputting data and transmitting data are considered to be insignificant extra-solution activity in step 2A, and are determined to be well-understood, routine, conventional activity in the field. Court decisions cited in MPEP 2106.05(d)(II) section (i), indicate that mere receiving or transmitting data over a network, is well-understood, routing, conventional function when it is claimed in a merely generic manner. Thus, the additional element fails to ensure the claim as a whole amount to significantly more than the judicial exception itself. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claim 10 further recites details of determining the optimization function. Such limitations cover mathematical calculations, relationship, and/or formula under step 2A prong one (see at least figure 5 step 503-505). The claim does not recite additional element to integrate the judicial exception into a practical application step 2A prong two or amounts to significantly more under step 2B. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claim 11 further recites details of determining the causal relationship between the multiple variables in the multivariable time series. Such limitations cover mathematical calculations, relationship, and/or formula under step 2A prong one (see at least [0056]). The claim does not recite additional element to integrate the judicial exception into a practical application step 2A prong two or amounts to significantly more under step 2B. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claim 13 further recites before determining, based on the values of the respective parameters in the causal matrix, the causal relationship between the multiple variables in the multivariable time series: determining a target parameter having a value less than a preset threshold in the causal matrix; and setting the value of the target parameter to 0. Such limitations cover mathematical calculations, relationship, and/or formula under step 2A prong one (see at least [0057] describing the step of comparing a parameter with a preset threshold and set the parameter to 0 based on the comparison). The claim does not recite additional element to integrate the judicial exception into a practical application step 2A prong two or amounts to significantly more under step 2B. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claim 14 further recites wherein obtaining the time series set comprises: obtaining an observation window and a sliding step; and moving the observation window with the sliding step over time series to obtain the plurality of the first time series segments. Such limitations cover mathematical calculations, relationship, and/or formula under step 2A prong one (see at least [0032-0033] describes the step of sliding the observation window to obtain a plurality of first time series segments based on observation window S0 and sliding step). The claim does not recite additional element to integrate the judicial exception into a practical application step 2A prong two or amounts to significantly more under step 2B. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claim 15 further recites wherein a length of the observation window is no less than a maximum time lag value. Such limitations cover mathematical calculations, relationship, and/or formula under step 2A prong one (see at least [0033] describes the length of the observation window is greater than or equal to an estimated maximum time lag value). The claim does not recite additional element to integrate the judicial exception into a practical application step 2A prong two or amounts to significantly more under step 2B. Accordingly, the claim is not patent-eligible under 35 U.S.C. 101.
Claims 1-3, 5-7 recite method claims that would be practiced by the apparatus claims 8, 10-11,13-15, respectively. Thus, they are rejected for the same reasons.
Claims 9, 16-17, 19-20 recite product claims having limitation similar to the apparatus claims 8, 10-11, 13-14, respectively. Thus, they are rejected for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUY DUONG whose telephone number is (571)272-2764. The examiner can normally be reached Mon-Friday 7:30-5:30.
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/HUY DUONG/Examiner, Art Unit 2182
(571)272-2764
/ANDREW CALDWELL/Supervisory Patent Examiner, Art Unit 2182
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