DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims X are new.
Claims 1-12 are pending and have been examined.
This action is in reply to the papers filed on 07/06/2023 (effective filing date 07/11/2022).
Information Disclosure Statement
The information disclosure statement(s) submitted: 07/06/2023, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method, system, and computer readable medium for recommending optimum combination of quantum circuits.
Claim 1 recites [a] processor implemented method, further comprising: receiving, via one or more hardware processors, an input data further comprising a) definition of each of a plurality of tasks for experimentation, b) information on orchestration of each of the plurality of tasks, and c) a search space for triggering the plurality of tasks; generating a training data, via the one or more hardware processors, further comprising: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space; and prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks; generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices; soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster; generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices; and iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached: selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward; computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster; computing a probability score for each vertex using softmax on the computed sum of edge weights, wherein the probability score determines probability of selecting the vertex for execution, wherein the plurality of high-level combinations are prioritized based on value of the associated probability score; selecting a vertex from the selected cluster, based on the computed probability score; and executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data; and training a GNN data model using the training data.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-4 recite a method and, therefore, are directed to the statutory class of a process. Claims 5-8 recite a system/apparatus and, therefore, are directed to the statutory class of machine. Claims 9-12 recite a non-transitory computer readable medium/computer product and, therefore, are directed to the statutory class of a manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A processor implemented method, further comprising:
No additional elements are positively claimed.
receiving, via one or more hardware processors, an input data further comprising a) definition of each of a plurality of tasks for experimentation, b) information on orchestration of each of the plurality of tasks, and c) a search space for triggering the plurality of tasks;
This limitation includes the step(s) of: receiving, via one or more hardware processors, an input data further comprising a) definition of each of a plurality of tasks for experimentation, b) information on orchestration of each of the plurality of tasks, and c) a search space for triggering the plurality of tasks.
But for the one or more hardware processors, this limitation is directed to processing and/or communicating known information (e.g., receiving and transmitting information) to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
receiving, via one or more hardware processors, an input data…
generating a training data, via the one or more hardware processors, further comprising: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space; and
This limitation includes the step(s) of: generating a training data, via the one or more hardware processors, further comprising: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space.
But for the one or more hardware processors, this limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
generating a training data, via the one or more hardware processors…
prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks;
This limitation includes the step(s) of: prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices;
This limitation includes the step(s) of: generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster;
This limitation includes the step(s) of: soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices; and
This limitation includes the step(s) of: generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached: selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward; computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster;
This limitation includes the step(s) of: iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached: selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward; computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
computing a probability score for each vertex using softmax on the computed sum of edge weights, wherein the probability score determines probability of selecting the vertex for execution, wherein the plurality of high-level combinations are prioritized based on value of the associated probability score;
This limitation includes the step(s) of: computing a probability score for each vertex using softmax on the computed sum of edge weights, wherein the probability score determines probability of selecting the vertex for execution, wherein the plurality of high-level combinations are prioritized based on value of the associated probability score.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
selecting a vertex from the selected cluster, based on the computed probability score; and
This limitation includes the step(s) of: selecting a vertex from the selected cluster, based on the computed probability score.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data; and
This limitation includes the step(s) of: executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
training a GNN data model using the training data.
This limitation includes the step(s) of: training a GNN data model using the training data.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information to aid in recommending optimum combination of quantum circuits which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of recommending optimum combination of quantum circuits, which, pursuant to MPEP 2106.04, is aptly categorized as a mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: a communication interface for displaying and transmitting information (system claim). Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: one or more hardware processors and memory to execute the method and system and a “non-transitory machine-readable storage device” for storing executable instructions. These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; 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).
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.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Independent system claim 5 and CRM claim 9 also contains the identified abstract ideas, with the additional elements of a processor and storage medium, which are a generic computer components, and thus not significantly more for the same reasons and rationale above.
Dependent claims 2-4, 6-8, and 10-12 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims receiving data, generating a combination of tasks, computing a score, generating a graph, etc… Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from recommending optimum combination of quantum circuits. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, 9 are rejected under 35 U.S.C. 103 as being unpatentable over: HUA Tian et al. : "Machine learning of percolation models using graph convolutional neural networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 7 July 2022 (2022-07-07), XP091265030 (hereinafter HUA Tian et al.); in view of Ying Rex et al. : "Hierarchical graph representation learning with differentiable pooling", arXiv.org, 20 February 2019 (2019-02-20), XP093102864, Ithaca DOI: 10.48550/arXiv.1806.08804 Retrieved from the Internet: URL:https://arxiv.org/pdf/1806.08804.pdf [retrieved on 2023-11-17] (hereinafter Ying Rex et al.).
18/218,744 – Claim 1. HUA Tian et al. teaches A processor implemented method, further comprising (HUA Tian et al. see method in Figures 2 and 3): receiving, via one or more hardware processors, an input data further comprising a) definition of each of a plurality of tasks for experimentation (HUA Tian et al. see plurality of tasks for experimentation in HUA Tian et al.: page 4, col. 1, paragraph 2: “Our first idea is to build a general GCN that can predict the percolation thresholds of different lattices simultaneously”), b) information on orchestration of each of the plurality of tasks (HUA Tian et al. see information on orchestration of each of the plurality of tasks in HUA Tian et al.: page 4, col. 1, paragraph 2: “Our first idea is to build a general GCN that can predict the percolation thresholds of different lattices simultaneously.”), and c) a search space for triggering the plurality of tasks (HUA Tian et al. see respective search space in HUA Tian et al.: Figure 2, (a1), Square lattice);
generating a training data, via the one or more hardware processors, further comprising: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space
(Ying Rex et al. see generating a plurality of initial high-level combinations of the plurality of tasks, in Ying Rex et al.: page 4, paragraph 3: “Formally, given Z = GNN(A, X), the output of a GNN module, and a graph adjacency matrix A ∈ ℝ n × n, we seek to define a strategy to output a new coarsened graph containing m < n nodes, with weighted adjacency matrix A’ ∈ ℝ m × m and node embeddings Z’ ∈ ℝ m × d.”
See also hyper parameters in Ying Rex et al.: page 5, paragraph 2: “The output dimension of GNNl,pool corresponds to a pre-defined maximum number of clusters in layer l, and is a hyperparameter of the model.”
see trial labels in confusion method (i.e. default parameters from the search space) in HUA Tian et al.: page 4, col. 1, first paragraph: “To perform this method, one has to prepare a dataset first. … Then, one can make the trial labels and perform training and testing. The pi is chosen as the trial threshold for generating trial labels, and all graphs generated with pj < pi are specified as the label 0, and the rest are specified as label 1. One repeats the previous two operations with all trial thresholds {pi} in the range [pmin , pmax], and would output the performance curve as expected.”, and
in random occupying sites (i.e. further default parameters from the search space), in HUA Tian et al.: page 2, col. 2, first paragraph: “For site-percolation on a square lattice, the configuration is obtained via randomly occupying sites according to a certain probability pi ∈ [pmin , pmax].”); and
prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks; generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices (HUA Tian et al. see use of DiffPool (i.e. respective negative dissimilarity score computation forming edge-weights of the vertices) in HUA Tian et al.: page 3, col. 1, last paragraph: “In the pooling layer, we use a differentiable graph pooling module [32], based on the graph hierarchical pooling”, and see Ying Rex et al.: section 3.2, Figure 1, equation 4);
soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster (HUA Tian et al. see use of DiffPool (i.e. soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster) in HUA Tian et al.: page 3, col. 1, last paragraph: "In the pooling layer, we use a differentiable graph pooling module [32], based on the graph hierarchical pooling", and Ying Rex et al.: section 3.2, Figure 1, equation 4);
generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices (HUA Tian et al. see subgraphs in Figure 2(b2) and see HUA Tian et al.: page 3, col. 1, last paragraph: “The way of graph collapse can be realized by a cluster assignment matrix S ∈ Rn1 × n2 , where n1 is the number of nodes in the graph Gl and n2 is the number of nodes of the new graph.”, see use of DiffPool in HUA Tian et al.: page 2, col. 1, paragraph 3: “In addition, technologically, we use state-of-the-art graph convolution and soft allocation of learnable pooling [32] to build our GCN”, and in HUA Tian et al.: Algorithm 1, line 13: " Gk = DiffPool(Gk-1)", wherein DiffPool applies soft clustering generating a plurality of subgraphs using pairwise dissimilarity,
see Ying Rex et al.: section 3.2, Figure 1, equation 4, page 4, paragraph 5: “Intuitively, S(l) provides a soft assignment of each node at layer l to a cluster in the next coarsened layer l+ 1.”, and paragraph 6: “Similarly, Equation (4) takes the adjacency matrix A(l) and generates a coarsened adjacency matrix denoting the connectivity strength between each pair of clusters.”); and
iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached (HUA Tian et al. see iteratively performing till a defined time-limit has been reached, in HUA Tian et al.: page 2, col. 1, paragraph 2: “After l times of convolution”):
selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward (HUA Tian et al. see use of Diff Pool in page 3, col. 1, last paragraph: “In the pooling layer, we use a differentiable graph pooling module [32], based on the graph hierarchical pooling”, and
see cluster assignment matrix (i.e. selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward) in Ying Rex et al.: section 3.2 and page 4, paragraph 4: “… DIFFPOOL, addresses the above challenges by learning a cluster assignment matrix over the nodes using the output of a GNN model.”);
computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster (HUA Tian et al. see computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster in HUA Tian et al. page 2, col. 2, last paragraph: “GCN learns the features from neighboring nodes through the edges. The features of each node,
h
i
l
, are updated by convolution of the features of neighboring nodes
h
j
(
l
-
1
)
”);
computing a probability score for each vertex using softmax on the computed sum of edge weights (HUA Tian et al. see respective computing of a probability score for each vertex using softmax on the computed sum of edge weights, in HUA Tian et al.: Algorithm 1, line 15: “p1 , p2 = Softmax(MLP(Gk))”),
wherein the probability score determines probability of selecting the vertex for execution (HUA Tian et al. see probabilistic matrix S (i.e. the probability score determining probability of selecting the vertex for execution) in HUA Tian et al.: page 3, col. 1, last paragraph: “In the real simulation, for a graph with nk nodes, a soft learn and probabilistic matrix S ∈ Rnk × nk+1 is used …”, equation 2,
and in Ying Rex et al.: page 4, paragraph 5: “Intuitively, S (l) provides a soft assignment of each node at layer l to a cluster in the next coarsened layer l + 1.”),
wherein the plurality of high-level combinations are prioritized based on value of the associated probability score (HUA Tian et al. see super nodes in k-th pooling layers (i.e. high level combinations), in HUA Tian et al.: page 3, col. 1, last paragraph - col. 2, first paragraph: “In the real simulation, for a graph with nk nodes, a soft learn and probabilistic matrix S ∈ Rnk × nk+1 is used … where X(k) is the feature vector of the super node in the k-th pooling layer.”, and equation 2, and see pooling equation 3 (i.e. prioritizing based on value of the associated probability score) in HUA Tian et al.: page 3, col. 2, first paragraph: “Θ is the filter parameter matrix representing the probability that the node is assigned to any clusters (super nodes) for the next hierarchical pooling layer.”);
selecting a vertex from the selected cluster, based on the computed probability score; and executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data (HUA Tian et al. see iteratively updating the feature information (i.e. selecting a vertex using the assignment matrix, i.e. the probability score, and executing the selected vertex based on the probability score to obtain a performance metric) in HUA Tian et al.: page 2, col. 1, paragraph 2: “The GCN performs a convolution operation with the feature (usually a vector) of the surrounding nodes and the edges between the nodes, and then iteratively updates the feature information of each node. After l times of convolution, each node contains the feature of its l-order neighboring nodes, and the local features of the configuration are extracted.”); and
training a GNN data model using the training data (see HUA Tian et al.: page 4, col. 1, paragraph 2: “… we train the GCN using the input graphs with a fixed number of nodes but different numbers of edges simultaneously, which is impossible for a NN.”).
The teaching of Ying Rex et al. with respect to the pooling is regarded as incorporated in HUA Tian et al. (see HUA Tian et al.: page 3, col. 1, last paragraph: “In the pooling layer, we use a differentiable graph pooling module [32], based on the graph hierarchical pooling.”, and page 2, col. 1, paragraph 3: “In addition, technologically, we use state-of-the-art graph convolution and soft allocation of learnable pooling [32] to build our GCN.”)
Document Ying Rex et al. was available at the time of publication of document HUA Tian et al.
Furthermore, before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified HUA Tian et al. to include the features as taught by Ying Rex et al.. One of ordinary skill in the art would have been motivated to do so to incorporate the pooling features of Ying Rex et al. into the differentiable graph pooling module features of HUA Tian et al. which should prove to improve user experience, maximize profits, and optimize revenue.
18/218,744 – Claim 5. A system, comprising: one or more hardware processors; a communication interface; and a memory storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to: receive an input data further comprising a) definition of each of a plurality of tasks for experimentation, b) information on orchestration of each of the plurality of tasks, and c) a search space for triggering the plurality of tasks; generate a training data, by: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space; and prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks; generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices; soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster; generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices; and iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached: selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward; computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster; computing a probability score for each vertex using softmax on the computed sum of edge weights, wherein the probability score determines probability of selecting the vertex for execution, wherein the plurality of high-level combinations are prioritized based on value of the associated probability score; selecting a vertex from the selected cluster, based on the computed probability score; and executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data; and train a GNN data model using the training data.
18/218,744 – Claim 9. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: receiving an input data further comprising a) definition of each of a plurality of tasks for experimentation, b) information on orchestration of each of the plurality of tasks, and c) a search space for triggering the plurality of tasks; generating a training data, further comprising: generating a plurality of initial high-level combinations of the plurality of tasks, based on a plurality of hyper parameters with default parameters from the search space; and prioritizing the plurality of high-level combinations, further comprising: computing a dissimilarity score between each two distinct experiments among a plurality of experiments associated with the plurality of tasks; generating a graph, wherein in the graph, the plurality of experiments form vertices of the graph and an exponentiation of negative pairwise dissimilarity score form edge-weights of the vertices; soft-clustering the vertices of the graph into a plurality of clusters to determine probability of each node of the graph belonging to a cluster; generating a plurality of subgraphs for each cluster, wherein in the subgraph, the plurality of experiments form vertices of the graph and pairwise dissimilarity score form edge-weights of the vertices; and iteratively performing till one of a) all the vertices have been evaluated, b) a maximum number of trials has been exhausted, and c) a defined time-limit has been reached: selecting a cluster based on an associated probability score, wherein the probability score depends on a cluster-reward; computing a sum of edge weights with respect to a neighbour edge of each vertex within the selected cluster; computing a probability score for each vertex using softmax on the computed sum of edge weights, wherein the probability score determines probability of selecting the vertex for execution, wherein the plurality of high-level combinations are prioritized based on value of the associated probability score; selecting a vertex from the selected cluster, based on the computed probability score; and executing the selected vertex based on the probability score to obtain a performance metric, wherein the input data and the associated performance metric forms the training data; and training a GNN data mod& using the training data.
Claims 5 and 9, have similar limitations as of Claim 1, therefore they are REJECTED under the same rationale as Claim 1.
Claims 2, 6, 10 are rejected under 35 U.S.C. 103 as being unpatentable over: HUA Tian et al.; in view of Ying Rex et al.
18/218,744 – Claim 2. HUA Tian et al. further teaches The method of claim 1, wherein the information on orchestration of each of the plurality of tasks is received as workflow defining execution order and concurrency of execution (HUA Tian et al. see simultaneous prediction of percolation thresholds in page 4, col. 1, paragraph 2).
18/218,744 – Claim 6. The system of claim 5, wherein the one or more hardware processors are configured to receive the information on orchestration of each of the plurality of tasks as workflow defining execution order and concurrency of execution.
18/218,744 – Claim 10. The one or more non-transitory machine-readable information storage mediums of claim 9, wherein the information on orchestration of each of the plurality of tasks is received as workflow defining execution order and concurrency of execution.
Claims 6 and 10, have similar limitations as of Claim 2, therefore they are REJECTED under the same rationale as Claim 2.
Claims 4, 8, 12 are rejected under 35 U.S.C. 103 as being unpatentable over: HUA Tian et al.; in view of Ying Rex et al.
18/218,744 – Claim 4. HUA Tian et al. further teaches The method of claim 1, wherein the edge weights and the cluster-reward are readjusted with respect to the performance metric (HUA Tian et al. see obeying iteration flow in equation at page 3, col. 2, first paragraph, last line, and equations 3-5).
18/218,744 – Claim 8. The system of claim 5, wherein the one or more hardware processors are configured to readjust the edge weights and the cluster-reward with respect to the performance metric.
18/218,744 – Claim 12. The one or more non-transitory machine-readable information storage mediums of claim 9, wherein the edge weights and the cluster-reward are readjusted with respect to the performance metric.
Claims 8 and 12, have similar limitations as of Claim 4, therefore they are REJECTED under the same rationale as Claim 4.
Claims 3, 7, 11 are rejected under 35 U.S.C. 103 as being unpatentable over: HUA Tian et al.; in view of Ying Rex et al.; in further view of Castrillo et al. 2022/0230087.
18/218,744 – Claim 3. The method of claim 1, HUA Tian et al. may not expressly disclose the following features, however, Castrillo et al. 2022/0230087 teaches these features as follows wherein the dissimilarity score between the two distinct experiments is computed using fidelity, hilbert space, and estimated probability distribution of fidelities (Castrillo et al. 2022/0230087 teaches the “Hilbert space” features [0009] In some implementations processing the estimated polarization parameter values to obtain an estimate of the fidelity of the n-qubit quantum logic gate comprises: fitting the estimated polarization parameter values corresponding to each circuit depth d as an exponential decay in d; determining an estimated polarization per cycle p.sub.n for the n-qubit quantum logic gate based on the exponential decay in d and an obtained estimate of polarization for a single qubit gate in a quantum circuit that operates on n qubits; and determining an estimate of the fidelity of the n-qubit quantum logic gate using F=p.sub.n+(1−p.sub.n)/D, where D=2.sup.n represents the Hilbert space dimension.).). Furthermore, before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified HUA Tian et al. to include the features as taught by Castrillo et al. 2022/0230087. One of ordinary skill in the art would have been motivated to do so to incorporate the Hilbert space features in order to facilitate methods and systems for recommending optimum combination of quantum circuits which should prove to improve user experience, maximize profits, and optimize revenue.
18/218,744 – Claim 7. The system of claim 5, wherein the one or more hardware processors are configured to compute dissimilarity score between the two distinct experiments using fidelity, hilbert space, and estimated probability distribution of fidelities.
18/218,744 – Claim 11. The one or more non-transitory machine-readable information storage mediums of claim 9, wherein the dissimilarity score between the two distinct experiments is computed using fidelity, hilbert space, and estimated probability distribution of fidelities.
Claims 7 and 11, have similar limitations as of Claim 3, therefore they are REJECTED under the same rationale as Claim 3.
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Lin et al. 2024/0289355 – Abstract: A computer obtains node embeddings, node periodicity classifications, edge embeddings, and edge periodicity classifications for each time of a time period. The computer determines subgraph embeddings based on a subgraph of the graph, times in the time period, the node embeddings for nodes in the subgraph, the edge embeddings for edges in the subgraph, the node periodicity classifications for the nodes in the subgraph, and the edge periodicity classifications for the edges in the subgraph. The computer translates each subgraph embedding of the subgraph embeddings for each time of the time period into projected subgraph embeddings. For the subgraph, the computer aggregates the plurality of projected subgraph embeddings into an aggregated subgraph embedding. The computer determines if the subgraph is periodic based upon at least the aggregated subgraph embedding.
Crabtree et al. – Abstract: A semantic search system integrates with an AI platform to provide advanced search capabilities by leveraging automatically generated ontologies and knowledge graphs. The system employs natural language processing, machine learning, and large language models to create, update, and align ontologies from diverse data sources. It supports context-aware query interpretation, personalized results, and complex reasoning by incorporating user context, feedback, and domain knowledge. The system optimizes search performance and efficiency through indexing techniques, distributed computing, and continuous learning. With a modular architecture and scalable infrastructure, the semantic search system enables users to retrieve relevant, meaningful, and context-specific information from vast amounts of structured and unstructured data. The integration of the semantic search system with the AI platform's components, such as knowledge graphs and model blending, enhances the platform's overall reasoning, decision-making, and problem-solving capabilities, empowering users with intelligent and intuitive search experiences across various domains and applications.
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
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 extension fee 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.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
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 extension fee 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 date of this final action.
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b