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 .
Drawings
The drawings were received on 09/29/2023. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on the following date(s): 10/05/2023 has been considered by the examiner.
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.
Regarding claims 1-6, the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the claim is directed to signal per se/data per se. Specifically, the permeable recites a “system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a system configured to” where the processor can be considered software data and the memory system does not expressly exclude transitory signals in the claim nor in the specification. Thus, the system is directed signal per se as claimed and does not fall within at least one of the four categories of patent eligible subject matter.
Examiner suggested that claim 1 limitation include non-transitory storage medium/memory, for example.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claim 1: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
… (Considered directed to a Mathematical concepts – mathematical relationships, as claimed; see MPEP § 2106.04(a)(2), subsection I)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a system configured to: (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
receive code that causes a Markov Chain Monte Carlo sampling technique to be performed with respect to a Bayesian model comprising a plurality of random variables… (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
… a Markov Chain Monte Carlo sampling technique to be performed with respect to a Bayesian model comprising a plurality of random variables, representing different parameterized probability distributions and connected via edges in a directed acyclical graph (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
… generate instructions to execute the code that causes the Markov Chain Monte Carlo sampling technique, wherein the instructions comprise performing the first order and the second order (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… performing the first order and the and the second order for different respective iterations of the Markov Chain Monte Carlo sampling technique; … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Performing repetitive calculations)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation are directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink."
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the Markov Chain Monte Carlo sampling technique is a Gibbs sampling technique. (Considered directed to a Mathematical concepts – mathematical relationships, as claimed; see MPEP § 2106.04(a)(2), subsection I)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract recited in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the second order is a reverse of the first order. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Does claim fall within a statutory category? No
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
. (Mental process for evaluating and making judgements based on input operations as claimed) (see MPEP § 2106.04(a)(2), subsection III).
(Considered directed to a Mathematical concepts – mathematical relationships, as claimed; see MPEP § 2106.04(a)(2), subsection I)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the code that causes the Markov Chain Monte Carlo sampling technique to be performed also generates an inference … (Claimed limitations merely including instructions to implement an abstract idea on a computer and merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and elements invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the first order is performed on odd numbered iterations of the Markov Chain Monte Carlo sampling technique and wherein the second order is performed on even numbered iterations of the Markov Chain Monte Carlo sampling technique. (Considered directed to a Mathematical concepts – mathematical relationships, as claimed; see MPEP § 2106.04(a)(2), subsection I)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the first order is performed on odd numbered iterations (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Performing repetitive calculations)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation are directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
wherein the Markov Chain Monte Carlo sampling technique is a Metropolis Hastings sampling technique. (Considered directed to a Mathematical concepts – mathematical relationships, as claimed; see MPEP § 2106.04(a)(2), subsection I)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claims 7 and 14: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 1 and are rejected under the same rationale.
Claims 8 and 15: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 2 and are rejected under the same rationale.
Claims 9 and 16: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 3 and are rejected under the same rationale.
Claims 10 and 17: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 4 and are rejected under the same rationale.
Claims 11 and 18: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 5 and are rejected under the same rationale.
Claims 13 and 20: Does claim fall within a statutory category? Yes. And the limitations are similar to claim 6 and are rejected under the same rationale.
Claims 12 and 19: Does claim fall within a statutory category? No.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea noted in claim 7 and 14 rejection respectively.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the code is specified in a probabilistic programming language and wherein the instructions are generated in a non-probabilistic programming language. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Specifically, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined or as an ordered combination, that are directed to what have the courts have identified as "significantly more”, than the identified abstract idea, see MPEP 2106.05.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-5, 7-8, 10-11, 14-15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20210005278, herein after ‘Chang’) in view of Yang et al. (US 20190087294, hereinafter ‘Yang’).
Regarding independent claim 1, Chang teaches a system, comprising: at least one processor; a memory, comprising program instructions that when executed by the at least one processor cause the at least one processor to implement a system configured to: (in [0174] The system and method of the present invention may be implemented by computer software that permits the accessing of data from an electronic information source. The software and the information in accordance with the invention may be within a single, free-standing computer or it may be in a central computer networked to a group of other computers or other electronic devices. The information may be stored on a computer hard drive, on a CD-ROM disk or on any other appropriate data storage device.)
receive code that causes a Markov Chain Monte Carlo sampling technique to be performed with respect to a Bayesian model comprising a plurality of random variables, representing different parameterized probability distributions and connected via edges in a directed acyclical graph; (in [0055] A Bayesian network [receive code that causes a Markov Chain Monte Carlo sampling technique to be performed with respect to a Bayesian model comprising a plurality of random variables,] represents a joint probability distribution over a set of random variables [a plurality of random variables] v={x.sub.1, . . . , x.sub.n}, which are assumed to be categorical. It can be defined by a graph structure G and a vector of parameter θ [representing different parameterized probability distributions and connected via edges in a directed acyclical graph], i.e. conditional probability distribution (CPD), i.e. m={G, θ}. The structure consists of a set of variables v={v.sub.1, . . . , v.sub.n} and edges e={e.sub.1, . . . , e.sub.m}, i.e. G={v, e}, where G is a directed acyclic graph (DAG); θ is a collection of conditional mass functions p(X.sub.i|π.sub.i) where π.sub.i denotes a set of parents of i-th node x.sub.i in the graph respecting the relations of e…. [0056] The following notion is used by the present invention: X.sub.i represents i-th node or random variable in the network and x.sub.ik denotes the k-th state of X.sub.i whose state space contains r.sub.i=|x.sub.i| number of discrete values. π.sub.ij represents the vector of j-th configuration on parents of X.sub.i. q.sub.i=|π.sub.i| denotes the number of total instantiations of the parent set π.sub.i of X.sub.i. The model parameter θ={θ.sub.ijk|∀ijk} is a vector of conditional probabilities where θ.sub.ijk=p(x.sub.ik|π.sub.ij) [representing different parameterized probability distributions and connected via edges in a directed acyclical graph], with i∈{1, . . . , n}, j∈{1, . . . , q.sub.i}, k∈{1, . . . , r.sub.i}. In the above, bold letters are used to denote vectors or sets, however, throughout this disclosure, bold and normal letters are used interchangeably wherever it is clear. The Bayesian network represents a joint probability distribution p(x)=p(x.sub.1, . . . , x.sub.n)=Π.sub.ip(x.sub.i|π.sub.i), for every X in v.)
and evaluate the directed acyclical graph to determine a first order for sampling from the plurality of random variables for a first iteration of the Markov Chain Monte Carlo sampling technique and performing a second order, different than the first order, for sampling from the plurality of random variables for a second iteration of the Markov Chain Monte Carlo sampling technique; ([0071] To describe the approach of the present invention, the process begins by reformatting the general posterior probability corresponding to any given graphical structure [evaluate the directed acyclical graph] as defined in conventional Bayesian network approaches. Let X represent the vector of variables represented as nodes in the network; E is the evidence; D denotes the observed data; G is the graphical network structure to infer; and θ is a vector of model parameters. From this the posterior probability may be written as P(G|D)=P(D|G)P(G)/P(D), where the marginal probability P(D|G) can be expressed as an integral over the parameters, given a particular graphical structure G: P(D|G)=∫.sub.θP(D|G,θ)P(θ|G)dθ. Unlike the traditional Bayesian Dirichlet score, D in this case contains continuous values, and thus, the likelihood of the data is not derived from a multi-nominal distribution, but rather a continuous density function whose form is estimated using a kernel density estimation procedure… The data likelihood is then optimized by estimating θ using maximum-a-posteriori (MAP) estimation:… where {circumflex over (θ)}=argmax {P(D|G,θ)P(θ|G)}. P(D|G, θ) equals to the data likelihood and P(θ|G) denotes the prior distribution over parameters. A Monte Carlo sampling [iteration of the Markov Chain Monte Carlo sampling technique] procedure is used to efficiently sample θ from P(θ|G), evaluating the likelihood for each parameter sample.; And claim 5: The method of claim 1, further comprising the step of updating the computed top-down causal model and the bottom-up predictive model by cycling said models through one or more iterations of Monte Carlo Markov Chain sampling [performing a second order, different than the first order, for sampling from the plurality of random variables for a second iteration of the Markov Chain Monte Carlo sampling technique].)
generate instructions to execute the code that causes the Markov Chain Monte Carlo sampling technique, wherein the instructions comprise performing the first order and the second order for different respective iterations of the Markov Chain Monte Carlo sampling technique; (in claim 5: The method of claim 1, further comprising the step of updating the computed top-down causal model and the bottom-up predictive model by cycling said models through one or more iterations of Monte Carlo Markov Chain sampling [generate instructions to execute the code that causes the Markov Chain Monte Carlo sampling technique, wherein the instructions comprise performing the first order and the second order for different respective iterations of the Markov Chain Monte Carlo sampling technique].)
and perform the instructions to execute the code. (in [0095]…Both the top-down score (328&330) and predictive score (332&334) are periodically passed to the hill-climbing, MCMC at step 316, the global MCMC at step 318, and the order-based MCMC at step 320 to update them as the progress of structure learning by the software [perform the instructions to execute the code]…; Examiner notes that one of ordinary skill in the art would understand that software operations are executed on a computing device as executed software code/instructions, as noted in [0174] The system and method of the present invention may be implemented by computer software that permits the accessing of data from an electronic information source [perform the instructions to execute the code]. The software and the information in accordance with the invention may be within a single, free-standing computer or it may be in a central computer networked to a group of other computers or other electronic devices [perform the instructions to execute the code]. The information may be stored on a computer hard drive, on a CD-ROM disk or on any other appropriate data storage device.)
Chang teaches the use of an Markov Chain Monte Carlo sampling techniques for sampling data through one or more iterations as noted above. One of ordinary skill in the art would understand that each iteration is associated with a different selected data sample.
Additionally, Yang teaches each iteration is associated with a different selected data sample, in [0074] Step (7) reasoning a fault of the contingent Bayesian networks by utilizing a Markov chain Monte Carlo Method, wherein a establishing method is as follows: the Markov chain Monte Carlo Method is denoted as MCMC, based on the Markov process, in order to sample on a specified distribution, according to the Markov process, … [0075] Gibbs sampling is a special case in MCMC [evaluate the directed acyclical graph to determine a first order for sampling from the plurality of random variables for a first iteration of the Markov Chain Monte Carlo sampling technique and performing a second order, different than the first order, for sampling from the plurality of random variables for a second iteration of the Markov Chain Monte Carlo sampling technique], wherein a first dimension is alternately fixed, and values of other dimension are adopted to sample a value in the first dimension; if a probability of occurrence of an event A={X.sub.1, X.sub.2, . . . X.sub.s} needs to be reasoned, wherein X.sub.1, X.sub.2, . . . , X.sub.s are nodes of CBNs, i.e., software fault Fault and environmental constrain of the fault Constrain, and s≤n, wherein a joint probability distribution of an event A is denoted as Φ(A), wherein a process of the Gibbs sampling comprises steps of: [0076] (1) initializing comprising: randomly giving X.sub.1, X.sub.2, . . . , X.sub.s initial values A.sub.0={X.sub.1.sup.0, X.sub.2.sup.0, . . . X.sub.s.sup.0}; [0077] (2) generating X.sub.1.sup.1 by p(X.sub.1|X.sub.2.sup.0, . . . X.sub.s.sup.0); [0078] (3) in a same way as the step (2), fixing other variables, generating X.sub.2.sup.1, . . . , X.sub.s.sup.1 in sequence to obtain a new event A.sub.1={X.sub.1.sup.1, X.sub.2.sup.1, . . . X.sub.s.sup.1} [generate instructions to execute the code that causes the Markov Chain Monte Carlo sampling technique, wherein the instructions comprise performing the first order and the second order for different respective iterations of the Markov Chain Monte Carlo sampling technique at each iteration a new sample set order is generated]; and [0079] (4) repeating steps 2-3 to obtain a Markov chain {A.sub.1, A.sub.2, . . . A.sub.t} wherein t is a sampling time; [0080] wherein through steps mentioned above, the conditional distribution is simulated to simulate the joint distribution, and then the conditional distribution is directly derived through the joint distribution simulated, in such a manner that the contingent Bayesian networks reasoning is achieved to complete fault diagnosis.
Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving and processing data using on probabilistic reasoning and modeling techniques to express and analyze uncertainty in probabilistic events, as disclosed by Yang with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as disclosed by Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Yang and Chang as noted above; Doing so allows for developing a method for establishing data processing technique for modeling probabilistic events based on a contingent Bayesian network that is configured to represent the uncertainty of the dependency by marking each edge of the network model with a condition indicating an activation state, (Chang, 0007).
Regarding claim 2, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the Markov Chain Monte Carlo sampling technique is a. (in [0092] The final posterior in the top-down & bottom-up (“TDBU”) method is a combination of the original top-down & bottom-up score. As in Bayesian network, optimizing this score function (Eq. 13) is an NP-hard problem due to the size of the possible structure space. However, any heuristic search method, such as Monte Carlo Markov Chain (“MCMC”) [wherein the Markov Chain Monte Carlo sampling technique is a] or Hill Climbing etc., can be applied to optimize this posterior function…; And claim 1: A computer-implemented method for predictive network modeling comprising the steps of: aggregating computational data from one or more databases; computing a multiscale network based on the computational data; passing the multiscale network through Monte Carlo Markov Chain sampling [wherein the Markov Chain Monte Carlo sampling technique is a]…)
Yang teaches wherein the Markov Chain Monte Carlo sampling technique is a Gibbs sampling technique. (in [0035] Preferably, the step (7) of reasoning a fault of the contingent Bayesian networks by utilizing a Markov chain Monte Carlo Method, the Markov chain Monte Carlo Method is denoted as MCMC, wherein the MCMC is widely applied in Bayesian inference, Gibbs sampling which is of widest use in MCMC [wherein the Markov Chain Monte Carlo sampling technique is a Gibbs sampling technique] is adopted in the present invention to perform fault reasoning on the contingent Bayesian networks; Gibbs sampling is a special case in MCMC, wherein a first dimension is alternately fixed, and values of other dimension are adopted to sample a value in the first dimension; if a probability of occurrence of an event A={X.sub.1, X.sub.2, . . . X.sub.s} needs to be reasoned, wherein X.sub.1, X.sub.2, . . . , X.sub.s are nodes of CBNs, i.e., software fault Fault and environmental constrain of the fault Constrain, and s≤n wherein a joint probability distribution of an event A is denoted as Φ(A), wherein a process of the Gibbs sampling comprises steps of: …)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Yang and Chang for the same reasons disclosed above.
Regarding claim 4, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the code that causes the Markov Chain Monte Carlo sampling technique to be performed also generates an inference for a given input value. ([0052] The systems and methods of the present invention build on existing work on pairwise causality to a full spectrum of causality inference in a multi-dimensional setting [wherein the code that causes the Markov Chain Monte Carlo sampling technique to be performed also generates an inference for a given input value]. In a network, it is particularly challenging to discern direct causality from correlations. The challenge of learning a causal network includes two sub-tasks: i) infer conditional independence among multiple variables; and ii) infer direct causality between two or more variables… [0053] The present invention leverages an intuitive linear regression treatment encrypted in the frame of bottom-up Bayesian inference [wherein the code that causes the Markov Chain Monte Carlo sampling technique to be performed also generates an inference for a given input value] based on a set of constraints over the conditional probability distribution given hypothesis on causality direction and function.)
Additionally Yang teaches in [0074] Step (7) reasoning a fault of the contingent Bayesian networks by utilizing a Markov chain Monte Carlo Method, wherein a establishing method is as follows: the Markov chain Monte Carlo Method is denoted as MCMC, based on the Markov process, in order to sample on a specified distribution, according to the Markov process, first simulate the Markov process from any state, continuously perform state transition, and finally converge to a stationary distribution; wherein the MCMC is widely applied in Bayesian inference [wherein the code that causes the Markov Chain Monte Carlo sampling technique to be performed also generates an inference for a given input value], Gibbs sampling which is of widest use in MCMC is adopted in the present invention to perform fault reasoning on the contingent Bayesian networks;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Yang and Chang for the same reasons disclosed above.
Regarding claim 5, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the first order is performed on odd numbered iterations of the Markov Chain Monte Carlo sampling technique and wherein the second order is performed on even numbered iterations of the Markov Chain Monte Carlo sampling technique. (in claim 5: The method of claim 1, further comprising the step of updating the computed top-down causal model and the bottom-up predictive model by cycling said models through one or more iterations of Monte Carlo Markov Chain sampling [wherein the first order is performed on odd numbered iterations of the Markov Chain Monte Carlo sampling technique and wherein the second order is performed on even numbered iterations of the Markov Chain Monte Carlo sampling technique].)
Regarding claims 7 and 14, the claim limitations are similar to those examined in claim 1 and are rejected under the same rationale.
Regarding claims 8 and 15, the claim limitations are similar to those examined in claim 2 and are rejected under the same rationale.
Regarding claims 10 and 17, the claim limitations are similar to those examined in claim 4 and are rejected under the same rationale.
Regarding claims 11 and 18, the claim limitations are similar to those examined in claim 5 and are rejected under the same rationale.
Claims 3, 6, 9, 13, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20210005278, herein after ‘Chang’) in view of Yang et al. (US 20190087294, hereinafter ‘Yang’) in further view of Learning in Salakhutdinov (NPL: Learning in Markov random fields using tempered transitions, hereinafter ‘Sal’).
Regarding claim 3, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the second order ((in claim 5: The method of claim 1, further comprising the step of updating the computed top-down causal model and the bottom-up predictive model by cycling said models through one or more iterations of Monte Carlo Markov Chain sampling [wherein the second order as different sampling ordering per iteration].)
Chang and Yan does not expressly teach the sampling process iteration that reverses sampling order.
Sal does expressly teach the sampling process iteration that reverses sampling order, in Sec. 3.1: Suppose that our goal is to sample from p(x;θ). We first define a sequence of intermediate probability distributions: p0, ..., pS, with pS = p(x;θ) and p0 being more spread out and easier to sample from than pS. Constructing a suitable sequence of intermediate probability distributions will in general depend on the problem. One general way to define this sequence is: … r. We also need to define a reverse transition operator [wherein the second order is a reverse of the first order]
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is the same as Ts. Many commonly used transition operators, such as Metropolis–Hastings, are reversible. Non-reversible operators are usually composed of several reversible sub-transitions applied in sequence Ts = Q1...QK, such as the single component updates in a Gibbs sampler. The reverse operator can be simply constructed from the same sub-transitions, but applied in the reverse order
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Sal, Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving and processing data using on probabilistic reasoning modeling techniques and reverse transition operations to express probabilistic sequences, as disclosed by Sal with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as collectively disclosed by Yang and Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Sal, Yang and Chang as noted above; Doing so allows for developing a method for using Markov chain Monte Carlo operators based on tempered transitions enables the stochastic approximation algorithm to better explore highly multimodal distributions, which considerably improves parameter estimates in large, densely-connected Markov random fields, (Sal, abstract).
Regarding claims 9 and 16, the claim limitations are similar to those examined in claim 3 and are rejected under the same rationale.
Regarding claim 6, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the Markov Chain Monte Carlo sampling technique is a . (in [0092] The final posterior in the top-down & bottom-up (“TDBU”) method is a combination of the original top-down & bottom-up score. As in Bayesian network, optimizing this score function (Eq. 13) is an NP-hard problem due to the size of the possible structure space. However, any heuristic search method, such as Monte Carlo Markov Chain (“MCMC”) [wherein the Markov Chain Monte Carlo sampling technique is a ] or Hill Climbing etc., can be applied to optimize this posterior function. FIGS. 2C and 2D exemplarily show network scoring, and the improvement over standard Bayesian techniques in the precision and recall using the algorithms of the present invention, respectively. And claim 1: 1. A computer-implemented method for predictive network modeling comprising the steps of: aggregating computational data from one or more databases; computing a multiscale network based on the computational data; passing the multiscale network through Monte Carlo Markov Chain sampling [wherein the Markov Chain Monte Carlo sampling technique is a ]; computing a top-down causal model;…)
Chang and Yan does not expressly teach the sampling technique as a Metropolis Hastings sampling technique.
Sal does expressly teach the sampling technique as a Metropolis Hastings sampling technique, in Sec. 3.1: Suppose that our goal is to sample from p(x;θ). We first define a sequence of intermediate probability distributions: p0, ..., pS, with pS = p(x;θ) and p0 being more spread out and easier to sample from than pS. Constructing a suitable sequence of intermediate probability distributions will in general depend on the problem. One general way to define this sequence is: … r. We also need to define a reverse transition operator
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is the same as Ts. Many commonly used transition operators, such as Metropolis–Hastings, are reversible [wherein the Markov Chain Monte Carlo sampling technique is a Metropolis Hastings sampling technique]. Non-reversible operators are usually composed of several reversible sub-transitions applied in sequence Ts = Q1...QK, such as the single component updates in a Gibbs sampler. The reverse operator can be simply constructed from the same sub-transitions, but applied in the reverse order
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Sal, Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving and processing data using on probabilistic reasoning modeling techniques and reverse transition operations to express probabilistic sequences, as disclosed by Sal with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as collectively disclosed by Yang and Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Sal, Yang and Chang as noted above; Doing so allows for developing a method for using Markov chain Monte Carlo operators based on tempered transitions enables the stochastic approximation algorithm to better explore highly multimodal distributions, which considerably improves parameter estimates in large, densely-connected Markov random fields, (Sal, abstract).
Regarding claims 13 and 20, the claim limitations are similar to those examined in claim 6 and are rejected under the same rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20210005278, herein after ‘Chang’) in view of Yang et al. (US 20190087294, hereinafter ‘Yang’) in further view of Learning in Krishnamurthy et al. (US 20220209891, hereinafter ‘Krishna’).
Regarding claim 6, the rejection of claim 1 is incorporated and Chang in combination with Yang teaches the system of claim 1, wherein the Markov Chain Monte Carlo sampling technique is a . (in [0092] The final posterior in the top-down & bottom-up (“TDBU”) method is a combination of the original top-down & bottom-up score. As in Bayesian network, optimizing this score function (Eq. 13) is an NP-hard problem due to the size of the possible structure space. However, any heuristic search method, such as Monte Carlo Markov Chain (“MCMC”) [wherein the Markov Chain Monte Carlo sampling technique is a ] or Hill Climbing etc., can be applied to optimize this posterior function. FIGS. 2C and 2D exemplarily show network scoring, and the improvement over standard Bayesian techniques in the precision and recall using the algorithms of the present invention, respectively. And claim 1: 1. A computer-implemented method for predictive network modeling comprising the steps of: aggregating computational data from one or more databases; computing a multiscale network based on the computational data; passing the multiscale network through Monte Carlo Markov Chain sampling [wherein the Markov Chain Monte Carlo sampling technique is a ]; computing a top-down causal model;…)
Chang and Yan does not expressly teach the sampling technique as a Metropolis Hastings sampling technique.
Krishna does expressly teach the sampling technique as a Metropolis Hastings sampling technique, in [0087] In an exemplary aspect, in Metropolis-Hastings, samples [wherein the Markov Chain Monte Carlo sampling technique is a Metropolis Hastings sampling technique] are iteratively generated from a proposal distribution, which could be a normal distribution with manually tuned mean and variance. Samples are accepted or rejected depending upon certain acceptance ratio, which indicates how likely the new sample is probable as compared to the previous accepted sample. Metropolis-Hastings algorithm constructs a Markov chain, which asymptotically reaches a unique stationary distribution. When the proposal density is symmetric, acceptance ratio further simplifies to just check if new sample is more likely than the old sample for acceptance… [0089] In an exemplary aspect, the sampler 306 is configured as a gradient-descent based Metropolis-Hastings sampler for both communication tasks of channel estimation and symbol demodulation. In an exemplary aspect, the first derivative (Gradient) of an error function, such as the Negative of Log Posterior (NLP, deduced from Equation (6)), or of a loss function, is used as the mean of proposal move, and its second derivative (Hessian) as the variance of the proposal move. Advantageously, the MH-GD sampler enables MCMC sampling [wherein the Markov Chain Monte Carlo sampling technique is a Metropolis Hastings sampling technique] with faster convergence compared to Gibbs sampling.
Krishna, Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving and processing data using Bayesian Inference to get optimal Maximum a posteriori Probability (MAP) estimates based on Markov-Chain Monte-Carlo (MCMC) sampling (e.g. Gibbs and/or Metropolis Hastings sampling), as disclosed by Krishna with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as collectively disclosed by Yang and Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Krishna, Yang and Chang as noted above; Doing so allows for employing MCMC using improved Gibbs sampling and/or Metropolis Hastings sampling techniques to achieve improved MAP performance, (Krishna, 0017-0018).
Regarding claims 13 and 20, the claim limitations are similar to those examined in claim 6 and are rejected under the same rationale.
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20210005278, herein after ‘Chang’) in view of Yang et al. (US 20190087294, hereinafter ‘Yang’) in further view of Learning in Wood et al. (US 20170090881 hereinafter ‘Wood’).
Regarding claim 12, the rejection of claim 7 is incorporated and Chang in combination with Yang teaches the method of claim 7, wherein the code is specified in a. (in [0095] …. Similarly, the bottom-up predictive score 332 is used to calculate a predicted GE and clinical profile 334. Both the top-down score (328&330) and predictive score (332&334) are periodically passed to the hill-climbing, MCMC at step 316, the global MCMC at step 318, and the order-based MCMC at step 320 to update them as the progress of structure learning by the software [wherein the code is specified in a probabilistic programming language and wherein the instructions are generated in a non-probabilistic programming language]. … And in [0174] The system and method of the present invention may be implemented by computer software [wherein the code is specified in a ] that permits the accessing of data from an electronic information source…)
Chang and Yan does not expressly teach code for specifying modeling functions for executing computer instructions as a probabilistic programming language.
Wood does expressly teach code for specifying modeling functions for executing computer instructions as a probabilistic programming language, in [0047] A probabilistic programming language and execution method in accordance with an embodiment of the invention are now described. The language is a probabilistic programming [code for specifying modeling functions for executing computer instructions as a probabilistic programming language; wherein the code is specified in a probabilistic programming language] intermediate representation language, which can be compiled to machine code by standard compilers, and linked to operating system libraries. Thus, it can be used as an efficient, scalable and portable probabilistic programming compilation target. (In other words, compilers for probabilistic programming languages can be provided that compile programs in a probabilistic programming language into a program in the intermediate representation language, which can then be efficiently/scalably/portably executed.)
Wood, Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing information processing techniques to help perform Sequential Monte Carlo sampling using , as disclosed by Wood with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as collectively disclosed by Yang and Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Wood, Yang and Chang as noted above; Doing so allows for enabling parallel execution across multiple separate processors/computers, to allow execution-intensive tasks to be efficiently processed, (Wood, 0004-0005).
Regarding claim 19, the claim limitations are similar to those examined in claim 12 and are rejected under the same rationale.
Claims 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20210005278, herein after ‘Chang’) in view of Yang et al. (US 20190087294, hereinafter ‘Yang’) in further view of Tristan et al. (US 20150095277, hereinafter ‘Tristan’).
Regarding claim 12, the rejection of claim 7 is incorporated and Chang in combination with Yang teaches the method of claim 7, wherein the code is specified in a. (in [0095] …. Similarly, the bottom-up predictive score 332 is used to calculate a predicted GE and clinical profile 334. Both the top-down score (328&330) and predictive score (332&334) are periodically passed to the hill-climbing, MCMC at step 316, the global MCMC at step 318, and the order-based MCMC at step 320 to update them as the progress of structure learning by the software [wherein the code is specified in a probabilistic programming language and wherein the instructions are generated in a non-probabilistic programming language]. … And in [0174] The system and method of the present invention may be implemented by computer software [wherein the code is specified in a ] that permits the accessing of data from an electronic information source…)
Chang and Yan does not expressly teach code for specifying modeling functions for executing computer instructions as a probabilistic programming language.
Tristan does expressly teach code for specifying modeling functions for executing computer instructions as a probabilistic programming language, in [0023] Thus, described herein is a probabilistic programming language [code for specifying modeling functions for executing computer instructions as a probabilistic programming language; wherein the code is specified in a probabilistic programming language] and compiler [wherein the instructions are generated in a non-probabilistic programming language] for Bayesian networks designed to make effective use of data-parallel architectures such as Graphics Processing Units (GPUs). A GPU is a processor with a highly parallel structure, having a plurality of execution units called "cores" (e.g., tens, hundreds, or thousands of cores, etc.). Each core of a GPU is configured to process a thread of computation in parallel with the functioning of the other cores of the GPU [wherein the instructions are generated in a non-probabilistic programming language].
Tristan, Chang and Yang are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for retrieving and processing data using a modeling framework of described computer functions that utilizes an intermediate representation (IR) that symbolically represents features of probability distributions, as disclosed by Tristan with the method for developing computer-implemented systems and methods related to predictive network modeling using statistical analysis techniques, as collectively disclosed by Yang and Chang.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Tristan, Yang and Chang as noted above; Doing so allows for employing statistical modeling techniques related to Bayesian network models, that can effectively use data-parallel architectures such as Graphics Processing Units (GPUs) and therefore speed up processing the computations, (Tristan, 0023-).
Regarding claim 19, the claim limitations are similar to those examined in claim 12 and are rejected under the same rationale.
Response to Arguments
Applicant's arguments filed 07/29/2026 have been fully considered by the examiner.
Applicant argues, regarding the rejection of claims under 35 UCS 103, the following:
First, the Yang reference is silent regarding the one or more iteration of Markov chain sampling as to the sampling order of the variables. And the Yang (US 20190087294) and Chang (US 20210005278) do not teaches determining multiple different orders and doing so by evaluating a directed acyclical graph as claimed in the limitation: “evaluate the directed acyclical graph to determine a first order for sampling from the plurality of random variables for a first iteration of the Markov Chain Monte Carlo sampling technique and performing a second order, different than the first order, for sampling from the plurality of random variables for a second iteration of the Markov Chain Monte Carlo sampling technique”, see pages 6-10 of remarks.
MPEP 2111 provides guidance regarding the interpretation of claims in light of the specification. Examiner notes that the claim recites the use of random sampling from a plurality of random variables using a Monte Carlo sampling techniques. The claim limitation also only requires the sampling to occurring twice using the same sampling techniques from the same sample of variables. The claim provides no insight on how the orders are determined to be different or evaluated as such. It is reasonable for one of ordinary skill in the art to interpret that a random sampling using the same sampling techniques will result in a different set of ordered samples as claimed second order of random sampled variables.
The applicant also attacks the teaches noted in Yang in the office action without considering the teaches in the reference Chang. MPEP 2145(IV) that notes "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., Inc., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Where a rejection of a claim is based on two or more references, a reply that is limited to what a subset of the applied references teaches or fails to teach, or that fails to address the combined teaching of the applied references may be considered to be an argument that attacks the reference(s) individually. Where an applicant’s reply establishes that each of the applied references fails to teach a limitation and addresses the combined teachings and/or suggestions of the applied prior art, the reply as a whole does not attack the references individually as the phrase is used in Keller and reliance on Keller would not be appropriate. This is because "[T]he test for obviousness is what the combined teachings of the references would have suggested to [a PHOSITA]." In re Mouttet, 686 F.3d 1322, 1333, 103 USPQ2d 1219, 1226 (Fed. Cir. 2012)."
Yang teaches in a probabilistic graphical model, a Bayesian network as a network set of nodes where the directed acyclic graph (DAG) structure encoded conditional independencies. The concepts were cited in the office action; the applicant appears to be looking for key-word mapping directly from the claim, applicant is reminded of the guidance in MPEP 2111.
The Yang explicitly teaches the use of a DAG structure as a Bayesian network and the use of the Markov Chain Monte Carlo sampling technique per the claim limitation, in [0074] Step (7) reasoning a fault of the contingent Bayesian networks [directed acyclical graph to determine a first order for sampling] by utilizing a Markov chain Monte Carlo Method [iteration of the Markov Chain Monte Carlo sampling technique], wherein a establishing method is as follows: the Markov chain Monte Carlo Method is denoted as MCMC, based on the Markov process, in order to sample on a specified distribution, according to the Markov process, … [0075] Gibbs sampling is a special case in MCMC, wherein a first dimension is alternately fixed, and values of other dimension are adopted to sample a value in the first dimension; if a probability of occurrence of an event A={X.sub.1, X.sub.2, . . . X.sub.s} needs to be reasoned, wherein X.sub.1, X.sub.2, . . . , X.sub.s are nodes of CBNs, i.e., software fault Fault and environmental constrain of the fault Constrain, and s≤n, wherein a joint probability distribution of an event A is denoted as Φ(A), wherein a process of the Gibbs sampling comprises steps of: [0076] (1) initializing comprising: randomly giving X.sub.1, X.sub.2, . . . , X.sub.s initial values [evaluate the directed acyclical graph to determine a first order for sampling from the plurality of random variables for a first iteration of the Markov Chain Monte Carlo sampling technique] A.sub.0={X.sub.1.sup.0, X.sub.2.sup.0, . . . X.sub.s.sup.0}; [0077] (2) generating X.sub.1.sup.1 by p(X.sub.1|X.sub.2.sup.0, . . . X.sub.s.sup.0); [0078] (3) in a same way as the step (2) [performing a second order, different than the first order, for sampling from the plurality of random variables for a second iteration of the Markov Chain Monte Carlo sampling technique], fixing other variables, generating X.sub.2.sup.1, . . . , X.sub.s.sup.1 in sequence to obtain a new event A.sub.1={X.sub.1.sup.1, X.sub.2.sup.1, . . . X.sub.s.sup.1} ;
Yang expressly teaches the iterative sampling method using the same sampling techniques of a variables in the same set of Bayesian networks, in [0079] (4) repeating steps 2-3 to obtain a Markov chain {A.sub.1, A.sub.2, . . . A.sub.t} wherein t is a sampling time; [0080] wherein through steps mentioned above, the conditional distribution is simulated to simulate the joint distribution, and then the conditional distribution is directly derived through the joint distribution simulated, in such a manner that the contingent Bayesian networks reasoning is achieved to complete fault diagnosis.
The claims do not exclude the teaches of the Yang reference. The applicant cannot argue features not required by the claim language. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., excluding the use of a DAG including Bayesian networks ) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthemore, the more the applicant fails to consider the combination as Yang is provide as a reference indicating what one of ordinary skill in the art would know as how computational evaluations based on Bayesian networks by utilizing a Markov chain Monte Carlo Method is completed. The secondary highlights the use of the Markov Chain Monte Carlo sampling method including Gibbs sampling that random sampling of different set of variables in a iteration, in 0075.
The Chang reference was used to teach the use of a probability graph model that could include the use the Bayesian network in the Yang reference. While Chang does not expressly teach the probabilistic network disclosed in Yang, it does disclose the use a Markov Chain Monte Carlo sampling to evaluating probabilistic networks. The examiner has provided a prima facie case of obviousness and has failed to provide objective evidence to support the noted deficiencies in the office action.
When a prima facie case of obviousness is established, the burden shifts to the applicant to come forward with arguments and/or evidence to rebut the prima facie case. See, e.g., In re Dillon, 919 F.2d 688, 692, 16 USPQ2d 1897, 1901 (Fed. Cir. 1990) (en banc). In this case, applicant remarks amount to mere allegation of patentability, per the examiner remarks above.
Applicant argues, regarding the rejection of claims under 35 USC 101: Abstract idea, that the claims as a whole are directed to an improvement in computing technology because the claims are describe a technique that reduces the amount of computing resources used to perform a sampling of a Bayesian model that utilizes different sampling orders for different iterations such as pocking an ordering and reversing the order requirement in the following iterations, as noted in specification paragraphs 0021-0031, see remarks pages 9-10.
Examiner disagrees, and provides the following sections in the MPEP for determining if claims as a whole are directed to a noted improvement in computer technology. The MPEP 2106.04(d)(1) discloses the evaluation of claimed improvements in the functioning of a computer or improvement to a technical field in step 2A prong two. The MPEP section discloses “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification…” In addition, MPEP 2106.05(a) discloses ...It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))…
In this case, the recited claims failed to reflect the disclosed improvement, highlighted in the specification, for reducing computing resources based on setting a different sampling technique for each iteration. The claims do not reflect the disclosed improvement.
At best, the claims as a whole describe an system/method for computing mathematical relationships utilizing known Markov mathematical sampling techniques where the computer is merely used as a tool for encoding the claimed system/method as executable computer instructions. How the computer reduces it’s use of computing resources is not reflected in the claimed invention, as alleged by the applicant, when the claims were considered as whole and in light of applicant’s remarks.
The rejection made in the previous office action has been maintained.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Silva et al. (NPL: The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models): teaches generating a sampled S within a Gibbs sampling procedure. Sampling other rows can be done by redefining a new order where the corresponding target variable is the last one… The procedure can be interpreted as calling a modification of the importance sampler with a dynamic ordering ≺i which, at every step, moves Yi to the end of the global ordering ≺.
Gordon et al. (US 20150058337): teaches a probabilistic programming language is a programming language in which belief about the value of a variable is represented using a probability distribution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWATOSIN ALABI whose telephone number is (571)272-0516. The examiner can normally be reached Monday-Friday, 8:00am-5:00pm EST..
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/OLUWATOSIN ALABI/ Primary Examiner, Art Unit 2129
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