CTNF 18/401,021 CTNF 92068 DETAILED ACTION This office action is responsive to the above identified application filed 12/29/2023. The application contains claims 1-20, all examined and rejected. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 14-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 14 recites the limitation "non-transitory storage medium of claim 3". There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner consider “claim 3” is “claim 13”. Claim 15 recites the limitation "non-transitory storage medium of claim 1". There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner consider “claim 1” is “claim 11”. Claim 16 recites the limitation "non-transitory storage medium of claim 1". There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner consider “claim 1” is “claim 11”. Claim 17 recites the limitation "non-transitory storage medium of claim 1". There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner consider “claim 1” is “claim 11”. Claim 18 inherit the deficiency of claim 17; therefore it is rejected based on dependency. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 1 and 11 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept). Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include process and machine as in independent Claim 1 and 11, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, The invention is directed to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: “predicting a distribution of an integer variable associated with a problem input to an orchestration system”, “determining a range within[vi',vf'] based on the predicted distribution; and generating a one-hot encoding of problem using only integers in the range” (Mental process, observation, evaluation and judgment) The claim recites additional elements as “with a machine learning model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “wherein the integer variable includes [vi,vf]” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: “with a machine learning model” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)); “wherein the integer variable includes [vi,vf]” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. Claim 11 recites “ A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” to perform the same method as set forth in claim 1, the added element of “ A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer. Claim 11 is therefore rejected according to the same findings and rationale as provided above. Independent claim 11 are the same analogy and rejected using similar analysis as claim 1. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. claims 2 disclose “wherein the distribution is a Gaussian distribution” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 3 disclose “ the machine learning model is trained using historical data that includes previously executed problems, the problems including QUBO problems” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 4 disclose “wherein the historical data includes a vector related to the problem, wherein the vector, for each instance of the problem, encodes a number of integer variables, a number of multiplications between the integer variables, a minimum integer, a maximum integer, a mean, and a median for each of the integer variables” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 5 disclose “comprising inputting a vector for the problem into the machine learning model” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 6 disclose “wherein the range includes all probable points from a distribution, wherein the probable points are translated to a new interval.” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 7 disclose “further comprising fine-tuning the range to increase a granularity or to increase the range” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 8 disclose “wherein the one-hot encoding requires qubits equal to a number of integers in the range, which is less than a number of integers in vi,vf” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 9 disclose “wherein the problem includes integer variables, further comprising using the machine learning model (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)) to predict a distribution for each of the integer variables and determining a range for each of the integer variables” (Mental process) This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claim 10 disclose “comprising one-hot encoding each of the ranges and submitting the encoded problem to a quantum annealing system” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed and rejected under similar rationale. For at least these reasons, the claimed inventions of each of dependent claims 2-10 and 12-20 ,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-3, 5, 7-15, 17-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by “Job Shop Scheduling Solver based on Quantum Annealing” Published 2016 [hereinafter D1] . With regard to Claim 1, D1 teach a method comprising: predicting a distribution, with a machine learning model, of an integer variable associated with a problem input to an orchestration system (Fig. 5) , wherein the integer variable includes [vi,vf] (Fig. 2, P. 2, II, “Here PNG media_image1.png 34 38 media_image1.png Greyscale is bounded from above by the timespan T, which represents the maximum time we allow for the jobs to complete. The timespan itself is bounded from above by the total work of the problem”, P. 5, A. Makespan Estimation, “In fig. 2, we show the distribution of the optimal makespans PNG media_image1.png 34 38 media_image1.png Greyscale for different ensembles of instances parametrized by their size … For each set of parameters, we can compute solutions with a classical exhaustive solver in order to identify the median of the distribution PNG media_image2.png 25 110 media_image2.png Greyscale as well as the other quantiles. These could also be inferred from previously solved instances with the proposed annealing solver … Figure 2 indicates that a normal distribution is an adequate approximation, so we need only to estimate its average PNG media_image1.png 34 38 media_image1.png Greyscale and variance σ 2 … This linear ansatz allows for the extrapolation of approximate resource requirements for classes of problems which have not yet been pre-characterized, and it constitutes an educated guess for classes of problems which cannot be precharacterized due to their difficulty or size”, “The distributions are histograms of occurrences for 1000 random instances, fitted with a Gaussian function of mean PNG media_image1.png 34 38 media_image1.png Greyscale ”, Fig. 2, “The mean and the variance are well fitted respectively by PNG media_image3.png 33 36 media_image3.png Greyscale = PNG media_image4.png 46 357 media_image4.png Greyscale ) ; determining a range within [vi',vf'] based on the predicted distribution (P. 12, “T should be selected by solving the following equation and rounding to the nearest integer”, Eq. (B10), “The reason for using this guided search is that the average number of calls to find the optimal makespan is dramatically reduced with respect to a linear search on the range (Tmin; Tmax]”, P. 3-4, “binary variables required for the mapping can be pruned by applying simple restrictions on the time index t (whose computation is polynomial as the system size increases and therefore trivial here). Namely, we can definean effective release time for each operation corresponding to the sum of the execution times of the preceding operations in the same job. A similar upper bound corresponding to the timespan minus all of the execution times of the subsequent operations of the same job can be set. The bits corresponding to these invalid starting times can be eliminated from the QUBO problem altogether since any valid solution would require them to be strictly zero”, P. 13, Col. 2, 1. Variable pruning, “An operation cannot start before its head and must leave enough time after finishing to fit its tail, so the window of possible start times, the processing window, for operation PNG media_image5.png 37 193 media_image5.png Greyscale ”) ; and generating a one-hot encoding of problem using only integers in the range (P. 2, II, “We assign a set of binary variables for each operation, corresponding to the various possible discrete starting times the operation can have:”, Eq. 2, “an operation must start once and only once, leading to the constraint and associated penalty function”, Eq. 3, Eq. 9, Fig. 1-c, P. 3-4, “binary variables required for the mapping can be pruned by applying simple restrictions on the time index t (whose computation is polynomial as the system size increases and therefore trivial here). Namely, we can definean effective release time for each operation corresponding to the sum of the execution times of the preceding operations in the same job. A similar upper bound corresponding to the timespan minus all of the execution times of the subsequent operations of the same job can be set. The bits corresponding to these invalid starting times can be eliminated from the QUBO problem altogether since any valid solution would require them to be strictly zero”) . With regard to Claim 2, D1 teach the method of claim 1, wherein the distribution is a Gaussian distribution Figure 2 indicates that a normal distribution is an adequate approximation, so we need only to estimate its average PNG media_image1.png 34 38 media_image1.png Greyscale and variance σ 2 ”, “The distributions are histograms of occurrences for 1000 random instances, fitted with a Gaussian function of mean PNG media_image1.png 34 38 media_image1.png Greyscale ”) . With regard to Claim 3, D1 teach the method of claim 1, wherein the machine learning model is trained using historical data that includes previously executed problems, the problems including QUBO problems (P. 2, I.B, “The optimizer is best described as an oracle that solves an Ising problem with a given probability [15]. This Ising problem is equivalent to a quadratic unconstrained binary optimization (QUBO) problem”, P. 5, A. Makespan Estimation, “ These could also be inferred from previously solved instances with the proposed annealing solver”) . With regard to Claim 5, D1 teach the method of claim 1, further comprising inputting a vector for the problem into the machine learning model (P. 5, A. “Interestingly, from the characterization of the families of instances up to N = 10 we find that, at least in the region explored, the average minimum makespan PNG media_image3.png 33 36 media_image3.png Greyscale i is proportional to the average execution time of a job PNG media_image6.png 33 73 media_image6.png Greyscale , where PNG media_image7.png 30 34 media_image7.png Greyscale is the mean of Pp. This linear ansatz allows for the extrapolation of approximate resource requirements for classes of problems which have not yet been pre-characterized”, Fig. 2, “The mean and the variance are well fitted respectively by PNG media_image3.png 33 36 media_image3.png Greyscale = PNG media_image4.png 46 357 media_image4.png Greyscale ”, formula represent linear regression model, variables represent input vector ) . With regard to Claim 7, D1 teach the method of claim 1, further comprising fine-tuning the range to increase a granularity or to increase the range (P. 12, B. 3, “For our current purpose, an inexpensive approximation of the error function is sufficient. In most cases this condition means initializing the search at T = hT i. We produce a query q0 for the annealing of HT . If no schedule is found (condition (B7)) we simply let Tmin = T”, Eq. (B10), P. 13, 4. “The described binary search assumes that a lower bound Tmin and an upper bound Tmax are readily available.”, P. 3, Fig. 1-d, “Red edges/circles represent the variations with respect to HT=6. Yellow stars indicate the bits which are penalized with local fields for timespan discrimination”, P. 4, III.B, “We explore a method of extracting more information regarding the actual optimal makespan of a problem within a single call to the solver by breaking the degeneracy of the ground states and spreading them over some finite energy scale”, P. 3, III.A, “Window shaving”, P. 13-14, C.1., Algorithm 1, “Shaving algorithm”) . With regard to Claim 8, D1 teach the method of claim 7, wherein the one-hot encoding requires qubits equal to a number of integers in the range, which is less than a number of integers in vi,vf (P. 2, B.II, “We assign a set of binary variables for each operation, corresponding to the various possible discrete starting times the operation can have:”, Eq. (2), P. 2, II.A, “an operation must start once and only once, leading to the constraint and associated penalty function”, Eq. (3), P. 4, III.A. “reducing the execution windows of operations (i.e., shaving) [20], or in the disjunctive approach, adjusting the heads and tails of operations [21, 22], or more generally, by applying constraints propagation techniques (e.g. [23]), together constitute the basis for a number of classical approaches to solving the JSP. Shaving is sometimes used as a pre-processing step or as a way to obtain a lower bound on the makespan before applying other methods. The interest from our perspective is to showcase how such classical techniques remain relevant, without straying from our quantum annealing approach, when applied to the problem of pruning as many variables as possible. This enables larger problems to be considered and improves the success rate of embeddability in general”, Fig. 3-b embedding probability vs problem size, P. 5, A. Makespan Estimation, “ These could also be inferred from previously solved instances with the proposed annealing solver”, “t his linear ansatz allows for the extrapolation of approximate resource requirements for classes of problems which have not yet been pre-characterized, and it constitutes an educated guess for classes of problems which cannot be precharacterized due to their difficulty or size”, P. 14, “Algorithm 1”, P.3-4, B., “This simplification eliminates an estimated number of variables equal to PNG media_image8.png 30 168 media_image8.png Greyscale , where PNG media_image9.png 36 30 media_image9.png Greyscale represents the average execution time of the operations. This result can be generalized to consider the previously defined ratio θ, such that the total number of variables required after this simple QUBO problem pre-processing is PNG media_image10.png 42 220 media_image10.png Greyscale ”) . With regard to Claim 9, D1 teach the method of claim 1, wherein the problem includes integer variables, further comprising using the machine learning model to predict a distribution for each of the integer variables and determining a range for each of the integer variables (P.3-4, B., “This simplification eliminates an estimated number of variables equal to PNG media_image8.png 30 168 media_image8.png Greyscale , where PNG media_image9.png 36 30 media_image9.png Greyscale represents the average execution time of the operations. This result can be generalized to consider the previously defined ratio θ, such that the total number of variables required after this simple QUBO problem pre-processing is PNG media_image10.png 42 220 media_image10.png Greyscale ”, P. 13, Appendix C.1. “we define qi as the sum of the execution times of all operations following Oi. The numbers ri and qi are referred to as the head and tail of operation Oi, respectively. An operation cannot start before its head and must leave enough time after finishing to fit its tail, so the window of possible start times, the processing window, for operation PNG media_image11.png 36 191 media_image11.png Greyscale . If we consider the one-machine subproblems induced on each machine separately, we can update the heads and tails of each operation and reduce the processing windows further.”, P. 5, A. Makespan Estimation, “ These could also be inferred from previously solved instances with the proposed annealing solver”, “t his linear ansatz allows for the extrapolation of approximate resource requirements for classes of problems which have not yet been pre-characterized, and it constitutes an educated guess for classes of problems which cannot be precharacterized due to their difficulty or size”) . With regard to Claim 10, D1 teach the method of claim 9, further comprising one-hot encoding each of the ranges and submitting the encoded problem to a quantum annealing system (Eq. (2), Eq. (3), Eq(9), Fig. 3-a, P.5, B, “This process is covered in more details in the appendix. An example of embedding for a 5 x 5 JSP instance with θ = 1 and T = 7 is shown in Figure 3-a, where the 72 logical variables of the QUBO problem are embedded using 257 qubits”, Fig. 4a, P. 7, V, “On the quantum annealer installed at NASA Ames (it has 509 working qubits”) . With regard to Claim 11, Claim 11 is similar in scope to claim 1 therefore it is rejected under similar rationale. D1 further teach a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations (P. 7, “On the quantum annealer installed at NASA Ames (it has 509 working qubits”, P. 8, “classical algorithm run on a modern single core processor”, P. 14, “Benchmarking of classical methods was performed on an off-the-shelf Intel Core i7-930 processor clocked at 2.8 GHz.”) . With regard to Claim 12, Claim 12 is similar in scope to claim 2 therefore it is rejected under similar rationale. With regard to Claim 13, Claim 13 is similar in scope to claim 3 therefore it is rejected under similar rationale. With regard to Claim 14, Claim 14 is similar in scope to claim 4 therefore it is rejected under similar rationale. With regard to Claim 15, Claim 15 is similar in scope to claim 5 therefore it is rejected under similar rationale. With regard to Claim 17, Claim 17 is similar in scope to claim 7 therefore it is rejected under similar rationale. With regard to Claim 18, Claim 18 is similar in scope to claim 8 therefore it is rejected under similar rationale. With regard to Claim 19, Claim 19 is similar in scope to claim 9 therefore it is rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 10 therefore it is rejected under similar rationale . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over “Job Shop Scheduling Solver based on Quantum Annealing” Published 2016 [hereinafter D1] in view of “Black-box optimization for integer-variable problems using Ising machines and factorization machines” Published 9/1/2022 [hereinafter D2] . With regard to Claim 6, D1 teach The method of claim 1, wherein the range includes all probable points from a distribution (P. 12, 3. “This fitted distribution is the same Pp described in Figure 2-a of the main text whose tails have been cut off at locations corresponding to an instance-dependent upper bound Tmax and strict lower bound Tmin (see the following section on bounds). Once the initial Tmin and Tmax are set, the binary search proceeds as follows. To ensure a logarithmic scaling for the search, we need to take into account the normal distribution of makespans by attempting to bisect the range (Tmin; Tmax] such that the probability of finding the optimal makespan on either side is roughly equal. In other words, T should be selected by solving the following equation and rounding to the nearest integer”, Eq. (B10)) . D1 does not explicitly disclose the probable points are translated to a new interval. D2 teach the probable points are translated to a new interval (IV. B, Eq. (6), “The parameter n0 in Eq. (6) is an integer, which shifts the range of integers represented by one-hot encoding”). D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of using Ising/annealing machines to search for low energy solutions of Ising problems and quadratic unconstrained binary optimization (QUBO) problems. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to allow hardware-efficient parameterization. By dynamically translating a distribution's range by a integer parameter, quantum circuits (such as quantum annealers) map integers to spin configurations without needing extra qubits. This preserves all probable points within a compact, manageable footprint. This is simply combining prior art elements according to known methods to yield predictable results and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143). With regard to Claim 16, Claim 16 is similar in scope to claim 6 therefore it is rejected under similar rationale. Examiner Notes Claim 4 is not rejected under art as the claim disclose features including “historical data includes a vector related to the problem, wherein the vector, for each instance of the problem, encodes a number of integer variables, a number of multiplications between the integer variables, a minimum integer, a maximum integer, a mean, and a median for each of the integer variables” A remarkable art in this area, D1 , disclose the ability to translate job scheduling tasks into binary variables to be processed. D1 also disclose the usage of historical data to predict the distribution, determine valid search range, and one-hot encoding. However D1 does not disclose historical data includes a vector related to the problem, wherein the vector, for each instance of the problem, encodes a number of integer variables, a number of multiplications between the integer variables, a minimum integer, a maximum integer, a mean, and a median for each of the integer variables. Another remarkable art in this area, “A GNN-GUIDED PREDICT-AND-SEARCH FRAMEWORK FOR MIXED-INTEGER LINEAR PROGRAMMING”, discloses speeding up optimization by using machine learning trained on historical data to predict the probability distribution of variables. It also disclose translating problem constraints into vectors. However it does not disclose multiplication between integer variables and its feature vector lacks the specific mean, median, minimum, maximum calculation for each integer variable. Another remarkable art in this area, “What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO”, discloses the ability to evaluate algorithms of QUBO problems by training a machine learning model on a historical library of problem instances and create a vector of 58 distinct statistical features including the mean, minimum, maximum. However the calculated features is for the network structure properties and not for the integer variables. It also does not disclose the multiplication number between integer variables. However further evaluation/search will be provided based on the applicant’s response. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. “What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO” that disclose discloses the ability to evaluate algorithms of QUBO problems by training a machine learning model on a historical library of problem instances and create a vector of 58 distinct statistical features including the mean, minimum, maximum. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/401,021 Page 2 Art Unit: 2148 Application/Control Number: 18/401,021 Page 3 Art Unit: 2148 Application/Control Number: 18/401,021 Page 4 Art Unit: 2148 Application/Control Number: 18/401,021 Page 5 Art Unit: 2148 Application/Control Number: 18/401,021 Page 6 Art Unit: 2148 Application/Control Number: 18/401,021 Page 7 Art Unit: 2148 Application/Control Number: 18/401,021 Page 8 Art Unit: 2148 Application/Control Number: 18/401,021 Page 9 Art Unit: 2148 Application/Control Number: 18/401,021 Page 10 Art Unit: 2148 Application/Control Number: 18/401,021 Page 11 Art Unit: 2148 Application/Control Number: 18/401,021 Page 12 Art Unit: 2148 Application/Control Number: 18/401,021 Page 13 Art Unit: 2148 Application/Control Number: 18/401,021 Page 14 Art Unit: 2148 Application/Control Number: 18/401,021 Page 15 Art Unit: 2148 Application/Control Number: 18/401,021 Page 16 Art Unit: 2148 Application/Control Number: 18/401,021 Page 17 Art Unit: 2148 Application/Control Number: 18/401,021 Page 18 Art Unit: 2148 Application/Control Number: 18/401,021 Page 19 Art Unit: 2148 Application/Control Number: 18/401,021 Page 20 Art Unit: 2148 Application/Control Number: 18/401,021 Page 22 Art Unit: 2148 Application/Control Number: 18/401,021 Page 23 Art Unit: 2148 Application/Control Number: 18/401,021 Page 24 Art Unit: 2148 Application/Control Number: 18/401,021 Page 25 Art Unit: 2148
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