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 .
Status of the Claims
The amended claim set received 22 December 2022 has been entered into the application.
Claims 1-20 are pending.
Priority
Applicant claims foreign priority benefit to European Application EP22383137 filed 26 November 2022.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP22383137, filed on 26 November 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01 February 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings were received on 22 December 2022. These drawings are accepted.
Specification
The specification received 22 December 2022 has been entered into the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Following the flowchart of MPEP 2106
Step I - Process, Machine, Manufacture or Composition
Claims 1-6 are drawn to a computer implemented method, so a process.
Claims 7-19 are drawn to an apparatus, so a machine.
Claim 20 is drawn computer-readable media, so a manufacture.
2A Prong I - Identification of an Abstract Ideas
Claims 1 drawn is a computer-implemented method while claim 7 is drawn to an apparatus while claim 20 is drawn to a manufacture. However, claims 1, 7, and 12 encompass similar limitations and are therefore examined similarly.
Claims 1, 7. and 12 recite:
processing a predetermined machine learning routine in the form of a tensor network that defines layers of tensors in the routine, the routine being adapted for a regression problem of fermionic systems that are molecules or chemical reactions
This step encompasses using a predetermined machine learning routine in the form of a tensor network that performs regression mathematics/statistics using fermionic system data (i.e., molecules or chemical reactions) which reads on abstract ideas. Here, the predetermined machine learning routine that uses tensor network that performs regression problems reads on mathematics/statistical analysis and reads on abstract ideas performed in a computing environment. See MPEP 2106.04(a)(2)(C)(1-3). This step encompasses using tensor network which encompasses using utilizing tensor calculus, linear and multilinear algebra, and vector mathematic which reads on abstract ideas.
the routine being processed such that: each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor
This step encompasses performing a routine which encompassing performing a series of mathematical steps/instructions to convert tensor network into a parity preserving tensor network and introduce a sign swap tensor into the tensor network for implementing anticommutation fermionic operator which reads on instruction and is therefore an abstract idea. This step encompasses organizing mathematical functions and quantitative data to convert a tensor network using a machine learning routine into a parity preserving tensor and is therefore an abstract idea. This step encompasses processing tensor networks of the predetermined machine learning routine is converted into a parity preserving tensor which encompasses performing mathematic computation to convert a tensor network to a parity preserving tensor which reads on abstract ideas. This is step involves using such mathematically organizing and rearranging quantitative data using methods such as parity eigenvalues “p being the eigenvalue of the parity operation P” [Spec page 11] which reads on abstract ideas. This step encompasses using machine learning routine which encompasses performing a series mathematical computation (i.e., series of mathematical steps) for converting tensor network to a parity preserving tensor which reads on abstract ideas.
a sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network of the predetermined machine learning routine, thereby implementing anticommutation fermionic operator
This step encompasses step encompassing following a routine/series of steps/instructions to introduce a sign swap function at the legs of the tensors of the predetermined machine learning routine to implement anticommutation fermionic operator and is therefore an abstract idea. This step encompassing performing a series of mathematical computation for swapping signs (i.e., multiplying data by -1) to flipping signs of fermionic system which reads on abstract ideas.
Claims 1-7, and 8-19 are further drawn to limitations that describe the abstract ideas of claims 1 and 7 and are therefore abstract ideas.
2A Prong II - Consideration of Practical Application.
Claims 1 and 12 do not recite any additional element which integrate the recited judicial exception into a practical application.
Here, in the instant case, the claims merely set forth a method of data analysis for outputting at least one parameter for the first fermionic system. As such, practicing the claims merely results in the generation of information (i.e., at least one parameter for the first fermionic system). Such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv).
Claims 1, 7, and 20 recite a predetermined machine learning routine (PMLR) in the form of a tensor network that defines layers of tensors in the routine, the routine being adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Here, even though the claimed steps utilize a PMLR, the PMLR is broadly and generically recited and reads on mere instructions to implement an abstract idea on a generic computer and reads on mathematical/statistical computations (i.e., regression analysis). See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(III)(C)(1-3), and 2106.05(f).
Furthermore, and for sake of compact prosecution, even if PLMR is also considered an additional element, the PMLR is used to generally apply the abstract idea without limiting how the trained (MLM) functions. These limitations only recite the outcomes for “the predetermined machine learning routine to output at least one parameter for the first fermionic system” without any details about how the predetermined machine learning routine determines at least one parameter for the first fermionic system (i.e., molecules) which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
2B Analysis - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of using computer process, components, and equipment of claims 1, 7, and 20 does not add significantly more than the recited judicial exception because using computers to analyze abstract ideas is merely tangential to the claimed method and is deemed well-understood, routine, and conventional. See MPEP 2106.05(b), 2106.05(d)(II), and 2106.05(g). To provide evidence of conventionality of using computer processes/algorithms and component for processing molecule data using tensors and machine learning methods,
Marti et al. (Marti) teaches tensor networks with respect to fermionic wave functions ansatz for molecules [title]. Marti teaches using the density matrix renormalization group (DMRG) algorithm (i.e., computer processes) (page 2 introduction) (New Journal of Physics, 2010-10, Vol.12 (10), p.103008).
To provide further evidence of conventionality of using computer processes (i.e., algorithm) for processing fermionic systems data, Corboz et al. (Corboz) teaches a tensor network algorithm that uses iterative processes (page 12). Corboz teaches the leading computational cost O(Dk) which is in reference to measuring the computational cost of the algorithm computer components and equipment [page 22] (Philippe Corboz "Fermionic tensor networks" 2013).
The recited additional element of using extracting data of claims 1, 12, 21, and 24 does not add significantly more than the recited judicial exception because extracting data that is subsequently analyzed by abstract ideas is deemed a well-understood, routine, and conventional extra-solution activity. See MPEP 2106.05(d)(II).
The recited additional element of using data inputting of claims 1, 5, 7, 11, and 20 does not add significantly more than the recited judicial exception because inputting data into a computer or computer program/software for subsequently analysis is merely tangential to the claimed method and is deemed well-understood, routine, and conventional extra-solution activity. See MPEP 2106.05(b), 2106.05(d)(II), and 2106.05(g).
The recited additional element of using data outputting of claims 1, 7, 9, and 20 does not add significantly more than the recited judicial exception because outputting data is merely tangential to the claimed method and is deemed well-understood, routine, and conventional extra-solution activity. See MPEP 2106.05(b), 2106.05(d)(II), and 2106.05(g).
The recited additional element of data transmitting of claims 4-5 and 10-11 does not add significantly more than the recited judicial exception because is an inherent computer processing step that transmitting data signal is merely tangential to the computer component and processes of the claimed method and is deemed well-understood, routine, and conventional extra-solution activity.
In conclusion and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 1, 2, 6-8, and 12-20 rejected under 35 U.S.C. 103(a) as being unpatentable over Stojevic et al. (WO 2020/095051, Int’l Patent Pub Date: 14 May 2020) in view Corboz (Philippe Corboz "Fermionic tensor networks" 2013).
Claims 1 drawn is a computer-implemented method while claim 7 is drawn to an apparatus while claim 20 is drawn to a manufacture. However, claims 1, 7, and 12 encompass similar limitations and are therefore examined similarly.
Claim 1 recites processing a predetermined machine learning routine in the form of a tensor network that defines layers of tensors in the routine, the routine being adapted for a regression problem of fermionic systems that are molecules or chemical reactions.
Claim 1 recites the routine being processed such that: each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor.
Claim 1 recites a sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network of the predetermined machine learning routine, thereby implementing anticommutation fermionic operator.
Claim 1 recites inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction.
Claim 1 recites outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem, the at least one parameter being inferred by the processed predetermined machine learning routine.
Stojevic et al. (Stojevic) discloses a tensor network implementing using a machine learning system [Stojevic, claims 1-2]. Stojevic discloses using the Fermi correlation due to election exchange [Stojevic, page 69]. Stojecvic discloses the definition of the application is based on the concept of entanglement entropy to fermionic systems [Stojevic, page 31]. Stojevic discloses the results of figures 33 which contains regression analysis regarding full tensorial molecular input [Stojevic, page 38-39, figure 33]. Stojevic discloses the claim is configured to optimize chemical properties for candidate drug-like molecules [Stojevic, claims 11-18], as in instant claim 1 processing a predetermined machine learning routine in the form of a tensor network that defines layers of tensors in the routine, the routine being adapted for a regression problem of fermionic systems that are molecules or chemical reactions. Here, although Stojevic does not directly disclose regression problems for fermionic system, Stojevic discloses the tensorial machine learning and using Fermi correlations and regression analysis for molecules which suggest the machine learning systems can evaluate regression problem of fermionic systems that are molecules or chemical reactions (i.e., drug-like molecules).
Stojevic discloses using the machine learning system to process drug-like molecules [Stojevic, claims 11-18]. Stojevic discloses tensor network representations include networks describing states with volume law entanglement, which can, for example, provide descriptions of highly excited states present in transition states of small molecules in a reaction or a binding process [Stojevic, page 58]. Stojevic discloses an input and output process for the machine learning tensor [Stojevic, figure 34]. Stojevic discloses using training sets of molecules [Stojevic, figure 32A], as in instant claim 1 inputting a first many-body problem modeling a first fermionic system in the processed predetermined machine learning routine, the first fermionic system being a molecule or a chemical reaction.
Stojevic discloses a GTN discovery platform – Pharm View, analysis pipeline for analyzing molecules for drug properties and binding [Stojevic, figure 32C]. Stojevic discloses outputting entanglement and binding path [Stojevic, figure 32C]. Stojevic discloses the system is configured to simulate and calculate energies of excited states [claim 17] and light absorption properties [claim 18] (i.e., least one parameter for the first fermionic system) [Stojevic, claim 17-18]. Stejovic discloses training dataset for screening libraries of novel molecules [Stojevic, figure 32A, page 35 section 1.3], as in instant claim 1 outputting from the processed predetermined machine learning routine at least one parameter for the first fermionic system after having inputted the first many-body problem, the at least one parameter being inferred by the processed predetermined machine learning routine.
Dependent claim(s):
Stojevic discloses optimizing and calculating molecular properties such as energy and crystal properties [Stojevic claims 8 and 11-18], as in instant claims 2 and 8.
Stojevic discloses a schematic decomposition of a mode-5 tensor as a matrix product [Stojevic page 52 figures 38-39]. Stojevic discloses GTN discovery platform that discloses the pipeline/system analyzing different molecule (i.e., first fermionic system) and molecular conformations interacting with a Phe molecule (i.e., second fermionic system [Stojevic figure 32B]. Stojevic discloses tensorial decomposition into five smaller tensors [Stojevic page 113], as in instant claims 6 and 12-13. Here, it is obvious the computer system contains instructions to decompose tensors into multi-dimensional tensor network. It is noted claim 13 is an inherent computer processing step.
Stojevic discloses a drug discovery pipeline for analyzing drug-like compounds and drug binding [Stojevic figure 32C]. Stojevic discloses the pipeline analyzing different compounds/molecules [Stojevic figures 31-32A-C], as in instant claim 14.
Stojevic discloses using neural networks comprise tensorial network decompositions or tensor networks [Stojevic page 63], as in instant claim 15.
Stojevic discloses the method can use data from rea-world measurements [Stojevic page 105]. Stojevic discloses chemical compound dataset [Stojevic page 109], as in instant claim 16.
Stojevic discloses using MPS matrix product operator (MPO’s) [Stojevic page 54 figure 47], as in instant claim 17.
Stojevic discloses using a two-dimensional MERA (Multi-scale Entanglement Renormalization Ansatz) which is a known tensor [Stojevic page 102]. Stojevic discloses using MPS matrix product operator (MPO’s), as in instant claim 18.
Stojevic discloses using Hamiltonian [Stojevic page 68], as in instant claim 19.
Stojevic does not teach
Stojevic does not teach claim 1 the routine being processed such that: each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor.
Stojevic does not teach claim 1 a sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network of the predetermined machine learning routine, thereby implementing anticommutation fermionic operator.
Corboz (Corboz) teaches the mathematical theory required for utilizing and implementing fermionic tensor networks [Corboz page 1]. Corboz teaches using parity preserving tensors and swap tensors for swapping signs [Corboz page 14], as in instant claim 1 the routine being processed such that: each tensor of the tensor network of the predetermined machine learning routine is converted into a parity preserving tensor.
Corboz teaches operators in anti-commute [Corboz page 13]. Corboz teaches anticommutation rules to evaluate fermionic operator network by replacing crossing by swap tensors [Corboz page 17]. Corboz teach swap tensor for sign swapping [Corboz page 14]. Stojevic discloses using a Utensor with swapped legs [Stojevic top of page 23], as in instant claim 1 a sign swap tensor is introduced in the tensor network at each crossing of legs of different tensors in the tensor network of the predetermined machine learning routine, thereby implementing anticommutation fermionic operator. Thus, it would be obvious to combine the mathematical theory and guidance of swap tensors and parity preserving tensor of Corboz with swapping methods of Stojevic because although Stojevic does not directly teach the mathematics behind the swap tensors Corboz expands on the method of Stojevic by providing the direct guidance required for implementing anticommutation fermionic operator using fermionic system data.
It would be obvious to one of ordinary skill in the art by the effective filing date of the claimed invention to modify Stojevic in view of Corboz because Corboz provides the mathematical theory for utilizing fermionic systems. Here, one of ordinary skill would recognize that while Stojevic provides fermi correlation which implicitly evaluates fermionic systems, Corboz expands on the use of specific fermi/fermionic tensor network for evaluating fermionic data. As such, one of ordinary skill in the art would be motivated to combine the mathematical guidance and theory of Corboz with the quantum circuit system of Stojevic for representing quantum states of effectively infinite physical or chemical system [Stojevic, claim 1-11]. Thus, one or ordinary skill in the art would have a reasonable expectation of success combining/incorporating the mathematical theory of Corboz with the machine learning system of Stojevic because Corboz teaches the mathematics required for using swap tensors and parity preserving tensors which could be coded in the machine learning system of Stojevic. Therefore, combining parity preserving tensor and anticommutation operators and rules of Corboz into the machine learning system of Stojevic would yield predictable method steps for using parity tensors and sign swapping tensors for implementing anticommutation fermionic operator.
Conclusion
Claims 1-20 are rejected.
No claims are allowed.
Finality
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
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/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights