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. DETAILED ACTION 2 . This office action is in response to the original filing of 09/05/2023. Claims 1-10 are canceled and claims 11-19 are pending and have been considered below. Claim Rejections - 35 USC § 112 3. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim 19 is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim. Claim Rejections - 35 USC § 101 4 . 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 1 - 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 1 , the claim recites a method which falls into one of the statutory categories. 2A – Prong 1: Claim 1 1 , in part, recites “ providing a directed graph having an input node and output node connected by a plurality of edges and nodes; randomly drawing a plurality of paths through the directed graph along drawn edges of the directed graph, wherein each respective edge is assigned a probability which characterizes with which probability the respective edge is drawn, wherein the probabilities are ascertained depending on a sequence of previously drawn edges of the respective path ; drawing a path depending on the adjusted probabilities and creating the machine learning system corresponding to the drawn path ” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, identifying a follow-up action covers someone mentally writing down an action. 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 1 further recites “ training machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). Step 2B : The claim does not contain significantly more than the judicial exception. The claim further recites “ training machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” .” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) . As an ordered whole, the claim is directed to a mentally performable process of developing a model for estimating a parameter of a topic model. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible. Claim 1 8 , the claim recites : 2A – Prong 1: Claim 1 8 , in part, recites “ providing a directed graph having an input node and output node connected by a plurality of edges and nodes; randomly drawing a plurality of paths through the directed graph along drawn edges of the directed graph, wherein each respective edge is assigned a probability which characterizes with which probability the respective edge is drawn, wherein the probabilities are ascertained depending on a sequence of previously drawn edges of the respective path ; drawing a path depending on the adjusted probabilities and creating the machine learning system corresponding to the drawn path ” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, identifying a follow-up action covers someone mentally writing down an action. 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 11 further recites “ training machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). “… machine-readable storage element on which is stored a computer program including instructions for creating a machine learning system, the instructions, when executed by a computer, causing the computer to perform ” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) . The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory "). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr , 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea . Step 2B : The claim does not contain significantly more than the judicial exception. The claim further recites “ training machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” .” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) . As an ordered whole, the claim is directed to a mentally performable process of developing a model for estimating a parameter of a topic model. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible. “… machine-readable storage element on which is stored a computer program including instructions for creating a machine learning system, the instructions, when executed by a computer, causing the computer to perform ” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) . The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory "). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr , 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea . Claim 1 9 , the claim recites : 2A – Prong 1: Claim 11, in part, recites “ provid e a directed graph having an input node and output node connected by a plurality of edges and nodes; randomly draw a plurality of paths through the directed graph along drawn edges of the directed graph, wherein each respective edge is assigned a probability which characterizes with which probability the respective edge is drawn, wherein the probabilities are ascertained depending on a sequence of previously drawn edges of the respective path ; draw a path depending on the adjusted probabilities and creating the machine learning system corresponding to the drawn path ” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, identifying a follow-up action covers someone mentally writing down an action. 2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 11 further recites “ train machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). Step 2B : The claim does not contain significantly more than the judicial exception. The claim further recites “ train machine learning systems corresponding to the drawn paths, wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ” .” This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) . As an ordered whole, the claim is directed to a mentally performable process of developing a model for estimating a parameter of a topic model. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible. Claim 12 recites “ wherein a parameterized function ascertains the probabilities of the edges depending on an order of previously drawn edges of the path, wherein the parameterization of the function is adjusted during training with respect to the cost function ” This limitation could encompass mentally calculating the probabilities . Claim 1 3 recites “wherein the previously drawn edges and/or nodes are assigned a unique coding of their order and the function ascertains the probabilities depending on the coding ” This limitation could encompass mentally calculating the representation of the probabilities . Claim 1 4 recites “wherein the function ascertains a probability distribution over possible edges, from a set of edges that can be drawn nex t” This limitation could encompass mentally calculating the probabilities. Claim 1 5 recites “wherein the function is an affine transformation or a neural network ” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f) . Claim 1 6 recites “ wherein the function is an affine transformation or a neural network, and wherein the parameterization of the affine transformation describes a linear transformation and a shift of the unique coding, and a scaling is composed of a low-rank approximation and the scaling depending on a number of edges ” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). Claim 1 7 recites “wherein a plurality of functions are used and the functions are each provided by a neural network, wherein a parameterization of a plurality of layers of the neural networks are shared among all neural networks. ” these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f). Claim Rejections - 35 USC § 102 5 . 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 – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim(s) 11-12, 14-15, and 17-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Guo et al. (Towards Accurate and Compact Architectures via Neural Architecture Transformer 2021) . Claim 11. Guo discloses a computer-implemented method for creating a machine learning system, comprising the following steps: providing a directed graph having an input node and output node connected by a plurality of edges and nodes ( Section 3, 3.1 , fig. 2) ; randomly drawing a plurality of paths through the directed graph along drawn edges of the directed graph ( Section 6, 6.1 , and optimization in equation 5 , Section 3.4 Training the parameters of the controller model : For each input architecture, we sample n optimized architectures …) , wherein each respective edge is assigned a probability which characterizes with which probability the respective edge is drawn ( Section 3.3 paragraph 2 , “ Z refers to the probability distribution[ ... ] over 3 transitions on the edges, i.e., "remaining unchanged", "replacing with null connection", and "replacing with skip connection " and Section 3.4 Training the parameters of the controller model “ the probability to sample some architecture …wherein the distribution depends on Z; Section 3. 3 Policy Learning by Graph Convolutional Network “Note that a graph ….) wherein the probabilities are ascertained depending on a sequence of previously drawn edges of the respective path ( see use of the adjacency matrix A in equation 3 (i.e. allocation of probabilities to edges … and Section 3. 3 Policy Learning by Graph Convolutional Network : " Since the optimization of an operation/edge in the architecture graph depends on the adjacent nodes and edges, we consider both the current edge and its neighbors to learn the optimal policy. Therefore, we build the controller model with a graph convolutional networks (GCN) [ ... ] to exploit the adjacency information of the operations in the architecture. Here, an architecture graph can be represented by a data pair (A,X), where A denotes the adjacency matrix of the graph and X denotes the attributes of the nodes together with their two input edges ." and Section 3.4 Training the parameters of the controller model : " the probability to sample some architecture a from the distribution [ ... ]", wherein the distribution depends on Z ; Section 3. 3 Policy Learning by Graph Convolutional Network : Note that a graph convolutional layer… and equation 3 ) ; training machine learning systems corresponding to the drawn paths (Algorithm 1 Training method for NAT… Section 3.4 Training method for NAT ) , wherein parameters of the machine learning system are adjusted during training so that a cost function is optimized, the parameters that are adjusted include the probabilities of the edges of the drawn paths ( Section 3.4 Training the parameters of the controller model : see maximization of the expected reward in equation 4, which uses an entropy regularization term; as a result of which the distribution is encouraged to have high entropy during the training ; and drawing a path depending on the adjusted probabilities and creating the machine learning system corresponding to the drawn path ( optimized architectures in fig. 3 and assessing the respective machine learning systems in table 1) . Claim 12. Guo discloses t he method according to claim 11, wherein a parameterized function ascertains the probabilities of the edges depending on an order of previously drawn edges of the path, wherein the parameterization of the function is adjusted during training with respect to the cost function ( Section 3.4 rule in equations 4 and 5; To encourage exploration, we use an entropy regularization … ; see "Details of the MDP formulation" Section 3.2 Markov Decision Process for NAT and the corresponding use of the "adjacency matrix", and see respective maximizing of the expected reward in equation 4, which is adjusted during the training based on an entropy regularization term, Section 3.4 Training the parameters of the controller model ) Claim 14. Guo discloses the method according to claim 12, wherein the function ascertains a probability distribution over possible edges, from a set of edges that can be drawn next ( Section 3. 3 Policy Learning by Graph Convolutional Network : Note that a graph convolutional layer… and equation 3 ). Claim 15. Guo discloses the method of claim 12, wherein the function is an affine transformation or a neural network (abstract, Section 2.2). Claim 17. Guo discloses the method according to claim 15, wherein a plurality of functions are used and the functions are each provided by a neural network, wherein a parameterization of a plurality of layers of the neural networks are shared among all neural networks ( Section 3.4 Training method for NAT Training the parameters of the supernet w). Claims 18-19 represent the storage and apparatus of claim 11, respectively and are rejected along the same rationale. Claim Rejections - 35 USC § 103 6 . 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 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al. (Towards Accurate and Compact Architectures via Neural Architecture Transformer 2021) in view of Ben- dror et al. (US 2022/0327386). Claim 13. Guo discloses the method according to claim 12, but fails to explicitly disclose wherein the previously drawn edges and/or nodes are assigned a unique coding of their order and the function ascertains the probabilities depending on the coding. However, Ben- dror discloses wherein the previously drawn edges and/or nodes are assigned a unique coding of their order and the function ascertains the probabilities depending on the coding ([0066]). Therefore, It would have been obvious to one of ordinary skill in the art, at or before the effective filing date of the instant application, to use the feature of Ben- dror in Guo . One would have been motivated to reduce the number of operations performed by the neural network . Claim 16. Guo discloses the method according to claim 13, but fails to explicitly disclose wherein the function is an affine transformation or a neural network, and wherein the parameterization of the affine transformation describes a linear transformation and a shift of the unique coding, and a scaling is composed of a low-rank approximation and the scaling depending on a number of edges. However, Ben- dror discloses wherein the function is an affine transformation or a neural network, and wherein the parameterization of the affine transformation describes a linear transformation and a shift of the unique coding, and a scaling is composed of a low-rank approximation and the scaling depending on a number of edges ([0073 ],[ 0066],[0008]) . Therefore, It would have been obvious to one of ordinary skill in the art, at or before the effective filing date of the instant application, to use the feature of Ben- dror in Guo . One would have been motivated to effectively learn of complex patterns in the data. Conclusion 7 . The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 27 2 - 4046 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov . Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /PHENUEL S SALOMON/ Primary Examiner, Art Unit 2146