CTNF 18/292,368 CTNF 87151 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. DETAILED ACTION This action is responsive to the following communication: Non-Provisional Application filed Jan. 26, 2024. Claims 1-16 are pending in the case. Claims 1, 15 and 16 are independent claims. Claim Rejections - 35 U.S.C. § 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 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. As to claim 1 : Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03. Yes, the claim is to a process. Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1). Yes , the limitation “receiving data comprising a plurality of data points, each data point comprising a respective value of each of a plurality of features for each data point in the received data, except that for at least one of the plurality of data points a respective value of at least one but not all of the features is missing such that the received data comprises at least one missing value;” is mere insignificant extra solution activity and something the courts have recognized as being well-understood, routine and conventional. Yes , the limitation “encoding, using a first neural network having at least one parameter, the values of the received data into a distribution of a first plurality of latent vectors, the distribution of the first plurality of latent vectors having a mean and a variance;” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C). Yes , the limitation “decoding, using a second neural network having at least one parameter, a random sample of the distribution of the first plurality of latent vectors into a computed vector for each data point; ” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Yes , the limitation “inputting the computed vector for each data point into a third neural network having at least one parameter to determine a computed plurality of mask vectors, the plurality of mask vectors comprising a computed mask vector for each data point, wherein each computed mask vector comprises a computed binary value for each feature to indicate whether a value for each feature is missing or not;” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Yes , the limitation “encoding, using a fourth neural network having at least one parameter, background data for each data point in the received data into a distribution of a second plurality of latent vectors, wherein the background data comprises fully observed data comprising a value for each feature and for each data point in the background data;” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). Yes , the limitation “tuning the at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network by minimizing a loss function, the loss function comprising a sum of: a measure of difference between the distribution of the first plurality of latent vectors and the distribution of the second plurality of latent vectors; and an error determined based on the computed vector for each data point, the computed at least one mask vector and ground truth data for the received data.” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C). Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d). No , the limitation “receiving data comprising a plurality of data points, each data point comprising a respective value of each of a plurality of features for each data point in the received data, except that for at least one of the plurality of data points a respective value of at least one but not all of the features is missing such that the received data comprises at least one missing value;” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2). No , the limitation “encoding, using a first neural network …” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). No , the limitation “decoding, using a second neural network … ” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). No , the limitation “inputting the computed vector for each data point into a third neural network having … ” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(1), 2106.05(f)(2). No , the limitation “encoding, using a fourth neural network …” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). No , the limitation “tuning the at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network by minimizing a loss function, the loss function …” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h). Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05. No Claims 2-14 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 2-18 recite the same abstract idea. The claims recite additional limitations directed, but do not otherwise add any meaningful limits beyond the abstract idea. Claims 15 and 16 are rejected for the similar reasons discussed above with respect to claim 1. Claims 15 and 16 do not additional elements that amount to significantly more than the judicial exception. Claim Rejections - 35 USC § 103 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. Claims 1-16 are rejected under 35 U.S.C. 103 as being anticipated by Gong et al. (hereinafter Gong) U.S. Patent Publication No. 2020/0372369. With respect to independent claim 1 , Gong teaches a computer-implemented method comprising: receiving data comprising a plurality of data points (see e.g., Fig. 1) , each data point comprising a respective value of each of a plurality of features for each data point in the received data, except that for at least one of the plurality of data points a respective value of at least one but not all of the features is missing such that the received data comprises at least one missing value (see e.g., Fig. 1 Para [63][64] – “Input data 102 is received by an attributive proposal network 106 , and the input data 102 is typically only partially-observed … input data 102 can also be heterogeneous data … The data is split between observed and unobserved data to reflect the “completeness” of the input data 102 ”) ; encoding, using a first neural network having at least one parameter (see e.g., Fig. 1 – 106 Attributive Proposal Network) , the values of the received data into a distribution of a first plurality of latent vectors, the distribution of the first plurality of latent vectors having a mean and a variance (see e.g., Para [67][90][109]-[119][120] [203] – “All priors p(z.sub.i) can be standard Gaussians and proposal distributions q(z.sub.i|x, m) are diagonal Gaussians with means and covariances inferred by neural networks. This factorization separates encoding of each attribute and yields an efficient distribution for the latent space by assuming the latent variables are conditionally independent given the data and mask.”) ; decoding, using a second neural network having at least one parameter (see e.g., Fig. 1 – 110 Data Generative Network) , a random sample of the distribution (see e.g., Para [76][110]-[116] – “The latent variables for all attributes are sampled using the selective proposal distributions in (8). Next, to capture the intricate dependency relationships between observed and unobserved attributes, the variational latent variables are aggregated with an aggregator … “) of the first plurality of latent vectors into a computed vector for each data point (see e.g., Fig. 1 Para [58] [76][115]) ; inputting the computed vector (see e.g., Para [115][133] – “The latent variables for all attributes are sampled using the selective proposal distributions … the variational latent variables are aggregated with an aggregator … before providing to the decoders”) for each data point into a third neural network (see e.g., Fig. 1 – 116 Mask Generative Network Para [11]) having at least one parameter to determine a computed plurality of mask vectors, the plurality of mask vectors comprising a computed mask vector for each data point, wherein each computed mask vector comprises a computed binary value for each feature to indicate whether a value for each feature is missing or not (see e.g., Para [11][119] – “The latent codes representing each attribute are then aggregated and provided to decoders to reconstruct a mask (e.g., using a mask generative network) and all attributes independently. The latent codes and the generated mask are provided to a data generative network to sample the attributes. “ “Mask VAE consists of an encoder which encodes the mask vector to stochastic latent variables and a decoder which decodes the latent variables to a reconstruction of the mask.”) ; encoding, using a fourth neural network having at least one parameter (see e.g., ., Fig. 1 – 108 Collective Proposal Network Para [114] – “Collective Proposal Network 108 : Differently, for an unobserved attribute, the proposal distribution is selected as q.sub.Ψ(z.sub.i|x.sub.o, m), which collects all observed ones and the mask to produce the proposal distribution.”) , background data for each data point in the received data into a distribution of a second plurality of latent vectors (see e.g., Abstract Para [4][120] – “The mask can be fully-observed and available during training and testing stages. ”) , wherein the background data comprises fully observed data comprising a value for each feature and for each data point in the background data (see e.g., Para [4][72]); tuning the at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network by minimizing a loss function (see e.g., Para [121] – the Objective Function) , the loss function comprising a sum of (Gong does not expressly show the loss function is a sum of the measure of difference and error. However, based on the teachings of Gong, it would have been obvious for a person of ordinary skill in the art to try the algorithm. It is merely a calculation choice.) : a measure of difference between the distribution of the first plurality of latent vectors and the distribution of the second plurality of latent vectors (see e.g., Para [12][144] – “The architecture was compared against benchmark computing architectures on benchmark data sets (e.g., Fashion MNIST+label, MNIST+MNIST, CMU-MOSI), and was found to yield technical benefits and improvements, including improved computational accuracy (e.g., improved mean square error scores, shown both in mean and standard deviations over independent runs).”” Mean-squared error, cross-entropy and binary cross-entropy are used as reconstruction loss for numerical, categorical and mask variables, respectively.”) ; and an error determined based on the computed vector for each data point, the computed at least one mask vector and ground truth data for the received data (see e.g., Equation 11 Para [107][121][222]) . With respect to dependent claim 2 , Gong teaches the received data comprises at least one data value representing sensor values of at least one device (see e.g., Para [3] – “data are collected from different measurement tools with different recording mechanisms”) . With respect to dependent claim 3 , Gong teaches the method is used to diagnose at least one malfunction in the at least one device (Gong does not expressly show this feature. However, Gong expressly indicates “[l]earning from data is an objective of artificial intelligence. Learning algorithms often rely heavily on clean homogeneous data, whereas in the real world, data is filled with noisy heterogeneous data. Heterogeneity is ubiquitous in a variety of applications and platforms from healthcare and finance to social networks and manufacturing systems.” See Para [3]. Therefore, it would have been obvious to apply the method to diagnose of malfunction.) With respect to dependent claim 4 , Gong teaches the background data comprises fully observed data for at least one of the plurality of features of each data point (see e.g., Para [120] – “The mask can be fully-observed and available during training and testing stages.”) . With respect to dependent claim 5 , Gong teaches determining the received data by removing, from the ground truth data, values for at least one of the plurality of features outside of a respective range of values for each of the at least one feature (see e.g., Para [7]) . With respect to dependent claim 6 , Gong teaches the received data comprises: at least one observed value for at least one of the data points; and a mask vector for each data point, where each mask vector comprises a binary value for each feature to indicate whether a value for each feature is missing or not (see e.g., Para [66][67] – “ a mask 104 can be generated from the input data 102 … mask 104 can be a data structure, such as an array, a linked list, among others … he mask 104 could be a vector or an array of Booleans indicative of whether each modality is observed or not observed.”) . With respect to dependent claim 7 , Gong teaches determining the mask vector for each data point in the received data from the at least one observed value and the at least one missing value of the received data (see e.g., Para [66][67] – “a mask 104 can be generated from the input data 102 ”) . With respect to dependent claim 8 , Gong teaches after the tuning at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network, setting the at least one parameter of the first neural network and the at least one parameter of the second neural network to tuned values to provide at least one tuned parameter of the first neural network and at least one tuned parameter of the second neural network; receiving second received data comprising a plurality of data points, each data point comprising a respective value of each of a plurality of features for each data point in the received data, except that for at least one of the plurality of data points a respective value of at least one but not all of the features is missing such that the second received data comprises at least one missing value; encoding, using the first neural network having the at least one tuned parameter, the second received data into a distribution of a third plurality of latent vectors; sampling a random sample of latent vectors from the distribution of the third plurality of latent vectors; decoding, using the second neural network having the at least one tuned parameter, the random sample of latent vectors from the distribution of the third plurality of latent vectors to obtain the at least one missing value in the second received data (see e.g., Para [5][55] – “the trained model can be deployed for later use in both data generation and imputation. In particular, once trained, the model can impute missing data from any combination of observed variables while being trained efficiently with a single variational objective.”) . With respect to dependent claim 9 , Gong teaches the background data is input by a user (see e.g., Para [22] – “The system can be interoperated with, for example, through various application programming interfaces (APIs), whereby data sets are provided through upload or data stream and an output data set is returned.”) . With respect to dependent claim 10 , Gong teaches the tuning the at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network by minimizing a loss function comprises: using a gradient descent algorithm for each of the at least one parameter of the first neural network, the at least one parameter of the second neural network and the at least one parameter of the third neural network (see e.g., Para [72][137] – “the model is jointly trained following stochastic gradient variational Bayes.” “using neural networks and optimizing the parameters via backpropagation techniques, according to some embodiments.”) . With respect to dependent claim 11 , Gong teaches the at least one missing value in the received data comprises Missing Not At Random, MNAR, data (see e.g., Para [87][92] – “Not Missing At Random (NMAR). Missingness depends on unobserved variables or both observed and unobserved variables.”) . With respect to dependent claim 12 , Gong teaches the received data comprises at least one data value representing physical measurements of at least one patient (see e.g., Para [50] – “patient records like gender, age, the history of allergies, symptoms and etc.”) . With respect to dependent claim 13 , Gong teaches the method is used to diagnose the at least one patient (Gong does not expressly show this feature. However, Gong expressly indicates “[l]earning from data is an objective of artificial intelligence. Learning algorithms often rely heavily on clean homogeneous data, whereas in the real world, data is filled with noisy heterogeneous data. Heterogeneity is ubiquitous in a variety of applications and platforms from healthcare and finance to social networks and manufacturing systems.” See Para [3]. Therefore, it would have been obvious to apply the method to diagnose of patient.) . With respect to dependent claim 14 , Gong teaches the background data comprises an age and a gender of each patient (see e.g., Para [50]) . Claim 15 is rejected for the similar reasons discussed above with respect to claim 1. Claim 16 is rejected for the similar reasons discussed above with respect to claim 1. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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://portal.uspto.gov/external/portal. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /PEI YONG WENG/Primary Examiner, Art Unit 2141 Application/Control Number: 18/292,368 Page 2 Art Unit: 2141 Application/Control Number: 18/292,368 Page 3 Art Unit: 2141 Application/Control Number: 18/292,368 Page 4 Art Unit: 2141
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