DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The present application, filed on September 06, 2024, is accepted.
Claims 1 – 20 are being considered on the merits.
Drawings
The drawings, filed on September 06, 2024, are accepted.
Specification
The specification, filed on September 06, 2024, is accepted.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 10, and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 9, and 17 of US Patent No. 12105847 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because both the application and the patent are implementing a differentially private variational autoencoder for data obfuscation are disclosed. They both encode input data into a latent space that is a representation of the input. Claims of the instant application therefore are not patently distinct from the earlier application claims and as such are unpatentable over obvious-type double patenting. A later patent/application claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim.
A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a 35-patent claim to a species within that genus). “ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim Rejections - 35 USC § 103
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.
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 – 3, 8 – 12, and 17 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210004677 A1 to Menick et al., (hereinafter, “Menick”) in view of US 20200090050 A1 to Rolfe et al., (hereinafter, “Rolfe”).
Regarding claim 1, Menick teaches a system of comprising: at least one hardware processor; a variational autoencoder comprising: an encoder configured to: access input data; [Menick, para. 5 discloses training the encoder neural network, the decoder neural network, and the prior neural network on the training data by repeatedly performing the following operations: selecting a batch of training data; for each given observation in the selected batch: providing the given observation as input to the encoder neural network] encode the input data into a latent space representation by inferring a mean of the latent space representation bounded within a finite space [Menick, para. 5 discloses process the given observation in accordance with current parameter values of the encoder neural network to generate as output parameters of a data-conditional encoding probability distribution over a latent state space; determining an updated code for the given observation based on the parameters of the data-conditional encoding probability distribution. Para. 67 discloses the observation distribution may define a respective probability distribution over possible intensity values for each pixel of an image. For example, a probability distribution over possible intensity values for a pixel may be a Normal distribution parameterized by mean and standard deviation parameters. The observation distribution may assign a probability value to an image based on a product, over each pixel of the image, of the likelihood of the intensity value of the pixel according to the probability distribution over possible intensity values for the pixel.] and using a global value for a standard deviation of the mean, the bounding of the mean using a hyperbolic tangent or a stereographic projection, [Menick, para. 65 discloses the system selects a batch of observations 114 from the training data 108, and processes the observations 114 using the encoder neural network 102. The encoder neural network 102 is configured to process an observation to generate an output that defines the parameters of an encoding distribution 116 (sometimes referred to as a “data conditional” encoding distribution) for the observation. The encoding distribution 116 is a probability distribution over a latent (state) space that represents a space of possible latent variables. Each latent variable can be represented in as an ordered collection of numerical values, for example, as a vector, matrix, or higher order tensor of numerical values.] and sample data from the latent space representation; [Menick, para. 5 discloses sampling one or more latent variables from the data-conditional encoding probability distribution; providing the latent variables as input to the decoder neural network, which is configured to process the latent variables in accordance with current parameter values of the decoder neural network to generate as output parameters of an observation probability distribution over the observation space;] and a decoder configured to: decode the sampled data of the latent space representation into output data [Menick, para. 22 discloses processing the one or more latent variables using the decoder neural network to generate parameters of an observation probability distribution over an observation space, where each observation in the set of observations lies in the observation space; determining an approximate reconstruction of the observation using the observation probability distribution; determining residual data required for lossless reconstruction of the observation based on a difference between the observation and the approximate reconstruction of the observation], but Menick does not teach the global value being independent of the input data.
However, Rolfe does teach the global value being independent of the input data. [Rolfe, para. 67 discloses the values of the active continuous latent variables identify a point or region in the relevant latent subspace. Alternatively presented, the set of active latent variables can be thought of as identifying a set of filters to apply to the input, and the operation of each filter is dependent on the value of the corresponding active latent variable(s). This effectively separates the modes of the prior and/or approximating posterior distributions, thereby promoting sparsity.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling to combine Rolfe’s system with Menick’s system, with a motivation to select a subset of active latent variables to define a latent subspace with probability mass largely bounded away from the origin and disjoint from subspaces associated with other disjoint subsets of active latent variables. [Rolfe, para. 67]
As per claim 2, modified Menick teaches the system of claim 1, wherein the encoder is a first neural network. [Menick, para. 15 discloses the encoder neural network includes a convolutional neural network.]
As per claim 3, modified Menick teaches the system of claim 2, wherein the decoder is a second neural network. [Menick, para. 16 discloses the decoder neural network includes an autoregressive neural network.]
As per claim 8, modified Menick teaches the system of claim 1, wherein the system is located on a server machine of a trusted third-party that facilitates interactions between two parties other than the trusted third-party. [Menick, para. 72 discloses for each of the observations 114 in the current batch, the system 100 processes the neighboring code 128 for the observation using the prior neural network 106. The prior neural network 106 is configured to process the neighboring code to generate an output that includes the parameters of a prior probability distribution 124 that models the code for the observation. Like the encoding probability distribution 116, the prior probability distribution 124 is a probability distribution over the latent space.]
As per claim 9, modified Menick teaches the system of claim 8, wherein the variational autoencoder further comprises an output module configured to send the output data to a non-trusted third-party. [Menick, para. 10 discloses providing the observation as input to the encoder neural network, which is configured to process the observation in accordance with current parameter values of the encoder neural network to generate as output parameters of a data-conditional encoding probability distribution over the latent state space; determining an updated code for the observation based on the parameters of the data-conditional encoding probability distribution; and assigning the updated code to the observation.]
Regarding claims 10 – 12, they recite features similar to features within claims 1 – 3, therefore, they are rejected in a similar manner.
Regarding claims 17 – 18, they recite features similar to features within claims 8 - 9, therefore, they are rejected in a similar manner.
Regarding claims 19 – 20, they recite features similar to features within claims 1 – 2, therefore, they are rejected in a similar manner.
Claims 4 – 7 and 13 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210004677 A1 to Menick et al., (hereinafter, “Menick”) in view of US 20200090050 A1 to Rolfe et al., (hereinafter, “Rolfe”) in further view of US 11294756 B1 to Sadrieh et al., (hereinafter, “Sadrieh”).
Regarding claim 4, modified Menick teaches the system of claim 2, but modified Menick does not teach wherein the first neural network comprises a first plurality of long short-term memory cells.
However, Sadrieh does teach wherein the first neural network comprises a first plurality of long short-term memory cells. [Sadrieh, col. 3 lines 31 – 35 discloses an encoder 320 includes an array of Long-Short Term Memory (LSTM) elements shown as Layer 1 through Layer L, wherein L is any integer number. By using networks with LSTM cells, the temporal nature of the data is taken into account.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Sadrieh’s system with Menick’s system, with a motivation for time series events, such as BGP update packets, can be analyzed using artificial intelligence such as a recurrent neural network. An encoder of a recurrent neural network can be used to calculate latent variables, one for each time-sequence input data stream. The latent variables can be used to generate a parameterized latent distribution. A decoder can then be used to generate an output time-series stream with reduced noise. A reconstruction probability can be calculated and an anomaly detection can be computed to obtain a single anomaly score for all of the time series streams or a different anomaly score per stream. [Sadrieh, col. 1 lines 53 – 63]
Regarding claim 5, modified Menick teaches the system of claim 3, but modified Menick does not teach wherein the second neural network comprises a second plurality of long short-term memory cells.
However, Thomson does teach wherein the second neural network comprises a second plurality of long short-term memory cells. [Sadrieh, col. 5 lines 16 – 20 discloses the decoder 350 can also include an array of LSTM cells, or similar cell types used by the encoder. Both the encoder 320 and the decoder 350 can be trained using backpropagation, reparameterization and other known techniques for training neural networks.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Sadrieh’s system with Menick’s system, with a motivation for time series events, such as BGP update packets, can be analyzed using artificial intelligence such as a recurrent neural network. An encoder of a recurrent neural network can be used to calculate latent variables, one for each time-sequence input data stream. The latent variables can be used to generate a parameterized latent distribution. A decoder can then be used to generate an output time-series stream with reduced noise. A reconstruction probability can be calculated and an anomaly detection can be computed to obtain a single anomaly score for all of the time series streams or a different anomaly score per stream. [Sadrieh, col. 1 lines 53 – 63]
Regarding claim 6, modified Menick teaches the system of claim 2, but modified Menick does not teach wherein the first neural network comprises a first plurality of gated recurrent units.
However, Sadrieh does teach wherein the first neural network comprises a first plurality of gated recurrent units. [Sadrieh, col. 5 lines 5 – 11 discloses Other cell types can be used in a neural network, such as Recurrent Neural Networks (RNN) or Gated Recurrent Units (GRU). In process block 530, a parameterized latent distribution is calculated. For example, in FIG. 3, the parameterized distribution of latent variables 340 provides two vectors describing the mean and the variance of the latent state distributions.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Sadrieh’s system with Menick’s system, with a motivation for time series events, such as BGP update packets, can be analyzed using artificial intelligence such as a recurrent neural network. An encoder of a recurrent neural network can be used to calculate latent variables, one for each time-sequence input data stream. The latent variables can be used to generate a parameterized latent distribution. A decoder can then be used to generate an output time-series stream with reduced noise. A reconstruction probability can be calculated and an anomaly detection can be computed to obtain a single anomaly score for all of the time series streams or a different anomaly score per stream. [Sadrieh, col. 1 lines 53 – 63]
Regarding claim 7, modified Menick teaches the system of claim 3, but modified Menick does not teach wherein the second neural network comprises a second plurality of gated recurrent units.
However, Sadrieh does teach wherein the first neural network comprises a first plurality of gated recurrent units. [Sadrieh, col. 5 lines 5 – 11 discloses Other cell types can be used in a neural network, such as Recurrent Neural Networks (RNN) or Gated Recurrent Units (GRU). In process block 530, a parameterized latent distribution is calculated. For example, in FIG. 3, the parameterized distribution of latent variables 340 provides two vectors describing the mean and the variance of the latent state distributions.]
Therefore, it would have been obvious to one of ordinary skill within the art before the effective filling date to combine Sadrieh’s system with Menick’s system, with a motivation for time series events, such as BGP update packets, can be analyzed using artificial intelligence such as a recurrent neural network. An encoder of a recurrent neural network can be used to calculate latent variables, one for each time-sequence input data stream. The latent variables can be used to generate a parameterized latent distribution. A decoder can then be used to generate an output time-series stream with reduced noise. A reconstruction probability can be calculated and an anomaly detection can be computed to obtain a single anomaly score for all of the time series streams or a different anomaly score per stream. [Sadrieh, col. 1 lines 53 – 63]
Regarding claims 13 – 16, they recite features similar to features within claims 4 – 7, therefore, they are rejected in a similar manner.
Conclusion
Pertinent prior art made of record however not relied upon:
US 11374952 B1 to Coskun et al.
“Techniques for monitoring a computing environment for anomalous activity are presented. An example method includes receiving a request to invoke an action within a computing environment, with the request including a plurality of request attributes and a plurality of contextual attributes. A normalcy score is generated for the received request by encoding the received request into a code in latent space of an autoencoder, reconstructing the request from the code, and generating a probability distribution indicating a likelihood that the reconstructed request attributes exist in a data set of non-anomalous activity. Based on the calculated normalcy score, one or more actions are taken to process the request such that execution of non-anomalous requests is allowed, and execution of potentially anomalous requests may be blocked pending confirmation.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phuc Pham whose telephone number is (571)272-8893. The examiner can normally be reached Monday - Thursday 7:30 AM - 4:30 PM; Friday 8:00 AM - 12:00 PM.
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/P.P./Patent Examiner, Art Unit 2408
/LINGLAN EDWARDS/Supervisory Patent Examiner, Art Unit 2408