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 . This action is responsive to the Amendment filed on 01/27/2026. Claims 1-20 are pending in the case. Claims 1, 15, and 20 are independent claims.
Response to Arguments
Applicant's prior art arguments have been fully considered but are moot in view of the new grounds of rejection presented below.
Claim Rejections - 35 U.S.C. § 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 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-3, 16, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chaudhury et al. (U.S. Pat. App. Pub. No. 2019/0318040, hereinafter Chaudhury) in view of Li et al. (Li, Xiaopeng, Zhourong Chen, Leonard KM Poon, and Nevin L. Zhang. "Learning latent superstructures in variational autoencoders for deep multidimensional clustering." arXiv preprint arXiv:1803.05206 (2018), hereinafter Li) and Lillicrap et al. (Int’l. Pat. App. Pub. No. WO-2015011688-A2, hereinafter Lillicrap).
As to independent claim 1, Chaudhury teaches
A computer implemented method for learning a model for clustered encoding and decoding from a latent probability distribution, comprising (Title and abstract):
mapping, by a system operatively coupled to a processor, high-dimensional modalities of data from one or more latent probability distributions corresponding to a plurality of encoder and decoder pairs to a plurality of independent latent spaces (Figure 2, 201 down to 232 and 202 down to 235); and
mapping, by the system, the plurality of independent latent spaces to a common latent space representing one or more features of one or more input classes associated with the plurality of encoder and decoder pairs (Figure 2, common latent space 237)….
Chaudhury does not appear to expressly teach the latent probability distribution represents a likelihood of different multi-dimensional images input to each encoder of the plurality of encoder and decoder pairs via multi-stage latent learning.
Li teaches the latent probability distribution represents a likelihood of different multi-dimensional images input to each encoder of the plurality of encoder and decoder pairs via multi-stage latent learning (Title and abstract. Page 3, "learn this process by maximizing the marginal loglikelihood p". Page 6, "LTVAE model on two image datasets and two other datasets". Page 7, "the weights of encoder and decoder networks of above").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the latent learning of Li to discover the multi-facet structures of data, especially for high-dimensional data (see Li at page 1).
Chaudhury does not appear to expressly teach initializing, by the system, a plurality of learnable weight matrices with random values.
Lillicrap teaches initializing, by the system, a plurality of learnable weight matrices with random values (Paragraph 8, "an initial step of initialising the neural network with random connection weight values").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the neural network techniques of Lillicrap to break the symmetry of neurons in a layer, ensuring they receive different gradient updates and learn diverse features (see Lillicrap at paragraph 38).
As to dependent claim 2, Chaudhury further teaches a first quantity of independent latent spaces is the same as a second quantity of the plurality of encoder and decoder pairs (Figure 2, 201 down to 240 and 202 down to 250).
As to dependent claim 3, Li further teaches the plurality of encoder and decoder pairs learn characteristics of one or more different input images fed to the plurality of encoder and decoder pairs at the same time (Page 6, "LTVAE model on two image datasets and two other datasets". Page 7, "the weights of encoder and decoder networks of above").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the latent learning of Li to discover the multi-facet structures of data, especially for high-dimensional data (see Li at page 1).
As to dependent claim 16, Li further teaches the plurality of encoder and decoder pairs learn characteristics of one or more different input images fed to the plurality of encoder and decoder pairs at the same time (Page 6, "LTVAE model on two image datasets and two other datasets". Page 7, "the weights of encoder and decoder networks of above").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the latent learning of Li to discover the multi-facet structures of data, especially for high-dimensional data (see Li at page 1).
As to independent claim 20, Chaudhury teaches
A system for learning a model for clustered encoding and decoding from a latent probability distribution, comprising (Title and abstract. Paragraph 112 et seq.):
a memory that stores computer executable components (Paragraph 113); and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise (Paragraph 112):
a plurality of encoder and decoder pairs that map high-dimensional modalities of data from a latent probability distribution to a plurality of independent latent spaces (Figure 2, 201 down to 232 and 202 down to 235),
wherein the processor can combine the plurality of independent latent spaces to generate a common latent space that represents one or more features of one or more input classes associated with the plurality of encoder and decoder pairs (Figure 2, common latent space 237)….
Chaudhury does not appear to expressly teach the latent probability distribution represents a likelihood of different multi-dimensional images input to each encoder of the plurality of encoder and decoder pairs via multi-stage latent learning.
Li teaches the latent probability distribution represents a likelihood of different multi-dimensional images input to each encoder of the plurality of encoder and decoder pairs via multi-stage latent learning (Title and abstract. Page 3, "learn this process by maximizing the marginal loglikelihood p". Page 6, "LTVAE model on two image datasets and two other datasets". Page 7, "the weights of encoder and decoder networks of above").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the latent learning of Li to discover the multi-facet structures of data, especially for high-dimensional data (see Li at page 1).
Chaudhury does not appear to expressly teach the processor initializes a plurality of learnable weight matrices with a random variety of weights equal to a shape of a first one of the plurality of independent latent spaces.
Lillicrap teaches the processor initializes a plurality of learnable weight matrices with a random variety of weights equal to a shape of a first one of the plurality of independent latent spaces (Paragraph 8, "an initial step of initialising the neural network with random connection weight values").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the neural network techniques of Lillicrap to break the symmetry of neurons in a layer, ensuring they receive different gradient updates and learn diverse features (see Lillicrap at paragraph 38).
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over Chaudhury in view of Li, Lillicrap, and Park et al. (U.S. Pat. App. Pub. No. 2006/0222079, hereinafter Park).
As to dependent claim 4, the rejection of claim 3 is incorporated.
Chaudhury does not appear to expressly teach the plurality of encoder and decoder pairs encode and decode spatial and temporal features of the one or more different input images.
Park teaches the plurality of encoder and decoder pairs encode and decode spatial and temporal features of the one or more different input images (Paragraph 13, "a scalable multi-view image encoding method is provided. M images are input from M cameras and are filtered on a spatial axis. The M images are filtered by using spatial motion compensated temporal filtering (MCTF) or hierarchical B-pictures").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the image encoding and decoding techniques of Park to filter multi-view images input from multiple cameras in the spatial-axis and temporal-axis directions to support a variety of spatio-temporal scalabilities (see Park at paragraph 9).
Claim 5 is rejected under 35 U.S.C. § 103 as being unpatentable over Chaudhury in view of Li, Park, and Choo et al. (U.S. Pat. App. Pub. No. 2022/0166976, hereinafter Choo).
As to dependent claim 5, the rejection of claim 4 is incorporated.
Chaudhury does not appear to expressly teach the plurality of encoder and decoder pairs each correspond with an image class from the one or more different input images.
Choo teaches the plurality of encoder and decoder pairs each correspond with an image class from the one or more different input images (Paragraph 166, "the input image may be classified and processed as one group based on the colors and/or textures of the one or more pixels". Paragraph 80, "a target image may be an encoding target image that is the target to be encoded and/or a decoding target image that is the target to be decoded").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the encoding/decoding techniques of Choo to provide high compression efficiency while maintaining the performance of a machine vision using properties required for the machine vision, rather than human visual properties (see Choo at paragraph 97).
Claim 15 is rejected under 35 U.S.C. § 103 as being unpatentable over Chaudhury in view of Li and Phelps et al. (U.S. Pat. App. Pub. No. 2020/0327186, hereinafter Phelps).
As to independent claim 15, Chaudhury teaches
A computer program product for learning a model for clustered encoding and decoding from a latent probability distribution, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to (Title and abstract. Paragraph 112 et seq.):
map, by the processor, high-dimensional modalities of data from one or more latent probability distributions corresponding to a plurality of encoder and decoder pairs to a plurality of independent latent spaces (Figure 2, 201 down to 232 and 202 down to 235); and
map, by the processor, the plurality of independent latent spaces to a common latent space representing one or more features of one or more input classes associated with the plurality of encoder and decoder pairs (Figure 2, common latent space 237)….
Chaudhury does not appear to expressly teach the latent probability distribution represents a likelihood of different multi-dimensional images input to each of encoder of the plurality of encoder and decoder pairs via multi-stage latent learning.
Li teaches the latent probability distribution represents a likelihood of different multi-dimensional images input to each of encoder of the plurality of encoder and decoder pairs via multi-stage latent learning (Title and abstract. Page 3, "learn this process by maximizing the marginal loglikelihood p". Page 6, "LTVAE model on two image datasets and two other datasets". Page 7, "the weights of encoder and decoder networks of above").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the latent learning of Li to discover the multi-facet structures of data, especially for high-dimensional data (see Li at page 1).
Chaudhury does not appear to expressly teach multiply, by the processor, the plurality of independent latent spaces by a plurality of learnable weight matrices.
Li teaches multiply, by the processor, the plurality of independent latent spaces by a plurality of learnable weight matrices (Claim 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the matrix multiplication techniques of Phelps to decrease latency across a matrix multiply unit by increasing the rate in which weight values are loaded into weight matrix registers within the matrix multiply unit (see Phelps at paragraph 3).
Claim 16 is rejected under 35 U.S.C. § 103 as being unpatentable over Chaudhury in view of Li, Phelps, and Park.
As to dependent claim 16, the rejection of claim 15 is incorporated.
Chaudhury does not appear to expressly teach the plurality of encoder and decoder pairs learn characteristics of one or more different input images fed to the plurality of encoder and decoder pairs at the same time.
Park teaches the plurality of encoder and decoder pairs learn characteristics of one or more different input images fed to the plurality of encoder and decoder pairs at the same time (Paragraph 13, "a scalable multi-view image encoding method is provided. M images are input from M cameras and are filtered on a spatial axis. The M images are filtered by using spatial motion compensated temporal filtering (MCTF) or hierarchical B-pictures").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the variational mapping between embedding spaces of Chaudhury to include the image encoding and decoding techniques of Park to filter multi-view images input from multiple cameras in the spatial-axis and temporal-axis directions to support a variety of spatio-temporal scalabilities (see Park at paragraph 9).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
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/Casey R. Garner/Primary Examiner, Art Unit 2123