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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
means for receiving, via an artificial neural network (ANN), …
means for generating, via the ANN, latent representation …,
means for assigning the latent variables…,
means for determining, via the ANN, a representation of causal factors in claim 22.
means for generating a causal graph… in claim 23.
means for generating a causal graph concurrently… in claim 24.
means for regularizing the latent representation… in claim 25.
means for generating the latent variables… in claim 26.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 22 – 28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AlA the applicant regards as the invention. The claims recite:
means for receiving, via an artificial neural network (ANN), …
means for generating, via the ANN, latent representation …,
means for assigning the latent variables…,
means for determining, via the ANN, a representation of causal factors in claim 22.
means for generating a causal graph… in claim 23.
means for generating a causal graph concurrently… in claim 24.
means for regularizing the latent representation… in claim 25.
means for generating the latent variables… in claim 26.
invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The correct requirement for satisfying the definiteness requirement is that the corresponding structure (or material or acts) of a means- (or step-) plus-function limitation must be disclosed in the specification itself in a way that one skilled in the art will understand what structure (or material or acts) will perform the recited function. If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b). See Atmel Corp. v. Information Storage Devices, Inc., 198 F.3d 1374, 1381, 53 USPQ2d 1225, 1230 (Fed. Cir. 1999).
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims do not resolve the deficiencies noted above; and are therefore appropriately rejected.
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.
Claim(s) 1 – 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, recites abstract idea but for the recitation of generic computer components:
Regarding claim 1,
generating, [ ], latent representation based on latent variables for the temporal sequence data;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing temporal sequence data and organizing or transforming the data into another representations based on latent variables. See (MPEP 2106.04)).
assigning the latent variables of the temporal sequence data to causal variables;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves associating or categorizing latent variables as causal variables based on identified relationships. See (MPEP 2106.04)).
determining, [ ], a representation of causal factors for each dimension of the temporal sequence data based on the assignment.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing the assigned information and organizing causal factors into a representation corresponding to dimensions of the temporal sequence data. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
… via the ANN …
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (II), recites mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (I), recites additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
further comprising generating a causal graph based on the causal factors via a causal discovery process.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing causal factors to identify relationships among them and organizing the identified relationships into a graph. See (MPEP 2106.04)).
Claim(s) 9, 16 and 23, recite similar subject matter as claim 2, so are rejected under the same rationale.
Regarding claim 3, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
in which a causal graph is generated concurrently with determining the representation of the causal factors.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 10, 17 and 24, recite similar subject matter as claim 3, so are rejected under the same rationale.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
further comprising regularizing the latent representation based on the latent variables to follow independencies between a causal variable and a causal parent of the causal variable under interventions.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves applying rules to a representation based on identified independence relationships between causal variables. See (MPEP 2106.04)).
Claim(s) 11, 18 and 25, recite similar subject matter as claim 4, so are rejected under the same rationale.
Regarding claim 5, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
in which the latent variables are generated based on a normalizing flow providing an invertible mapping for disentangling the causal factors.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 12, 19 and 26, recite similar subject matter as claim 5, so are rejected under the same rationale.
Regarding claim 6, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
in which the causal factors are multidimensional.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 13, 20 and 27 recite similar subject matter as claim 6, so are rejected under the same rationale.
Regarding claim 7, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
in which the temporal sequence data comprises a video.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim(s) 14, 21 and 28 recite similar subject matter as claim 7, so are rejected under the same rationale.
Regarding claim 8,
The rest of the limitations recite analogous subject matter as claim 1, so are rejected under similar rationale.
An apparatus, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 15,
The rest of the limitations recite analogous subject matter as claim 1, so are rejected under similar rationale.
A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising:
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 22,
The rest of the limitations recite analogous subject matter as claim 1, so are rejected under similar rationale.
An apparatus, comprising:
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1 – 3, 5, 7 – 10, 12, 14 – 17, 19, 21 – 24, 26 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over HABIBIAN et al., Pub. No.: US20200304804A1 in view of Yang et al., "Causalvae: Disentangled representation learning via neural structural causal models."
Regarding claim 1, HABIBIAN teaches: A processor-implemented method,
(HABIBIAN, “[0007] Certain aspects of the present disclosure are directed to a system for compressing video. The system includes at least one processor and a memory coupled to the at least one processor [A processor-implemented method].”)
comprising: receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations;
(HABIBIAN, “[0056] …In such a network, auto-encoder 401 may encode video in terms of a key frame (e.g., an initial frame marking the beginning of a sequence of frames in which subsequent frames in the sequence are described as a difference relative to the initial frame in the sequence) [receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations], warping (or differences) between the key frame and other frames in the video, and a residual factor. In other aspects, auto-encoder 401 may be implemented as a two-dimensional neural network conditioned on previous frames, a residual factor between frames, and conditioning through stacking channels or including recurrent layers.”)
generating, via the ANN, latent representation based on latent variables for the temporal sequence data;
(HABIBIAN, “[0053] Aspects of the present disclosure provide for the compression and decompression of video content using a deep neural network. The deep neural network may include: (1) an auto-encoder that maps frames of received video content into a latent code space [generating, via the ANN, latent representation based on latent variables for the temporal sequence data] (e.g., a space between an encoder and a decoder of an auto-encoder in which the video content has been encoded into code, which is also referred to as latent variables or latent representations) and (2) a probabilistic model that can losslessly compress codes from the latent code space..”)
assigning the latent variables of the temporal sequence data to causal variables; and
(HABIBIAN, “[0056] Auto-encoder 401 may be implemented using a convolutional architecture. In some aspects, auto-encoder 401 may be configured as a three-dimensional convolutional neural network (CNN) such that auto-encoder 401 learns spatio-temporal filters for mapping video to a latent code space [assigning the latent variables of the temporal sequence data to causal variables]. In such a network, auto-encoder 401 may encode video in terms of a key frame (e.g., an initial frame marking the beginning of a sequence of frames in which subsequent frames in the sequence are described as a difference relative to the initial frame in the sequence), warping (or differences) between the key frame and other frames in the video, and a residual factor. ”)
HABIBIAN does not teach:
determining, via the ANN, a representation of causal factors for each dimension of the temporal sequence data based on the assignment.
Yang teaches:
determining, via the ANN, a representation of causal factors for each dimension of the temporal sequence data based on the assignment.
(Yang, page: 2, “In this paper, we propose a VAE-based causal disentangled representation learning framework by introducing a novel Structural Causal Model layer (Mask Layer), which allows us to recover the latent factors with semantics and structure via a causal DAG [determining, via the ANN, a representation of causal factors for each dimension of the temporal sequence data based on the assignment]. The input signal passes through an encoder to obtain independent exogenous factors and then a Causal Layer to generate causal representation which is taken by the decoder to reconstruct the original input.”)
Yang and HABIBIAN are related to the same field of endeavor (i.e.: learning via neural structure). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Yang with teachings of HABIBIAN to improve latent representation of multimedia contents by accounting for causal dependencies among semantic factors to provide a more meaningful and structured representation for subsequent probabilistic compression. (Yang, Abstract).
Claim 22, recites limitations analogous to claim 1, so is rejected under the same rationale.
Regarding claim 2, HABIBIAN in view of Yang teach the method of claim 1.
Yang further teaches: further comprising generating a causal graph based on the causal factors via a causal discovery process.
(Yang, page: 2, “To train our model, we propose a new loss function which includes the VAE evidence lower bound loss and an acyclicity constraint imposed on the learned causal graph to guarantee its “DAGness” [further comprising generating a causal graph based on the causal factors via a causal discovery process]. In addition, we analyze the identifiablilty of the proposed model, showing that the learned parameters of the disentangled model recover the true one up to certain degree.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Yang with teachings of HABIBIAN for the same reasons disclosed for claim 1.
Claim(s) 9, 16 and 23 recite limitations analogous to claim 2, so are rejected under the same rationale.
Regarding claim 3, HABIBIAN in view of Yang teach the method of claim 1.
Yang further teaches: in which a causal graph is generated concurrently with determining the representation of the causal factors.
(Yang, page 3 – 4, “Our model is within the framework of VAE-based dis entanglement. In addition to the encoder and the decoder structures, we introduce a Structural Causal Model (SCM) layer to learn causal representations [with determining the representation of the causal factor]. To formalize causal representation, we consider n concepts of interest in data. The concepts in observations are causally structured by a Directed Acyclic Graph (DAG) [in which a causal graph is generated concurrently] with an adjacency matrix A. Though a general nonlinear SCM is preferred, for simplicity, in this work, the Causal Layer exactly implements a Linear SCM as described in Eq. 1.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Yang with teachings of HABIBIAN for the same reasons disclosed for claim 1.
Claim(s) 10, 17 and 24 recite limitations analogous to claim 3, so are rejected under the same rationale.
Regarding claim 5, HABIBIAN in view of Yang teach the method of claim 1.
Yang further teaches: in which the latent variables are generated based on a normalizing flow providing an invertible mapping
(Yang, page: 4, “The adjacency matrix associated with the causal graph is A = [A1|...|An] where Ai ∈ Rn is the weight vector such that Aji encodes the causal strength from zj to zi. We have a set of mild nonlinear and invertible functions [g1,g2,...,gn] that map parental variables [in which the latent variables are generated based on a normalizing flow providing an invertible mapping] to the child variable.”)
for disentangling the causal factors.
(Yang, page: 2, “In this paper, we propose a VAE-based causal disentangled representation learning framework [for disentangling the causal factors] by introducing a novel Structural Causal Model layer (Mask Layer), which allows us to recover the latent factors with semantics and structure via a causal DAG.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Yang with teachings of HABIBIAN for the same reasons disclosed for claim 1.
Claim(s) 12, 19 and 26 recite limitations analogous to claim 5, so are rejected under the same rationale.
Regarding claim 7, HABIBIAN in view of Yang teach the method of claim 1.
HABIBIAN further teaches: in which the temporal sequence data comprises a video.
(HABIBIAN, “[0053] Aspects of the present disclosure provide for the compression and decompression of video content using a deep neural network. The deep neural network may include: (1) an auto-encoder that maps frames of received video [in which the temporal sequence data comprises a video] content into a latent code space (e.g., a space between an encoder and a decoder of an auto-encoder in which the video content has been encoded into code, which is also referred to as latent variables or latent representations) and (2) a probabilistic model that can losslessly compress codes from the latent code space.”)
Claim(s) 14, 21 and 28 recite limitations analogous to claim 7, so are rejected under the same rationale.
Regarding claim 8, HABIBIAN teaches: An apparatus, comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured:
(HABIBIAN, “[0007] Certain aspects of the present disclosure are directed to a system for compressing video. The system includes at least one processor and a memory coupled to the at least one processor [An apparatus, comprising: a memory; and at least one processor coupled to the memory].”)
The rest of the limitations are analogous to claim 1, so are rejected under similar rationale.
Regarding claim 15, HABIBIAN teaches: A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising:
(HABIBIAN, “[0007] Certain aspects of the present disclosure are directed to a system for compressing video. The system includes at least one processor and a memory coupled to the at least one processor [A non-transitory computer-readable medium having program code recorded thereon, the program code executed by a processor and comprising].”)
The rest of the limitations are analogous to claim 1, so are rejected under similar rationale.
Claim(s) 4, 11, 18 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over HABIBIAN in view of Yang and in further view of WEI et al., Pub. No.: US20220004910A1.
Regarding claim 4, HABIBIAN in view of Yang teach the method of claim 1.
HABIBIAN in view of Yang do not teach:
further comprising regularizing the latent representation based on the latent variables to follow independencies between a causal variable and a causal parent of the causal variable under interventions.
WEI teaches:
further comprising regularizing the latent representation based on the latent variables to follow independencies between a causal variable and a causal parent of the causal variable under interventions.
(WEI, “[0035] According to further exemplary implementations of the present disclosure, the computing device 120 may first use the causal model obtained at block 204 to determine initial causality among variables in the group of variables obtained at block 202 based on the determined causal sequence between variables. Then, the computing device 120 may conduct a conditional independence test on the initial causality. Finally, the computing device 120 may determine the causality among variables based on a result of the conditional independence test [comprising regularizing the latent representation based on the latent variables to follow independencies between a causal variable] and the initial causality [and a causal parent of the causal variable under interventions]. Additionally, in this operation the computing device 120 may also first determine association between variables, and determine the initial causality based on the determined causal sequence between variables and the determined association between variables. Since both the causal sequence and the association are used, the initial causality determined at this point will become more accurate.”)
WEI, HABIBIAN and Yang are related to the same field of endeavor (i.e.: learning via neural structure). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of WEI with teachings of HABIBIAN and Yang to identify causal relationships among variables, including mixed continuous and discrete variables to capture underlying causal structure rather than merely encoding observed correlations. (WEI, Abstract).
Claim(s) 11, 18 and 25 recite limitations analogous to claim 4, so are rejected under the same rationale.
Claim(s) 6, 13, 20 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over HABIBIAN in view of Yang and in further view of O'Shaughnessy et al., Pub. No.: US20230229946A1.
Regarding claim 6, HABIBIAN in view of Yang teach the method of claim 1.
HABIBIAN in view of Yang do not teach:
in which the causal factors are multidimensional.
O'Shaughnessy teaches:
in which the causal factors are multidimensional.
(O'Shaughnessy, “[0046] The technology discloses a method to represent and move within the data distribution, and a rigorous metric for causal influence of different data aspects on the classifier output. To do this, the causal explanation computing apparatus 14 constructs a generative model consisting of a disentangled representation of the data and a generative mapping from this representation to the data space as shown in FIG. 3, by way of example. Further, the causal explanation computing apparatus 14 learns the disentangled representation in such a way that each factor controls a different aspect of the data [are multidimensional], and a subset of the factors have a large causal influence [in which the causal factors] on the classifier output.”)
O'Shaughnessy, HABIBIAN and Yang are related to the same field of endeavor (i.e.: learning via neural structure). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of O'Shaughnessy with teachings of HABIBIAN and Yang to make the latent representation of the content more interpretable and causally meaningful, by identifying latent factors that influence the model output and using causal relationships among those factors to improve understanding of the compressed representation. (O'Shaughnessy, Abstract).
Claim(s) 13, 20 and 27 recite limitations analogous to claim 6, so are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al., Pub. No.: US10706104B1.
Lee teaches creating graphical representations of the first and second datasets by applying conditional independence tests on them, and storing conditional independence information obtained by applying the conditional independence tests on the first and second datasets.
KohI et al., Pub. No.: US20200372654A1.
Dalli receiving a request to generate a plurality of possible segmentations of an image; sampling a plurality of latent variables from a latent space, wherein each latent variable is sampled from the latent space in accordance with a respective probability distribution over the latent space that is determined based on the image;.
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/M.T.M./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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