DETAILED ACTION
This action is in response to claims filed 20 June 2023 for application 18338174 filed 20 June 2023. Currently claims 1-30 are pending.
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.
The specification at [0092] is relied upon for interpretation of the “means”. Examiner is interpreting the means as any hardware or software.
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.
Claims 23-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the means in the claim language when interpreted in light of the specification can be solely software and thus the claims are rejected as being directed to software per se.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-12, 15-23, and 25-30 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Carlucci et al. (MultiDIAL: Domain Alignment Layers for (Multisource) Unsupervised Domain Adaptation).
Regarding claims 1, 12, 23 and 30, Carlucci discloses: A processor-implemented method, comprising:
identifying one or more domain-sensitive layers in a machine learning model based on differences between outputs generated by one or more layers in the machine learning model for inputs in a source domain and inputs in a shifted domain (“To summarize, the contribution of this paper is threefold. First, we present an approach for unsupervised domain adaptation, based on the introduction of DA-layers to explicitly address the domain shift problem, which act in synergy with an entropy loss that exploits unsupervised target data during learning. Our solution simultaneously aligns feature representations and learns where and to which extent adaptation should take place.” P4442 ¶4, “However, the networks contain also a number of special layers, the DA-layers, which implement a domain-specific operation. Indeed, the role of such layers is to apply a data transformation that aligns the observed input distribution with a reference distribution. Since in general the input distributions of the domain-specific predictors differ, while the reference distribution stays the same, we have that the predictors undergo different transformations in the corresponding DA-layers.” P4444 §3.1 ¶1);
updating normalizing values for each respective domain-sensitive layer of the one or more domain-sensitive layers based on a mixing factor, fixed normalizing values for data in the source domain, and calculated normalizing values for data in the shifted domain (“This is achieved in two ways: first we introduce an entropy-based prior distribution for the network parameters based on the target observations; second, we endow the architecture with the ability of learning the degree of adaptation by introducing a parametrized, cross-domain bias to the input distribution of each domain-specific DA-layer. The rest of this subsection is devoted to describe the new layer, while we defer to the next subsection the description of the prior distribution.” P4444 §3.1 ¶2, “The rationale behind the introduction of the mixing factor a is that we can move from having an independent alignment of the domains akin to AdaBN, when α = 1, to having a coupled normalization when α = 0. In the former case the DA-layer computes different functions in each domain-specific predictor and is equivalent to considering a full degree of domain alignment. The latter case, instead, yields the same function in each predictor thus transforming the domains equally, which yields no domain alignment” p4444 §3.1 ¶5); and
applying the updated normalizing values to each respective domain-sensitive layer of the one or more domain-sensitive layers in the machine learning model (“This is achieved in two ways: first we introduce an entropy-based prior distribution for the network parameters based on the target observations; second, we endow the architecture with the ability of learning the degree of adaptation by introducing a parametrized, cross-domain bias to the input distribution of each domain-specific DA-layer. The rest of this subsection is devoted to describe the new layer, while we defer to the next subsection the description of the prior distribution.” P4444 §3.1 ¶2).
Regarding claims 4, 15, and 25, Carlucci discloses: The method of claim 1, wherein the normalizing values for each respective domain-sensitive layer are updated further based on the mixing factor applied to the normalizing values for data in the source domain and 1 minus the mixing factor applied to the normalizing values for data in the shifted domain (Fig 2 p4447, domain statistics use α and global statistics use 1-α).
Regarding claims 5, 16, and 26, Carlucci discloses: The method of claim 4, wherein the mixing factor comprises a learnable parameter fixed for the machine learning model prior to deployment (“This is achieved in two ways: first we introduce an entropy-based prior distribution for the network parameters based on the target observations; second, we endow the architecture with the ability of learning the degree of adaptation by introducing a parametrized, cross-domain bias to the input distribution of each domain-specific DA-layer. The rest of this subsection is devoted to describe the new layer, while we defer to the next subsection the description of the prior distribution.” P4444 §3.1 ¶2, “Both our variants, a free or fixed, outperform the existing state of the art in this setting.” P4450 ¶6).
Regarding claims 6 and 17, Carlucci discloses: The method of claim 1, wherein the normalizing values for the data in the shifted domain comprise an average and a variance calculated over the data in the shifted domain (
PNG
media_image1.png
184
344
media_image1.png
Greyscale
p4444 §3.1 ¶3).
Regarding claims 7, 18, and 27, Carlucci discloses: The method of claim 1, further comprising generating the inputs in the shifted domain by applying one or more transformations to the inputs in the source domain (“However, the networks contain also a number of special layers, the DA-layers, which implement a domain-specific operation. Indeed, the role of such layers is to apply a data transformation that aligns the observed input distribution with a reference distribution. Since in general the input distributions of the domain-specific predictors differ, while the reference distribution stays the same, we have that the predictors undergo different transformations in the corresponding DA-layers” p4444 §3.1 ¶1).
Regarding claims 8 and 19, Carlucci discloses: The method of claim 1, wherein the inputs in the shifted domain comprise inputs obtained at inference time (“Moreover, we keep track of moving estimates of μa and σ2a’, to be used at inference time. Note that in this work we assume the α parameter to be learned and shared between matching DA-layers of each domain-specific predictor. However, α’s can differ across DA-layers within the same predictor” p4444 §3.1 ¶3).
Regarding claims 9, 20, and 28, Carlucci discloses: The method of claim 1, further comprising deploying the machine learning model with the updated normalizing values for each respective domain-sensitive layer of the one or more domain-sensitive layers in the machine learning model (Fig 2 p4446, “Moreover, we keep track of moving estimates of μa and σ2a’, to be used at inference time. Note that in this work we assume the α parameter to be learned and shared between matching DA-layers of each domain-specific predictor. However, α’s can differ across DA-layers within the same predictor” p4444 §3.1 ¶3).
Regarding claims 10 and 21, Carlucci discloses: The method of claim 1, further comprising defining the normalizing values for the data in the source domain as one or more constants in the machine learning model (Fig 2 p4446, “Moreover, we keep track of moving estimates of μa and σ2a’, to be used at inference time. Note that in this work we assume the α parameter to be learned and shared between matching DA-layers of each domain-specific predictor. However, α’s can differ across DA-layers within the same predictor” p4444 §3.1 ¶3).
Regarding claims 11, 22, and 29, Carlucci discloses: The method of claim 1, further comprising:
receiving input data (Fig 2 p4446);
generating one or more inferences based on the received input data and the machine learning model with the updated normalizing values for each respective domain-sensitive layer of the one or more domain-sensitive layers in the machine learning model (“Moreover, we keep track of moving estimates of μa and σ2a’, to be used at inference time. Note that in this work we assume the α parameter to be learned and shared between matching DA-layers of each domain-specific predictor. However, α’s can differ across DA-layers within the same predictor” p4444 §3.1 ¶3); and
taking one or more actions based on the generated one or more inferences (Fig 5 p4448 data is classified).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 is advised of the obligation under 37 CFR 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.
Claim(s) 2-3, 13-14 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carlucci in view of Chung et al. (Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation).
Regarding claims 2, 13 and 24, Carlucci discloses: The method of claim 1, wherein identifying the one or more domain-sensitive layers in the machine learning model comprises, for each respective layer of the layers in the machine learning model:
calculating a source domain gradient vector for the respective layer based on forward-propagating and backpropagating the inputs in the source domain through the respective layer (p4446 §4.1.2 ¶1 and p4447 ¶1, note: networks are trained using gradient descent which includes forwards and backwards propagation);
calculating a domain-shifted gradient vector for the respective layer based on forward-propagating and backpropagating the inputs in the shifted domain through the respective layer (p4446 §4.1.2 ¶1 and p4447 ¶1, note: networks are trained using gradient descent which includes forwards and backwards propagation).
Carlucci does not disclose, however, Chung teaches: calculating a similarity score between the source domain gradient vector and the domain-shifted gradient vector, wherein a similarity score above a threshold percentile of scores across similarity scores calculated for the layers in the machine learning model indicates that the respective layer is a domain-sensitive layer (“Overview of our cosine similarity loss at the feature level. By computing the cosine similarity matrix, we can compare which target feature is similar to which source feature and selectively maximize the cosine similarity between them spatial-wise. Here, the threshold is set to 0.5. Broad arrows on top indicate that the images are forwarded through the feature extractor of the segmentation network.” Fig 1 P1).
Carlucci and Chung are in the same field of endeavor of Domain Adaptation and are analogous. Carlucci discloses a method using a mixing factor for domain and shift adaptation. Chung discloses identifying domain adaptation layers for maximization using a cosine similarity. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the known DA with a mixing factor of Carlucci with the known maximization and identification of similar layers using cosine similarity as taught by Chung to yield predictable results.
Regarding claims 3 and 14, Carlucci does not disclose, however, Chung teaches: The method of claim 2, wherein the similarity score comprises a normalized cosine similarity score (“Overview of our cosine similarity loss at the feature level. By computing the cosine similarity matrix, we can compare which target feature is similar to which source feature and selectively maximize the cosine similarity between them spatial-wise. Here, the threshold is set to 0.5. Broad arrows on top indicate that the images are forwarded through the feature extractor of the segmentation network.” Fig 1 P1).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trujillo can be reached at (571)-272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ERIC NILSSON/ Primary Examiner, Art Unit 2151