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 .
Response to Amendment
This Office Action is in response to applicant’s communication filed 2 October 2025, in response to the Office Action mailed 2 July 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 March 2026 has been entered.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statement, dated 24 April 2026, is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
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) 1-42 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-14 recite a method, claims 15-28 recite a device, and claims 29-42 recite a manufacture. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
annotates the one or more unannotated samples with one or more pseudo-labels corresponding to output of the trained machine learning model – a data scientist annotates unannotated samples with one or more pseudo-labels based on the output of a ML model.
generating a third sample set that includes at least one sample formed by concatenating a first sample from the first sample set and a second sample from the second sample set – the data scientist concatenates samples together to form a new sample set. Alternatively/additionally – concatenation is a mathematical function.
wherein the first sample set and the second sample set are sliced into stripes and concatenated at corresponding range-view projection pixel locations while maintaining spatial consistency – the data scientist decides how the samples are to be sliced into stripes to maintain spatial consistency and concatenates them. Alternatively/additionally – this slicing and concatenation is mathematical function (see, e.g., para. [89] of the specification as filed).
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a method comprising: training a machine learning model to perform a segmentation task for a source domain – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
using the at least one processor (multiple instances) – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the machine learning model being trained based on a first sample set that includes one or more annotated samples associated with the source domain – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating a second sample set by applying the trained machine learning model to one or more unannotated samples associated with a target domain – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
wherein the trained machine learning model annotates – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and updating, based on the third sample set, the trained machine learning model to perform the segmentation task for the target domain – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a method comprising: training a machine learning model to perform a segmentation task for a source domain – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
using the at least one processor (multiple instances) – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
the machine learning model being trained based on a first sample set that includes one or more annotated samples associated with the source domain – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
generating a second sample set by applying the trained machine learning model to one or more unannotated samples associated with a target domain – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
wherein the trained machine learning model annotates – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and updating, based on the third sample set, the trained machine learning model to perform the segmentation task for the target domain – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the third sample set is generated to include the second sample from the second sample set based at least on a first confidence of a first label assigned to the second sample satisfying a threshold – the data scientist determines whether to include the second sample in the third sample set based upon their confidence in the label of the sample. Alternatively/additionally – comparing a confidence value to a threshold is a mathematical calculation.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
by the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
by the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the output of the trained machine learning model comprises a probability distribution across a plurality of labels – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the first confidence of the first label corresponds to an entropy of the probability distribution – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the output of the trained machine learning model comprises a probability distribution across a plurality of labels – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the first confidence of the first label corresponds to an entropy of the probability distribution – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the third sample set excludes a third sample from the second sample set based at least on a second confidence of a second label assigned to the third sample failing to satisfy the threshold – the data scientist determines whether to include the second sample in the third sample set (or not) based upon their confidence in the label of the sample. Alternatively/additionally – comparing a confidence value to a threshold is a mathematical calculation.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
by the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
by the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
The claim recites the following additional mental process and/or mathematical concept elements:
annotating the third sample from the second sample set to generate a third pseudo-label for the third sample – the data scientist annotates the third sample with a pseudo-label.
generating a fourth sample set that includes at least one sample formed by concatenating the first sample from the first sample set and the third sample including the third pseudo-label – the data scientist concatenates samples together to form a new sample set. Alternatively/additionally – concatenation is a mathematical function.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
using the at least one processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
by applying the updated trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
refining, based on the fourth sample set, the updated machine learning model to perform the segmentation task for the target domain – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
using the at least one processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
by applying the updated trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
refining, based on the fourth sample set, the updated machine learning model to perform the segmentation task for the target domain – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the threshold is determined based on a quantity of samples included in the first sample set having the first label – this is a mathematical relationship between a quantity value and the threshold value. Alternatively/additionally – the data scientist can set a threshold based on the quantity of samples in the first sample set having a specific label.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the third sample is formed by stitching together the first sample and the second sample – the data scientist stitches together the first and second sample. Alternatively/additionally – stitching together data values is a mathematical function.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the third sample is formed by stitching together alternating portions of the first sample and the second sample – the data scientist stitches together alternating portions of the first and second sample. Alternatively/additionally – stitching together alternating portions of data values is a mathematical function.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the machine learning model comprises a neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the machine learning model comprises a neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the machine learning model includes an anti-aliasing filter configured to suppress high frequency components present in an input of the machine learning model – an anti-aliasing filter suppressing high frequency components is a mathematical function. Alternatively/additionally – the data scientist determines a max frequency of components to keep and removes the rest.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the anti-aliasing filter comprises a low-pass filter that is applied – an anti-aliasing low-pass filter is a mathematical function. Alternatively/additionally – the data scientist determines a max frequency/size of components to keep and removes the rest.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
to determine class-wise confidence thresholds used to generate pseudo labels – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
to determine class-wise confidence thresholds used to generate pseudo labels – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the source domain and the target domain comprise at least one of different geographical locations – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the source domain and the target domain comprise at least one of different geographical locations – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 13:
The claim recites the following additional mental process and/or mathematical concept elements:
assigns a semantic label to one or more points in a LiDAR point cloud – the data scientist assigns semantic labels to points of the LiDAR point cloud (e.g., 3D objects in the scene)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the segmentation task comprises a LiDAR segmentation – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
in which the machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the segmentation task comprises a LiDAR segmentation – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
in which the machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 14:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein the semantic label identifies at least one of a physical feature or an object according to the one or more points – the data scientist assigns semantic labels to points of the LiDAR point cloud (e.g., 3D objects in the scene)
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 15:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a system comprising: at least one processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to: [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a system comprising: at least one processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to: [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 16, see the rejection of claim 2, above.
As per claim 17, see the rejection of claim 3, above.
As per claim 18, see the rejection of claim 4, above.
As per claim 19, see the rejection of claim 5, above.
As per claim 20, see the rejection of claim 6, above.
As per claim 21, see the rejection of claim 7, above.
As per claim 22, see the rejection of claim 8, above.
As per claim 23, see the rejection of claim 9, above.
As per claim 24, see the rejection of claim 10, above.
As per claim 25, see the rejection of claim 11, above.
As per claim 26:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the source domain and the target domain comprise at least one of different geographical locations, different times of the day, and different weather – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the source domain and the target domain comprise at least one of different geographical locations, different times of the day, and different weather – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 27, see the rejection of claim 13, above.
As per claim 28, see the rejection of claim 14, above.
As per claim 29, see the rejections of claims 1 and 15, above.
As per claim 30, see the rejection of claim 2, above.
As per claim 31, see the rejection of claim 3, above.
As per claim 32, see the rejection of claim 4, above.
As per claim 33, see the rejection of claim 5, above.
As per claim 34, see the rejection of claim 6, above.
As per claim 35, see the rejection of claim 7, above.
As per claim 36, see the rejection of claim 8, above.
As per claim 37, see the rejection of claim 9, above.
As per claim 38, see the rejection of claim 10, above.
As per claim 39, see the rejection of claim 11, above.
As per claim 40, see the rejection of claim 26, above.
As per claim 41, see the rejection of claim 13, above.
As per claim 42, see the rejection of claim 14, above.
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.
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.
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.
Claim(s) 1-5, 7-19, 21-33, and 35-42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jaritz et al. (xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation, March 2020, pgs. 1-12), in view of Ramamonjison et al. (SimROD: A Simple Adaptation Method for Robust Object Detection, July 2021, pgs. 1-25), and further in view of Oblak (US 2022/0180131).
As per claim 1, Jaritz teaches a method comprising: training, using at least one processor, a machine learning model to perform a segmentation task for a source domain [in this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) using 2D images and 3D point clouds for 3D (source domain) semantic segmentation (abstract, figs. 2-3, etc.; where fig. 3 shows the source and target domain segmentation models), where the training can be performed by a single GPU (at least one processor core) with 11GB RAM (pg. 6, “Training”)], the machine learning model being trained based on a first sample set that includes one or more annotated samples associated with the source domain [XMUDA is trained on a source dataset S, where each sample consists of 2D image xs2D, 3D point cloud xs3D, and 3D segmentation labels ys3D (pg. 3, section 3; see also: pg. 4, section 3.2 and fig. 3; pg. 5, section 4.1; etc.); where the segmentation labels are the annotations of the samples]; generating, using the at least one processor, a second sample set by applying the trained machine learning model to one or more unannotated samples associated with a target domain [the trained model is used to produce pseudo-labels for the unannotated target domain samples (pg. 5, “Additional self-training with Pseudo-Labels”; see also: pg. 4, section 3.2 and fig. 3; etc.); where the samples annotated with the generated pseudo-labels is the second sample set], wherein the trained machine learning model annotates the one or more unannotated samples with one or more pseudo-labels corresponding to output of the trained machine learning model [the trained model is used to produce pseudo-labels for the unannotated target domain samples (pg. 5, “Additional self-training with Pseudo-Labels”; see also: pg. 4, section 3.2 and fig. 3; etc.)]; and updating, using the at least one processor and based on the third sample set, the trained machine learning model to perform the segmentation task for the target domain [the segmentation model is trained with the annotated samples from the source domain (xs2D, xs3D, and ys3D) and target domain (xt2D, xt3D, and LPL) (pg. 4, section 3.2 and fig. 2; etc.), which can also include fusion of 2D and 3D features (pg. 7, section 4.4 and fig. 4; etc.); where the combined source and target domain samples are a third sample set].
While Jaritz teaches using annotated source domain samples and pseudo-labeled target domain samples (a third sample set) to train the segmentation model (see above), it has not been relied upon for teaching generating, using the at least one processor, a third sample set that includes at least one sample formed by concatenating a first sample from the first sample set and a second sample from the second sample set, wherein the first sample set and the second sample set are sliced into strips and concatenated at corresponding range-view projection pixel locations while maintaining spatial consistency.
Ramamonjison teaches generating, using the at least one processor, a third sample set that includes at least one sample formed by concatenating a first sample from the first sample set and a second sample from the second sample set [the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (pg. 4, section 3.2.2; etc.); for the source and target domain samples with (pseudo-)labels of Jaritz, above, where the collage with the combined labels is the third sample concatenation of the first and second samples]; and updating, using the at least one processor and based on the third sample set, the trained machine learning model [the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (pg. 4, section 3.2.2; etc.), which generated samples are used to optimize (train) the model (pgs. 4-5, section 3.2.3; etc.), for the training of the segmentation model of Jaritz, above].
Jaritz and Ramamonjison are analogous art, as they are within the same field of endeavor, namely unsupervised domain adaptation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the DomainMix augmentation to produce new (third) samples from combinations of source and target domain samples for further training, as taught by Ramamonjison, to augment the source and target domain samples for training the segmentation model in the system taught by Jaritz.
Ramamonjison provides motivation as [the DomainMIX augmentation of training samples helps overcome the challenging issues of domain shift and pseudo-label noise (abstract; pg. 4, section 3.2.2; etc.) and makes the model more robust to image corruption (pg. 2, section 2; etc.)].
Oblak teaches wherein the first sample set and the second sample set are sliced into strips and concatenated at corresponding range-view projection pixel locations while maintaining spatial consistency [the images can be sliced into strips of specified pixel ranges from projection locations (paras. 0049, 0060-61, 0077, 0080, etc.) and the segmentation results concatenated (para. 0054, etc.); for the augmentation/creation of samples of Jaritz/Ramamonjison/Oblak, above].
Jaritz/Ramamonjison and Oblak are analogous art, as they are within the same field of endeavor, namely applying neural networks to lidar point cloud data.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to slice the images into strips for specified locations, and concatenate results, as taught by Oblak, in producing the augmented/collage training samples in the system taught by Jaritz/Ramamonjison.
Oblak provides motivation as [by slicing the image into strips, levels of importance may be evaluated and areas of higher/lower importance may be analyzed accordingly (paras. 0060-61, 0077-80, etc.)].
As per claim 2, Jaritz/Ramamonjison/Oblak teaches wherein the third sample set is generated to include the second sample from the second sample set based at least on a first confidence of a first label assigned to the second sample by the trained machine learning model satisfying a threshold [the trained model is used to produce pseudo-labels for the unannotated target domain samples (Jaritz: pg. 5, “Additional self-training with Pseudo-Labels”; see also: pg. 4, section 3.2 and fig. 3; etc.) and the segmentation model is trained with the annotated samples from the source domain (xs2D, xs3D, and ys3D) and target domain (xt2D, xt3D, and LPL) (Jaritz: pg. 4, section 3.2 and fig. 2; etc.), where unconfident labels generated by the model are discarded through class-wise thresholding (i.e., a confidence of the label satisfying a class-wise threshold means the sample is kept) (Jaritz: pg. 6, section 4.3; etc.)].
As per claim 3, Jaritz/Ramamonjison/Oblak teaches wherein the output of the trained machine learning model comprises a probability distribution across a plurality of labels, and wherein the first confidence of the first label corresponds to an entropy of the probability distribution [the classification output of the trained machine learning model(s) is a vector of a probability distribution across a plurality of classes/labels and which corresponds to a cross-entropy of the output probabilities (Jaritz: pgs. 3-4, sections 3.1-3.2 and figs. 2-3; etc.)].
As per claim 4, Jaritz/Ramamonjison/Oblak teaches wherein the third sample set excludes a third sample from the second sample set based at least on a second confidence of a second label assigned to the third sample by the trained machine learning model failing to satisfy the threshold [the trained model is used to produce pseudo-labels for the unannotated target domain samples (Jaritz: pg. 5, “Additional self-training with Pseudo-Labels”; see also: pg. 4, section 3.2 and fig. 3; etc.) and the segmentation model is trained with the annotated samples from the source domain (xs2D, xs3D, and ys3D) and target domain (xt2D, xt3D, and LPL) (Jaritz: pg. 4, section 3.2 and fig. 2; etc.), where unconfident labels generated by the model are discarded through class-wise thresholding (i.e., a confidence of the label not satisfying a class-wise threshold means the sample is discarded/excluded) (Jaritz: pg. 6, section 4.3; etc.)].
As per claim 5, Jaritz/Ramamonjison/Oblak teaches annotating, using the at least one processor, the third sample from the second sample set by applying the updated trained machine learning model to generate a third pseudo-label for the third sample [the trained model is used to produce pseudo-labels for the unannotated target domain samples (Jaritz: pg. 5, “Additional self-training with Pseudo-Labels”; see also: pg. 4, section 3.2 and fig. 3; etc.) and the segmentation model is trained with the annotated samples from the source domain (xs2D, xs3D, and ys3D) and target domain (xt2D, xt3D, and LPL) (Jaritz: pg. 4, section 3.2 and fig. 2; etc.)]; generating, using the at least one processor, a fourth sample set that includes at least one sample formed by concatenating the first sample from the first sample set and the third sample including the third pseudo-label [the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (Ramamonjison: pg. 4, section 3.2.2; etc.), which generated samples are used to optimize (train) the model (Ramamonjison: pgs. 4-5, section 3.2.3; etc.)]; and refining, using the at least one processor and based on the fourth sample set, the updated machine learning model to perform the segmentation task for the target domain [the segmentation model is trained with the annotated samples from the source domain (xs2D, xs3D, and ys3D) and target domain (xt2D, xt3D, and LPL) (Jaritz: pg. 4, section 3.2 and fig. 2; etc.) where the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (Ramamonjison: pg. 4, section 3.2.2; etc.), which generated samples are used to optimize (train) the model (Ramamonjison: pgs. 4-5, section 3.2.3; etc.)].
As per claim 7, Jaritz/Ramamonjison/Oblak teaches wherein the third sample is formed by stitching together the first sample and the second sample [the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (Ramamonjison: pg. 4, section 3.2.2; etc.), which generated samples are used to optimize (train) the model (Ramamonjison: pgs. 4-5, section 3.2.3; etc.); where the collage is a third sample formed by stitching together the first and second (source and target domain) samples].
As per claim 8, Jaritz/Ramamonjison/Oblak teaches wherein the third sample is formed by stitching together alternating portions of the first sample and the second sample [the DomainMix augmentation samples images from the source and target domains and mixes crops of these images to create a new domain-mixed image in a 2 x 2 collage, in addition to collating the pseudo labels for the unlabeled examples (target domain) with ground-truth labels of source images (Ramamonjison: pg. 4, section 3.2.2; etc.), which generated samples are used to optimize (train) the model (Ramamonjison: pgs. 4-5, section 3.2.3; etc.); where the collage is a third sample formed by stitching together alternating portions of the first and second (source and target domain) samples].
As per claim 9, Jaritz/Ramamonjison/Oblak teaches wherein the machine learning model comprises a neural network [the machine learning model includes 2D and 3D neural networks (Jaritz: pgs. 3-4, figs. 2-3 and sections 3.1-3.2; etc.)].
As per claim 10, Jaritz/Ramamonjison/Oblak teaches wherein the machine learning model includes an anti-aliasing filter configured to suppress high frequency components present in an input of the machine learning model [the neural network can include an anti-alias filtering operation followed by removal of pixels (downsampling) (Oblak: para. 0060, etc.)].
Oblak provides motivation as [the down-sampling and anti-alias filter can be used to configure the input to the correct size expected by the neural network (para. 0060, etc.)].
As per claim 11, Jaritz/Ramamonjison/Oblak teaches wherein the anti-aliasing filter comprises a low-pass filter that is applied to determine class-wise confidence thresholds used to generate pseudo-labels [the neural network can include an anti-alias filtering operation followed by removal of pixels (downsampling) (Oblak: para. 0060, etc.), and where unconfident labels generated by the model are discarded through class-wise thresholding (i.e., a confidence of the label satisfying a class-wise threshold means the sample is kept) (Jaritz: pg. 6, section 4.3; etc.)].
As per claim 12, Jaritz/Ramamonjison/Oblak teaches wherein the source domain and the target domain comprise at least one of different geographical locations [the source and target domains may be day and night (different times of the day) and/or different cities (geographic locations) (Jaritz: fig. 1; pg. 9, section A.1; etc.)].
As per claim 13, Jaritz/Ramamonjison/Oblak teaches wherein the segmentation task comprises a LiDAR segmentation in which the machine learning model assigns a semantic label to one or more points in a LiDAR point cloud [the xMUDA model is used to perform semantic segmentation on 3D LiDAR point clouds (Jaritz: fig. 1, abstract, etc.)].
As per claim 14, Jaritz/Ramamonjison/Oblak teaches wherein the semantic label identifies at least one of a physical feature or an object according to the one or more points [the xMUDA model is used to perform semantic segmentation on 3D LiDAR point clouds (Jaritz: fig. 1, abstract, etc.) which can include labeling features and/or objects (Jaritz: pg. 5, section 4.1; see also: pg. 2, section 2; pg. 3, fig. 2; etc.)].
As per claim 15, see the rejection of claim 1, above, wherein Jaritz/Ramamonjison/Oblak also teaches a system comprising: at least one processor, and at least one non-transitory storage media storing instructions that, when executed by the at least one processor, cause the at least one processor to: [perform the method] [the training can be performed by a single GPU (at least one processor core) with 11GB RAM (Jaritz: pg. 6, “Training”); where a GPU executes instructions stored in at least one storage media].
As per claim 16, see the rejection of claim 2, above.
As per claim 17, see the rejection of claim 3, above.
As per claim 18, see the rejection of claim 4, above.
As per claim 19, see the rejection of claim 5, above.
As per claim 21, see the rejection of claim 7, above.
As per claim 22, see the rejection of claim 8, above.
As per claim 23, see the rejection of claim 9, above.
As per claim 24, see the rejection of claim 10, above.
As per claim 25, see the rejection of claim 11, above.
As per claim 26, Jaritz/Ramamonjison/Oblak teaches wherein the source domain and the target domain comprise at least one of different geographical locations, different times of the day, and different weather [the source and target domains may be day and night (different times of the day) (Jaritz: fig. 1; etc.)].
As per claim 27, see the rejection of claim 13, above.
As per claim 28, see the rejection of claim 14, above.
As per claim 29, see the rejections of claims 1 and 15, above.
As per claim 30, see the rejection of claim 2, above.
As per claim 31, see the rejection of claim 3, above.
As per claim 32, see the rejection of claim 4, above.
As per claim 33, see the rejection of claim 5, above.
As per claim 35, see the rejection of claim 7, above.
As per claim 36, see the rejection of claim 8, above.
As per claim 37, see the rejection of claim 9, above.
As per claim 38, see the rejection of claim 10, above.
As per claim 39, see the rejection of claim 11, above.
As per claim 40, see the rejection of claim 26, above.
As per claim 41, see the rejection of claim 13, above.
As per claim 42, see the rejection of claim 14, above.
Claim(s) 6, 20 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jaritz, Ramamonjison, and Oblak, as applied to claims 2, 16, and 30, above, and further in view of Zou et al. (Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, 2018, pgs. 289-305 – cited in an IDS).
As per claim 6, Jaritz/Ramamonjison/Oblak teaches the method of claim 2, as described above.
While Jaritz/Ramamonjison/Oblak teaches using class-wise confidence thresholding (see above), it has not been relied upon for teaching wherein the threshold is determined based on a quantity of samples included in the first sample set having the first label.
Zou teaches wherein the threshold is determined based on a quantity of samples included in the first sample set having the first label [the UDA framework utilizing iterative self-training (ST) can utilize a class-balanced self-training (CBST) framework (abstract, etc.) by normalizing class-wise confidence level thresholding for the pseudo-label generation, which is based on the proportion of selected pseudo-labels in each class (i.e., based on a quantity of samples having the label) (pgs. 7-8, section 4.2; pg. 9, section 4.3; etc.)].
Jaritz/Ramamonjison/Oblak and Zou are analogous art, as they are within the same field of endeavor, namely unsupervised domain adaptation for semantic segmentation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the class-balanced self-training, including confidence thresholding based on the quantity of samples having each label, as taught by Zou, for the class-wise thresholding of the labels and sample discarding in the system taught by Jaritz/Ramamonjison/Oblak.
Zou provides motivation as [the CBST avoids the usual, gradual dominance of large classes on pseudo-label generation and helps refine generated labels while providing state-of-the-art semantic segmentation performance (abstract; pg. 2, middle paragraph; pg. 3, penultimate paragraph; tables 2-3; etc.)].
As per claim 20, see the rejection of claim 6, above.
As per claim 34, see the rejection of claim 6, above.
Response to Arguments
Applicant's arguments filed 30 March 2026, with respect to the rejections under 35 U.S.C. 101, have been fully considered but they are not persuasive.
Applicant argues that the particular concatenation of the first and second sample sets used to update a trained machine learning model is not a mental process of mathematical function, but a technical feature.
However, as described above, this is a mental process and/or mathematical concept. Additionally, applicant has described an improvement to the annotation of training samples/generation of annotated training samples. Therefore, (assuming that the invention provides these advantages) this amounts to an improvement to an abstract idea rather than to a computer or technology. See MPEP 2106.05(a). It appears that any benefits to the computer itself are based solely on the use of an improvement to the abstract idea(s), using generic computer components to apply the abstract idea(s). Additionally, to find a valid improvement to a computer or technology the specification must disclose the improvement and the claim must include the necessary components to realize the improvement. MPEP 2106.05(d)(1).
Applicant also argues that the concatenation at corresponding range-view projection pixel locations while maintaining spatial consistency cannot practically be performed in the human mind.
However, as described above, a data scientist can select corresponding range-view projection pixel locations to slice the samples at and concatenate the samples to maintain spatial consistency. The claim does not describe how corresponding range-view pixel locations are determined or selected or how spatial consistency is assured, and so this appears to be a process that could reasonably be performed in the human mind (and/or with the aid of pen and paper, etc.).
Applicant’s arguments, see the remarks, filed 30 May 2026, with respect to the rejection(s) of claim(s) 1-5, 7-9, 12-19, 21-23, 26-33, 35-37, and 40-42 under 35 U.S.C. 103 have been fully considered and are persuasive in view of the amendments made to the independent claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Oblak, which has been relied upon for slicing samples into strips and concatenating the samples at corresponding range-view projection pixel locations while maintaining spatial consistency.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-42 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kong et al. (ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation, 30 Nov 2021, pgs. 1-12 – cited in an IDS and published after the effective filing date of the claimed invention) – paper by the inventors describing the invention.
Hegde et al. (Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection, Sept 2021, pgs. 9876-9887 – cited in an IDS) – discloses a pseudo-label based self training method for unsupervised domain adaptation for a LiDAR dataset, including an uncertainty-aware mean teacher framework to filter incorrect pseudo-labels.
Neksrasov et al. (Mix3D: Out-of-Context Data Augmentation for 3D Scenes, Oct 2021, pgs. 1-10) – discloses a data augmentation technique for segmenting 3D scenes, including concatenating point cloud data with labels.
Liu et al. (Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning, June 2021, pgs. 211-221) – discloses adversarial unsupervised domain adaptation (AUDA) based 3D semantic segmentation to include multi-modal learning, including threshold moving.
Alonso et al. (Domain Adaptation in LiDAR Semantic Segmentation, Oct 2020, pgs. 1-7 – cited in an IDS) – discloses an UDA method for LiDAR semantic segmentation including aligning the distribution of semantic classes of target and source domains.
Toldo et al. (Unsupervised Domain Adaptation in Semantic Segmentation: a Review, May 2020, pgs. 1-34) – discloses multiple systems and methods for UDA in semantic segmentation systems.
Spadotto et al. (Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training, April 2020, pgs. 1-8) – discloses UDA including self training utilizing adaptive thresholding to balance classes based on per-class overall confidence.
Achituve et al. (Self-Supervised Learning for Domain Adaptation on Point Clouds, March 2021, pgs. 1-18) – discloses self-supervised learning for domain adaptation on 3D point cloud data, including using a shared feature encoder on source and target domain data and concatenating a global feature vector with features of the point clouds.
Gabourie (US 11,176,477) – discloses UDA using a sliced-Wasserstein distance, and including a shared encoder/embedding on source and target domain data.
Habib (US 2020/0327696) – discloses a system/method for calibrating cameras/scanners of mobile devices/platforms including slicing a scene into strips and concatenating the acquired strips and utilizing tie points that correspond to a pixel or data point in multiple images of a scene that represent a projection of a physical point.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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