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 .
The examiner acknowledges receipt of remarks/amendments dated July 7, 2026.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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) 1-2 and 6-14 are rejected under 35 U.S.C. 103 as being unpatentable over “Uncertainty-aware Mean Teacher for Source-Free Unsupervised Domain Adaptive 3D Object Detection” by Hegde et al. (hereinafter ‘Hegde’) in view of “TarGAN: Generating target data with class labels for unsupervised domain adaptation” by Lv et al. (hereinafter ‘Lv’).
In regards to claim 1, Hegde teaches a method for measuring an ability of a trained machine learning model for processing of measurement data to generalize, with respect to a given task, to a target domain and/or distribution to which one or more input records of measurement data belong, the method comprising the following steps: (See Hegde Section 3.2, Hegde teaches domain adaption from Waymo to KITTI data.)
determining, from the input records of measurement data, a target style that characterizes the target domain and/or distribution; (See Hegde page 9881, Hedge teaches introducing target style such as weather conditions into KITTI domain data.)
obtaining, based at least in part on the target style, validation examples in the target domain and/or distribution,
processing, by the trained machine learning model, the validation examples into outputs; and (See Hedge page 7880, Hegde teaches determining outputs based on pseudo labels.)
determining, based on a comparison between the outputs and the respective ground truth labels, an accuracy of the trained machine learning model as the ability of the trained machine learning model to generalize to the target domain and/or distribution. (see Hegde section 5 on page 9882 and Figure 7, Hegde teaches the assessment of the model in the target domain against ground truth.).
However, Hedge does not expressly teach assigning ground truth labels to validation examples.
Lv teaches assigning ground truth labels to validation examples. (See Lv Section 3, Lv teaches assigning the same ground truth labels to target domain samples.)
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify Hedge to include class labels as taught by Lv. The determination of obviousness is predicated upon the following findings: One skilled in the art would have been motivated to modify Hedge in this manner because/in order to improve discriminative power in the target domain.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Hedge with Lv to obtain the invention as specified in claim 1.
In regards to claim 2, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein the determining of the target style includes: processing, by a trained feature extractor network, the input records of measurement data into target feature maps; and determining, from the target feature maps, features of the measurement data that characterize the target domain. (See Hegde Figure 2, Hegde teaches feature encoder.)
In regards to claim 6, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein the obtaining of the validation examples includes retrieving, based on the target style, validation examples from a library. (See Hegde page 9881, Hedge teaches acquiring datasets.)
In regards to claim 7, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein the input records of measurement data include: (i) images, and/or (ii) point clouds that assign measurement values of at least one measured quantity to locations in a plane and/or in space. (See Hegde page 9881).
In regards to claim 8, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein the trained machine learning model is a classifier that maps records of measurement data to classification scores with respect to one or more classes of a given classification. (See Hedge Figure 2 and Page 7880).
In regards to claim 9, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein the input records of measurement data include input records of measurement data that have been captured by at least one sensor carried on board a vehicle or robot. (See Hegde page 9881).
In regards to claim 10, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches wherein: the validation examples are obtained from an external server that is outside the vehicle or robot; and the processing of the validation examples, and the determining of the ability to generalize, are performed on board the vehicle or robot. (See Hedge page 9881).
In regards to claim 11, Hedge and Lv teach all the limitations of claim 1. Hegde also teaches further comprising: actuating, in response to determining that the determined ability of the trained machine learning model fulfils a predetermined criterion, a downstream technical system that uses outputs of the machine learning model to move the technical system into an operational state where it can better tolerate noisy or incorrect outputs. (See Hegde Section 4.2).
In regards to claim 12, Hedge teaches a method for generating validation examples from input records of measurement data, comprising the following steps: providing respective source examples in a source domain and/or distribution and corresponding ground truth labels; (See Hegde Section 3.2, Hegde teaches domain adaption from Waymo to KITTI data.)
determining, from the source examples, a source content that characterizes a content of the source examples within the source domain and/or distribution; determining, from the input records of measurement data, a target style that characterizes a target domain and/or distribution to which the input records of measurement data belong; and (See Hegde page 9881, Hedge teaches introducing target style such as weather conditions into KITTI domain data.)
combining each source content and the target style into a validation example in the target domain and/or distribution, (See Hedge page 7880, Hegde teaches determining outputs based on pseudo labels.)
However, Hedge does not expressly teach that the corresponding ground truth label of the respective source example remains valid for the validation example.
Lv teaches that the corresponding ground truth label of the respective source example remains valid for the validation example. (See Lv Section 3, Lv teaches assigning the same ground truth labels to target domain samples.)
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify Hedge to include class labels as taught by Lv. The determination of obviousness is predicated upon the following findings: One skilled in the art would have been motivated to modify Hedge in this manner because/in order to improve discriminative power in the target domain.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Hedge with Lv to obtain the invention as specified in claim 12.
Claims 13-14 recite limitations that are similar to that of claim 1. Therefore, claims 13-14 are rejected similarly as claim 1.
Allowable Subject Matter
Claims 3-5 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
In regards to claim 3, the applied art does not teach or suggest “wherein the obtaining of the validation examples includes: providing respective source examples in a source domain and/or distribution and corresponding ground truth labels (5*); determining, from each of the source examples, a source content that characterizes a content of the source examples within the source domain and/or distribution; and combining each source content and the target style into a validation example in the target domain and/or distribution, so that the corresponding ground truth label of the respective source example remains valid for the validation example.”
Claims 4-5 are indicated allowable for being dependent on claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UTPAL D SHAH whose telephone number is (571)272-5729. The examiner can normally be reached M-F: 7:30-5:30.
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, Vu Le can be reached at (571) 272-7332. 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.
/UTPAL D SHAH/Primary Examiner, Art Unit 2668
Prosecution Insights