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 .
Priority
Acknowledgement is made of Applicant’s claim of priority from Foreign Application No. DE10 2023 130 646.4, filed November 6, 2023.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on November 4, 2024 was reviewed and the listed references were noted.
Status of Claims
Claims 1-7 and 10-11 are pending. Claims 8-9 have been canceled.
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 11 is 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 “a computer-readable storage medium…” could refer to transitory forms of signal transmission (i.e., “signals per se”) which does not fall under any of the statutory categories (see MPEP 2106.03(I)).
Claim interpretation affects the evaluation of both criteria for eligibility. For example, in Mentor Graphics v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017), claim interpretation was crucial to the court’s determination that claims to a "machine-readable medium" were not to a statutory category. In Mentor Graphics, the court interpreted the claims in light of the specification, which expressly defined the medium as encompassing "any data storage device" including random-access memory and carrier waves. Although random-access memory and magnetic tape are statutory media, carrier waves are not because they are signals similar to the transitory, propagating signals held to be non-statutory in Nuijten. 851 F.3d at 1294, 112 USPQ2d at 1133 (citing In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)). Accordingly, because the BRI of the claims covered both subject matter that falls within a statutory category (the random-access memory), as well as subject matter that does not (the carrier waves), the claims as a whole were not to a statutory category and thus failed the first criterion for eligibility.
The rejection of claim 11 may be overcome by amending the claim to, for example, recite as: “a non-transitory computer-readable storage medium…”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation "the navigation of an at least partially autonomous robot and/or vehicle…". There is insufficient antecedent basis for this limitation in the claim.
The rejection could be overcome by, for example, simply reciting “navigation of an at least partially autonomous robot and/or vehicle…”.
Regarding claims 2-6, the phrase “preferably” renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05.
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.
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.
Claims 1, 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Virkar et al. (US 2010/0274539 A1) in view of Ansorregui et al. (US 2020/0380739 A1) further in view of Das et al. (US 2022/0108121 A1).
Regarding claim 1, Virkar teaches a method for dimension reduction of a multidimensional feature space for training a machine learning model by machine learning, comprising the following steps:
performing the dimension reduction on the basis of the determined task-specific feature space (Virkar, Para. [0112], a machine learned subspace is more than a simple reduction to lower dimension. Analysis taking place on the data in lower dimensions is computationally less burdened than analysis that is done on the original, larger scale data. For example, by combining SVM and PCA analysis, both supervised and unsupervised structure is revealed in the subspace defined by the SVM and PCA; subsequent data discovery with other machine learning methods or analysis methods can be applied to the new mapping (projection) of the data, and will take less time on this smaller dataspace).
Although Virkar teaches performing dimension reduction using PCA based on a task-specific feature space (Virkar, Para. [0112]), Virkar does not explicitly teach “providing at least one data pair, in which in each case an original data element and a modified data element have a feature difference (Δf) in relation to one another”. However, in an analogous field of endeavor, Ansorregui teaches first image reconstruction information my comprise residual pixel values calculated as the difference between values of corresponding pixels in the first image and the modified second image (Ansorregui, Para. [0026]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Virkar with the teachings of Ansorregui by including a feature difference is the difference between a data pair that includes an original data element and a modified data element. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for determining the difference between an original and modified data element, as recognized by Ansorregui.
Although Virkar in view of Ansorregui teaches determining a feature difference of a data pair (Ansorregui, Para. [0026]), they do not explicitly teach “which feature difference is specific to a respective defined task for machine learning, the at least one data pair being specific to sensor data resulting from a capture of a sensor” and “determining at least one task-specific feature space, which is specific to the at least one feature difference (Δf), on the basis of a comparison of the respective data pairs”. However, in an analogous field of endeavor, Das teaches utilizing one or more sensors, such as a camera, a proximity sensor and/or a three-dimensional depth sensor for obtaining information on the data pair (Das, Para. [0083]). Das further teaches comparing the feature vectors for the traffic-sign characters and the feature vectors for the card characters to generate the subtraction space which represents the differences between the different feature vectors (Das, Para. [0072]).
Therefore, it would have been obvious to one having ordinary skill in the art to modify the method of Virkar in view of Ansorregui with the teachings of Das by including the data pair being specific to sensor data and that the feature difference is specific to a respective defined task for machine learning (i.e., traffic-sign machine learning model) and that the feature space (i.e., subtraction space) is specific to the feature difference (i.e., differences between feature vectors). One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for obtaining more accurate approximations from a machine learning model, as recognized by Das. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 10 recites a device with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Virkar, Ansorregui and Das references, presented in rejection of Claim 1, apply to this claim.
Claim 11 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Virkar, Ansorregui and Das references, presented in rejection of Claim 1, apply to this claim. Finally, the combination of the Virkar, Ansorregui and Das references discloses a computer readable storage medium (Ansorregui, Para. [0053], computer-readable memory having stored therein computer program instructions).
Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Virkar et al. (US 2010/0274539 A1) in view of Ansorregui et al. (US 2020/0380739 A1) further in view of Das et al. (US 2022/0108121 A1), as applied to claims 1, 10 and 11 above, and further in view of Jaipuria et al. (US 12,430,899 B2, filed August 3, 2022).
Regarding claim 2, Virkar in view of Ansorregui further in view of Das teaches the method of claim 1, as described above.
Although Virkar in view of Ansorregui further in view of Das teaches performing dimension reduction using PCA (Virkar, Para. [0112]), they do not explicitly teach “training the machine learning model for the at least one defined task on the basis of the dimension reduction performed, wherein the at least one defined task comprises recognizing the at least one feature difference (Δf), preferably in the form of classification and/or object detection” and “providing the trained machine learning model for an application in which the at least one defined task is applied to the and/or further sensor data, preferably image data, resulting from a capture of the sensor and/or a further sensor, preferably image sensor”. However, in an analogous field of endeavor, Jaipuria teaches in a block 450, the computer 200 reduces the dimensionality of the matrix of principal components from the block 445 by mapping to a limited number of dimensions. Next, in a block 455, the computer 200 can sort the images 300 from the cluster 900 of interest into subclusters 1000 while determining an optimal number of the subclusters 1000. The computer 200 can use any suitable clustering algorithm, with either a fixed number of clusters or a variable number of clusters. The computer 200 can use the matrix outputted in the block 450 as an input to the clustering algorithm. A training set can be constructed from the images 300 using the subclusters 1000 such that the same or approximately the same number of images 300 are used from each subcluster 1000, thus providing less biased training for a second machine-learning program. Next, in a block 475, the computer 200 trains the second machine-learning program using the training set constructed from the images 300. The second machine-learning program may be an object-recognition program, e.g., using a convolutional neural network. For example, the second machine-learning program may be trained to recognized different types of trailer hitches. Once trained, the second machine-learning program may be installed on a vehicle computer of a vehicle (Jaipuria, Col. 7 line 43 – Col. 9, line 47).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Virkar in view of Ansorregui further in view of Das with the teachings of Jaipuria by including training the machine learning model (i.e., second machine-learning program) for the at least one defined task (i.e., object-recognition) on the basis of the dimension reduction performed in order to provide the machine learning model for object detection on images. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for performing autonomous operations such as object detection in a vehicle, as recognized by Jaipuria. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Regarding claim 3, Virkar in view of Ansorregui further in view of Das and Jaipuria teaches the method of claim 2, characterized in that
the data elements are each specific to the image data, wherein the at least one feature difference (Δf) is provided as a difference of an image feature of the image data, and the recognition of the at least one feature difference is performed on the basis of pixel values of the image data (Ansorregui, Para. [0026], the first image reconstruction information may comprise residual pixel values calculated as the difference between values of corresponding pixels in the first image and the modified second image).
The proposed combination as well as the motivation for combining the Virkar, Ansorregui, Das and Jaipuria references presented in the rejection of Claim 2, apply to Claim 3 and are incorporated herein by reference. Thus, the method recited in Claim 3 is met by Virkar in view of Ansorregui further in view of Das and Jaipuria.
Regarding claim 4, Virkar in view of Ansorregui further in view of Das and Jaipuria teaches the method of claim 2,
characterized in that the navigation of an at least partially autonomous robot and/or vehicle is performed on the basis of the recognition and preferably classification and/or object detection, wherein the image data represent a traffic scene during navigation (Das, Para. [0185], the vehicle subsystem can include an autonomous vehicle and can perform maneuvers, communicate, and otherwise function without the aid of a human provider, in accordance with available technology. Para. [0186], sensors or data input devices capable of receiving and/or recording information relating to navigating a route to pick up, transport, and/or drop off a requester), wherein the at least one feature difference (Δf) are provided as a difference in an image feature of the image data which indicates a navigation-relevant difference in the traffic scene, preferably in the form of different signals of a traffic light system and/or different traffic signs (Das, Para. [0072], comparing the feature vectors for the traffic-sign characters and the feature vectors for the card characters to generate the subtraction space which represents the differences between the different feature vectors).
The proposed combination as well as the motivation for combining the Virkar, Ansorregui, Das and Jaipuria references presented in the rejection of Claim 2, apply to Claim 4 and are incorporated herein by reference. Thus, the method recited in Claim 4 is met by Virkar in view of Ansorregui further in view of Das and Jaipuria.
Regarding claim 5, Virkar in view of Ansorregui further in view of Das and Jaipuria teaches the method of claim 2,
characterized in that a transformation result is obtained based on the performed dimension reduction, preferably by an application of a principal component analysis (PCA) of the reduced feature space, wherein the transformation result is preferably specific to a weighting or loading of the principal component analysis, wherein the transformation result is used in the application of the trained machine learning model for dimension reduction of the sensor data (Jaipuria, Col. 7 line 43 – Col. 9, line 47, the computer 200 reduces the dimensionality of the matrix of principal components from the block 445 by mapping to a limited number of dimensions. Next, in a block 455, the computer 200 can sort the images 300 from the cluster 900 of interest into subclusters 1000 while determining an optimal number of the subclusters 1000. The computer 200 can use any suitable clustering algorithm, with either a fixed number of clusters or a variable number of clusters. The computer 200 can use the matrix outputted in the block 450 as an input to the clustering algorithm. A training set can be constructed from the images 300 using the subclusters 1000 such that the same or approximately the same number of images 300 are used from each subcluster 1000, thus providing less biased training for a second machine-learning program. Next, in a block 475, the computer 200 trains the second machine-learning program using the training set constructed from the images 300. The second machine-learning program may be an object-recognition program, e.g., using a convolutional neural network. For example, the second machine-learning program may be trained to recognized different types of trailer hitches. Once trained, the second machine-learning program may be installed on a vehicle computer of a vehicle).
The proposed combination as well as the motivation for combining the Virkar, Ansorregui, Das and Jaipuria references presented in the rejection of Claim 2, apply to Claim 5 and are incorporated herein by reference. Thus, the method recited in Claim 5 is met by Virkar in view of Ansorregui further in view of Das and Jaipuria.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Virkar et al. (US 2010/0274539 A1) in view of Ansorregui et al. (US 2020/0380739 A1) further in view of Das et al. (US 2022/0108121 A1), as applied to claims 1, 10 and 11 above, and further in view of Ganesh et al. (US 2022/0092035 A1) and Price et al. (US 2024/0201832 A1, filed December 14, 2022).
Regarding claim 6, Virkar in view of Ansorregui further in view of Das teaches the method of claim 1, as described above.
Although Virkar in view of Ansorregui further in view of Das teaches performing dimension reduction using PCA (Virkar, Para. [0112]), they do not explicitly teach “the at least one defined task comprises a plurality of different tasks for which the dimension reduction is performed, wherein a feature space specific thereto is determined for this purpose in each case”. However, in an analogous field of endeavor, Ganesh teaches performing a function transformation to reduce a high-dimensional data associated with the feature set to a low-dimensional feature space dataset without loss of information (Ganesh, Para. [0044]). The system provides for preparing feature space specific to a given predictive task (Ganesh, Para. [0059]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the method of Virkar in view of Ansorregui further in view of Das with the teachings of Ganesh by including determining a feature space specific to a given task of a plurality of tasks for which the dimension reduction is performed. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for accelerating information processing tasks and aiding in obtaining optimal solutions, as recognized by Ganesh.
Although Virkar in view of Ansorregui further in view of Das and Ganesh teaches a feature space specific to a task of a plurality of tasks (Ganesh, Para. [0059]), they do not explicitly teach “wherein preferably the different tasks are provided by different task heads of a machine learning model”. However, in an analogous field of endeavor, Price teaches a model that is trained using multitask learning includes one or more shared backbone layers and heads dedicated to perform a specific task. Each head includes a machine learning model required to perform/learn the specific task associated with that head (Price, Para. [0080]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Virkar in view of Ansorregui further in view of Das and Ganesh with the teachings of Price by including that different task heads of a model are required to learn/perform the specific task. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for improved efficiency in task performance, as recognized by Price. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Virkar et al. (US 2010/0274539 A1) in view of Ansorregui et al. (US 2020/0380739 A1) further in view of Das et al. (US 2022/0108121 A1), as applied to claims 1, 10 and 11 above, and further in view of Liba et al. (US 2024/0346631 A1, filed June 30, 2022).
Regarding claim 7, Virkar in view of Ansorregui further in view of Das teaches the method of claim 1, as described above.
Although Virkar in view of Ansorregui further in view of Das teaches determining a feature difference between an original data element and a modified data element (Ansorregui, Para. [0026]), they do not explicitly teach that “the provision of the at least one data pair comprises at least one of the following steps: Masking one of the data elements of the data pair, Replacing a part of one of the data elements with a part of another data element, Performing an in-painting to modify one of the data elements of the data pair”. However, in an analogous field of endeavor, Liba teaches generating a bystander mask that segments the bystander from the initial image (i.e., masking one of the data elements). The application generates an inpainted image that replaces all pixels within the bystander mask with pixels that match a background in the initial image (i.e., replacing a part of one of the data elements with a part of another data element and performing an in-painting) (Liba, Para. [0030]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Virkar in view of Ansorregui further in view of Das with the teachings of Liba by including masking a data element and performing inpainting by replacing a part of one of the data elements with a part of another data element. One having ordinary skill in the art would have been motivated to combine these references because doing so would allow for generating a modified data element, as recognized by Liba. Thus, the claimed invention would have been obvious to one having ordinary skill in the art before the effective filing date.
Conclusion
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/Emma Rose Goebel/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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