DETAILED ACTION
[1] Remarks
I. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
II. Claims 1-10 are pending and have been examined, where claims 1-10 is/are rejected. Explanations will be provided below.
III. Inventor and/or assignee search were performed and determined no double patenting rejection(s) is/are necessary.
IV. Patent eligibility (updated in 2019) shown by the following: Claims 1-10 pass patent eligibility test because there is/are no limitation or a combination of limitations amounting to an abstract idea. Also, the following limitation or the combinations of the limitations: “providing training data, wherein the training data include image information that represents a respective scene of the surroundings, wherein the image information results from a variety of image sensor sources in order to show the surroundings with different views in the image information for the representation of the respective scene” effects a transformation or a reduction of a particular article to a different state or thing / adds a specific limitation(s) other than what is well-understood, routine and conventional in the field, or adding unconventional steps that confine the claim to a particular useful application and providing improvements to the technical field of deep learning training, which recite additional elements that integrate the judicial exception into a practical application and amounting significant more.
[2] Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function. Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim(s) 9 are not interpreted under 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph because of the following reason(s): limitations are modified by sufficient structure or material for performing the claimed function.
Claim(s) 1-8 and 10 do not require 35 U.S.C. 112(f) or pre-AIA U.S.C. 112 6th paragraph interpretation because they are method claims and / or they are CRM claims.
Upon examination of the specification and claims, the examiner has determined, under the best understanding of the scope of the claim(s), rejection(s) under 35 U.S.C. 112(a)/(b) is/is not necessitated because of the following reasons: sufficient support are provided in the written description / drawings of the invention / insufficient support are provided in the written description / drawings of the invention.
[3] Grounds of Rejection
Claim Rejections - 35 USC § 102
U.S.C. 102 Conditions for patentability; novelty.
[Editor Note: Applicable to any patent application subject to the first inventor to file provisions of the AIA (see 35 U.S.C. 100 (note) ). See 35 U.S.C. 102 (pre-AIA ) for the law otherwise applicable.]
(a) NOVELTY; PRIOR ART.—A person shall be entitled to a patent unless—
(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention; or
(2) the claimed invention was described in a patent issued under section 151 , or in an application for patent published or deemed published under section 122(b) , in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
(b) EXCEPTIONS.—
(1) DISCLOSURES MADE 1 YEAR OR LESS BEFORE THE EFFECTIVE FILING DATE OF THE CLAIMED INVENTION.—A disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) if—
(A) the disclosure was made by the inventor or joint inventor or by another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(B) the subject matter disclosed had, before such disclosure, been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor.
(2) DISCLOSURES APPEARING IN APPLICATIONS AND PATENTS.—A disclosure shall not be prior art to a claimed invention under subsection (a)(2) if—
(A) the subject matter disclosed was obtained directly or indirectly from the inventor or a joint inventor;
(B) the subject matter disclosed had, before such subject matter was effectively filed under subsection (a)(2), been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(C) the subject matter disclosed and the claimed invention, not later than the effective filing date of the claimed invention, were owned by the same person or subject to an obligation of assignment to the same person.
Claims 1-3 and 8-10 are rejected under 35 U.S.C. 102(b)(1) as being anticipated by Dal Mutto (US 20190108396).
Regarding claim 1, Dal Mutto discloses a method for training a machine learning model for semantic scene understanding, comprising the following steps:
providing training data, wherein the training data include image information that represents a respective scene of the surroundings, wherein the image information results from a variety of image sensor sources in order to show the surroundings with different views in the image information for the representation of the respective scene (see figure 6 illustration below, the 1st, 2nd and 3rd views show different view of the object which show the surrounding):
PNG
media_image1.png
230
543
media_image1.png
Greyscale
training the machine learning model based on the provided training data to ascertain semantic scene information, for which purpose a depiction is evaluated in accordance with the different views (see figure 22, 2220 and 2230); and
PNG
media_image2.png
111
545
media_image2.png
Greyscale
providing the trained machine learning model (see figure 22 last section):
PNG
media_image3.png
57
246
media_image3.png
Greyscale
.
Regarding claim 2, Dal Mutto discloses the method according to claim 1, wherein the image information is specifically for:
(i) acquisition of the different views of the surroundings using different image sensors, the different image sensors including cameras (see figure 6, 100d and 100e are read as the cameras placed at different views) and/or
PNG
media_image4.png
151
524
media_image4.png
Greyscale
(ii) results from acquisition by the different image sensor sources in the form of the different image sensors, so that different images with different views of the same surroundings are provided as the image information to represent the same scene and used for the training (see figure 6 below, the background of the images captured are read as the scene).
Regarding claim 3, Dal Mutto discloses the method according to claim 1, wherein the image information includes different images in which the views of the same surroundings differ in that an image angle and/or a viewing angle are different (see figure 12 illustration below differ angles):
PNG
media_image5.png
125
301
media_image5.png
Greyscale
.
Regarding claims 8-10 see the rationale and rejection for claim 1. In addition, see figure 1B 400 which includes a processor and memory requiring code instructions.
Claim Rejections - 35 USC § 103
1. 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.
2. Claims 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal Mutto (US 20190108396) in view of Zhou (US 20130293683).
Regarding claim 4, Dal Mutto discloses all the limitations of claim 1, but is silent in disclosing the method according to claim 1, wherein the image sensor sources include: at least one wide-angle front camera of a vehicle and/or at least one telephoto front camera of the vehicle and/or at least one side camera of the vehicle and/or at least one rear camera of the vehicle, in order to provide at least one and/or different zoom sections and/or image sections and/or overlaps for representing the scene in the image information.
Zhou ‘683 discloses the method according to claim 1, wherein the image sensor sources include: at least one wide-angle front camera of a vehicle and/or at least one telephoto front camera of the vehicle and/or at least one side camera of the vehicle and/or at least one rear camera of the vehicle, in order to provide at least one and/or different zoom sections and/or image sections and/or overlaps for representing the scene in the image information (see figure 7A):
PNG
media_image6.png
443
538
media_image6.png
Greyscale
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include camera in multiple directions in order to eliminate blind spots, this technology facilitates driver assisted systems that enhance passenger safety.
3. Claims 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal Mutto (US 20190108396) in view of Zhou (US 20180260651).
Regarding claim 5, Dal Mutto discloses all the limitations of claim 1, but is silent in disclosing the method according to claim 1, wherein the image sensor sources are embodied as cameras of a vehicle so that the image information for representing the respective scene is provided in the form of a traffic scene. Zhou ‘651 discloses the method according to claim 1, wherein the image sensor sources are embodied as cameras of a vehicle so that the image information for representing the respective scene is provided in the form of a traffic scene (see paragraph 32, and one or more cameras or image capture devices).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include the image sensor sources are embodied as cameras of a vehicle so that the image information for representing the respective scene is provided in the form of a traffic scene in order to provide the visual context required by machine learning models to detect lane markers, traffic signs, and surrounding obstacles, for preventing traffic accidents.
Regarding claim 6, Zhou ‘651 discloses the method according to claim 1, wherein the machine learning model is trained to ascertain and classify the semantic scene information based on image points including pixels of the image information in order to obtain a description of the surroundings from the image information, elating to a context and/or weather conditions and/or a time of day and/or a traffic situation, wherein the depiction is evaluated in accordance with the different views by aligning the depictions at the feature level, by minimizing a distance calculation in order to ascertain the semantic scene information (see figure 2, shows traffic condition and the surroundings of each vehicle, also see figure 4, where the semantic information is extracted and image comprises pixels). See the motivation for claim 5.
4. Claims 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dal Mutto (US 20190108396) in view of MA (US 20180060684).
Regarding claim 7, Dal Mutto discloses all the limitations of claim 1, but is silent in disclosing the method according to claim 1, wherein the machine learning model includes at least or exactly two sub-models in parallel paths and is configured with a teacher-student architecture and/or as a Siamese network, wherein the training of the machine learning model includes: feeding image information resulting from acquisition by a first camera type including a wide-angle front camera, and/or augmentations of the image information into a first model of the machine learning model including a teacher model, feeding image information resulting from: (i) acquisition by a second camera type including a telephoto, or side, or rear camera, and/or (ii) augmentations of the image information into a second model of the machine learning model including a student model.
MA discloses the method according to claim 1, wherein the machine learning model includes at least or exactly two sub-models in parallel paths and is configured with a teacher-student architecture and/or as a Siamese network, wherein the training of the machine learning model includes: feeding image information resulting from acquisition by a first camera type including a wide-angle front camera, and/or augmentations of the image information into a first model of the machine learning model including a teacher model (see paragraph 275, license plate features of the first image and each of the third images are determined with a Siamese neural network model instead of a traditional vehicle recognition device, such that the vehicle searching device provided by the present application is not limited by application scenes, which leads to an improved vehicle searching speed while reducing requirements of hardware such as cameras that collect images of a vehicle, also see figure 4 illustration below),
PNG
media_image7.png
300
730
media_image7.png
Greyscale
feeding image information resulting from: (i) acquisition by a second camera type including a telephoto, or side, or rear camera, and/or (ii) augmentations of the image information into a second model of the machine learning model including a student model (see paragraph 32, Ci is read as the first camera and Cj is read as second camera, where the combination of Ci and Cj as a whole is read as wide-angle front camera):
PNG
media_image8.png
322
834
media_image8.png
Greyscale
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include Siamese network because it is designed to measure similarity rather than relying on direct classification, where the traffic data overwhelmingly consists of "normal" behavior learning the core signature of normal traffic and flagging deviations.
CONTACT INFORMATION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX LIEW (duty station is located in New York City) whose telephone number is (571)272-8623 (FAX 571-273-8623), cell (917)763-1192 or email alexa.liew@uspto.gov. Please note the examiner cannot reply through email unless an internet communication authorization is provided by the applicant. The examiner can be reached anytime.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MISTRY ONEAL R, can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEX KOK S LIEW/Primary Examiner, Art Unit 2674 Telephone: 571-272-8623
Date: 6/24/26
Prosecution Insights