CTNF 19/039,939 CTNF 96720 DETAILED ACTION This non-final action is in reply to the application filed 29 January 2025. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority Claims 43-66 and 97-106 are pending, having a filing date of 29 January 2025, and claiming a national stage continuation of PCT/US2024/015740, filed 14 February 2024, and claiming priority to U.S. Provisional Application Number 63/445,395, filed 14 February 2023. Claims 1-42 and 67-96 have been canceled by a preliminary amendment filed 29 January 2025. Information Disclosure Statement The information disclosure statement (IDS), submitted 29 January 2025, complies with 37 C.F.R. 1.97. Accordingly, the IDS has been considered by the examiner. An initialed copy of the 1449 form is enclosed herewith. Drawings 06-31 AIA The lengthy drawings have not been checked to the extent necessary to determine the presence of all possible errors. Applicant’s cooperations is requested in correcting any errors of which applicant may become aware in the drawings. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 43-58, 60, 61 and 97-106 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if: • STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or • STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: o STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? o STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? o STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? Using the two-step inquiry, it is clear that claims 43, 60 and 61 are directed toward non-statutory subject matter as shown below. STEP 1: Do claim 43, 60 and 61 fall within one of the statutory categories? Yes, because claim 43 is directed toward a system, claim 60 is directed toward a method, and claim 61 is directed toward a non-transitory computer-readable medium storing instructions executable by at least one processor to perform a method, all of which fall within one of the statutory categories. STEP 2A (PRONG 1): Are the claims directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claims 43, 60 and 61 are directed to an abstract ideas. With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas: 1. Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; 2. Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and 3. Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion). As per claims 43, 60 and 61 , the system (claim 43), method (claim 60) and non-transitory computer readable medium storing instructions executable by at least one processor to perform a method (claim 61) are mental processes that can be performed in the mind and, therefore, are abstract ideas. In particular, claim 43, 60 and 61 recite the abstract ideas of: “ aggregating the drive information obtained from the plurality of vehicles to determine a refined first location indicator and a refined second location indicator associated with the landmark ”; and determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator …. ” These recitations merely consist of aggregating (collecting) drive information and determining a landmark orientation. This is equivalent to aggregating (collecting) drive information from a plurality of vehicles (e.g. images of road signs), and determining a landmark orientation (e.g. orientation of road signs). The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc. , 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson , 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc. , 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson , 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook , 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person, aggregating (collecting) drive information from a plurality of vehicles (e.g. images of road signs), and determining a landmark orientation (e.g. orientation of road signs). The mere nominal recitations that the aggregating and determining are implemented by “ at least one processor ” (claims 43, and 61), does not take the limitation out of the mental process grouping. STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application. With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application: • an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; • an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; • an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; • an additional element effects a transformation or reduction of a particular article to a different state or thing; and • an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application: • an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea; • an additional element adds insignificant extra-solution activity to the judicial exception; and • an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use. Claims 43, 60 and 61 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into practical application. Claims 43, 60 and 61 further recites the additional elements: “ receiving drive information obtained by a plurality of vehicles traversing or having traversed a road segment ”; “ storing the landmark orientation in a map ”; and “ distributing the map to one or more autonomous vehicles for use in navigating along the road segment .” These additional elements further limits the abstract idea without integrating the abstract idea into practical application or significantly more. In particular, the “receiving, storing and distributing … “ steps is recited at a high level of generality (i.e., as a general means of gathering an electronic representation of a landmark (e.g. a road sign) ) and amount to mere data gathering, a form of insignificant extra-solution activity added to the judicial exception per MPEP 2106.05(g), because the steps characterize pre and post solution activity, such as an individual observing and recalling the images of the landmarks. Claims 43 and 61 still further include the additional element “ at least one processor ”. This elements is not sufficient to amount to significantly more than the judicial exception because they fail to integrate the exception into practical application. The mere inclusion of instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the system accomplishes receiving, storing and distributing steps by “ at least one processor ” i.e. via computers. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated in the practical application. The “at least one processor” merely describes how to generally “apply” the otherwise metal judgements in a generic or general purpose computing environment. The at least one processor is recited at a high level of generality and merely automates the receiving, storing, and distributing steps. STEP 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception? No, claims 43, 60 and 61 1 does not recite additional elements that amount to significantly more than the judicial exception. With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements: • adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or • simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present. Claims 43, 60 and 61 recite limitations or combination of limitations that are well-understood, routine, conventional (WURC) activity in the field. Receiving. storing, and distributing data are fundamental, i.e. WURC, activities performed by processors operating on information (data) such as the processors recited in claim 43 and 61. Further, applicant’s specification does not provide any indication that the storing, and distributing data activities of the system are performed using anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC , 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc. , 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well ‐ understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC , 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co. , 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Thus, since claims 43, 60, and 61 are: (a) directed toward an abstract ideas; (b) do not recite additional elements that integrate the judicial exception into practical application; and (c) do not recites additional elements that amount to significantly more than the judicial exception, it is clear that claims 43, 60, 61 and 62 are directed to non-statutory subject matter. Dependent claims 44-59, and 97 to 106 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more. For example, the additional elements in claims 47-50, 55-58 are further limitations that under their broadest reasonable interpretation are abstract using the analysis for independent claims 43, 60 and 61. Claim 55 still further includes the additional element “a machine learning model”. This element is no sufficient to amount to significantly more than the judicial exception because it fails to integrate the exception into practical application. The mere inclusion of instructions to implement an abstract idea on a computer, or merely using a computer as a tool to preform and abstract idea is indicative that the judicial exception has not been integrated into practical application. It the instant case, the system accomplishes the system accomplishes the determining of the landmark’s orientation by applying the ”machine learning model”, i.e. via a computer. Thus, it is clear that the abstract idea has not been integrated into practical application. The “machine learning model” merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose computing environment. The “machine learning model” is recited at a high level of generality and merely automates the “determining” result. Conclusion : As such, claims 43-58, 60, 61 and 97-106 are rejected as being drawn to an abstract idea without significantly more, and thus are ineligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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 non-obviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 43, 52, 54-57, 60 and 61 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication Number 2023/0245469 to Holicki in view of U.S. Patent Publication Number 2018/0285663 to Viswanathan et al. (hereafter V) and U.S. Patent Publication Number 2020/0250443 to Suzuki et al. (hereafter Suzuki) . As per claim 43 , Holicki discloses [a] system for determining landmark orientation (see at least Holicki, Abstract), the system comprising: at least one processor programmed to execute operations (see at least Holicki, [0009] disclosing that a method for localizing a motor vehicle in an environment during a driving operation. Estimation cycles are repeatedly carried out by a processor circuit to estimate in each case a current position of the motor vehicle. In the respective estimation cycle, sensor data of landmarks of the environment are received from an environment sensor, and then from feature data, which are formed of map data of a map region of the environment and of the sensor data, position data of a respective estimated position that the motor vehicle has with respect to an environment are then ascertained by means of an estimation module.; [0026] disclosing that the estimation module and the observer model can, for example, each be implemented as software for the processor circuit. The processor circuit itself can be based, for example, on at least one microprocessor) comprising: receiving drive information obtained by a plurality of vehicles traversing or having traversed a road segment (see at least Holicki, [0009] disclosing that the sensor data are in particular camera images or a sequence of individual camera images (so-called frames), for which in each case a position estimation may be carried out by means of the estimation module for generating respective position data; [0061] disclosing that the movement path 19 can be estimated from individual estimated positions 21, which can in each case be formed into a two-dimensional depiction of the environment 12 or be estimated, that is, for example, formed into individual images or frames, such as can be received in the sensor data 16 from the environment sensor 13. The individual estimated positions 21 can be generated by comparing feature data 22, some of which can be formed of the sensor data 16 and some of which can be formed of a navigation database 23, of which map data 24 of a map region 25 of the environment 12 can be formed), the drive information comprising landmark detection information corresponding to a landmark located along the road segment (see at least Holicki, [0064] disclosing that FIG. 3 describes how, in contrast, the environment 12 can be represented by the sensor data 16 by a two-dimensional depiction in the form of pixel values 36 from a camera 14. In the sensor data 16 of the depiction, landmarks 30 can be marked by object recognition, such as is available per se in the prior art (feature detection) by means of detection data 40, which can define a bounding box 41, for example, around the particular landmark 30), ... (1) ... ; aggregating the drive information obtained from the plurality of vehicles to determine a refined first location indicator and a refined second location indicator associated with the landmark (see at least Holicki, [0061] disclosing that that the chronological succession or the sequence of current positions 21 can be linked by means of an observer model 27 to form the movement path 19 <interpreted as aggregation>, which can then be provided by means of the trajectory data 18, for example, to an automated vehicle guidance system 28, which can be a driver assistance system; [0113] disclosing that the training method for the artificial neural networks MLP.sub.offset and MLP.sub.meas: The system requires video data of vehicles having known positions relative to the map. <interpreted as a plurality of vehicles>); ... (2) ... ; ... (3) ... ; and ... (4) ... , the navigating comprising determining at least one navigational response based on the stored landmark orientation (see at least Holicki, [0048] disclosing that a motor vehicle comprising an environment sensor and an embodiment of the processor circuit coupled to the environment sensor, wherein the processor circuit is coupled to a control unit, which is designed for automated vehicle guidance, for transmitting an estimated movement path of a driving operation of the motor vehicle). But, the difference between Holicki and the claimed invention is that Holicki does not explicitly teach the following limitations taught in V, a comparable system where it was known to have: (1) wherein the landmark detection information includes at least a first location indicator associated with the landmark and a second location indicator associated with the landmark (see at least V, [0015] ; [0038] disclosing that such as the processor 22 or the like, for using homography to estimate the orientation and scale of the first type of road sign based upon the bounding boxes associated with the first type of road sign within the image and the predefined image of the first type of road sign, such as obtained from the road sign database 26. See block 60 of FIG. 4. For example, the apparatus, such as the processor, may be configured to use homography to estimate the orientation and scale of the first type of road sign based upon corner points of the bounding boxes associated with the first type of road sign within the image and the predefined image of the first type of road sign. For example, the apparatus, such as the processor, may be configured to determine the geometric transformation that must occur in order to convert the orientation and scale of the predefined image of the first type of road sign to the orientation and scale of the first type of road sign within the image. While this geometric transformation may be determined based upon the road signs themselves, the apparatus, such as the processor of an example embodiment, may determine this geometric transformation with reference to the bounding boxes, such as the corner points of the bounding boxes, thereby permitting the orientation and scale of the first type of road sign within the image to be estimated relative to the predefined image for the first type of road sign, such as stored by the road sign database, based upon the geometric transformation required to transform the bounding box about the predefined image of the first type of road sign to align with the bounding box about the first type of road sign within the image); and (2) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator (see at least V, [0038] disclosing that such as the processor 22 or the like, for using homography to estimate the orientation and scale of the first type of road sign based upon the bounding boxes associated with the first type of road sign within the image and the predefined image of the first type of road sign, such as obtained from the road sign database 26. See block 60 of FIG. 4. For example, the apparatus, such as the processor, may be configured to use homography to estimate the orientation and scale of the first type of road sign based upon corner points of the bounding boxes associated with the first type of road sign within the image and the predefined image of the first type of road sign. For example, the apparatus, such as the processor, may be configured to determine the geometric transformation that must occur in order to convert the orientation and scale of the predefined image of the first type of road sign to the orientation and scale of the first type of road sign within the image. While this geometric transformation may be determined based upon the road signs themselves, the apparatus, such as the processor of an example embodiment, may determine this geometric transformation with reference to the bounding boxes, such as the corner points of the bounding boxes, thereby permitting the orientation and scale of the first type of road sign within the image to be estimated relative to the predefined image for the first type of road sign, such as stored by the road sign database, based upon the geometric transformation required to transform the bounding box about the predefined image of the first type of road sign to align with the bounding box about the first type of road sign within the image; [0036] disclosing homography provides for local feature-based alignment and defines the difference in appearance of two planar objects, such as road signs, viewed from different points of view, such as a front view and a perspective view. The predefined image of the first type of road sign may be provided in various manners, but, in one embodiment, the road sign database 26 includes the predefined image of the first type of road sign.) ... . But, the difference between the combination of Holicki and V, and the claimed invention is that neither Holicki nor V explicitly teach the following limitations taught in Suzuki, a comparable system where it was known to have: (3) storing the landmark orientation in a map (see at least Suzuki, [0032] disclosing that he storage unit 14 stores the moving image generated by the image capturing unit 11 and the position information acquired by the position information acquisition unit 12 in association with time information at the time when the moving image is generated. In the present embodiment, it is also useful for the storage unit 14 to store map information indicating the installation position and the content of the traffic sign. In addition, the storage unit 14 may store information on a result of analysis and processing, by the control unit 15, of the generated moving image. Further, the storage unit 14 accumulates various kinds of information on an operation or control of the vehicle, such as storage of a program that controls the subject vehicle 10); and (4) distributing the map to one or more autonomous vehicles for use in navigating along the road segment (see at least Suzuki, [0008] disclosing that an information processing device of a vehicle having an image capturing unit, and includes a storage unit configured to store map information including at least an installation position of a traffic sign, a control unit configured to compare the installation position of the traffic sign in the map information with position information of the vehicle, and when the vehicle reaches a position where the traffic sign is visible, control the image capturing unit, such that the image capturing unit captures an image including the traffic sign, and a communication unit configured to transmit, to a server, the image including the traffic sign and the position information of the vehicle when the image is captured) ... . Holicki, V and Suzuki are analogous art to claim 43 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, to provide the benefit of (1) having the landmark detection information include at least a first location indicator associated with the landmark and a second location indicator associated with the landmark and (2) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator, as disclosed in V, with a reasonable expectation of success. It would have further been obvious to have further modified the combination of Holicki and V to provide the benefit of (2) storing the landmark orientation in a map and (3) distributing the map to one or more autonomous vehicles for use in navigating along the road segment, as disclosed in Suzuki, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill. As per claim 52 , the combination of Holicki, V, and Suzuki discloses all of the limitations of claim 43, as shown above. wherein the landmark is a traffic sign, a road marking, a traffic light, a convex blind spot mirror, or a construction indicator (see at least Holicki, [0062] disclosing that the landmarks 30, for example, can be described in each case by the feature data 22. A landmark 30 can in each case be a traffic sign 31 and/or a roadway marking 32). As per claim 54 , the combination of Holicki, V, and Suzuki discloses all of the limitations of claim 43, as shown above. Holicki further discloses the following limitation: wherein the landmark detection information further includes detections of both a front face and a back face of the landmark (see at least Holicki, [0081] disclosing that the comparison takes place image by image, such as by way of a deep learning approach, that is, there is an artificial neural network that computes for each image the pose relative to the external map (Δx, Δy, Δφ) as well as a covariance matrix. This is fed to a Kalman filter as an observer model to obtain a consistent trajectory of the movement path <interpreted as drivable paths of the road segment>). As per claim 55 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. Holicki further discloses the following limitation: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises applying a machine learning model (see at least Holicki, [0100]-[0102] disclosing that The input point lists contain more than just 2D coordinates, resulting not in a x2, but an xD block or landmark descriptor, D being the feature dimension (see next section). An encoder computes a landmark descriptor. Multiple point lists or lists of landmark descriptors are input both for maps and for recognitions/detections, one for each type of landmark. Each of these lists runs through a max pooling layer, and the resulting one-dimensional vectors are concatenated to obtain a visual descriptor for multiple landmark types. The final network, serving as the model 63 of machine learning, not only computes the position x, y of the vehicle, but also a covariance matrix or the covariance value; [0111] disclosing that “Learning” a covariance: The data are fed to a Kalman filter in order to obtain a consistent trajectory across multiple frames. The final pose estimation network (model 63) is expanded by an estimation for the six different elements of the 3×3 covariance matrix. The negative log likelihood may be used as an additional loss function; [0113]) ... . V further discloses the following limitation: wherein the first location indicator and the second location indicator are inputs to the machine learning model (see at least V, [0045], claim 1, disclosing a method for augmenting a training data set, the method comprising: identifying a first type of road sign within an image; estimating an orientation and scale of the first type of road sign within the image; identifying stylistic content associated with the first type of road sign within the image; transforming an image of a second type of road sign based upon the orientation and scale of the first type of road sign and also based upon the stylistic content associated with the first type of road sign, wherein the first type of road sign occurs more often in a plurality of images than the second type of road sign; and creating a synthetic image in which the first type of road sign within the image is replaced by a transformed representation of the second type of road sign, wherein creating the synthetic image comprises filling in one or more pixels in the synthetic image about the second type of road sign that were occluded by the first type of road sign within the image ). As per claim 56 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. Holicki further discloses the following limitation: wherein determining the at least one navigational response based on the stored landmark orientation comprises determining a relevancy of the landmark to a vehicle among the one or more autonomous vehicles (see at least Holicki, [0113] disclosing that training method for the artificial neural networks MLP.sub.offset and MLP.sub.meas: The system requires video data of vehicles having known positions relative to the map. These training data could stem from a combined differential GPS (DGPS)/a measuring system comprising inertial sensors or from successful runs with a previous localization method. The network can be trained using these ground truth position data. For the training, in contrast, no ground truth labels are necessary for correspondences of map landmarks and detection landmarks. These associations result automatically from the training. Ground truth position data (which may include position data) are sufficient; [0114] disclosing that this yields a method for the self-localization of vehicles based on landmarks relative to a predefined map. This method uses a deep learning procedure for regressing the position of the vehicle, together with a covariance matrix, into the system of the map). As per claim 57 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. V further discloses the following limitations: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises: determining a landmark type (see at least V, [0037] disclosing that FIG. 4 includes an image 50 from among a plurality of images captured along a roadway that includes a first type of road sign 52 <interpreted as a landmark>, that is, a speed limit sign posting a speed limit of 55 mph, and a bounding box 54 that has been defined so as to include the road sign. As shown, the bounding box is rectangular and includes the entirety of the road sign, but is sized so as to relatively closely approximate the size of the road sign within the image. And that the apparatus, such as the processor 22, may be configured to analyze the image, identify the road sign and then define a bounding box thereabout. As also shown in FIG. 4, a database, such as the road sign database, includes predefined images of a plurality of different types of road signs including, for example, a speed limit sign 56 and a railroad crossing sign 58; [0038] disclosing that the apparatus, such as the processor, may be configured to determine the geometric transformation that must occur in order to convert the orientation and scale of the predefined image of the first type of road sign to the orientation and scale of the first type of road sign within the image.; [0039] disclosing that As shown in block 34 of FIG. 3, the apparatus 20 of this example embodiment also includes means, such as the processor 22 or the like, for identifying the stylistic content associated with the first type of road sign within the image.); obtaining one or more geometric characteristics of the landmark based on the landmark type (see at least V, [0038]); and using the one or more geometric characteristics in conjunction with the refined first location indicator and a refined second location indicator to refine the landmark orientation (see at least V, [0038]; [0042] disclosing that as shown in block 40 of FIG. 3, the apparatus 20 of an example embodiment also includes means, such as the processor 22 or the like, for creating a synthetic image in which the first type of road sign within the image is replaced by a transformed representation of the second type of road sign. In this regard, the apparatus, such as the processor, of an example embodiment is configured to cause the transformed representation of the second type of road sign to be overlaid on the first type of road sign within the image. By transforming the image of the second type of road sign in accordance with the orientation, scale and stylistic content of the first type of road sign, the transformed representation of the second type of road sign overlays the first type of road sign within the image in such a manner as to completely remove the first type of road sign from view). As per claim 60 , similar to claim 43, Holicki discloses [a] method for determining landmark orientation (see at least Holicki, Abstract), the method comprising: receiving drive information obtained by a plurality of vehicles traversing or having traversed a road segment (see at least Holicki, [0009]; [0061]), the drive information comprising landmark detection information corresponding to a landmark located along the road segment (see at least Holicki, [0064]), ... (1) ... ; aggregating the drive information obtained from the plurality of vehicles to determine a refined first location indicator and a refined second location indicator associated with the landmark (see at least Holicki, [0061]; [0113] ); ... (2) ... ; ... (3) ... ; and ... (4) ... , the navigating comprising determining at least one navigational response based on the stored landmark orientation (see at least Holicki, [0048]). But, the difference between Holicki and the claimed invention is that Holicki does not explicitly teach the following limitations taught in V, a comparable system where it was known to have: (1) wherein the landmark detection information includes at least a first location indicator associated with the landmark and a second location indicator associated with the landmark (see at least V, [0015]; [0038]); and (2) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator (see at least V, [0038]; [0036]) ... . But, the difference between the combination of Holicki and V, and the claimed invention is that neither Holicki nor V explicitly teach the following limitations taught in Suzuki, a comparable system where it was known to have: (3) storing the landmark orientation in a map (see at least Suzuki, [0032]); and (4) distributing the map to one or more autonomous vehicles for use in navigating along the road segment (see at least Suzuki, [0008]) ... . Holicki, V and Suzuki are analogous art to claim 60 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, to provide the benefit of (1) having the landmark detection information include at least a first location indicator associated with the landmark and a second location indicator associated with the landmark and (2) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator, as disclosed in V, with a reasonable expectation of success. It would have further been obvious to have further modified the combination of Holicki and V to provide the benefit of (2) storing the landmark orientation in a map and (3) distributing the map to one or more autonomous vehicles for use in navigating along the road segment, as disclosed in Suzuki, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill. As per claim 61 , similar to claims 43 and 60, Holicki discloses ... (1) ... a method for determining landmark orientation (see at least Holicki, Abstract), the method comprising: receiving drive information obtained by a plurality of vehicles traversing or having traversed a road segment (see at least Holicki, [0009]; [0061]), the drive information comprising landmark detection information corresponding to a landmark located along the road segment (see at least Holicki, [0064]), ... (2) ... ; aggregating the drive information obtained from the plurality of vehicles to determine a refined first location indicator and a refined second location indicator associated with the landmark (see at least Holicki, [0061]; [0113]); ... (3) ... ; ... (4) ... ; and ... (5) ... , the navigating comprising determining at least one navigational response based on the stored landmark orientation (see at least Holicki, [0048]). But, the difference between Holicki and the claimed invention is that Holicki does not explicitly teach the following limitations taught in V, a comparable system where it was known to have: (1) A non-transitory computer-readable medium storing instructions executable by at least one processor to perform (see at least V, [0014]), (2) wherein the landmark detection information includes at least a first location indicator associated with the landmark and a second location indicator associated with the landmark (see at least V, [0015]; [0038]); and (3) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator (see at least V, [0038]; [0036] ) ... . But, the difference between the combination of Holicki and V, and the claimed invention is that neither Holicki nor V explicitly teach the following limitations taught in Suzuki, a comparable system where it was known to have: (4) storing the landmark orientation in a map (see at least Suzuki, [0032]); and (5) distributing the map to one or more autonomous vehicles for use in navigating along the road segment (see at least Suzuki, [0008]), ... . Holicki, V and Suzuki are analogous art to claim 61 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, to provide the benefit of (1) having a non-transitory computer-readable medium storing instructions executable by at least one processor to perform the method, (2) having the landmark detection information include at least a first location indicator associated with the landmark and a second location indicator associated with the landmark and (3) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator, as disclosed in V, with a reasonable expectation of success. It would have further been obvious to have further modified the combination of Holicki and V to provide the benefit of (4) storing the landmark orientation in a map and (5) distributing the map to one or more autonomous vehicles for use in navigating along the road segment, as disclosed in Suzuki, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill . 07-22-aia AIA Claim s 44-46, 49, 50, 53, 59, 97, 98, 101-103 and 106 are rejected under 35 U.S.C. 103 as being unpatentable over Holicki, V, Suzuki as applied to claim s 43, 60 and 61 above, and further in view of U.S. Patent Publication Number 2017/0308989 to Lee et al. (hereafter Lee) . As per claim 44 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee. wherein the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark (see at least Lee, [0041] disclosing that the normal determination unit 228 may receive the coordinates of the segmented region corresponding to the traffic sign 130 in the image 160 from the traffic sign detection unit 224. Upon receiving the coordinates of the segmented region, the normal determination unit 228 may process the image 160 including the traffic sign 130 to determine the direction 150 normal to a surface of the traffic sign 130. As used herein, the term “normal” or “normal direction” means a direction or vector that is perpendicular to a surface of an object such as a traffic sign. In some embodiments, the normal direction 150 of the traffic sign 130 may be determined based on the image 160. In one embodiment, the normal direction 150 of the traffic sign 130 may be determined to be a direction of the road 140 in the image 160 since a normal to the surface of the traffic sign 130 can be generalized or assumed to be parallel to a direction or shape of the road 140; [0053] ). Holicki, V, Suzuki and Lee are analogous art to claim 44 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of having the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 45 , the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 44, as shown above. V further discloses the following limitation: wherein the information comprises one or more of three-dimensional locations along a first edge of the planar surface, three-dimensional locations along a second edge of the planar surface, and three- dimensional locations of three or more corners of the planar surface (see at least V, [0035] disclosing that FIG. 4 includes an image 50 from among a plurality of images captured along a roadway that includes a first type of road sign 52, that is, a speed limit sign posting a speed limit of 55 mph, and a bounding box 54 <interpreted as including three dimensional locations along a first edge of the planar surface, along a second surface, and of three or more corners> that has been defined so as to include the road sign. As shown, the bounding box is rectangular and includes the entirety of the road sign, but is sized so as to relatively closely approximate the size of the road sign within the image. The bounding box may have been defined in advance of the analysis of the image by the apparatus 20. Alternatively, the apparatus, such as the processor 22, may be configured to analyze the image, identify the road sign and then define a bounding box thereabout; [0038]). As per claim 46 , the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 45, as shown above. V further discloses the following limitation: wherein the first edge of the planar surface corresponds to a right edge of the landmark and the second edge corresponds to a left edge of the landmark (see at least V, [0036] as cited from claim 45, discussing Fig. 4, which shows the first edge corresponding to the right, and the second edge corresponding to the left). As per claim 49 , the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 44, as shown above. V further discloses the following limitation: wherein the refined first location indicator or the refined second location indicator represents a three-dimensional location for at least one edge of a planar surface of the landmark (see at least V, [0038] disclosing that the processor, may determine this geometric transformation with reference to the bounding boxes, such as the corner points of the bounding boxes, thereby permitting the orientation and scale of the first type of road sign within the image to be estimated relative to the predefined image for the first type of road sign, such as stored by the road sign database, based upon the geometric transformation required to transform the bounding box about the predefined image of the first type of road sign to align with the bounding box about the first type of road sign within the image), wherein aggregating the drive information includes determining an average three-dimensional location for the at least one edge of the planar surface of the landmark based on the refined first location indicator and the refined second location indicator (see at least V, [0038]; [0042] disclosing that as shown in block 40 of FIG. 3, the apparatus 20 of an example embodiment also includes means, such as the processor 22 or the like, for creating a synthetic image in which the first type of road sign within the image is replaced by a transformed representation of the second type of road sign. In this regard, the apparatus, such as the processor, of an example embodiment is configured to cause the transformed representation of the second type of road sign to be overlaid on the first type of road sign within the image. By transforming the image of the second type of road sign in accordance with the orientation, scale and stylistic content of the first type of road sign, the transformed representation of the second type of road sign overlays the first type of road sign within the image in such a manner as to completely remove the first type of road sign from vie). As per claim 50 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises determining a normal to a plane defined by the refined first location indicator and the refined second location indicator (see at least Lee, [0041] disclosing that the normal determination unit 228 may receive the coordinates of the segmented region corresponding to the traffic sign 130 in the image 160 from the traffic sign detection unit 224. Upon receiving the coordinates of the segmented region, the normal determination unit 228 may process the image 160 including the traffic sign 130 to determine the direction 150 normal to a surface of the traffic sign 130 ; [0052]; [0053]). Holicki, V, Suzuki and Lee are analogous art to claim 50 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of determining a normal to a plane defined by the refined first location indicator and the refined second location indicator, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 53 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee: wherein the landmark detection information further includes detections of both a front face and a back face of the landmark (see at least Lee, [0026] disclosing that upon detecting the traffic sign 130, the electronic device 110 may determine a distance D1 between the traffic sign 130 and the image sensor 120 or vehicle 100 based on the image 160 and distance information at time T1, a direction 150 normal to a surface of the traffic sign 130 <interpreted as a direction of front face, one of ordinary skill in the art would know that if the direction of the front face is detected so is the direction back face, because the sign is planer, and the back face is in the opposite direction of the front face geometrically> in the image 160, and motion of the vehicle 100 (e.g., speed, rotation, translation, acceleration, or the like) from a vehicle electronic system as shown in FIG. 2.). Holicki, V, Suzuki and Lee are analogous art to claim 53 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of having the landmark detection information further includes detections of both a front face and a back face of the landmark, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 59 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee: wherein the at least one navigational response includes at least one of steering, braking, or accelerating the one or more autonomous vehicles (see at least Lee, [0026] disclosing that upon detecting the traffic sign 130, the electronic device 110 may determine a distance D1 between the traffic sign 130 and the image sensor 120 or vehicle 100 based on the image 160 and distance information at time T1, a direction 150 normal to a surface of the traffic sign 130 in the image 160, and motion of the vehicle 100 (e.g., speed, rotation, translation, acceleration) from a vehicle electronic system as shown in FIG. 2). Holicki, V, Suzuki and Lee are analogous art to claim 59 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of having the at least one navigational response include at least one of steering, braking, or accelerating the one or more autonomous vehicles, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by responding to the obtaining the obtained and identified images of traffic signs. As per claim 97 , similar to claim 44, the combination of Holicki, V and Suzuki discloses all of the limitations of claim 60, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee. wherein the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark (see at least Lee, [0041]; [0053]). Holicki, V, Suzuki and Lee are analogous art to claim 97 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of having the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 98 , similar to claim 45, the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 97, as shown above. V further discloses the following limitation: wherein the information comprises one or more of three-dimensional locations along a first edge of the planar surface, three- dimensional locations along a second edge of the planar surface, and three- dimensional locations of three or more corners of the planar surface ( see at least V, [0035]; [0038]). As per claim 101 , similar to claim 50, the combination of Holicki, V and Suzuki discloses all of the limitations of claim 60, as shown above. But neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises determining a normal to a plane defined by the refined first location indicator and the refined second location indicator (see at least Lee, [0041]; [0052]; [0053]). Holicki, V, Suzuki and Lee are analogous art to claim 101 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of determining a normal to a plane defined by the refined first location indicator and the refined second location indicator, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 102 , similar to claims 44 and 97, the combination of Holicki, V and Suzuki discloses all of the limitations of claim 61, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee. wherein the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark (see at least Lee, [0041]; [0053]). Holicki, V, Suzuki and Lee are analogous art to claim 102 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of having the first location indicator and the second location indicator each comprise information that when taken in combination represent a planar surface of the landmark, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 103 , similar to claims 45 and 98, the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 102, as shown above. V further discloses the following limitation: wherein the information comprises one or more of three-dimensional locations along a first edge of the planar surface, three-dimensional locations along a second edge of the planar surface, and three-dimensional locations of three or more corners of the planar surface (see at least V, [0035]; [0038]). As per claim 106 , similar to claim 50, the combination of Holicki, V and Suzuki discloses all of the limitations of claim 61, as shown above. But neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Lee: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises determining a normal to a plane defined by the refined first location indicator and the refined second location indicator (see at least Lee, [0041]; [0052]; [0053] ). Holicki, V, Suzuki and Lee are analogous art to claim 106 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of determining a normal to a plane defined by the refined first location indicator and the refined second location indicator, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs . 07-22-aia AIA Claim s 47, 48, 99, 100, 104 and 105 are rejected under 35 U.S.C. 103 as being unpatentable over Holicki, V, Suzuki and Lee as applied to claim s 44 and 99 above, and further in view of U.S. Patent Publication Number 2024/0078750 to Liu et al. (hereafter Liu) . As per claim 47 , the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 44, as shown above. But, neither Holicki, V, Suzuki nor Lee explicitly teach the following limitation taught in Liu: wherein the operations further comprise comprising determining an actual width of the landmark (see at least Liu, [0077] disclosing with regard to Fig. 10, determining a plurality of road sign-projected two-dimensional grids 420 from the plurality of two-dimensional grids 220, wherein the road sign-projected two-dimensional grids 420 encompass projections 430 of the plurality of road sign semantic data points 410 on the plane PL; fitting a plurality of vertices 230 of the plurality of road sign-projected two-dimensional grids 420 to obtain a road sign-projected fitted plane (not shown); determining, according to the road sign-projected fitted plane and the plurality of road sign semantic data points 410, the spatial geometric parameters of the road sign RS, wherein the spatial geometric parameters of the road sign RS comprise a center point c2 of a road sign rectangle 400, a normal vector (not shown) of the road sign rectangle 400, a first vector 402 of the road sign rectangle 400, a length L3 of the road sign rectangle 400 on the first vector 402, and a length L4 <interpreted as the width> of the road sign rectangle 400 on the second vector 404). Holicki, V, Suzuki, Lee and Liu are analogous art to claim 47 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Liu relates to a parameterization method for point cloud data and a map construction method based on point cloud data (see Liu, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V, Suzuki and Lee, to provide the benefit of determining an actual width of the landmark, as disclosed in Liu, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 48 , the combination of Holicki, V, Suzuki, Lee and Liu discloses all of the limitations of claim 47, as shown above. Liu further discloses the following limitations: wherein the operations further comprise determining a direction of a line normal relative to a surface of the landmark based on the actual width (see at least Liu, [0074] disclosing that he step of determining, according to the road mark-projected fitted plane and the plurality of road mark semantic data points 310, the spatial geometric parameters of the road mark RM comprises: calculating a normal vector of the road mark-projected fitted plane as a normal vector of the road mark rectangle 300; and calculating a feature vector of the plurality of road mark semantic data points 310 as the long side vector 302, wherein the short side vector 304 is perpendicular to the normal vector of the road mark rectangle and is also perpendicular to the long side vector 302; [0077]); and storing the direction in association with the map (see at least Liu, [0087] disclosing that since road marks of a real road have clear geometric rules and time sequence information of each road mark is stored during data acquisition, the topological relationship between the road marks may be constructed according to the geometric relationship and the time sequence information). As per claim 99 , similar to claim 47, the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 97, as shown above. But, neither Holicki, V, Suzuki nor Lee explicitly teach the following limitation taught in Liu: further comprising determining an actual width of the landmark (see at least Liu, [0077]). Holicki, V, Suzuki, Lee and Liu are analogous art to claim 99 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Liu relates to a parameterization method for point cloud data and a map construction method based on point cloud data (see Liu, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V, Suzuki and Lee, to provide the benefit of determining an actual width of the landmark, as disclosed in Liu, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 100 , similar to claim 48, the combination of Holicki, V, Suzuki, Lee and Liu discloses all of the limitations of claim 99, as shown above. Liu further discloses the following limitations: further comprising determining a direction of a line normal relative to a surface of the landmark based on the actual width (see Liu, [0074]; [0077]); and storing the direction in association with the map (see at least Liu, [0087]). As per claim 104 , similar to claims 47 and 99, the combination of Holicki, V, Suzuki and Lee discloses all of the limitations of claim 61, as shown above. But, neither Holicki, V, Suzuki nor Lee explicitly teach the following limitation taught in Liu: wherein the method further comprises determining an actual width of the landmark (see at least Lee, [0077]). Holicki, V, Suzuki, Lee and Liu are analogous art to claim 104 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Liu relates to a parameterization method for point cloud data and a map construction method based on point cloud data (see Liu, [0002]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V, Suzuki and Lee, to provide the benefit of determining an actual width of the landmark, as disclosed in Liu, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by more accuracy and reliability obtaining the images of traffic signs. As per claim 105 , similar to claims 48 and 100, the combination of Holicki, V, Suzuki, Lee and Liu discloses all of the limitations of claim 104, as shown above. Liu further discloses the following limitations: wherein the method further comprises determining a direction of a line normal relative to a surface of the landmark based on the actual width (see Liu, [0074]; [0077]); and storing the direction in association with the map (see at least Liu, [0087]) . 07-22-aia AIA Claim 51 is rejected under 35 U.S.C. 103 as being unpatentable over Holicki, V and Suzuki as applied to claim 43 above, and further in view of U.S. Patent Publication Number 2010/0217529 to Stroila et al. (hereafter Stroila) . As per claim 51 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Stroila: wherein determining the normal to the plane is performed via a triangulation process (see at least Stroila, [0054] disclosing that other clustering techniques may be used. Separate planes may be fitted to separate clusters, such as via a RANSAC technique, to further identify or determine separate road features, such as road signs. Also, triangulation methods may be used to determine if the data points correspond to one road sign or separate road signs based on geometry, symmetry, and/or other factors. Triangulation may also be used in conjunction with the original data set 401 to fill in a gap, such as the gap 60). Holicki, V, Suzuki, Lee and Stroila are analogous art to claim 51 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Stroila relates to determining geographic features corresponding to a travel path from collected data (see Stroila, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V, Suzuki and Lee, to provide the benefit of having the determination of the normal to the plane be performed via a triangulation process, as disclosed in Stroila, with a reasonable expectation of success. Doing so would provide the benefit of improving the landmark information collection and identification . 07-22-aia AIA Claim 58 is rejected under 35 U.S.C. 103 as being unpatentable over Holicki, V and Suzuki as applied to claim 43 above, and further in view of U.S. Patent Publication Number 2022/0380990 to Stenneth et al. (hereafter Stenneth) . As per claim 58 , the combination of Holicki, V and Suzuki discloses all of the limitations of claim 43, as shown above. V further discloses the following limitation: wherein determining the landmark orientation for the at least one feature of the landmark based on the refined first location indicator and the refined second location indicator comprises: determining a landmark type (as cited for claim 57, see at least V, [0037]-[0039) ... . But, neither Holicki, V nor Suzuki explicitly teach the following limitation taught in Stenneth: obtaining one or more color features of the landmark based on the landmark type (see at least Stenneth, [0058] disclosing that the road sign attribute data can indicate a road sign location, sign information displayed by the road sign, a position of the road sign with respect to one or more attributes of links, segments, and nodes, an orientation of the road sign with respect to a ground of which the road sign is mounted or a road of which the road sign is associated with, a size of the road sign, a classification/type of the road sign, yaw, pitch, and roll angles of the road sign, a height of the road sign, a color of the road sign, composition of the road sign, etc. The road sign records 1009 can include information indicating whether a road sign is associated with a specific segment of a road link (as opposed to an entire link) and information indicating a flow of traffic that the road sign is designed to be associated with within a given node. And the road sign attribute data indicates at least one attribute of the functional road sign 117.); and using the one or more color features in conjunction with the refined first location indicator and a refined second location indicator to refine the landmark orientation (see at least Stenneth, [0058]; [0068] disclosing that the machine learning module is trained to identify sign information from: (1) a first plurality of images of an obscured road sign that is captured using image capturing devices that mimic the way a human eye would see an image (e.g., a standard camera); and (2) a second plurality of images of the obscured road sign that is captured using image capturing devices that are different than the way a human eye would see an image). Holicki, V, Suzuki, and Stenneth are analogous art to claim 58 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Suzuki relates to an information processing device, a server, and a traffic management system that detects deterioration of a road sign over time (see Suzuki, [0002]). Stenneth relates to control a functional road object, such as a sign, for example, using a predicted state of visibility for the functional road sign (see Stenneth, [0001] ). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V and Suzuki, to provide the benefit of obtaining one or more color features of the landmark based on the landmark type, and using the one or more color features in conjunction with the refined first location indicator and a refined second location indicator to refine the landmark orientation, as disclosed in Stenneth, with a reasonable expectation of success. Doing so would provide the benefit of improving the landmark information collection and identification . 07-21-aia AIA Claim s 62-65 are rejected under 35 U.S.C. 103 as being unpatentable over Holicki and V . As per claim 62 , similar to claims 43, 60 and 61, Holicki discloses [a] navigation system for a host vehicle (see at least Holicki, Abstract), the system comprising: at least one processor comprising circuitry (see at least Holicki, [0026] disclosing that the estimation module and the observer model can, for example, each be implemented as software for the processor circuit. The processor circuit itself can be based, for example, on at least one microprocessor) and having access to a memory (see at least Holicki, [0049] a computer-readable memory medium comprising commands that, when executed by a computer or a computer network, prompt this computer or this computer network to carry out an embodiment of the method described herein. The memory medium can be designed at least partially as a volatile data memory and/or at least partially designed as a non-volatile data memory), wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to execute operations (see at least Holicki, [0049] disclosing that the commands can be provided as so-called binary code or assembler code or compiled program code, which can be executed by the computer or computer network by means of a processor circuit of the described kind provided by this computer or computer network, or as source code) comprising: receiving map data corresponding to a road segment on which the host vehicle is navigating or will navigate (see at least Holicki, [0024] disclosing an estimation module, it is already possible to recognize the covariance of the position coordinates that will result or is to be expected in the estimation module based on the map data and/or based on the sensor data. If it can be recognized, for example, based on the map data and/or sensor data that only roadway markings extending in the longitudinal direction of the driving direction are detectable, it can already be signaled during the estimation of the position coordinate for the longitudinal direction (here, the x coordinate) by the estimation module that the position coordinate of the longitudinal direction can have a large variance or dispersion. The reason is that, without further landmarks enabling orientation or fixation relative to the longitudinal direction, such as road signs, an estimation of the position of the motor vehicle in the longitudinal direction or driving direction is not unambiguously possible), wherein the map data comprises a landmark orientation for a landmark positioned relative to the road segment (see at least Holicki, [0024]; [0045] disclosing that the map data of the map region are selected from a digital environment map of the environment in a database by means of initial position data of at least one localization unit. In this way, it is possible to resort to a localization unit such as a GPS receiver and/or vehicle odometry for the selection of a map region or map section to be currently used), the landmark orientation having been determined based on: drive information obtained by a plurality of vehicles traversing or having traversed the road segment (see at least Holicki, [0009]; [0061]) , the drive information comprising landmark detection information corresponding to the landmark (see at least Holicki, [0064];[0113]; [0114], ), ... (1) ... ; aggregating the drive information obtained from the plurality of vehicles to determine a refined first location indicator and a refined second location indicator associated with the landmark (see at least Holicki, [0061]; [0113]); and ... (2) ...; determining, based at least on the map data and host vehicle sensor data, a presence of a target landmark in an environment of the host vehicle (see at least Holicki, [0024]); determining, based on at least the map data, that the target landmark in the environment of the host vehicle corresponds to the landmark (see at least Holicki, [0024]; [0045]); determining at least one navigational response based on the landmark orientation stored in the map data in association with the landmark (see at least Holicki, [0048]); and causing the host vehicle to implement the at least one navigational response (see at least Holicki, [0048]; claim 14, disclosing an environment sensor and a processor circuit, coupled to the environment sensor, according to claim 13, wherein the processor circuit is coupled to a control unit designed for automated vehicle guidance, for transmitting an estimated movement path of a driving operation of the motor vehicle to the control unit). (1) wherein the landmark detection information includes at least a first location indicator associated with the landmark and a second location indicator associated with the landmark (see at least V, [0015]; [0038]); and (2) determining the landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator (see at least V, [0038]; [0036]) ... . Holicki and V are analogous art to claim 62 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, to provide the benefit of (1) having the landmark detection information include at least a first location indicator associated with the landmark and a second location indicator associated with the landmark and (2) determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator, as disclosed in V, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill. As per claim 63 , similar to claim 52, the combination of Holicki and V disclose all of the limitations of claim 62, as shown above. Holicki further discloses the following limitation: wherein the landmark is a traffic sign, a road marking, a traffic light, a convex blind spot mirror, or a construction indicator (see at least Holicki, [0062]). As per claim 64 , similar to claim 56, the combination of Holicki and V disclose all of the limitations of claim 62, as shown above. Holicki further discloses the following limitation: wherein determining the at least one navigational response includes determining a relevancy of the landmark to the host vehicle (see at least Holicki, [0113]; [0114]). As per claim 65 , the combination of Holicki and V discloses all of the limitations of claim 62, as shown above. Holicki further discloses the following limitation: wherein determining the at least one navigational response includes determining a relevancy of the landmark to a drivable path associated with the road segment (see at least Holicki, [0048] disclosing that a motor vehicle comprising an environment sensor and an embodiment of the processor circuit coupled to the environment sensor, wherein the processor circuit is coupled to a control unit, which is designed for automated vehicle guidance, for transmitting an estimated movement path of a driving operation of the motor vehicle; [0081] disclosing that the comparison takes place image by image, such as by way of a deep learning approach, that is, there is an artificial neural network that computes for each image the pose relative to the external map (Δx, Δy, Δφ) as well as a covariance matrix. This is fed to a Kalman filter as an observer model to obtain a consistent trajectory of the movement path <interpreted as drivable paths of the road segment> ) . 07-22-aia AIA Claim 66 is rejected under 35 U.S.C. 103 as being unpatentable over Holicki and V as applied to claim 62 above, and further in view of Lee . As per claim 66 , similar to claim 59, the combination of Holicki and V discloses all of the limitations of claim 62, as shown above. But, neither Holicki nor V explicitly teach the following limitation taught in Lee: wherein the at least one navigational response includes at least one of steering, braking, or accelerating the host vehicle (see at least Lee, [0026]). Holicki, V and Lee are analogous art to claim 66 because they are in the same field of detecting and classifying various objects in an environment of a vehicle. Holicki relates to a method and to a processor circuit for determining the position of or for localizing a motor vehicle in an environment during a driving operation (see Holicki, [0001]). V relates to a method, apparatus and computer program product for augmenting a training data set to include additional synthetic images of a road sign that otherwise occurs infrequently (see V, [0001]). Lee relates to capturing an image of a traffic sign with an image sensor in a vehicle (see Lee, [0001]). Therefore, it would have been prima facie obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system, as disclosed in Holicki, as modified by V, to provide the benefit of having the at least one navigational response include at least one of steering, braking, or accelerating the one or more autonomous vehicles, as disclosed in Lee, with a reasonable expectation of success. Doing so would provide the benefit of improving safety by responding to the obtaining the obtained and identified images of traffic signs . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Patent Publication Number 2022/0380990 to Stenneth et al. (hereafter Stenneth) at [0070] disclosing that this artificial intelligence can also be used to recognize parking signs and identify them when an image collected by the disclosed parking spot information system of what the system believes to be a parking sign cannot be resolved to the speed of the camera vehicle, the orientation of the sign, the camera, or the vehicle, inclement weather conditions, and other circumstances yielding compromised image fidelity . Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICK M. BRADY III whose telephone number is (571)272-7458. The examiner can normally be reached Monday - Friday 7:00 am - 4;30 pm. 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, Erin Bishop can be reached at 571-270-3713. 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. PATRICK M. BRADY III Examiner Art Unit 3665 /PATRICK M BRADY/Examiner, Art Unit 3665 /Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665 Application/Control Number: 19/039,939 Page 2 Art Unit: 3665 Application/Control Number: 19/039,939 Page 3 Art Unit: 3665 Application/Control Number: 19/039,939 Page 4 Art Unit: 3665 Application/Control Number: 19/039,939 Page 5 Art Unit: 3665 Application/Control Number: 19/039,939 Page 6 Art Unit: 3665 Application/Control Number: 19/039,939 Page 7 Art Unit: 3665 Application/Control Number: 19/039,939 Page 8 Art Unit: 3665 Application/Control Number: 19/039,939 Page 9 Art Unit: 3665 Application/Control Number: 19/039,939 Page 10 Art Unit: 3665 Application/Control Number: 19/039,939 Page 11 Art Unit: 3665 Application/Control Number: 19/039,939 Page 12 Art Unit: 3665 Application/Control Number: 19/039,939 Page 13 Art Unit: 3665 Application/Control Number: 19/039,939 Page 14 Art Unit: 3665 Application/Control Number: 19/039,939 Page 15 Art Unit: 3665 Application/Control Number: 19/039,939 Page 16 Art Unit: 3665 Application/Control Number: 19/039,939 Page 17 Art Unit: 3665 Application/Control Number: 19/039,939 Page 18 Art Unit: 3665 Application/Control Number: 19/039,939 Page 19 Art Unit: 3665 Application/Control Number: 19/039,939 Page 20 Art Unit: 3665 Application/Control Number: 19/039,939 Page 21 Art Unit: 3665 Application/Control Number: 19/039,939 Page 22 Art Unit: 3665 Application/Control Number: 19/039,939 Page 23 Art Unit: 3665 Application/Control Number: 19/039,939 Page 24 Art Unit: 3665 Application/Control Number: 19/039,939 Page 25 Art Unit: 3665 Application/Control Number: 19/039,939 Page 26 Art Unit: 3665 Application/Control Number: 19/039,939 Page 27 Art Unit: 3665 Application/Control Number: 19/039,939 Page 28 Art Unit: 3665 Application/Control Number: 19/039,939 Page 29 Art Unit: 3665 Application/Control Number: 19/039,939 Page 30 Art Unit: 3665 Application/Control Number: 19/039,939 Page 31 Art Unit: 3665 Application/Control Number: 19/039,939 Page 32 Art Unit: 3665 Application/Control Number: 19/039,939 Page 33 Art Unit: 3665 Application/Control Number: 19/039,939 Page 34 Art Unit: 3665 Application/Control Number: 19/039,939 Page 35 Art Unit: 3665 Application/Control Number: 19/039,939 Page 36 Art Unit: 3665 Application/Control Number: 19/039,939 Page 37 Art Unit: 3665 Application/Control Number: 19/039,939 Page 38 Art Unit: 3665 Application/Control Number: 19/039,939 Page 39 Art Unit: 3665 Application/Control Number: 19/039,939 Page 40 Art Unit: 3665 Application/Control Number: 19/039,939 Page 41 Art Unit: 3665 Application/Control Number: 19/039,939 Page 42 Art Unit: 3665 Application/Control Number: 19/039,939 Page 43 Art Unit: 3665 Application/Control Number: 19/039,939 Page 44 Art Unit: 3665 Application/Control Number: 19/039,939 Page 45 Art Unit: 3665 Application/Control Number: 19/039,939 Page 46 Art Unit: 3665 Application/Control Number: 19/039,939 Page 47 Art Unit: 3665 Application/Control Number: 19/039,939 Page 48 Art Unit: 3665 Application/Control Number: 19/039,939 Page 49 Art Unit: 3665 Application/Control Number: 19/039,939 Page 50 Art Unit: 3665 Application/Control Number: 19/039,939 Page 51 Art Unit: 3665 Application/Control Number: 19/039,939 Page 52 Art Unit: 3665 Application/Control Number: 19/039,939 Page 53 Art Unit: 3665 Application/Control Number: 19/039,939 Page 54 Art Unit: 3665 Application/Control Number: 19/039,939 Page 55 Art Unit: 3665 Application/Control Number: 19/039,939 Page 56 Art Unit: 3665 Application/Control Number: 19/039,939 Page 57 Art Unit: 3665
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