DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are presented for examination.
Claims 1-5, 9-20 are rejected.
Claims 6-8 are objected to.
Examiner’s Note
Here, regarding contingent or conditional clauses, the examiner applies the guidance of MPEP 2111.04, II. and the PTAB Decision in Ex parte RANDAL C. SCHULHAUSER et al. (Precedential), Appeal 2013-007847, decided 28 April 2016, where the Board decided:
"A proper interpretation of claim language, under the broadest reasonable interpretation of a claim during prosecution, must construe the claim language in a way that at least encompasses the broadest interpretation of the claim language for purposes of infringement. . . . [In a method claim, if] the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed. . . . [However, the] broadest reasonable interpretation of a system claim having structure that performs a function, which only needs to occur if a condition precedent is met, still requires structure for performing the function should the condition occur. This interpretation of the system claim differs from the method claim because the structure [] is present in the system regardless of whether the condition is met and the function is actually performed. Unlike [the method claim], which is written in a manner that does not require all of the steps to be performed should the condition precedent not be met, [the system claim] is limited to the structure capable of performing all the recited functions."
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 9-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Behrendt et al. (US Pub. No.: 2020/0240790 A1: hereinafter “Behrendt”).
Consider claims 1, 17:
Behrendt teaches a computing system (Figs. 1-3 elements 10-206, steps 208-314), a method of processing a first digital map and a second digital map (See Behrendt, e.g., “…localizing an entity includes a processing system with at least one processing device…obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors…produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors…configured to extract sets of localization features from the sensor data of the second set of sensors...generate visualization images in which each visualization image includes a respective set of localization features…generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), the method comprising the following steps performed by at least one integrated circuit (Figs. 1-3 elements 10-206, steps 208-314): using the first digital map to generate a first ordered array of pixel values, the first ordered array of pixel values representing a first digital map object in a region contained in both the first digital map and the second digital map the first digital map object being included in the first digital map (See Behrendt, e.g., “…receive the sensor data and the map data simultaneously or at different times. Upon receiving the sensor data (or the sensor data and the map data), the processing system 130 proceeds to step 304. Also, upon receiving the sensor data in real-time, the processing system 130 proceeds to step 306. In an example embodiment, the processing system 130 is configured to perform step 304 and step 306 simultaneously or at different times…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604); using the second digital map to generate a second ordered array of pixel values, the second ordered array of pixel values representing a second digital map object in the region, the second digital map object being included in the second digital map (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604); and using a machine learning, ML, model to determine at least one score indicative of a likelihood that the first digital map object corresponds to the second digital map object (See Behrendt, e.g., “…the processing system 130 (e.g., the DNN module 180) is configured to employ the trained DNN model to optimize the registration of at least the map image 400 with the visualization image 402 in real-time. In this regard, FIG. 4D illustrates a non-limiting example of a representation 410 of an optimized registration of at least the map image 400 with the visualization image 402. As shown in FIG. 4D, when the map image 400 is optimally registered with the visualization image 402, the map features (e.g., lane-markings 400A) are aligned with the localization features (e.g., lane-markings 402A). In this case, the localization update data includes offset data, which indicates an offset between a position of the map image, as aligned via the localization estimate, relative to a position of the map image, as optimally registered via the trained DIN model…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), wherein the ML model has an input layer that receives at least the first ordered array of pixel values and the second ordered array of pixel values (See Behrendt, e.g., “…upon optimizing registration of the various images and determining the localization update data, the processing system 130 generates localization output data with high accuracy in real-time based on computations involving the localization estimate and the localization output data. In this regard, for example, the processing system 130 adjusts the localization estimate based on the offset data to provide the localization output data.…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), and wherein the ML model has an output layer that outputs the at least one score (See Behrendt, e.g., “…generate confidence output data. In an example embodiment, the confidence output data includes at least a confidence value, which provides a quantifiable assurance of reliability of the localization output data based on the current input. In this regard, for example, if there aren't enough map features or localization features to perform the image registration reliably, then the processing system 130 generates a low confidence value…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 2:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches wherein all pixel values of the first ordered array of pixel values and of the second ordered array of pixel values are selected from a group comprising a first set of one or more first pixel values and a second set of one or more second pixel values (See Behrendt, e.g., “…a map image of a mapping region that includes the localization estimate; extracting, via the processing system, sets of localization features from a second selection of sensor data; generating, via the processing system, visualization images, each visualization image including a respective set of localization features; generating, via the processing system, localization update data in-real-time via optimizing registration of the map image relative to the visualization images; generating, via the processing system, localization output data based on the localization estimate and the localization update data; and providing, via the processing system, the localization output data to an application system relating to navigation of the vehicle…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), wherein the first ordered array of pixel values and of the second ordered array of pixel values are generated such that, at a pixel, a pixel value of the first ordered array of pixel values is a pixel value included in the first set of one or more first pixel values if the first digital map object overlaps the pixel (See Behrendt, e.g., “…optimizing alignment of map features of the map image relative to corresponding sets of localization features of the visualization images: and computing offset data based on positions of the map image (i) when aligned based on the localization estimate and (ii) when aligned with the visualization images via the optimized registration…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), a pixel value of the first ordered array of pixel values is a pixel value included in the second set of one or more second pixel values if the first digital map object does not overlap the pixel (See Behrendt, e.g., “…optimizing alignment of map features of the map image relative to corresponding sets of localization features of the visualization images: and computing offset data based on positions of the map image (i) when aligned based on the localization estimate and (ii) when aligned with the visualization images via the optimized registration…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), a pixel value of the second ordered array of pixel values is a pixel value included in the first set of one or more first pixel values if the object overlaps the pixel, and a pixel value of the second ordered array of pixel values is a pixel value included in the second set of one or more second pixel values if the object does not overlap the pixel (See Behrendt, e.g., “…producing the map image includes generating a two-dimensional top-view image of the map region that is rendered based on the localization estimate; the step of generating the visualization images includes producing visualization images as two-dimensional top-views of respective sets of localization features; and the map image and each visualization image reside in the same coordinate system…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 3:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches wherein the first ordered array of pixel values and the second ordered array of pixel values are arrays of binary pixel values (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 4:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches wherein the input layer further receives a third ordered array of pixel values representing one or several additional first digital map objects in the region, the one or several one or several additional first digital map objects being included in the first digital map and being different from the first digital map object (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), and a fourth ordered array of pixel values representing one or several additional second digital map objects in the region, the one or several one or several additional second digital map objects being included in the second digital map and being different from the second digital map object (See Behrendt, e.g., “…localizing an entity includes a processing system with at least one processing device…obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors…produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors…configured to extract sets of localization features from the sensor data of the second set of sensors...generate visualization images in which each visualization image includes a respective set of localization features…generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claims 5, 18-19:
Behrendt teaches everything claimed as implemented above in the rejection of claims 1, 17-18. In addition, Behrendt teaches wherein the first digital map comprises first nodes and first edges interconnecting the first nodes (See Behrendt, e.g., “…localizing an entity includes a processing system with at least one processing device…obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors…produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors…configured to extract sets of localization features from the sensor data of the second set of sensors...generate visualization images in which each visualization image includes a respective set of localization features…generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), wherein the second digital map comprises second nodes and second edges interconnecting the second nodes, wherein the first digital map object is one of the first edges, and wherein the second digital map object is one of the second edges (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 9:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches further comprising: generating a three-dimensional tensor that comprises at least the first ordered array of pixel values and the second ordered array of pixel values (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), wherein the input layer receives the three-dimensional tensor, and wherein the three-dimensional tensor has size X×Y×Z, wherein X, Y, and Z are integers, wherein X and Y are greater than 1, and wherein Z is equal to or greater than 2, equal to or greater than 4, or equal to or greater than 8 (See Behrendt, e.g., “…automatically optimize the localization estimate by registering real-time detections (e.g., sets of localization features of the visualization images based on sensor data from sensor system) with map features of a localized map region. The system 100 is also advantageously configured to select and align detected features in the range/view of the vehicle 10 relative to map features of the map, e.g. when these features are determined to be detected in a correct or appropriate manner…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 10:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches wherein the ML model is a deep learning model, and/or wherein the method further comprises: training the ML model using training data, wherein the training data comprises labels (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), and wherein the labels indicate, for each of a plurality of sets of first and second edges of two distinct digital training maps, whether the first edge corresponds to the second edge (See Behrendt, e.g., “…with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 11:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches wherein the first digital map and/or second digital map comprises data generated using probe trace data (See Behrendt, e.g., “…the processing system 130 directly yields localization update data with high accuracy. Additionally, by employing the trained neural network, the processing system 130 is configured to determine and generate confidence output data for assessing and quantifying a confidence or assurance of the reliability of the trained DNN model's likely performance with respect to the localization output data. Advantageously, the processing system 130 is configured to run in real-time and generate the localization output data in a single shot or iteratively…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), and wherein the method further comprises using the at least one score to at least one of: determine whether a map update is to be performed (See Behrendt, e.g., “…the processing system 130 directly yields localization update data with high accuracy. Additionally, by employing the trained neural network, the processing system 130 is configured to determine and generate confidence output data for assessing and quantifying a confidence or assurance of the reliability of the trained DNN model's likely performance with respect to the localization output data. Advantageously, the processing system 130 is configured to run in real-time and generate the localization output data in a single shot or iteratively…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604); and initiate a map merging of the first digital map and the second digital map (See Behrendt, e.g., “…generate confidence output data. In an example embodiment, the confidence output data includes at least a confidence value, which provides a quantifiable assurance of reliability of the localization output data based on the current input. In this regard, for example, if there aren't enough map features or localization features to perform the image registration reliably, then the processing system 130 generates a low confidence value…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claims 12, 20:
Behrendt teaches everything claimed as implemented above in the rejection of claims 1, 17. In addition, Behrendt teaches further comprising: performing a map merging to generate a third digital map from the first digital map and the second digital map (See Behrendt, e.g., “…generate a localization estimate for the vehicle based on a first selection of sensor data from the sensor system; produce a map image of a mapping region that includes the localization estimate; extract sets of localization features from a second selection of sensor data from the sensor system; generate visualization images, each visualization image including a respective set of localization features; generate localization update data in real-time via optimizing registration of the map image relative to the visualization images; generate localization output data based on the localization estimate and the localization update data; and provide the localization output data to an application system relating to navigation of the vehicle…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604) wherein the map merging uses the score to determine whether the first digital map object in the first digital map corresponds to the second digital map object in the second digital map (See Behrendt, e.g., “…generate visualization images, each visualization image including a respective set of localization features; generate localization update data in real-time via optimizing registration of the map image relative to the visualization images; generate localization output data based on the localization estimate and the localization update data; and provide the localization output data to an application system relating to navigation of the vehicle…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 13:
Behrendt teaches everything claimed as implemented above in the rejection of claim 12. In addition, Behrendt teaches wherein the map merging comprises combining attributes of the first digital map object in the first digital map and attributes of the second digital map object in the second digital map if the score indicates that the first digital map object corresponds to the second digital map object (See Behrendt, e.g., “…generate visualization images, each visualization image including a respective set of localization features; generate localization update data in real-time via optimizing registration of the map image relative to the visualization images; generate localization output data based on the localization estimate and the localization update data; and provide the localization output data to an application system relating to navigation of the vehicle…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 14:
Behrendt teaches everything claimed as implemented above in the rejection of claim 13. In addition, Behrendt teaches further comprising at least one of: outputting the third digital map to at least one navigation device for use in a vehicle navigation operation or a vehicle control operation, wherein data of the third digital map is added in an additional layer of map data; using, by at least one server, the third digital map to perform a vehicle navigation operation; and providing the third digital map for storage in a map data repository (See Behrendt, e.g., “…generate confidence output data. In an example embodiment, the confidence output data includes at least a confidence value, which provides a quantifiable assurance of reliability of the localization output data based on the current input. In this regard, for example, if there aren't enough map features or localization features to perform the image registration reliably, then the processing system 130 generates a low confidence value…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 15:
Behrendt teaches everything claimed as implemented above in the rejection of claim 14. In addition, Behrendt teaches further comprising using, by at least one navigation device or control circuit onboard a vehicle, data of the third digital map to perform at least one: a vehicle control operation, wherein the vehicle control operation comprises an autonomous driving operation or a driving assist operation; a vehicle navigation operation, wherein the vehicle navigation operation comprises one or several of a route search, route guidance, generation of warnings or alarms (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Consider claim 16:
Behrendt teaches everything claimed as implemented above in the rejection of claim 1. In addition, Behrendt teaches further comprising: generating a third digital map by merging the first digital map and the second digital map (See Behrendt, e.g., “…generate visualization images, each visualization image including a respective set of localization features; generate localization update data in real-time via optimizing registration of the map image relative to the visualization images; generate localization output data based on the localization estimate and the localization update data; and provide the localization output data to an application system relating to navigation of the vehicle…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604); and using the at least one score to provide or update at least part of the digital map data of the navigation device or a vehicle control device (See Behrendt, e.g., “…generate confidence output data. In an example embodiment, the confidence output data includes at least a confidence value, which provides a quantifiable assurance of reliability of the localization output data based on the current input. In this regard, for example, if there aren't enough map features or localization features to perform the image registration reliably, then the processing system 130 generates a low confidence value…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604), wherein an additional digital map layer is stored in the digital map data of the navigation device, and wherein the additional digital map layer includes at least some data elements of the third digital map (See Behrendt, e.g., “…upon obtaining one or more visualization images and map images, the DNN module 180 is configured to generate localization output data based at least on the localization estimate and localization update data, which is provided via the trained DNN model. More specifically, with the trained DNN model, the DNN module 180 is configured to determine any discrepancy between the aligned rending of the map image based on the localization estimate and the various generated images (e.g., sensor images, visualization images, etc.). Also, by utilizing the trained DNN model, the DNN module 180 is configured to optimize the registration of the various images (e.g., visualization images, map images, etc.), which are input into the processing system 130 such that one or more of the map images and the visualization images are aligned…”, of Abstract, ¶ [0004]-¶ [0006], ¶ [0023]-¶ [0025], ¶ [0027]-¶ [0054], ¶ [0056]-¶ [0058], and Figs. 1-3 elements 10-206, steps 208-314, Figs. 5-6 elements 500-604).
Allowable Subject Matter
Claims 6-8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Further, the prior art on record fails to teach or suggest, either in singularity or in combination, the claimed subject matter of Claims 6-8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KUBOTA et al. (US Pub. No.: 2020/0380318 A1) teaches “An analysis method implemented by a computer includes: generating a refine image by changing an incorrect inference image such that a correct label score of inference is maximized, the incorrect inference image being an input image when an incorrect label is inferred in an image recognition process; and narrowing, based on a score of a label, a predetermined region to specify an image section that causes incorrect inference, the score of the label being inferred by inputting to an inferring process an image obtained by replacing the predetermined region in the incorrect inference image with the refine image.”
Miyajima (US Pat. No.: 8,452,103 B2) teaches “A scene matching reference data generation system inputs a set of probe data. The set of the probe data includes captured images sequentially obtained by a plurality of probe cars and the vehicle positions of the probe cars. The system temporarily stores the captured images, evaluates accuracy reliability degrees of the image-capturing positions of the captured images, and assigns the accuracy reliability degrees to the captured images. The system selects, as a plurality of processing target captured images, a plurality of the captured images having accuracy reliability degrees equal to or higher than a first predetermined degree, extracts image feature points from the selected processing target captured images, and generates image feature point data based on the extracted image feature points. The system generates reference data for scene matching by associating the generated image feature point data with a reference image-capturing position corresponding to the generated image feature point data.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667
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