DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
The preliminary amendment filed 11/18/2024 have been entered and made of record. The Applicant has canceled claim(s) 9-10. The application has pending claim(s) 1-8.
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 Objections
Claims 5-6 and 8 are objected to because of the following informalities:
Claim 5 at line 6: “GAT;” should be -- Graph Attention Network (GAT); --.
Claim 6 at line 6: “the object” should be -- an object --.
Claim 6 at line 7: “the object” should be -- the selected object --.
Claim 8 at line 2: “wherein providing the reference map comprises:” should be -- further comprising providing the reference map by: --.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 4 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. More specifically, the claim limitation “extracting features … by a first Resnet; extracting features … by a first PointNet; and providing the extracted features to a common PointNet” are drafted as unlimited functional claim limitations that extend to all means or methods of resolving a problem that are not adequately supported by the written description (See MPEP 2173.05(g)). The Examiner suggests incorporating the subject matter of claim 5’s Graph Attention Network (GAT) respectively as supported by Figure 10 which depicts the architecture of a Resnet, a PointNet, and also a GAT feeding into the common PointNet.
The Examiner notes that claim 5 resolves claim 4’s 112(a) rejection.
Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Re Claim 7 at lines 2-4: The claim limitations “the landmark map network part” and “the object of the measurement map part” lack antecedent basis. Therefore “wherein determining the similarity between the features of the object of the landmark map network part and the features of the object of the measurement map part comprises” should be -- wherein determining the similarity comprises --.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the method of independent claim 1 is directed to one of the four statutory categories of eligible subject matter; thus, the claim passes Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, prong 1 analysis
The independent claims are directed to “determining segmentations represented by a vertex of the measurement image and neighborhood graphs, wherein the neighborhood graph comprises the vertex and edges containing information to identify neighboring vertices of the vertex, to obtain a measurement map; comparing segmentations of a reference map comprising segmentations of a reference image with landmarks to the segmentations represented by the vertices of the measurement image and the neighborhood graphs; and determining segmentations contained in the reference map and in the measurement image; and estimating the position of the vehicle based on the reference map during movement of the vehicle based on a result of the comparison”.
Each of the above limitations of “determining segmentations represented by a vertex of the measurement image and neighborhood graphs, wherein the neighborhood graph comprises the vertex and edges containing information to identify neighboring vertices of the vertex, to obtain a measurement map”, “comparing segmentations of a reference map comprising segmentations of a reference image with landmarks to the segmentations represented by the vertices of the measurement image and the neighborhood graphs”, “determining segmentations contained in the reference map and in the measurement image”, and “estimating the position of the vehicle based on the reference map during movement of the vehicle based on a result of the comparison” as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the human mind which falls within the “Mental Processes” grouping of abstract ideas.
Additional elements
The additional elements recited in independent claim 1 are “capturing a measurement image of a vehicle environment”.
Step 2A, prong 2 analysis
The above-identified additional elements do not integrate the judicial exception into a practical application.
The step “capturing a measurement image of a vehicle environment” merely constitutes activity involving data gathering. Such extra-solution activity does not integrate the abstract idea into a practical application. Please see MPEP §2106.05(g).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the step of “capturing a measurement image of a vehicle environment” amounts to insignificant extra-solution activity. Such insignificant extra-solution activity does not constitute significantly more than the claimed data gathering (See MPEP 2106.05(g)).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claim 1 does not recite eligible subject matter under 35 USC 101.
Regarding Dependent Claims 2-4 and 8:
Claims 2-4 and 8 are dependent on corresponding independent claim 1 respectively and therefore include all the limitations of corresponding independent claim 1. Thus claims 2-4 and 8 recite “Mental Processes”. Further, claims 2-4 and 8 further describe:
Dependent claims 2 merely describes “wherein comparing … comprises performing a rough localization to determine a road partition … and selecting a reference image …” which are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the human mind which falls within the same “Mental Processes” grouping of abstract ideas and it does not integrate the abstract idea into a practical application or add significantly more.
Dependent claims 3 merely describes “wherein comparing … comprises selecting an object from a set of objects contained in an image, and wherein a segmentation represents an object” which are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the human mind which falls within the same “Mental Processes” grouping of abstract ideas and it does not integrate the abstract idea into a practical application or add significantly more.
Dependent claim 4 merely describes “wherein comparing … comprises: … extracting features from a segmentation of a selected object … extracting features from the LiDAR data of the selected object …” which are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the human mind which falls within the same “Mental Processes” grouping of abstract ideas and it does not integrate the abstract idea into a practical application or add significantly more; and further merely describes “capturing real-time LiDAR data of the vehicle environment … and providing the extracted features …” which are processes that, under broadest reasonable interpretation, merely constitute insignificant extra-solution activity [data gathering and data outputting] and it does not integrate the abstract idea into a practical application or add significantly more; and further merely describes “by a first Resnet … by a first PointNet … to a common PointNet” which are processes that due to their broad generality amount to merely using a generic computer as a tool to implement generic computer functions [e.g. using a Resnet and PointNet(s)] that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system which neither integrates the abstract idea into a practical application nor adds significantly more.
Dependent claim 8 merely describes “wherein providing the reference map comprises: mapping the LiDAR data points to the reference image; determining objects … and determining segmentations … constructing a graph topological landmark map …” which are processes that due to their broad generality, under broadest reasonable interpretation, covers the performance of the limitation in the human mind which falls within the same “Mental Processes” grouping of abstract ideas and it does not integrate the abstract idea into a practical application or add significantly more; and further merely describes “capturing LiDAR data points and a reference image …” which are processes that, under broadest reasonable interpretation, merely constitute insignificant extra-solution activity [data gathering] and it does not integrate the abstract idea into a practical application or add significantly more; and further merely describes “using a semantic segmentation neural network” which are processes that due to their broad generality amount to merely using a generic computer as a tool to implement generic computer functions [e.g. using a neural network] that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system which neither integrates the abstract idea into a practical application nor adds significantly more
Thus, claims 2-4 and 8 do not recite eligible subject matter under 35 USC 101.
Regarding Claim 5 (and its dependent claims 6-7 respectively):
Claim 5 is dependent on claim 4 respectively and therefore includes all the limitations of claims 1 and 4. Thus claim 5 recites “Mental Processes”. Claim 5 further recites additional elements:
“wherein comparing the segmentations of the reference map with the segmentations represented by the vertices of the measurement image and the neighborhood graphs comprises: extracting features from neighbor segmentations of the segmentation of the selected object and providing the extracted features to a GAT; extracting features from LiDAR points cloud of a neighboring object of the selected object and providing the extracted features to the GAT; describing the extracted features containing spatial information; and providing the described reference image features to the common PointNet”.
The combination of the additional elements integrates the “Mental Processes” abstract idea into a practical application. Specifically, as discussed in paragraphs [0027], [0070]-[0071] of the originally filed specification and Figure 10 of the subject application, the architecture outputting the outputs of the Resnet, PointNet, and GAT to the common PointNet improves the position determination and estimation. As such, the additional elements of claim 5 [in combination with all the limitations of claims 1 and 4] integrate the “Mental Processes” into a practical application. Therefore, claim 5 recites eligible subject matter.
Claims 6-7 are dependent on claim 5 respectively and therefore also recite eligible subject matter by virtue of their dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ivanov (US 2021/0327084 A1, provided by Applicant’s Information Disclosure Statement IDS) in view of Saito (US 2018/0252548 A1).
Claim 1: Ivanov discloses a method of determining a position of a vehicle (see Ivanov, Fig. 1, [0031], Fig. 3, [0052]-[0053], determining an estimated position and/or estimated pose of the mobile vehicle), the method comprising: capturing a measurement image of a vehicle environment (see Ivanov, Fig. 1, [0031], Fig. 3, [0052]-[0053], capture an image surrounding the mobile vehicle); determining segmentations represented by a vertex of the measurement image and neighborhood graphs, wherein the neighborhood graph comprises the vertex and edges containing information to identify neighboring vertices of the vertex (see Ivanov, Fig. 3, [0057], segmentation analysis of the image, wherein the road segment data records are links or segments, e.g., maneuvers of a maneuver graph, representing roads, streets, or paths, and wherein node data are end points corresponding to the respective links or segments of the road segment data); comparing segmentations of a reference map comprising segmentations of a reference image with landmarks to the segmentations represented by the vertices of the measurement image and the neighborhood graphs (see Ivanov, Fig. 3, [0029], [0058]-[0059], comparing the attributes of the segments / sections from the segmentation analysis of the image with attributes [e.g., building, sky, road surface, ground, other corresponding to landmarks] of the segments / sections from the analysis of the artificial reference image, [0096], road segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, speed limits, turn restrictions at intersections, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, buildings, stores, parks, etc [corresponding to landmarks]); and determining segmentations contained in the reference map and in the measurement image (see Ivanov, Fig. 3, [0058]-[0060], substantially matching is identified); and estimating the position of the vehicle based on the reference map during movement of the vehicle based on a result of the comparison (see Ivanov, Fig. 3, [0063], determining an estimated position and/or estimated pose of the mobile vehicle based at least in part on the image position and/or image pose corresponding to the identified substantially matching artificial reference image).
However Ivanov fails to explicitly disclose where Saito discloses determining segmentations represented by a vertex and neighborhood graphs, wherein the neighborhood graph comprises the vertex and edges containing information to identify neighboring vertices of the vertex, to obtain a measurement map (see Saito, [0035], [0050]-[0054], map generator).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ivanov’s method using Saito’s teachings by including the map generator to Ivanov’s segmentation analysis in order to improve the representation of the segments in the graph (see Saito, [0035], [0050]-[0054]).
Re Claim 2: Ivanov further discloses wherein comparing the segmentations of the reference map to the segmentations represented by the vertices of the measurement image and the neighborhood graphs comprises performing a rough localization to determine a road partition on which the vehicle moves and selecting a reference image from the reference map related to the road partition (see Ivanov, Fig. 3, [0028]-[0029], [0036], [0058]-[0059], localization by comparing the attributes of the segments / sections from the segmentation analysis of the image with attributes [e.g., building, road surface, ground, etc.] of the segments / sections from the analysis of the artificial reference image, [0096], road segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, buildings, etc.).
Re Claim 3: Ivanov further discloses wherein comparing the segmentations of the reference map with the segmentations represented by the vertices of the measurement image and the neighborhood graphs comprises selecting an object from a set of objects contained in an image, and wherein a segmentation represents an object (see Ivanov, Fig. 3, [0028]-[0029], [0036], [0058]-[0059], [0096], comparing the attributes of the segments / sections from the segmentation analysis of the image with attributes [e.g., building, road surface, ground, etc.] of the segments / sections from the analysis of the artificial reference image, wherein the segmentation models determine and select an attribute [e.g., building, road surface, ground, etc.] of respective sections of the first image).
Re Claim 8: Ivanov as modified by Saito further discloses wherein providing the reference map comprises: capturing LIDAR data points and a reference image along a road for a road partition (see Ivanov, Fig. 3, [0037], capturing lidar point cloud surrounding the mobile vehicle, [0029], [0058]-[0059], providing the artificial reference image, [0096], road segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, etc.); mapping the LIDAR data points to the reference image (see Ivanov, Fig. 3, [0037], [0058]-[0060], substantially matching is identified where sections of the artificial reference image and the lidar point cloud match); determining objects on the reference image and determining landmark segmentations from the reference image using a semantic segmentation neural network (see Ivanov, Fig. 3, [0029], [0057]-[0059], attributes [e.g., building, sky, road surface, ground, other corresponding to landmarks] of the segments / sections from the analysis of the artificial reference image, [0096], road segments and nodes can be associated with attributes, such as geographic coordinates, street names, address ranges, buildings, etc. [corresponding to landmarks], wherein neural network segmentation models determine attributes); and constructing a graph topological landmark map containing vertices corresponding each to a segmentation and edges, wherein an edge identifies neighboring vertices of a vertex (see Saito, [0035], [0050]-[0054], map generator generates a graph containing vertices and edges of the road segments). See claim 1 for obviousness and motivation statements.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ivanov as modified by Saito, and further in view of Xie et al (“Large-Scale Place Recognition Based on Camera-LiDAR Fused Descriptor” – Sensors 2020 – 5/19/2020 – pages 1-21, provided by Applicant’s Information Disclosure Statement IDS). The teachings of Ivanov as modified by Saito have been discussed above.
Re Claim 4: However Ivanov as modified by Saito fails to explicitly disclose where Xie discloses wherein comparing comprises capturing real-time LiDAR data of the vehicle environment (see Xie, Fig. 5, first paragraph of Section 3, Section 3.1.1, 3D point cloud data acquired from LiDAR); extracting features from a segmentation of a selected object by a first and Resnet (see Xie, Fig. 5, first paragraph of Section 3, first paragraph of Section 3.2, Image Feature Extraction using ResNet); extracting features from the LiDAR data of the selected object by a first PointNet (see Xie, Fig. 5, first paragraph of Section 3, Section 3.1.1, Local feature extraction from the 3D point cloud data acquired from LiDAR via PointNet); and providing the extracted features to a common FC layer (see Xie, Fig. 5, first paragraph of Section 3, first two paragraphs of Section 3.3, Fused global descriptor via Fully Connected Concatenate).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Ivanov’s method, as modified by Saito, using Xie’s teachings by including similarity evaluation process to Ivanov’s comparing process in order to improve the similarity evaluation between features / attributes (see Xie, Fig. 5, first paragraph of Section 3, first two paragraphs of Section 3.3, Fused global descriptor via Fully Connected Concatenate).
Further, the Examiner takes Official Notice that it would have been exceedingly obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Ivanov's method [as modified by Saito and Xie] by including the limitations of a PointNet as part of the architecture to perform the common FC layer processing for the fused global descriptor [as taught by Xie as discussed above]. These limitations are exceedingly well known and typical in the image vision deep learning field of endeavor and therefore would be exceedingly obvious modifications toward Ivanov's method [as modified by Saito and Xie] in order to broaden the applicability of Ivanov's method [as modified by Saito and Xie] and provide improved similarity evaluation results.
Allowable Subject Matter
Claims 5-7 [claims 6-7 are dependent upon claim 5 respectively] 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zheng et al ‘429 discloses route computation and trajectories by constructing the landmark graph based at least in part on map matching and landmark graph building; Kroepfl et al ‘200 discloses map creation and localization for autonomous driving; Vorlander ‘530 discloses route determination using graphs comprising road segments with vertices and edges.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD KRASNIC whose telephone number is (571)270-1357. The examiner can normally be reached Mon. - Thur. and every other Friday from 8am - 4pm.
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/Bernard Krasnic/Primary Examiner, Art Unit 2671 July 17, 2026
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