DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
Figures 1, 2, and 4-6 are objected to as depicting a block diagram without “readily identifiable” descriptors of each block, as required by 37 CFR 1.84(n). Rule 84(n) requires “labeled representations” of graphical symbols, such as blocks; and any that are “not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.” In the case of figures 1 (num. 122, 124, and 126), 2, and 4-6, the blocks are not readily identifiable per se and therefore require the insertion of text that identifies the function of that block. That is, each vacant block should be provided with a corresponding label identifying its function or purpose.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the length is greater than 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an image acquisition module”, “a critical point determination module”, and “an indication sign determination module” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed towards a “machine-readable storage medium”, and the broadest reasonable interpretation of the instant claims in light of the specification encompasses transitory signals. But, transitory signals are not within one of the four statutory categories (i.e. non-statutory subject matter). See MPEP 2106.03.
However, claims directed towards non-transitory computer readable medium which exclude transitory forms of signal transmission may qualify as a manufacture and make the claim patent-eligible subject matter. MPEP 2106.03.
Claims 1-5 and 9-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of mental process type abstract idea (concepts performable in the human mind, including an observation, evaluation, judgement, and opinion) without significantly more.
Independent claims 1 and 9 recite the subject matter,
“determining, based on the image, locations and categories of critical points of the constituent elements; and determining, based on the categories of the critical points, semantics of the indication signs in response to the locations of the critical points satisfying a location condition” (claim 1); and
“determine, based on the image, locations and categories of critical points of the constituent elements; … determine, based on the categories of the critical points, semantics of the indication signs in response to the locations of the critical points satisfying a location condition” (claim 9).
The noted subject matter of claims 1 and 9 refers to determining locations and categories of points and determining semantics of the indication signs in response to the determined locations of the points satisfying a location condition, which are described with a high level of generality that a person may practically perform in the human mind by viewing an image and recognizing the locations of points of an indication sign. Thus, the broadest reasonable interpretation, in light of the specification, of the claimed subject matter directs to performing mental observations and evaluations, falling within the “mental processes” grouping of abstract ideas.
The judicial exceptions of claims 1 and 8 are not integrated into a practical application because the additional claim limitations of, “acquiring an image comprising the indication signs, constituent elements of the indication signs comprising at least one of an arrow element and a line segment element” (claim 1) and “acquire an image comprising the indication signs, the constituent elements of the indication signs comprising at least one of an arrow element and a line segment element” (claim 9); describe the data gathering steps and selecting particular data source or type of data to be manipulated, such that the claims merely add insignificant extra-solution activity to the judicial exceptions. See MPEP 2106.04(d) and MPEP 2106.05(g).
The judicial exception of claim 9 is further not integrated into a practical application because the additional claim limitations of, “an image acquisition module”, “a critical point determination module”, and “an indication sign determination module” describe generic units performing functions, which given its broadest reasonable interpretation, includes the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts to merely implementing the abstract idea on generic computing elements. See MPEP 2106.04(d), MPEP 2106.05(b), and MPEP 2106.05(f).
Claims 1 and 9 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass performing additional insignificant extra solution activity to the noted abstract idea activity, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(g). Claim 9 above noted additional claim limitations which describe generic units performing functions, which given their broadest reasonable interpretation, continue to merely encompass the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts to merely implementing the abstract idea on generic computing elements, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(b) and MPEP 2106.05(f).Furthermore, in consideration of the claims 1 and 9 additional elements as a combination, the additional elements continue to merely perform additional insignificant extra solution activity to the noted abstract idea activity and implementing the abstract idea on generic computing elements, and thus do not provide significantly more than the noted judicial exception.
Claims 2, 3 and 5 are not integrated into a practical application because the additional claim limitations of,
“determining, based on the image, areas of boundary frames of the indication signs; determining, based on the locations of the critical points and the areas of the boundary frames, whether the critical points are within the boundary frames; and determining, based on the categories of the critical points, the semantics of the indication signs in response to the critical points being within the boundary frames” (claim 2);
“determining, based on the image, parameters of the rotating rectangular frames, the parameters of the rotating rectangular frames comprising at least center point locations, dimension parameters, and angles; and determining, based on the parameters of the rotating rectangular frames, areas of the rotating rectangular frames” (claim 3);
“wherein the categories of the critical points further comprise invisible points, and determining, based on the image, the locations and the categories of the critical points of the constituent elements comprises: determining whether the constituent elements have a blocked area; determining whether the critical points are present in the blocked area in response to the constituent element having the blocked area; and determining, based on the image, the locations and categories of the critical points in response to the critical points being present in the blocked area, the categories of the critical points being the invisible points” (claim 5)
refer to additional steps of the noted mental process type abstract idea that are described with a high level of generality such that a person may continue to practically perform the broadest reasonable interpretation of the claimed steps within the human mind. See MPEP 2106.04(a)(2) III. If the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017). See MPEP 2106.04 II. A. 2.
Claims 2, 3 and 5 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass performing respective additional steps of the mental processes type abstract idea, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.04(a)(2) III. Furthermore, in consideration of the claims 2, 3 and 5 additional elements as a combination, the additional elements continue to merely perform respective additional steps of the mental process activity, and do not provide significantly more than the noted judicial exception.
Claim 4 is not integrated into a practical application because the additional claim limitations of, “wherein the categories of the critical points comprise at least one of: a bottom left corner point of a straight arrow, a bottom right corner point of the straight arrow, a vertex of the straight arrow; a bottom left corner point of a turn arrow, a bottom right corner point of the turn arrow, a vertex of the turn arrow; a bottom left corner point of a U-turn arrow, a bottom right corner point of the U-turn arrow, a vertex of the U-turn arrow, an inflection point of the U-turn arrow; and an endpoint of the line segment element” describe selecting particular data source or type of data to be manipulated, such that the claim merely add insignificant extra-solution activity to the judicial exceptions. See MPEP 2106.04(d) and MPEP 2106.05(g).
Claim 4 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass performing additional insignificant extra solution activity to the noted abstract idea activity, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(g). Furthermore, in consideration of the claim 4 additional elements as a combination, the additional elements continue to merely perform additional insignificant extra solution activity to the noted abstract idea activity, and do not provide significantly more than the noted judicial exception.
Claims 10 and 12 are not integrated into a practical application because the additional claimed subject matter of a “controller, comprising: at least one processor; and a memory, coupled to the at least one processor, and having instructions stored thereon” (claim 10); and
a “machine-readable storage medium having machine-executable instructions stored thereon, wherein the machine-executable instructions are executed by a processor” (claim 12),
which given its broadest reasonable interpretation, describes the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts to merely implementing the abstract idea on generic computing elements. See MPEP 2106.04(d), MPEP 2106.05(b), and MPEP 2106.05(f).
Claims 10 and 12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely encompass the use of generic computing elements to implement the noted abstract idea with a high level of generality, such that the claims amounts to merely implementing the abstract idea on generic computing elements, and are insufficient to provide significantly more than the noted judicial exceptions. See MPEP 2106.05(b) and MPEP 2106.05(f).Furthermore, in consideration of the claims 10 and 12 additional elements as a combination, the additional element continue to merely implement the abstract idea on generic computing elements, and thus do not provide significantly more than the noted judicial exception.
Claim 11 is not integrated into a practical application because the additional claim limitations of a “vehicle comprising the controller according to claim 10” merely recites the noted judicial exception being comprised in a vehicle, which generally links the noted judicial exception to a field of use in a vehicle. See MPEP 2106.04(d) and MPEP 2106.05(h).
Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the above noted additional claim limitations, similarly as discussed above, continue to merely describe generally linking the noted abstract idea to the field of use of a vehicle, and is insufficient to provide significantly more than the noted judicial exception. See MPEP 2106.05(h). Furthermore, in consideration of the claim 11 additional elements as a combination, the additional elements continue to merely generally link the noted abstract idea to the field of use of a vehicle, and do not provide significantly more than the noted judicial exception.
Examiner notes that claim 6 recites additional features of “determining, based on the image, the locations and the categories of the critical points of the constituent elements comprises: determining, by a backbone network and a neck network of a trained neural network model based on the image, a corresponding feature map; and determining, by a first head network of the neural network model based on the feature map, the locations and the categories of the critical points”, which would apply or use 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. See MPEP 2106.04(d) and MPEP 2016.05(e).
Thus, claims 6-8 are directed to statutory eligible subject matter.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 4, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Moon et al. (KR 101261409), herein Moon.
Regarding claim 1, Moon discloses a method for detecting indication signs, comprising:
acquiring an image comprising the indication signs, constituent elements of the indication signs comprising at least one of an arrow element and a line segment element (see Moon [0046], where an image is captured including road markings; see Moon Fig. 5 and [040], where the road markings that are the subject of recognition include selecting left turn, no left turn, no turn, straight, no straight, and U-turn markings, which include arrows and line segment elements);
determining, based on the image, locations and categories of critical points of the constituent elements (see Moon [0063]-[0068], where contours are extracted from the perspective transformed image and corner detection is performed to identify candidate road marking regions); and
determining, based on the categories of the critical points, semantics of the indication signs in response to the locations of the critical points satisfying a location condition (see Moon [0070]-[0077], where pattern recognition is performed on the extracted candidate road marking areas recognize captured road markings).
Although Moon does not explicitly disclose all features within the same embodiment, Moon does provide the various teachings as relevant embodiments and will occur to those skilled in the art, that various combinations of the disclosed embodiments of the invention described herein may be employed in practicing the invention.
Thus, one of ordinary skill in the art, in view of the suggested disclosed teachings of the embodiments of Moon, would have found it obvious and led to combine the disclosed features and arrive at the claimed invention.
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In this instance, Moon provide the various teachings as relevant embodiments and will occur to those skilled in the art, that various combinations of the disclosed embodiments of the invention described herein may be employed in practicing the invention.
One of ordinary skill in the art would have reasonable expectation of success of combining the disclosed features in the same embodiment for employing the disclosed road surface marking recognition device.
Regarding claim 4, please see the above rejection of claim 1. Moon discloses the method according to claim 1, wherein the categories of the critical points comprise at least one of: a bottom left corner point of a straight arrow, a bottom right corner point of the straight arrow, a vertex of the straight arrow; a bottom left corner point of a turn arrow, a bottom right corner point of the turn arrow, a vertex of the turn arrow; a bottom left corner point of a U-turn arrow, a bottom right corner point of the U-turn arrow, a vertex of the U-turn arrow, an inflection point of the U-turn arrow; and an endpoint of the line segment element (see Moon Fig. 4, Fig. 5, [0039]-[0040], and [0062]-[0070], where various road markings can be recognized and the road markings are recognized from extracted contours and corners of the road markings).
Regarding claim 9, Moon discloses the device for detecting indication signs, comprising: Regarding claim 9, it recites a device performing the method of claim 1. Moon teach a device performing the method of claim 1 (see Moon [0023], where the road surface marking recognition device may be operated as an independent server computer or installed on the image service device). Please see above for detailed claim analysis, with the exception to the following further limitations:
an image acquisition module, a critical point determination module, and an indication sign determination module (see Moon [0023], where the road surface marking recognition device may be operated as an independent server computer or installed on the image service device).
Please see the above rejection for claim 1, as the rationale to combine the teachings of Moon are similar, mutatis mutandis.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Moon as applied to claim 1 above, and further in view of Greenhalgh et al. (“Automatic Detection and Recognition of Symbols and Text on the Road Surface”), herein Greenhalgh.
Regarding claim 2, please see the above rejection of claim 1. Moon does not explicitly disclose the method according to claim 1, wherein determining, based on the categories of the critical points, the semantics of the indication signs comprises:
determining, based on the image, areas of boundary frames of the indication signs;
determining, based on the locations of the critical points and the areas of the boundary frames, whether the critical points are within the boundary frames; and
determining, based on the categories of the critical points, the semantics of the indication signs in response to the critical points being within the boundary frames.
Greenhalgh teaches in a related and pertinent method for automatic detection and recognition of road markings (see Greenhalgh Abstract), where detected candidate regions are fitted with a rotated minimum area rectangle which are used to determine features for sorting the candidates (see Greenhalgh sect. 3 Detection and Sorting of Candidate Regions).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Greenhalgh to the teachings of Moon, such that the detected road marking candidate areas are further fitted with a rotated minimum area rectangle to further determine features for sorting the candidate road markings.
This modification is rationalized as an application of a known technique to a known method ready for improvement to yield predictable results
In this instance, Moon teaches a base method for recognizing road markings in captured images by extracted contours and corner points of the road marking and identifying candidate areas.
Greenhalgh teaches a known technique for detecting and recognizing road markings, where detected candidate regions are fitted with a rotated minimum area rectangle which are used to determine features for sorting the candidates.
One of ordinary skill in the art would have recognized that by applying Greenhalgh’s technique to the teachings of Moon, the detected road marking candidate areas are further fitted with a rotated minimum area rectangle to further determine features for sorting the candidate road markings, predictably leading to an improved road marking recognizing method.
Regarding claim 3, please see the above rejection of claim 2. Moon and Greenhalgh disclose the method according to claim 2, wherein the boundary frames are rotating rectangular frames, and determining, based on the image, the areas of the boundary frames of the indication signs comprises:
determining, based on the image, parameters of the rotating rectangular frames, the parameters of the rotating rectangular frames comprising at least center point locations, dimension parameters, and angles; and determining, based on the parameters of the rotating rectangular frames, areas of the rotating rectangular frames (see Greenhalgh sect. 3 Detection and Sorting of Candidate Regions, Fig. 4 and Table 1, where the fitted rotated rectangles have angle, dimension, and position parameters, where the area of the rectangles can be determined).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Moon as applied to claim 1 above, and further in view of Wen et al. (“A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds”), herein Wen.
Regarding claim 5, please see the above rejection of claim 4. Moon does not explicitly disclose the method according to claim 4, wherein the categories of the critical points further comprise invisible points, and determining, based on the image, the locations and the categories of the critical points of the constituent elements comprises:
determining whether the constituent elements have a blocked area;
determining whether the critical points are present in the blocked area in response to the constituent element having the blocked area; and
determining, based on the image, the locations and categories of the critical points in response to the critical points being present in the blocked area, the categories of the critical points being the invisible points.
Wen teaches in a related and pertinent deep learning based framework for road marking extraction, classification, and completion (see Wen Abstract), where a trained deep learning based image completion translates an image with an incomplete road marking to an image with a complete road marking (see Wen sect. 3.3. Joint learning and context completion).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Wen to the teachings of Moon, such that obstructed or incomplete road markings can be translated to complete road markings for recognition.
This modification is rationalized as an application of a known technique to a known method ready for improvement to yield predictable results
In this instance, Moon teaches a base method for recognizing road markings in captured images by extracted contours and corner points of the road marking and identifying candidate areas.
Wen teaches a known technique for road marking extraction, classification and completion, where a trained deep learning based image completion translates an image with an incomplete road marking to an image with a complete road marking.
One of ordinary skill in the art would have recognized that by applying Wen’s technique to the teachings of Moon, obstructed or incomplete road markings can be translated to complete road markings for recognition, predictably leading to an improved road marking recognizing method.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Moon as applied to claim 1 above, and further in view of Qin et al. (“MRDet: A Multihead Network for Accurate Rotated Object Detection in Aerial Images”), herein Qin.
Regarding claim 6, please see the above rejection of claim 1. Moon does not explicitly disclose the method according to claim 1, wherein determining, based on the image, the locations and the categories of the critical points of the constituent elements comprises: determining, by a backbone network and a neck network of a trained neural network model based on the image, a corresponding feature map; and determining, by a first head network of the neural network model based on the feature map, the locations and the categories of the critical points.
Qin teaches in a related and pertinent arbitrary oriented region proposal network to generate oriented proposals (see Qin Abstract), where the MRDet network model uses a FPN with ResNet as the backbone network to obtain candidate regions and provide a feature map for producing classification scores, center locations, scales, and orientations of bounding boxes (see Qin Fig. 1 , sect. B. RRoI Align and sect. B. Implementation Details).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Qin to the teachings of Moon, such that the MRDet network model is used to provide region proposals and classification to the road markings in the captured images.
This modification is rationalized as an application of a known technique to a known method ready for improvement to yield predictable results
In this instance, Moon teaches a base method for recognizing road markings in captured images by extracted contours and corner points of the road marking and identifying candidate areas.
Qin teaches a known technique for object detection where the MRDet network model uses a FPN with ResNet as the backbone network to obtain candidate regions and provide a feature map for producing classification scores, center locations, scales, and orientations of bounding boxes.
One of ordinary skill in the art would have recognized that by applying Qin’s technique to the teachings of Moon, the MRDet network model is used to provide region proposals and classification to the road markings in the captured images, predictably leading to an improved road marking recognizing method.
Regarding claim 7, please see the above rejection of claim 6. Moon and Qin disclose the method according to claim 6, wherein determining, based on the categories of the critical points, the semantics of the indication signs comprises:
determining, by a second head network of the neural network model based on the feature map, parameters of rotating rectangular frames external to the indication signs, the parameters of the rotating rectangular frames comprising at least center point locations, dimension parameters, and angles (see Qin Fig. 1 and sect. III. PROPOSED METHOD, and sect. C. Multihead Network, where the detections produce center locations, scales, and orientations of bounding boxes);
determining, based on the locations of the critical points and the parameters of the rotating rectangular frames, whether the critical points are within the rotating rectangular frames (see Qin sect. C. Multihead Network, where fully connected heads are used for classification and orientation regression); and determining, based on the categories of the critical points, the semantics of the indication signs in response to the critical points being within the rotating rectangular frame (see Qin sect. C. Multihead Network, where fully connected heads are used for classification; see Moon [0070]-[0077], where pattern recognition is performed on the extracted candidate road marking areas to recognize captured road markings).
Regarding claim 8, please see the above rejection of claim 7. Moon and Qin disclose the method according to claim 7, wherein a training method of the neural network model comprises: determining, based on the first head network, a first offset amount corresponding to the locations of the critical points (see Qin, sect. C. Multihead Network, where during the training stage, differences between the candidate, prediction and ground truth of the parameters are computed);
determining, based on the second head network, a second offset amount corresponding to the center point locations; and adjusting, based on the first offset amount and the second offset amount, parameters in the neural network model, such that the first offset amount and the second offset amount satisfy a convergence condition (see Qin, sect. C. Multihead Network, where during the training stage, where training the network uses cross-entropy loss function for classification and smooth L1 loss function for regression in three sibling heads).
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Moon as applied to claim 1 above, and further in view of Cox et al. (US 2022/0270358), herein Cox.
Regarding claim 10, Moon does not explicitly disclose a controller, comprising: at least one processor; and a memory, coupled to the at least one processor, and having instructions stored thereon that, when executed by the at least one processor, cause the controller to perform the method according to claim 1.
Cox teaches in a related and pertinent devices and systems for a vehicular sensor system to detect and classify objects (see Cox Abstract), where a device for performing the disclosed teachings includes a CPU and memory (see Cox [0031] and [0140]-[0142]).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Cox to the teachings of Moon, such that a device with a processor and memory are configured to implement the disclosed teachings.
This modification is rationalized as an application of a known technique to a known method ready for improvement to yield predictable results
In this instance, Moon teaches a base method for recognizing road markings in captured images by extracted contours and corner points of the road marking and identifying candidate areas.
Cox teaches a known technique for performing the disclosed teachings on a device including a CPU and memory.
One of ordinary skill in the art would have recognized that by applying Cox’s teachings to the teachings of Moon, the disclosed teachings can be implemented on the device with a processor and memory, predictably leading to an improved road marking recognizing method.
Regarding claim 11, Moon and Cox disclose a vehicle, comprising the controller according to claim 10 (see Cox [0031]-[0033] and [0140]-[0142], where the device may be included and mounted in a vehicle).
Regarding claim 12, Moon and Cox disclose a machine-readable storage medium having machine-executable instructions stored thereon, wherein the machine-executable instructions are executed by a processor to implement the method according to claim 1 (see Cox [0031] and [0140]-[0142]).).
Please see the above rejection for claim 10, as the rationale to combine the teachings of Moon and Cox are similar, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY WING HO CHOI whose telephone number is (571)270-3814. The examiner can normally be reached 9:00 AM to 5:00 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, VINCENT RUDOLPH can be reached at (571) 272-8243. 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.
/TIMOTHY CHOI/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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