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 .
Specification
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
Claim 6 is objected to because of the following informalities: Lines 2 - 4 of claim 6 recite, in part, “and the inputting the initial association matrix into the second association network to generate a target association matrix,” which appears to contain a minor informality. The Examiner suggests amending the claim to --and the inputting the initial association matrix into the second association network to generate [[a]] the target association matrix,-- in order to more clearly link back to the element(s) in lines 8 - 9 of claim 4 and to improve the clarity and precision of the claim(s). Appropriate correction is required.
Claim 10 is objected to because of the following informalities: Line 2 of claim 10 recites, in part, “performing at least one of following items” which appears to contain a grammatical error and/or a minor informality. The Examiner suggests amending the claim to --performing at least one of the following items-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: Lines 2 - 4 of claim 17 recite, in part, “and the inputting the initial association matrix into the second association network to generate a target association matrix,” which appears to contain a minor informality. The Examiner suggests amending the claim to --and the inputting the initial association matrix into the second association network to generate [[a]] the target association matrix,-- in order to more clearly link back to the element(s) in lines 8 - 9 of claim 15 and to improve the clarity and precision of the claim(s). Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 3 - 6 and 9 - 19 are 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.
Claim 3 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 because it is unclear as to which change type “the change type” recited on line 1 is referencing. Is it referring to the ”change type of the updated lane element” recited on line 2 of claim 2, the “change type of the target lane element” recited on line 8 of claim 2, both of the ”change type of the updated lane element” recited on line 2 of claim 2 and the “change type of the target lane element” recited on line 8 of claim 2 or some other change type? Clarification and appropriate correction are required. For purposes of examination, the Examiner will treat the claim as referencing at least one of the ”change type of the updated lane element” recited on line 2 of claim 2 and the “change type of the target lane element” recited on line 8 of claim 2.
Claim 9 recites the limitation "the prior encoding network" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 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 because it is unclear as to which lane update information “the lane update information” recited on line 13, along with subsequent recitation(s) of “the lane update information”, are referencing. Are they referring to the “lane update information” recited on line 5 of claim 9 or the “lane update information” recited on line 12 of claim 9? Clarification and appropriate correction are required. For purposes of examination, the Examiner will treat “the lane update information” recited on line 13 of claim 9 as referencing the “lane update information” recited on line 12 of claim 9 and “the lane update information” recited on line 17 of claim 9 as referencing the “lane update information” recited on line 5 of claim 9.
Claim 9 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 because it is unclear as to which map update model “the map update model” recited on line 15 is referencing. Is it referring to the “map update model” recited on lines 4 - 5 of claim 9 or the “map update model” recited on lines 5 - 6 of claim 9? Clarification and appropriate correction are required. For purposes of examination, the Examiner will treat the claim as referencing a single same map update model.
Claim 12 recites the limitation "the update network" in lines 11 - 12. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 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 because it is unclear as to which change type “the change type” recited on line 1 is referencing. Is it referring to the ”change type of the updated lane element” recited on line 2 of claim 13, the “change type of the target lane element” recited on line 8 of claim 13, both of the ”change type of the updated lane element” recited on line 2 of claim 13 and the “change type of the target lane element” recited on line 8 of claim 13 or some other change type? Clarification and appropriate correction are required. For purposes of examination, the Examiner will treat the claim as referencing at least one of the ”change type of the updated lane element” recited on line 2 of claim 13 and the “change type of the target lane element” recited on line 8 of claim 13.
Claims 4 - 6, 10, 11, 13, 15 - 18 and 20 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, due to being dependent upon a rejected base claim(s) but would be withdrawn from the rejection if their base claim(s) overcome the rejection(s).
Claim Rejections - 35 USC § 102
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 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.
Claims 1, 8, 9, 12, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Heo et al. U.S. Publication No. 2021/0333124 A1.
- With regards to claims 1 and 12, Heo et al. disclose a method for training a map update model, (Heo et al., Abstract, Fig. 15, Pg. 1 ¶ 0011 - 0012, Pg. 2 ¶ 0017, Pg. 3 ¶ 0042, Pg. 9 ¶ 0094 - 0098, Pg. 10 ¶ 0108 - 0111, Pg. 13 ¶ 0136 - 0144) the map update model comprising a prior encoding network (Heo et al., Figs. 4, 5, 7 & 9, Pg. 3 ¶ 0042 - 0043, Pg. 6 ¶ 0067 - Pg. 7 ¶ 0072, Pg. 8 ¶ 0089 - Pg. 9 ¶ 0093, Pg. 9 ¶ 010 - 0102, Pg. 12 ¶ 0131 - 0134, Pg. 13 ¶ 0140 - 0141) and an update network; (Heo et al., Figs. 4, 5, 10, 14 & 15, Pg. 1 ¶ 0011 - 0013, Pg. 2 ¶ 0015, Pg. 3 ¶ 0045, Pg. 5 ¶ 0056, Pg. 7 ¶ 0075 - 0077, Pg. 8 ¶ 0080 - 0082, Pg. 10 ¶ 0108 - 0110, Pg. 11 ¶ 0120 - 0123, Pg. 12 ¶ 0133 - 0134, Pg. 13 ¶ 0137 - 0144) and an electronic device, (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) comprising: at least one processor; (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) and a memory, communicatively connected to the at least one processor; (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) wherein the memory stores instructions executable by the at least one processor, (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) and the instructions are executed by the at least one processor, to enable the at least one processor to perform operations, (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) the method and operations comprising: inputting a historical lane element corresponding to a road top view sample into the prior encoding network to generate a prior encoding feature, (Heo et al., Figs. 2, 4 - 7 & 9, Pg. 1 ¶ 0008 - 0010, Pg. 2 ¶ 0016 - 0017, Pg. 3 ¶ 0041 - 0045, Pg. 4 ¶ 0055 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0067 - 0069, Pg. 7 ¶ 0071 - 0073, Pg. 8 ¶ 0089 - Pg. 9 ¶ 0093, Pg. 12 ¶ 0131 - 0133, Pg. 13 ¶ 0139 - 0141) the prior encoding network being used to encode geometric features, semantic features and spatial relationship features of lane elements; (Heo et al., Figs. 8, 11 & 12, Pg. 1 ¶ 0003 - 0004, Pg. 2 ¶ 0015, Pg. 3 ¶ 0041 - 0043, Pg. 4 ¶ 0048 - 0049, Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 - 0069, Pg. 7 ¶ 0071 - 0073, Pg. 8 ¶ 0082, 0085 and 0090, Pg. 9 ¶ 0095 - 0098, Pg. 11 ¶ 0115 - 0118 [“map data including information necessary for a vehicle to travel, such as road lanes, road markers, and the like. For example, the digital map DB 120 may be stored in the form of vector data including location information generated based on high-definition aerial photographs, spatial coordinates of road lanes, road markers, and the like, and geometric information (connection information such as point, line, plane, polygon, and the like) of the components. In the digital map DB 120, each of road lanes, road markers, and the like may be stored as one object, and each of the objects may be classified by a class or an identifier”, “Map data stored in the digital map DB 120 in the form of vectors may be expressed in the form of a road-layout map in two-dimensional or three-dimensional representation. In the present embodiment, a ‘road-layout map’ may refer to at least a part of a digital map or a high-definition map including map information stored in the form of vector. More specifically, the road-layout map may refer to a map in which information about each lane unit such as a road center line and a road boundary line, and the like, and information about crosswalks, signs, curbs, road markers, various structures, and the like, are stored in the form of vector. For example, the road-layout map may include spatial coordinates (x, y, z), type, and connectivity information for road lanes and road markers. In the road-layout map, each of the interconnected road lanes and road markers can be expressed as a single object, and for example, the road marker may be represented by a closed polygon, and the road lane may be represented by a straight line crossing the center” and “the received road-layout map 620 may include coordinates, type, connectivity information, and the like for the location of a road lane, or a road marker, and each of the connected lanes, road marker, and the like may correspond to one object. In the road-layout map 620, the road markers may be represented by closed polygons, and the road lane may be represented by the straight line crossing the center”]) inputting the prior encoding feature and the road top view sample into the update network to generate lane update information; (Heo et al., Abstract, Figs. 2, 4, 5, 10 - 12, 14 & 15, Pg. 1 ¶ 0010 - 0013, Pg. 3 ¶ 0042 - 0043 and 0045, Pg. 4 ¶ 0055 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 - Pg. 7 ¶ 0071, Pg. 7 ¶ 0077, Pg. 8 ¶ 0080 - 0082, Pg. 8 ¶ 0090 - Pg. 9 ¶ 0092, Pg. 10 ¶ 0109 - 0111, Pg. 10 ¶ 0114 - Pg. 11 ¶ 0119, Pg. 12 ¶ 0131 - 0134, Pg. 13 ¶ 0137 - 0143) constructing a loss function based on the lane update information and a target lane element corresponding to the road top view sample; (Heo et al., Figs. 8, 11, 12 & 15, Pg. 1 ¶ 0011, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0106 - 0110, Pg. 11 ¶ 0115 - 0116, 0119 and 0121, Pg. 12 ¶ 0134 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144) and adjusting a parameter of the map update model based on the loss function. (Heo et al., Figs. 8, 11, 12 & 15, Pg. 1 ¶ 0011, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0106 - 0110, Pg. 11 ¶ 0115 - 0116, 0119 and 0121, Pg. 12 ¶ 0134 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144)
- With regards to claim 8, Heo et al. disclose the method according to claim 1, wherein both the historical lane element and the target lane element comprise an element vectorization point set and element style information. (Heo et al., Pg. 1 ¶ 0003 and 0007 - 0008, Pg. 3 ¶ 0041 - 0043 and 0045, Pg. 4 ¶ 0048 - 0051, Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 and 0068 - 0069, Pg. 7 ¶ 0071 - 0075, Pg. 8 ¶ 0081 - 0082 and 0085, Pg. 8 ¶ 0090 - Pg. 9 ¶ 0092, Pg. 9 ¶ 0095 - 0097, Pg. 12 ¶ 0134, Pg. 13 ¶ 0139 - 0143)
- With regards to claim 9, Heo et al. disclose a method for updating a lane-level map, (Heo et al., Abstract, Figs. 11, 12 & 14, Pg. 1 ¶ 0002 - 0004 and 0008 - 0012, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0041 - 0042 and 0045, Pg. 5 ¶ 0056, Pg. 6 ¶ 0066, Pg. 8 ¶ 0082, Pg. 10 ¶ 0113 - Pg. 11 ¶ 0119, Pg. 13 ¶ 0137 [“According to some embodiments, by detecting a difference between the projected road-layout map and the image captured by a camera, it is possible to automatically detect a region where a change occurs in road information on the road-layout map. By correcting the road information on the road-layout map according to the detected changes in road information as described above, it is possible to maintain the up-to-dateness of the road-layout map at low cost”, “the system 180 for detecting changes in road information may determine what changes (e.g., addition, deletion, change of an object, and the like) occur in the current road information of the corresponding location in the map data previously stored in the digital map DB 120. As another example, the system 180 for detecting changes in road information may determine a region where the road information change occurs on the road-layout map (e.g., a location, a size of the region, and the like at which the road information change occurs)” and “the generated change value map may refer to a map displaying a region of change on the projected road-layout map compared with the captured road image”]) the method comprising: acquiring a road top view and a historical lane element corresponding to the road top view; (Heo et al., Abstract, Figs. 1, 2, 6 & 14, Pg. 1 ¶ 0008 - 0010, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0045, Pg. 4 ¶ 0051 - 0055, Pg. 8 ¶ 0084 - 0088, Pg. 12 ¶ 0131) inputting the road top view and the historical lane element into a map update model to generate lane update information, (Heo et al., Abstract, Figs. 1, 2, 4, 5, 11, 12, 14 & 15, Pg. 1 ¶ 0008 - 0013, Pg. 2 ¶ 0015 - 0017, Pg. 3 ¶ 0044 - 0045, Pg. 4 ¶ 0052 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0065 - 0067, Pg. 7 ¶ 0073 and 0075 - 0076, Pg. 8 ¶ 0080 - 0082 and 0090, Pg. 10 ¶ 0113 - Pg. 11 ¶ 0119, Pg. 12 ¶ 0130 - 0131 and 0134, Pg. 13 ¶ 0137) wherein the map update model is a map update model obtained by: inputting a historical lane element corresponding to a road top view sample into the prior encoding network to generate a prior encoding feature, (Heo et al., Figs. 2, 4 - 7 & 9, Pg. 1 ¶ 0008 - 0010, Pg. 2 ¶ 0016 - 0017, Pg. 3 ¶ 0041 - 0045, Pg. 4 ¶ 0055 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0067 - 0069, Pg. 7 ¶ 0071 - 0073, Pg. 8 ¶ 0089 - Pg. 9 ¶ 0093, Pg. 12 ¶ 0131 - 0133, Pg. 13 ¶ 0139 - 0141) the prior encoding network being used to encode geometric features, semantic features and spatial relationship features of lane elements; (Heo et al., Figs. 8, 11 & 12, Pg. 1 ¶ 0003 - 0004, Pg. 2 ¶ 0015, Pg. 3 ¶ 0041 - 0043, Pg. 4 ¶ 0048 - 0049, Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 - 0069, Pg. 7 ¶ 0071 - 0073, Pg. 8 ¶ 0082, 0085 and 0090, Pg. 9 ¶ 0095 - 0098, Pg. 11 ¶ 0115 - 0118 [“map data including information necessary for a vehicle to travel, such as road lanes, road markers, and the like. For example, the digital map DB 120 may be stored in the form of vector data including location information generated based on high-definition aerial photographs, spatial coordinates of road lanes, road markers, and the like, and geometric information (connection information such as point, line, plane, polygon, and the like) of the components. In the digital map DB 120, each of road lanes, road markers, and the like may be stored as one object, and each of the objects may be classified by a class or an identifier”, “Map data stored in the digital map DB 120 in the form of vectors may be expressed in the form of a road-layout map in two-dimensional or three-dimensional representation. In the present embodiment, a ‘road-layout map’ may refer to at least a part of a digital map or a high-definition map including map information stored in the form of vector. More specifically, the road-layout map may refer to a map in which information about each lane unit such as a road center line and a road boundary line, and the like, and information about crosswalks, signs, curbs, road markers, various structures, and the like, are stored in the form of vector. For example, the road-layout map may include spatial coordinates (x, y, z), type, and connectivity information for road lanes and road markers. In the road-layout map, each of the interconnected road lanes and road markers can be expressed as a single object, and for example, the road marker may be represented by a closed polygon, and the road lane may be represented by a straight line crossing the center” and “the received road-layout map 620 may include coordinates, type, connectivity information, and the like for the location of a road lane, or a road marker, and each of the connected lanes, road marker, and the like may correspond to one object. In the road-layout map 620, the road markers may be represented by closed polygons, and the road lane may be represented by the straight line crossing the center”]) inputting the prior encoding feature and the road top view sample into an update network to generate lane update information; (Heo et al., Abstract, Figs. 2, 4, 5, 10 - 12, 14 & 15, Pg. 1 ¶ 0010 - 0013, Pg. 3 ¶ 0042 - 0043 and 0045, Pg. 4 ¶ 0055 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 - Pg. 7 ¶ 0071, Pg. 7 ¶ 0077, Pg. 8 ¶ 0080 - 0082, Pg. 8 ¶ 0090 - Pg. 9 ¶ 0092, Pg. 10 ¶ 0109 - 0111, Pg. 10 ¶ 0114 - Pg. 11 ¶ 0119, Pg. 12 ¶ 0131 - 0134, Pg. 13 ¶ 0137 - 0143) constructing a loss function based on the lane update information and a target lane element corresponding to the road top view sample; (Heo et al., Figs. 8, 11, 12 & 15, Pg. 1 ¶ 0011, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0106 - 0110, Pg. 11 ¶ 0115 - 0116, 0119 and 0121, Pg. 12 ¶ 0134 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144) and adjusting a parameter of the map update model based on the loss function; (Heo et al., Figs. 8, 11, 12 & 15, Pg. 1 ¶ 0011, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0106 - 0110, Pg. 11 ¶ 0115 - 0116, 0119 and 0121, Pg. 12 ¶ 0134 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144) and updating the lane-level map based on the lane update information. (Heo et al., Abstract, Figs. 11, 12 & 14, Pg. 1 ¶ 0002 - 0004 and 0008 - 0013, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0041 - 0042 and 0045, Pg. 4 ¶ 0055 - Pg. 5 ¶ 0056, Pg. 6 ¶ 0066, Pg. 8 ¶ 0081 - 0082, Pg. 10 ¶ 0109, Pg. 10 ¶ 0113 - Pg. 11 ¶ 0119, Pg. 12 ¶ 0130 - 0131, Pg. 13 ¶ 0137 [“According to some embodiments, by detecting a difference between the projected road-layout map and the image captured by a camera, it is possible to automatically detect a region where a change occurs in road information on the road-layout map. By correcting the road information on the road-layout map according to the detected changes in road information as described above, it is possible to maintain the up-to-dateness of the road-layout map at low cost”, “the system 180 for detecting changes in road information may determine what changes (e.g., addition, deletion, change of an object, and the like) occur in the current road information of the corresponding location in the map data previously stored in the digital map DB 120. As another example, the system 180 for detecting changes in road information may determine a region where the road information change occurs on the road-layout map (e.g., a location, a size of the region, and the like at which the road information change occurs)” and “the generated change value map may refer to a map displaying a region of change on the projected road-layout map compared with the captured road image”])
- With regards to claim 19, Heo et al. disclose an electronic device, (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) comprising: at least one processor; (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) and a memory, communicatively connected to the at least one processor; (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) wherein the memory stores instructions executable by the at least one processor, (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) and the instructions are executed by the at least one processor, to enable the at least one processor to perform (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) the method according to claim 9. ([The Examiner asserts that Heo et al. disclose the method according to claim 9, see analysis of claim 9 included herein above.])
- With regards to claim 20, Heo et al. disclose a non-transitory computer readable storage medium, storing a computer instruction, wherein the computer instruction is used to cause a computer to perform (Heo et al., Fig. 3, Pg. 1 ¶ 0009 and 0012 - 0013, Pg. 3 ¶ 0040 and 0049, Pg. 5 ¶ 0057 - 0061, Pg. 14 ¶ 0145 - 0153) the method according to claim 1. ([The Examiner asserts that Heo et al. disclose the method according to claim 1, see analysis of claim 1 included herein above.])
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 - 7 and 13 - 18 are rejected under 35 U.S.C. 103 as being unpatentable over Heo et al. U.S. Publication No. 2021/0333124 A1 as applied to claims 1 and 12 above, and further in view of Xia et al., “DuARUS: Automatic Geo-object Change Detection with Street-view Imagery for Updating Road Database at Baidu Maps”, Proceedings of the 31st ACM International Conference on Information and Knowledge Management”, Oct. 2022, pages 3565 - 3574.
- With regards to claims 2 and 13, Heo et al. disclose the method and electronic device according to claims 1 and 12, respectively, wherein the lane update information comprises an updated lane element (Heo et al., Figs. 8, 11, 12, 14 & 15, Pg. 1 ¶ 0002 - 0004, 0008 and 0010 - 0011, Pg. 2 ¶ 0015 - 0016, Pg. 3 ¶ 0041 - 0042 and 0045, Pg. 4 ¶ 0048 - 0049, Pg. 5 ¶ 0056, Pg. 6 ¶ 0066 - 0068, Pg. 7 ¶ 0075, Pg. 8 ¶ 0082, Pg. 10 ¶ 0109, Pg. 10 ¶ 0114 - Pg 11 ¶ 0119, Pg. 13 ¶ 0137 and 0143) and a change type of the updated lane element, (Heo et al., Pg. 5 ¶ 0056, Pg. 8 ¶ 0082, Pg. 11 ¶ 0115 - 0119 [“the system 180 for detecting changes in road information may determine what changes (e.g., addition, deletion, change of an object, and the like) occur in the current road information of the corresponding location in the map data previously stored in the digital map DB 120” and “the region displayed on the change value map may be displayed in different colors according to the type of change (e.g., deletion, change, addition of object)”]) and constructing the loss function based on the lane update information and the target lane element corresponding to the road top view sample, comprises: constructing a first sub-loss function, based on the updated lane element and the target lane element; (Heo et al., Figs. 8, 11, 12 & 15, Pg. 1 ¶ 0011, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0106 - 0110, Pg. 11 ¶ 0115 - 0116, 0119 and 0121, Pg. 12 ¶ 0134 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144) constructing a second sub-loss function; (Heo et al., Fig. 15, Pg. 1 ¶ 0011, Pg. 6 ¶ 0070, Pg. 7 ¶ 0073, Pg. 9 ¶ 0100 - 0101, Pg. 10 ¶ 0104 - 0105, Pg. 11 ¶ 0121, Pg. 12 ¶ 0132 - 0134, Pg. 13 ¶ 0141) and determining the loss function, based on the first sub-loss function and the second sub-loss function. (Heo et al., Fig. 15, Pg. 1 ¶ 0011, Pg. 6 ¶ 0070, Pg. 7 ¶ 0073, Pg. 9 ¶ 0094 - 0098 and 0101, Pg. 10 ¶ 0104 - 0110, Pg. 11 ¶ 0121, Pg. 12 ¶ 0132 - Pg. 13 ¶ 0136, Pg. 13 ¶ 0141 - 0144 [“The adversarial learning artificial neural network for domain adaptation may be trained in conjunction with the metric learning artificial neural network included in the system 180 for detecting changes in road information”]) Heo et al. fail to disclose explicitly constructing a second sub-loss function, based on the change type of the updated lane element and a change type of the target lane element; and determining the loss function, based on the second sub-loss function constructed based on the change type of the updated lane element and a change type of the target lane element. Pertaining to analogous art, Xia et al. disclose wherein the lane update information comprises an updated lane element (Xia et al., Pg. 3565 Abstract, Pg. 3566 Right-Hand Column First-Full Paragraph, Pg. 3566 Fig. 1, Pg. 3567 Fig. 3, Pg. 3569 § 2.2.1 - Pg. 3570 § 2.2.3, Pg. 3570 Fig. 6) and a change type of the updated lane element, (Xia et al., Pg. 3565 Abstract, Pg. 3566 Right-Hand Column First-Full Paragraph, Pg. 3566 Fig. 1, Pg. 3567 Fig. 3, Pg. 3570 § 2.2.3) and constructing the loss function based on the lane update information and the target lane element corresponding to the road top view sample, comprises: constructing a first sub-loss function, based on the updated lane element and the target lane element; (Xia et al., Pg. 3569 § 2.2.1 - Pg. 3570 § 2.2.2, Pg. 3570 Fig. 6) constructing a second sub-loss function, based on the change type of the updated lane element and a change type of the target lane element; (Xia et al., Pg. 3569 § 2.2.1 - Pg. 3570 § 2.2.3, Pg. 3572 § 3.3.3) and determining the loss function, based on the first sub-loss function and the second sub-loss function. (Xia et al., Pg. 3567 Fig. 3, Pg. 3569 § 2.2.1 - Pg. 3570 § 2.2.3, Pg. 3570 Fig. 6, Pg. 3572 § 3.3.3) Heo et al. and Xia et al. are combinable because they are both directed towards image processing systems for detecting changes in road layout map data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Heo et al. with the teachings of Xia et al. This modification would have been prompted in order to enhance the base device of Heo et al. with the well-known and applicable technique Xia et al. applied to a comparable device. Utilizing a second sub-loss function based on the change type of the updated lane element and a change type of the target lane element, as taught by Xia et al., would enhance the base device of Heo et al. by enabling its map update model to learn and detect types of changes associated with changes it detects thereby facilitating its ability to detect the presence or absence of changes and display information in different colors according to the type of change associated with a detected change. Furthermore, this modification would have been prompted by the teachings and suggestions of Heo et al. that their system may detect the presence or absence of changes in road information, may output a value representing changes in road information and may display regions of change in different colors according to the type of change associated with the regions, such as deletion, change and addition, see at least page 1 paragraphs 0010 - 0011, page 3 paragraph 0045 and page 8 paragraph 0082 of Heo et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a second sub-loss function based on the change type of the updated lane element and a change type of the target lane element would be utilized by the base device of Heo et al. in order to enable its map update model to learn to detect the types of changes associated with detected changes so as to facilitate its ability to detect the presence or absence of changes and display information in different colors according to the type of change associated with a detected change. Therefore, it would have been obvious to combine Heo et al. with Xia et al. to obtain the invention as specified in claims 2 and 13.
- With regards to claims 3 and 14, Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 2 and 13, respectively, wherein the change type comprises at least one of: element style change, no change in element style, element addition, or element deletion; (Heo et al., Pg. 5 ¶ 0056, Pg. 8 ¶ 0082, Pg. 11 ¶ 0115 - 0119 [“the system 180 for detecting changes in road information may determine what changes (e.g., addition, deletion, change of an object, and the like) occur in the current road information of the corresponding location in the map data previously stored in the digital map DB 120” and “the region displayed on the change value map may be displayed in different colors according to the type of change (e.g., deletion, change, addition of object)”]) and/or wherein the update network comprises a first update sub-network and a second update sub-network, and inputting the prior encoding feature and the road top view sample into the update network to generate the lane update information, comprises: inputting the prior encoding feature and the road top view sample into the first update sub-network to generate the updated lane element; and inputting the updated lane element and the prior encoding feature into the second update sub-network to generate the change type of the updated lane element. ([The Examiner asserts that the broadest reasonable interpretation of claims 3 and 14, which recite “and/or” on line 3, only require the limitations corresponding to one of either the change type or the update network. As such, the Examiner asserts that none of the limitations corresponding to the update network are required by the broadest reasonable interpretation of claims 3 and 14.]) In addition, analogous art Xia et al. disclose wherein the change type comprises at least one of: element style change, no change in element style, element addition, or element deletion; (Xia et al., Pg. 3566 Right-Hand Column First-Full Paragraph, Pg. 3566 Fig. 1, Pg. 3567 Fig. 3, Pg. 3570 § 2.2.3) and/or wherein the update network comprises a first update sub-network and a second update sub-network, and inputting the prior encoding feature and the road top view sample into the update network to generate the lane update information, comprises: inputting the prior encoding feature and the road top view sample into the first update sub-network to generate the updated lane element; and inputting the updated lane element and the prior encoding feature into the second update sub-network to generate the change type of the updated lane element. ([The Examiner asserts that the broadest reasonable interpretation of claims 3 and 14, which recite “and/or” on line 3, only require the limitations corresponding to one of either the change type or the update network. As such, the Examiner asserts that none of the limitations corresponding to the update network are required by the broadest reasonable interpretation of claims 3 and 14.])
- With regards to claims 4 and 15, Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 3 and 14, respectively, ([The Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 3 and 14, see analysis of claims 3 and 14 included herein above.]) wherein the second update sub-network comprises a first association network and a second association network, and inputting the updated lane element and the prior encoding feature into the second update sub-network to generate the change type of the updated lane element, comprises: inputting the updated lane element and the prior encoding feature into the first association network to generate an initial association matrix; and inputting the initial association matrix into the second association network to generate a target association matrix, wherein one element in the target association matrix is used to indicate a change type of one lane element. ([The Examiner asserts that none of the limitations of claims 4 and 15 are required by their broadest reasonable interpretation(s) at least because all of the limitations of claims 4 and 15 correspond to further limitations of the update network and because claims 4 and 15 depend from claims 3 and 14, respectively, which, as explained above with respect to claims 3 and 14, do not require further limitations corresponding to the update network. As such, the Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 4 and 15 at least because none of the limitations of claims 4 and 15 are required by their broadest reasonable interpretation(s).])
- With regards to claims 5 and 16, Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 4 and 15, respectively, ([The Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 4 and 15, see analysis of claims 4 and 15 included herein above.]) wherein the first association network comprises: a multilayer perceptron network, an outer product network and a classification network, and inputting the updated lane element and the prior encoding feature into the first association network to generate the initial association matrix, comprises: inputting the updated lane element and the prior encoding feature respectively into the multilayer perceptron network to generate a first feature and a second feature; inputting the first feature and the second feature into the outer product network to generate a feature-level association matrix; and inputting the feature-level association matrix into the classification network to generate the initial association matrix. ([The Examiner asserts that none of the limitations of claims 5 and 16 are required by their broadest reasonable interpretation(s) at least because all of the limitations of claims 5 and 16 correspond to further limitations of the update network and because claims 5 and 16 depend from claims 4 and 15, respectively, which, as explained above with respect to claims 4 and 15, do not require further limitations corresponding to the update network. As such, the Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 5 and 16 at least because none of the limitations of claims 5 and 16 are required by their broadest reasonable interpretation(s).])
- With regards to claims 6 and 17, Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 4 and 15, respectively, ([The Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 4 and 15, see analysis of claims 4 and 15 included herein above.]) wherein the second association network comprises a matching network and a filtering network, and the inputting the initial association matrix into the second association network to generate a target association matrix, comprises: inputting the initial association matrix into the matching network to generate a matched association matrix, the matching network being used to match the initial association matrix using a preset matching strategy; and inputting the matched association matrix into the filtering network to generate the target association matrix, the filtering network being used to filter an element in the matched association matrix based on a confidence of the updated lane element and/or a matching degree between the updated lane element and the target lane element. ([The Examiner asserts that none of the limitations of claims 6 and 17 are required by their broadest reasonable interpretation(s) at least because all of the limitations of claims 6 and 17 correspond to further limitations of the update network and because claims 6 and 17 depend from claims 4 and 15, respectively, which, as explained above with respect to claims 4 and 15, do not require further limitations corresponding to the update network. As such, the Examiner asserts that Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 6 and 17 at least because none of the limitations of claims 6 and 17 are required by their broadest reasonable interpretation(s).])
- With regards to claims 7 and 18, Heo et al. in view of Xia et al. disclose the method and electronic device according to claims 2 and 13, respectively. Heo et al. fail to disclose explicitly wherein the first sub-loss function comprises: a regression loss function, a cosine similarity loss function and a classification loss function; and/or wherein the second sub-loss function is a classification loss function constructed based on the change type of the updated lane element and the change type of the target lane element. Pertaining to analogous art, Xia et al. disclose wherein the first sub-loss function comprises: a regression loss function, a cosine similarity loss function and a classification loss function; ([The Examiner asserts that the broadest reasonable interpretation of claims 7 and 18, which recite “and/or” on line 3, only require the limitations corresponding to one of either the first sub-loss function or the second sub-loss function. As such, the Examiner asserts that none of the limitations corresponding to the first sub-loss function are required by the broadest reasonable interpretation of claims 7 and 18.]) and/or wherein the second sub-loss function is a classification loss function constructed based on the change type of the updated lane element and the change type of the target lane element. (Xia et al., Pg. 3569 § 2.2.1 - Pg. 3570 § 2.2.3, Pg. 3572 § 3.3.3)
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Heo et al. U.S. Publication No. 2021/0333124 A1 as applied to claim 9 above, and further in view of Efland et al. U.S. Publication No. 2021/0406559 A1.
- With regards to claim 10, Heo et al. disclose the method according to claim 9. Heo et al. fail to disclose explicitly wherein the method further comprises: performing at least one of following items based on the updated lane-level map: determining road lane-level traffic information; determining a lane-level candidate planning trajectory; and determining a lane-level control strategy for a vehicle. Pertaining to analogous art, Efland et al. disclose wherein the method further comprises: performing at least one of following items based on the updated lane-level map: determining road lane-level traffic information; determining a lane-level candidate planning trajectory; and determining a lane-level control strategy for a vehicle. (Efland et al., Abstract, Figs. 3 & 6, Pg. 2 ¶ 0025 and 0027, Pg. 2 ¶ 0029 - Pg. 3 ¶ 0033, Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043, Pg. 5 ¶ 0056 and 0061, Pg. 6 ¶ 0063 - 0064, Pg. 11 ¶ 0119 - 0121, Pg. 12 ¶ 0123 - 0124, Pg. 14 ¶ 0150, Pg. 16 ¶ 0169 - 0171) Heo et al. and Efland et al. are combinable because they are both directed towards image processing systems that detect changes in road layout map data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Heo et al. with the teachings of Efland et al. This modification would have been prompted in order to enhance the base device of Heo et al. with the well-known and applicable technique Efland et al. applied to a comparable device. Determining at least one of road lane-level traffic information, a lane-level candidate planning trajectory and a lane-level control strategy for a vehicle, based on the updated lane-level map, as taught by Efland et al., would enhance the base device of Heo et al. by allowing for it to be utilized in an increased number of practical applications, such as various autonomous driving related applications, thereby improving the overall appeal and usefulness of the base device of Heo et al. to potential end-users. Furthermore, this modification would have been prompted by the teachings and suggestions of Heo et al. that road layout maps include information necessary for autonomous driving vehicles to travel, see at least page 1 paragraph 0003 of Heo et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that at least one of road lane-level traffic information, a lane-level candidate planning trajectory and a lane-level control strategy for a vehicle would be determined based on the updated lane-level map so as to allow for the base device of Heo et al. device to be utilized in an increased number of practical applications, such as various autonomous driving related applications, in order to improve the overall appeal and usefulness of the base device of Heo et al. to potential end-users. Therefore, it would have been obvious to combine Heo et al. with Efland et al. to obtain the invention as specified in claim 10.
- With regards to claim 11, Heo et al. disclose the method according to claim 9. Heo et al. fail to disclose explicitly wherein the method further comprises: performing route navigation based on the updated lane-level map. Pertaining to analogous art, Efland et al. disclose wherein the method further comprises: performing route navigation based on the updated lane-level map. (Efland et al., Abstract, Figs. 3 & 6, Pg. 2 ¶ 0025 and 0027, Pg. 2 ¶ 0029 - Pg. 3 ¶ 0037 Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043, Pg. 4 ¶ 0049, Pg. 5 ¶ 0054 - 0056 and 0061, Pg. Pg. 6 ¶ 0063 - 0064, Pg. 14 ¶ 0150, Pg. 16 ¶ 0169 - Pg. 17 ¶ 0176) Heo et al. and Efland et al. are combinable because they are both directed towards image processing systems that detect changes in road layout map data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Heo et al. with the teachings of Efland et al. This modification would have been prompted in order to enhance the base device of Heo et al. with the well-known and applicable technique Efland et al. applied to a comparable device. Performing route navigation based on the updated lane-level map, as taught by Efland et al., would enhance the base device of Heo et al. by allowing for it to be utilized in an increased number of practical applications, such as autonomous driving related applications, thereby improving the overall appeal and usefulness of the base device of Heo et al. to potential end-users. Furthermore, this modification would have been prompted by the teachings and suggestions of Heo et al. that road layout maps include information necessary for autonomous driving vehicles to travel, see at least page 1 paragraph 0003 of Heo et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that route navigation would be performed based on the updated lane-level map so as to allow for the base device of Heo et al. device to be utilized in an increased number of practical applications, such as autonomous driving related applications, in order to improve the overall appeal and usefulness of the base device of Heo et al. to potential end-users. Therefore, it would have been obvious to combine Heo et al. with Efland et al. to obtain the invention as specified in claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tang U.S. Publication No. 2023/0358563 A1; which is directed to an image processing method and system for generating and updating road map data.
Heo et al., “HD Map Change Detection with Cross-Domain Deep Metric Learning”, IEEE, International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pages 10218 - 10224; which is directed towards a map change detection algorithm based on measuring a similarity between camera images and a high-definition (HD) map of a driving environment.
Wild et al., “ExelMap: Explainable Element-based HD-Map Change Detection and Update”, arXiv, arXiv:2409.10178v1, Sept. 16, 2024, pages 1 - 17; which is directed towards an element-based updating strategy for updating road lane maps.
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/ERIC RUSH/Primary Examiner, Art Unit 2677
