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 . This office action is in response to an amendment filed on 3/4/26. Claims 1-20 are pending.
Response to Amendments
Amendments filed on 3/4/26 are under consideration. Claims 1, 2, 8, 9, 11, 12, 13 , 14, and 17-20 are amended. Claim Interpretation regarding claims 1 and 7 have been upheld upon amendment.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 11, 19, and 20 of this application are patentably indistinct from claims 1, 10, 17, and 20 of Application No. 18/425,175. Pursuant to 37 CFR 1.78(f), when two or more applications filed by the same applicant or assignee contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
Claims 1, 11, 19, and 20 are provisionally rejected on the ground of obviousness-type double patenting as being unpatentable over claims 1, 10, 17, and 20 of copending Application No. 18/425,175. Although the claims at issue are not identical, they are not patentably distinct from each other because the functions performed by the system of application 18/428,438 encompass the system claimed in the application 18/425,175 (see below for correspondence).
Claim 1 rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of Application No. 18/425,175 in view of Long II (US 2025/0052593 Al).
Claim 1 of Application No. 18/428,438
Claim 1 of Application No. 18/425,175
“A system, comprising: a processor; and a memory storing:
a set determination module including instructions that, when executed by the processor, cause the processor to determine
from geographic position traces of vehicles that traversed a region of interest, a set of geographic position traces to be used to produce a digital map of the region of interest
wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map
the count being between a first threshold count and a second threshold count
a production module including instructions that, when executed by the processor, cause the processor to produce, from the set, the digital map;
and a communications module including instructions that, when executed by the processor, cause the processor to transmit the digital map to a specific vehicle to be used to control movement of the specific vehicle.”
A system, comprising: a processor; and
a memory storing:
a set determination module including instructions that, when executed by the processor, cause the processor to determine,
from: positions of a vehicle that traversed a region, and data affiliated with images produced by a camera on the vehicle
a set of information to be used to produce a map of the region, an optimal state of the set being based on at least one of: a characteristic of the information, or an aspect, in the region, to be included in the map;
X
a production module including instructions that, when
executed by the processor, cause the processor to
produce, from the set, the map; and
communications module including instructions that, when executed by the processor, cause the processor to transmit the map to a specific vehicle to control movement of the specific vehicle
Application No. 18/425,175 fails to mention the count being between a first threshold count and a second threshold count.
Zhang teaches the count (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”)
Yet Zhang fails to teach being between a first threshold count and a second threshold count.
SHAOJUN teaches being between a first threshold count and a second threshold count; (Pg. 5 – “Further preferably, the triggering, by the monocular SLAM positioning module, monocular SLAM positioning includes: when the matching number of the current frame and the local map points is smaller than a first threshold and larger than a second threshold, the positioning quality is poor” & See Also Pg. 7 – “Preferably, the first threshold value is one value in a range of 80 to 100, and the second threshold value is one value in a range of 20 to 40.” (equates to being between a first threshold count and a second threshold count; as the first quote shows the matching number in a SLAM method being less than a first threshold and more than a second threshold.)) It would have been an advantageous addition to the system disclosed by Zhang to include being between a first threshold count and a second threshold count as this would allow for the count to fall within a range that does not exceed the processing capabilities of the system or require extraneous processing power to work.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include being between a first threshold count and a second threshold count as this allows for a minimum number of counts to be included ensuring a large enough dataset while not overloading the processing abilities of the system with too much data.
Claim X of Application No. 18/428,438
Claim X of Application No. 18/425,175
Claim 11
Claim 19
Claim 20
Claim 10
Claim 17
Claim 20
This is a nonstatutory obviousness-type double patenting rejection because the patentably indistinct
claims have not in fact been patented.
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 limitation(s) is/are: determination module including instructions that, when executed by the processor, cause the processor to determine, from geographic position traces of vehicles that traversed a region of interest, a set of geographic position traces to be used to produce a digital map of the region of interest, in claim 1. Additionally, production module including instructions that, when executed by the processor, cause the processor to produce, from the set, the digital map in claim 1. Finally, a communications module including instructions that, when executed by the processor, cause the processor to transmit the digital map to a specific vehicle to be used to control movement of the specific vehicle in claims 1 and 7.
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. See at least,
[0008] – “The memory can store a set determination module, a production module, and a communications module.”
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 § 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.
Claims 1-10, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 10,395,117 Bl) in view of SHAOJUN (CN 107990899 B).
Regarding Claim 1 Zhang teaches A system, (Pg. 39 – Col. 1 – lines 15-20 – “The technology disclosed generally relates to detecting location and positioning of a mobile device, and more particularly relates to application of visual processing and inertial sensor data to positioning and guidance technologies.” & See Also Pg. 39 – Col. 2 – lines 9-10 – “FIG. 3 illustrates an example of a visual-inertial sensory system.”) comprising: a processor;(Pg. 69 – Col. 61 – lines 17-20 – “Other implementations of the method described in this section can include a non-transitory computer-readable storage medium storing instructions executable by a processor to perform any of the methods described above” ) and a memory (Pg. 69 – Col. 61 – lines 20-24 – “Yet another implementation of the method described in this section can include a system including a memory and one or more processors operable to execute instructions, stored in the memory,”) storing: a set determination module including instructions that, (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 47 – Col. 18 – lines 29 – 34 – “In action 520, the keyrigs are searched using the selected search strategy in order to find among the keyrigs a keyrig with bag of words description closest to a bag of words description of a current image. In action 530, determine whether the match quality is sufficient” (equates to a set determination module including instructions as the quote shows a system able to execute instruction that lead to 3d mapping of the surrounding environment, by way of matching the traces together across a plurality of images, which is what the set determination module does by building a scene with the geographic positional traces )) when executed by the processor, cause the processor to determine, (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above”) from geographic position traces of vehicles that traversed a region of interest, a set of geographic position traces to be used to produce a digital map of the region of interest, (Pg. 40 – Col. 3 – lines 8-9 – “FIG. 32 illustrates detection of features points to build a sparse 3D mapping of object feature points.” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” (equates to determine, from geographic position traces of vehicles that traversed a region of interest, a set of geographic position traces to be used to produce a digital map of the region of interest as the quote shows the keyrigs being combination data of gps imu, etc. and that is equivalent to the geographic position traces wherein a digital map is made based on the data collected.)) wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map; (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”(equates to wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map as the first quote shows the classification of objects in the mapping that is performed wherein the object detected in this cited art is equivalent to the application aspect of basing the mapping upon. The last quote shows a count of traces as multiple poses and previous imaging is considered in the mapping. )) the count (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,” ) a production module including instructions that, (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to a production module including instructions that as the quote shows a system able to execute instructions that lead to 3d producing mapping of the surrounding environment which is what the production module does by building a scene with the geographic positional traces ) when executed by the processor, cause the processor to produce, from the set, the digital map; (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to hen executed by the processor, cause the processor to produce, from the set, the digital map; as the processor is shown to handle any method step implementation from the art and the second quote shows the 3d mapping being done and thus producing a digital map.)) and a communications module including instructions that, when executed by the processor, cause the processor to transmit the digital map to a specific vehicle to control an operation of a vehicle system to control movement of the specific vehicle. (Pg. 44 – Col. 11 – lines 11-17 – “Communications interface 342 can include hardware and/ or software that enables communication between visual inertial positioning system 300 and other systems controlling or enabling customer hardware and applications (hereinafter, a "host system" or "host") such as for example, a robot or other guided mobile platform, an autonomous vehicle,” (equates to and a communications module including instructions that, when executed by the processor, cause the processor to transmit the digital map to a specific vehicle to control an operation of a vehicle system to control movement of the specific vehicle as the quote shows the communication interface allowing the sensor and the mapping data it is handling to be used on controlling movement of an autonomous vehicle.))
Yet Zhang fails to teach being between a first threshold count and a second threshold count;
SHAOJUN teaches being between a first threshold count and a second threshold count; (Pg. 5 – “Further preferably, the triggering, by the monocular SLAM positioning module, monocular SLAM positioning includes: when the matching number of the current frame and the local map points is smaller than a first threshold and larger than a second threshold, the positioning quality is poor” & See Also Pg. 7 – “Preferably, the first threshold value is one value in a range of 80 to 100, and the second threshold value is one value in a range of 20 to 40.” (equates to being between a first threshold count and a second threshold count; as the first quote shows the matching number in a SLAM method being less than a first threshold and more than a second threshold.)) It would have been an advantageous addition to the system disclosed by Zhang to include being between a first threshold count and a second threshold count as this would allow for the count to fall within a range that does not exceed the processing capabilities of the system or require extraneous processing power to work.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include being between a first threshold count and a second threshold count as this allows for a minimum number of counts to be included ensuring a large enough dataset while not overloading the processing abilities of the system with too much data.
Regarding Claim 2 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 1, wherein a geographic position trace, of the geographic position traces: (Pg. 55 – col. 34 – lines 55-57 – “The sparse 3D mapping is created with visual information from the visual data from the quadocular-auxiliary sensor” (equates to wherein a geographic position trace, of the geographic position traces as the quote shows 3d mapping of the environment being done.)) is for a vehicle of the vehicles that traversed the region of interest, (Pg. 66 – Col. 56 – lines 56-60 – “The 3D map building technology disclosed will be illustrated with reference to a set of keyrigs captured by an autonomous vehicle 2905 with a quadocular-auxiliary sensor in FIG. 29. FIG. 29 illustrates an example path of travel 2930 of the autonomous vehicle 2905” (equates to is for a vehicle of the vehicles that traversed the region of interest as the quote shows the mapping being done by a vehicle travelling and taking in the data.)) and comprises a recording of a sequence of points, wherein the recording includes, for a point in the sequence of points, (Pg. 48 – Col. 20 – lines 17-19 – “(iii) a sequence of IMU readings starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to comprises a recording of a sequence of points, wherein the recording includes, for a point in the sequence of points as the quote shows the plurality of readings being taken between time stamps and thus establishing a sequence of readings.)) information about: a position of the point, and a time at which the position was documented. (Pg. 3 – Fig. 1 – 107 – “Time Stamping” & 115 – “IMU-Image Coord Information” & See Also Pg. 41 – Col. 6 – Lines 27 – 30 – “The Inertial component 120 includes an Inertial Measurement engine 105 that implements a time stamping processor 107 that time stamps sets of inertial data from an inertial sensor (not shown in FIG. 1 for clarity sake),” & See Also Pg. 41 – Col. 6 – lines 36 – 39 – “!MU-Image coordinate transformation processor 115 that computes transformations describing differences between a frame of reference of the inertial data and a frame of reference of the image data.” (Equates to information about: a position of the point, and a time at which the position was documented as the quotes provided show the points of data taken being time stamped and positionally reference via the use of an IMU.))
Regarding Claim 3 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 2, wherein the recording further includes, for the point, information about a height of the point with respect to a reference height. (Pg. 67 – Col. 58 – lines 46-49 – “illustrates an example map entry for a feature point of an object located above ground view in extensible markup language. Examples of objects above ground level view include traffic light signals, sidewalks, traffic signs, benches, buildings, fire hydrants, other vehicles, pedestrians, motorbikes, bicycles, trains, etc. The map entry includes a longitude 3504, a latitude 3502, a height 3506 and an ORB feature description 3508.” & See Also Pg. 67 – Col. 57 – lines 29 – 35 – “The non-moving objects are used to build the 3D map. In some implementations, a convolution neural network (CNN) is employed to identify the semantic information of the feature points. Thereby, the lane dividers 3006, the roadside curbs 3008, a mailbox 3016, and traffic signal posts 3018, 3020, 3022, 3024 are identified as non-moving objects and will be included in the 3D map” (equates to wherein the recording further includes, for the point, information about a height of the point with respect to a reference height as the first quote shows the gps providing a reference height for the mapping to be based upon, and the second quote shows the 3d mapping for the same objects being done wherein each and every object is mapped and the 3d mapping includes height.))
Regarding Claim 4 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 2, wherein the position is determined from proprioception information affiliated with the vehicle. (Pg. 67 – Col. 58 – lines 26 – 27 – “A location of feature points will be estimated related to the current position of the autonomous unit” & See Also Pg. 68 – lines 23-27 – “where the map server receives from the autonomous unit with a quadocular-auxiliary sensor, a set of keyrigs, each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit” & See Also Pg. 69 – Col. 62 – lines 26-29 – “selecting a subset of keyrig from the set of keyrigs and determining a sparse 3D mapping of object feature points taken from visual information of the surrounding scenery” (equates to wherein the position is determined from proprioception information affiliated with the vehicle as the first quote shows the location of feature points being determine and thus the position of the scenery is estimated here wherein the other two quotes shows how the 3d mapping is done via proprioception with the imu and gps and visual information being taken in to determine said feature points for position estimation. ))
Regarding Claim 5 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 4, wherein the proprioception information comprises at least one of global navigation satellite system information, inertial measurement unit information, or odometry information. (Pg. 68 – lines 23-27 – “where the map server receives from the autonomous unit with a quadocular-auxiliary sensor, a set of keyrigs, each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit”(equates to wherein the proprioception information comprises at least one of global navigation satellite system information, inertial measurement unit information, or odometry information as the above quote shows the proprioception unit containing an IMU. ))
Regarding Claim 6 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 2, wherein the sequence of points: includes a first point as a first element, includes a second point as a second element, and is characterized by a duration of time between the first element and the second element, (Pg. 66 – Col. 56 – lines 29-41 – “The descriptive point cloud is a set of sparse 3D points, where each point encodes a 3D geometric location, an uncertainty of the geometric location, and a set of 2D location plus appearance-based descriptors, each observed by a keyrig. A keyrig contains (i) a set of 360-degrees images; (ii) a timestamp for when the set of 360-degrees images in the keyrig is captured; (iii) a pose (i.e. the transformation from the quadocular-auxiliary sensor's coordinate to the map coordinate at the current time) and its uncertainty; and (iv) a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to wherein the sequence of points: includes a first point as a first element, includes a second point as a second element, is characterized by a duration of time between the first element and the second element as the quote shows a sequence of readings being included in the point cloud data wherein the first point is one of the readings at one timestamp and the second reading another image at the next timestamp. As the points are mapped to be geographic position traces which equate to the 3d mapping as it is built up from the point cloud data.)) the second element being an element, in the sequence of points, that immediately follows the first element. (Pg. 66 – Col. 56 – lines 29-41 – “The descriptive point cloud is a set of sparse 3D points, where each point encodes a 3D geometric location, an uncertainty of the geometric location, and a set of 2D location plus appearance-based descriptors, each observed by a keyrig. A keyrig contains (i) a set of 360-degrees images; (ii) a timestamp for when the set of 360-degrees images in the keyrig is captured; (iii) a pose (i.e. the transformation from the quadocular-auxiliary sensor's coordinate to the map coordinate at the current time) and its uncertainty; and (iv) a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to the second element being an element, in the sequence of points, that immediately follows the first element as the quote shows a sequence of images from the sensor being captured wherein each reading is timestamped and thus the second sensor data point in the set following from the starting point coordinate is done in a way that immediately follows the first in order to create a sensible map. ))
Regarding Claim 7 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 2, wherein the communications module further includes instructions to receive, from the vehicle, the geographic position trace. (Pg. 69 – Col. 61. – lines 12-13 – “At action 3950, the autonomous unit provides the 3D map via a communications link to the map server” & See Also Pg. 70 – Col. 63 – lines 52 – 57 - “the position of the autonomous unit generated using combinations of global positioning system, inertial measurement unit, and visual information of the surrounding scenery by 55 the first autonomous unit during travel from a starting point to an end point;” & See Also Pg. 55 – Col. 33 – line 54 – “Autonomous units such as autonomous vehicles,” (equates to wherein the communications module further includes instructions to receive, from the vehicle, the geographic position trace as the first quote shows the communications link providing the map from the autonomous unit wherein the last quote shows the autonomous unit being the vehicle itself which does the mapping. ))
Regarding Claim 8 Zhang- SHAOJUN I teaches (Zhang discloses the following limitations:) The system of claim 2, wherein: the aspect, in the region of interest, includes a first road, (Pg. 66 – col. 56 – lines 66-67 and pg. 67 – Col. 57 – line 1 – “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” ) the set of the geographic position traces to be used to produce the digital map includes the geographic position traces of the vehicles that traversed the first road, (Pg. 66 – col. 56 – lines 66-67 and pg. 67 – Col. 57 – line 1 – “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” & See Also Pg. 67 – Col. 57 – lines 4-20 – “Other obstacles situated near the autonomous vehicle 2905 include a pedestrian crossing the road 3012, a pedestrian on the sidewalk 3014, a curbside 3008, a mailbox 3016, and traffic lights/signs 3018, 3020, 3022, 3024. A set of keyrigs will be captured by the quadocular auxiliary sensor of autonomous vehicle 2905 at location 2910, each keyrig containing: (i) a timestamp where the images in the keyrig is captured; (ii) a pose (latitude, longitude position of the car, and orientation); (iii) a pair of 360-degrees images captured by the cameras, and (iv) a sequence of readings from the auxiliary sensors. The pair of360-degrees images will have visual information regarding the car in front 3026, the car at the back 3028, the lane divider 3006, the pedestrian crossing 3010, the pedestrian crossing the road 3012, the pedestrian on the sidewalk 3014, the curbside 3008, the mailbox 3016, and the traffic lights/signs 3018, 3020, 3022, 3024” (equates to the set of the geographic position traces to be used to produce the digital map includes the geographic position traces of the vehicles that traversed the first road, as the first quote shows the road itself being travelled upon by the vehicle and the second quote showed the traces being captured that form the 3d map being taken of various objects on or near the road.)) the count of the geographic position traces in the set is a count of the geographic position traces of the vehicles that traversed the first road, (Pg. 46 – Col. 15 – lines 50-56 – “One implementation of feature extractor 402 adaptively adjusts a threshold that is applied to a number of features needed in order for the system to keep track of a moving object. Such intelligent thresholds include a threshold that is adaptively adjusted based upon device movement, sensor readings, situational or environmental variables (e.g., low light, fog, bright light, and so forth) or combinations thereof.” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 66 – col. 56 – lines 66-67 and pg. 67 – Col. 57 – line 1 – “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” (equates to and the count of the geographic position traces in the set is a count of the geographic position traces of the vehicles that traversed the first road as the first quote shows the number of sensor readings being included within a threshold comparison for understanding a scenery, wherein the second quote shows the sensor being used for the collection of geographic position traces, and the last quote showing the vehicle traversing a road wherein these position traces would be taken while the vehicle is travelling.)) and the count of the geographic position traces of the vehicles that traversed the first road is greater than the first threshold count. (Pg. 46 – Col. 15 – lines 50-56 – “One implementation of feature extractor 402 adaptively adjusts a threshold that is applied to a number of features needed in order for the system to keep track of a moving object. Such intelligent thresholds include a threshold that is adaptively adjusted based upon device movement, sensor readings, situational or environmental variables (e.g., low light, fog, bright light, and so forth) or combinations thereof.” & See Also Pg. 46 – Col. 16 – lines 54-64 – “Then 2D-3D correspondence information for the 55 optical flow tracked 2D features is obtained by directly using 2D-2D correspondences from optical flow tracking results. For the remaining 3D map points, smartly search over 3D with adaptive parameters by: (i) if the remaining number of points is small, e.g., below a threshold, perform a linear 60 search, otherwise, (ii) if the remaining number of points is fairly big, e.g., above a threshold, perform a log(n) search with the use of a kd-tree or octree. During search, use previous time period velocity/acceleration to predict a pose so as to narrow the search region” & See Also Pg. 66 – col. 56 – lines 66-67 and pg. 67 – Col. 57 – line 1 – “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” (equates to and the count of the geographic position traces of the vehicles that traversed the first road is greater than a first threshold count as the first quote shows the sensor readings being considered for accuracy determination and the number of readings or counts being used against a threshold value, the second quote showing the information gleaned from the 3d map extraction being compared to a threshold and being over s threshold, and finally the last quote showing the sensor readings would be taken while the vehicle is traversing a first road. ) )
Regarding Claim 9 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 8, wherein the count of the geographic position traces of the vehicles that traversed the first road is less than the second threshold count. (Pg. 46 – Col. 16 – lines 54-60 – “Then 2D-3D correspondence information for the 55 optical flow tracked 2D features is obtained by directly using 2D-2D correspondences from optical flow tracking results. For the remaining 3D map points, smartly search over 3D with adaptive parameters by: (i) if the remaining number of points is small, e.g., below a threshold, perform a linear 60 search, otherwise” (equates to wherein the count of the geographic position traces of the vehicles that traversed the first road is less than a second threshold count as the quote shows the information gleaned from the 3d mapping results being less than a prescribed threshold as shown previously this information can be a number of counts of the geographic positioning traces via the sensor readings.))
Regarding Claim 10 Zhang- SHAOJUN teaches (Zhang discloses the following limitations:) The system of claim 8, wherein: the geographic position trace: is included in the set of the geographic position traces, (Pg. 41 – Col. 6 – lines 27-30 – “The Inertial component 120 includes an Inertial Measurement engine 105 that implements a time stamping processor 107 that time stamps sets of inertial data from an inertial sensor (not shown in FIG. 1 for clarity sake),” & See Also Pg. 47 – Col. 17 – lines 23-26 – “As used herein, the term keyrig denotes a set of image data received. In some implementations, the sets of image data include feature points and pose information.”(equates to : is included in the set of the geographic position traces as the quotes show sets of data from the sensors that make up the position traces to be including into this way of grouping data. )) and comprises a sequence of geographic position traces, (Pg. 41 – Col. 6 – lines 27-30 – “The Inertial component 120 includes an Inertial Measurement engine 105 that implements a time stamping processor 107 that time stamps sets of inertial data from an inertial sensor (not shown in FIG. 1 for clarity sake),” & See Also Pg. 48 – Col. 20 – lines 17-19 – “iii) a sequence of IMU readings starting from a first temporal point before the timestamp to a second temporal point after the timestamp” & See Also Pg. 66 – Col. 56 – lines 38 -41 – “a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to comprises a sequence of geographic position traces as we see from both quotes that sensors used to make up geographic position traces transmit the data of readings in a time based sequence.)) the sequence of geographic position traces includes a first geographic position trace and a second geographic position trace, (Pg. 48 – Col. 20 – lines 17-19 – “iii) a sequence of IMU readings starting from a first temporal point before the timestamp to a second temporal point after the timestamp” & See Also Pg. 66 – Col. 56 – lines 38 -41 – “a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to the sequence of geographic position traces includes a first geographic position trace and a second geographic position trace as tach sensor that makes up the data of the geographic position trace contains a first and second point within the sequence that shows a time difference between the sensor readings. )) the first geographic position trace is affiliated with a traversal during a first duration of time, (Pg. 48 – Col. 20 – lines 17-19 – “iii) a sequence of IMU readings starting from a first temporal point before the timestamp to a second temporal point after the timestamp” & See Also Pg. 66 – Col. 56 – lines 38 -41 – “a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to the first geographic position trace is affiliated with a traversal during a first duration of time as we see a position trace sensor reading being associated with a first time before a timestamp.) ) the second geographic position trace is affiliated with a traversal during a second duration of time, (Pg. 48 – Col. 20 – lines 17-19 – “iii) a sequence of IMU readings starting from a first temporal point before the timestamp to a second temporal point after the timestamp” & See Also Pg. 66 – Col. 56 – lines 38 -41 – “a sequence of readings from the auxiliary sensors starting from a first temporal point before the timestamp to a second temporal point after the timestamp.” (equates to the second geographic position trace is affiliated with a traversal during a second duration of time as we see a position trace sensor reading being associated with a second time after a timestamp.) ) the second duration of time lacking an overlap with the first duration of time, (Pg. 49 – [Co.. 21 ] – Lines 24-37 – “If the keyrig being considered for addition contains features not included in the current descriptive point cloud, triangulate the new features from images captured from the device at this timestamp to obtain the points in the device's coordinate frame. Add the new points to the map by transforming the points from the device's coordinate frame to the map's coordinate frame. Noteworthy is that some implementations include one or more of (i) triangulating new feature points across images from a current/same keyrig (e.g. between left and right cameras), (ii) triangulating new feature points across images from two different keyrigs, wherein the two different keyrigs are not necessarily in sequence (e.g. left camera from keyrig 1 to left camera from keyrig 10)” & See Also Pg. 49 – Col. 21 – lines 7-10 – “The first image frame is selected as a keyrig, and the device coordinate frame at that timestamp become the coordinates of the descriptive point cloud. This establishes a frame of reference.” (equates to the second duration of time lacking an overlap with the first duration of time as the first quote shows a selection of keyrigs from different timestamps being used to triangulate an object that was not previously found but by implementing data from a stamp 10 into data from 1 the object can be accurately shown in the first coordinate frame even if detected at a another timestamp in another coordinate frame. The second quote shows how the reference frame for each keyrig is based on a timestamp.)) and the count of the geographic position traces in the set includes: the first geographic position trace as one count, and the second geographic position trace as another count. (Pg. 49 – [Co.. 21 ] – Lines 24-37 – “If the keyrig being considered for addition contains features not included in the current descriptive point cloud, triangulate the new features from images captured from the device at this timestamp to obtain the points in the device's coordinate frame. Add the new points to the map by transforming the points from the device's coordinate frame to the map's coordinate frame. Noteworthy is that some implementations include one or more of (i) triangulating new feature points across images from a current/same keyrig (e.g. between left and right cameras), (ii) triangulating new feature points across images from two different keyrigs, wherein the two different keyrigs are not necessarily in sequence (e.g. left camera from keyrig 1 to left camera from keyrig 10)” (equates to and the count of the geographic position traces in the set includes: the first geographic position trace as one count, and the second geographic position trace as another count. As the quote shows a keyrig 1 and keyrig 10 and thus establishing a first and second geographic position trace and each being a count based on timestamp analysis.))
Regarding Claim 19 Zhang teaches A method, (Pg. 40 – col. 3 – lines 21 -23 – “FIG. 37 is a representative method for building 3D maps using information sourced by one or more moving autonomous units with quadocular-auxiliary sensory systems”) comprising: determining, by a processor (Pg. 69 – Col. 61 – lines 17-20 – “Other implementations of the method described in this section can include a non-transitory computer-readable storage medium storing instructions executable by a processor to perform any of the methods described above”) and from geographic position traces of vehicles that traversed a region of interest, (Pg. 40 – Col. 3 – lines 8-9 – “FIG. 32 illustrates detection of features points to build a sparse 3D mapping of object feature points.” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” (equates to and from geographic position traces of vehicles that traversed a region of interest, as the quote shows the keyrigs being combination data of gps imu, etc. and that is equivalent to the geographic position traces wherein a digital map is made based on the data collected from the autonomous vehicle while it is in transit.)) a set of geographic position traces to be used to produce a digital map of the region of interest, (Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to a set of geographic position traces to be used to produce a digital map of the region of interest, as the quote shows the 3d mapping being done and thus producing a digital map.)) wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map; (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”(equates to wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map; as the first quote shows the classification of objects in the mapping that is performed wherein the object detected in this cited art is equivalent to the application aspect of basing the mapping upon. The last quote shows a count of traces as multiple poses and previous imaging is considered in the mapping. )) the count (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”) producing, by the processor and from the set, the digital map; (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to producing, by the processor and from the set, the digital map as the processor is shown to handle any method step implementation from the art and the second quote shows the 3d mapping being done and thus producing a digital map.)) and transmitting, by the processor, the digital map to a specific vehicle to be used to control movement of the specific vehicle. (Pg. 44 – Col. 11 – lines 11-17 – “Communications interface 342 can include hardware and/ or software that enables communication between visual inertial positioning system 300 and other systems controlling or enabling customer hardware and applications (hereinafter, a "host system" or "host") such as for example, a robot or other guided mobile platform, an autonomous vehicle,” (equates to and transmitting, by the processor, the digital map to a specific vehicle to be used to control movement of the specific vehicle as the quote shows the communication interface allowing the sensor and the mapping data it is handling to be used on controlling movement of an autonomous vehicle.))
Yet Zhang fails to teach being between a first threshold count and a second threshold count;
SHAOJUN teaches being between a first threshold count and a second threshold count; (Pg. 5 – “Further preferably, the triggering, by the monocular SLAM positioning module, monocular SLAM positioning includes: when the matching number of the current frame and the local map points is smaller than a first threshold and larger than a second threshold, the positioning quality is poor” & See Also Pg. 7 – “Preferably, the first threshold value is one value in a range of 80 to 100, and the second threshold value is one value in a range of 20 to 40.” (equates to being between a first threshold count and a second threshold count; as the first quote shows the matching number in a SLAM method being less than a first threshold and more than a second threshold.)) It would have been an advantageous addition to the system disclosed by Zhang to include being between a first threshold count and a second threshold count as this would allow for the count to fall within a range that does not exceed the processing capabilities of the system or require extraneous processing power to work.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include being between a first threshold count and a second threshold count as this allows for a minimum number of counts to be included ensuring a large enough dataset while not overloading the processing abilities of the system with too much data.
Regarding Claim 20 Zhang teaches A non-transitory computer-readable medium for determining a set of geographic position traces to be used to produce a digital map of a region of interest, (Pg. 52 – Col. 28 – lines 57 – 60 – “Other implementations of the method described in this section can include a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described above”) the non-transitory computer- readable medium including instructions that, (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 47 – Col. 18 – lines 29 – 34 – “In action 520, the keyrigs are searched using the selected search strategy in order to find among the keyrigs a keyrig with bag of words description closest to a bag of words description of a current image. In action 530, determine whether the match quality is sufficient” & See Also Pg. 52 – Col. 28 – lines 57 – 60 – “Other implementations of the method described in this section can include a non-transitory computer readable storage medium storing instructions executable by a processor to perform any of the methods described above” (equates to the non-transitory computer- readable medium including instructions that, as the quote shows a system able to execute instruction that lead to 3d mapping of the surrounding environment, by way of matching the traces together across a plurality of images, which is what the set determination module does by building a scene with the geographic positional traces )) when executed by one or more processors, cause the one or more processors to: determine, (Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above”) from geographic position traces of vehicles that traversed the region of interest, (Pg. 40 – Col. 3 – lines 8-9 – “FIG. 32 illustrates detection of features points to build a sparse 3D mapping of object feature points.” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” (equates to and from geographic position traces of vehicles that traversed a region of interest, as the quote shows the keyrigs being combination data of gps imu, etc. and that is equivalent to the geographic position traces wherein a digital map is made based on the data collected from the autonomous vehicle while it is in transit.)) the set of geographic position traces to be used to produce the digital map of the region of interest, (Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to a set of geographic position traces to be used to produce a digital map of the region of interest, as the quote shows the 3d mapping being done and thus producing a digital map.)) wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map; (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”(equates to wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map; as the first quote shows the classification of objects in the mapping that is performed wherein the object detected in this cited art is equivalent to the application aspect of basing the mapping upon. The last quote shows a count of traces as multiple poses and previous imaging is considered in the mapping. )) the count (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”) produce, from the set, the digital map; Pg. 64 – Col. 51 – lines 52-56 – “Yet another implementation of the method described in this section can include a system including one or more memory and one or more processors operable to execute instructions, stored in the memory, to perform any of the methods described above” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to produce, from the set, the digital map; as the processor is shown to handle any method step implementation from the art and the second quote shows the 3d mapping being done and thus producing a digital map.))and transmit the digital map to a specific vehicle to be used to control movement of the specific vehicle. (Pg. 44 – Col. 11 – lines 11-17 – “Communications interface 342 can include hardware and/ or software that enables communication between visual inertial positioning system 300 and other systems controlling or enabling customer hardware and applications (hereinafter, a "host system" or "host") such as for example, a robot or other guided mobile platform, an autonomous vehicle,” (equates to and transmit the digital map to a specific vehicle to be used to control movement of the specific vehicle as the quote shows the communication interface allowing the sensor and the mapping data it is handling to be used on controlling movement of an autonomous vehicle.))
Yet Zhang fails to teach being between a first threshold count and a second threshold count;
SHAOJUN teaches being between a first threshold count and a second threshold count; (Pg. 5 – “Further preferably, the triggering, by the monocular SLAM positioning module, monocular SLAM positioning includes: when the matching number of the current frame and the local map points is smaller than a first threshold and larger than a second threshold, the positioning quality is poor” & See Also Pg. 7 – “Preferably, the first threshold value is one value in a range of 80 to 100, and the second threshold value is one value in a range of 20 to 40.” (equates to being between a first threshold count and a second threshold count; as the first quote shows the matching number in a SLAM method being less than a first threshold and more than a second threshold.)) It would have been an advantageous addition to the system disclosed by Zhang to include being between a first threshold count and a second threshold count as this would allow for the count to fall within a range that does not exceed the processing capabilities of the system or require extraneous processing power to work.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include being between a first threshold count and a second threshold count as this allows for a minimum number of counts to be included ensuring a large enough dataset while not overloading the processing abilities of the system with too much data.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang-Shaojun in view of PHILIPPE et al. (CN115892017A).
Regarding Claim 11 Zhang- Shaojun teaches The system of claim 8, wherein: the aspect further includes: a first lane of the first road, and a second lane of the first road, (Pg. 56 – Col. 35 – lines 19-21 – “Some existing digital maps may have sub-meter level accuracy. Some existing digital maps may have lane-level accuracy.” & See Also Pg. 58 – Col. 40 – lines 33-35 – “In autonomous vehicles, CNNs can be used to perform lane and vehicle detection while running at frame rates required for a real-time system” & See Also Pg. 66-67 – Col. 56-57 – lines 66 – line 1 - “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” (equates to the aspect further includes: a first lane of the first road, and a second lane of the first road, as the quote shows the ability to detect the vehicle is within a certain lane as detection of a lane divider is possible and thus a first and second lane exist.))
Yet Zhang- Shaojun fails to teach the geographic position traces of the vehicles that traversed the first road includes a first subset and a second subset, the first subset includes the geographic position traces of the vehicles that traversed the first lane, the second subset includes the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first road includes a first count subset and a second count subset, the first count subset is a count of the geographic position traces of the vehicles that traversed the first lane, the second count subset is a count of the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first lane is greater than a third threshold count, and the count of the geographic position traces of the vehicles that traversed the second lane is greater than a fourth threshold count.
PHILIPPE teaches the geographic position traces of the vehicles that traversed the first road includes a first subset and a second subset, (Pg. 9 – [76 ] – “At 210, if first preliminary lane estimates based on multiple lane markings are available and the number of first preliminary lane estimates based on multiple lane markings is above a predetermined lane marking threshold,” & See Also Pg. 8 – [65] – “At 104 , the location and type of lane markings may be determined based on sensor data 122 . Therefore, appropriate methods can be used, such as image recognition methods using neural networks or classical methods that are not based on machine learning. Sensor data 122 may be determined using cameras and/or LiDAR sensors or any other suitable sensors.” & See Also Pg. 8 – [68] – “In other words, the plurality of estimated lane markings 132 may be combined from multiple trips and/or from multiple trips of recorded vehicles recorded at the same location 136 to determine a combined, more accurate estimates” & See Also Pg. 9 – [74] – “At 206 , if a second estimated lane based on multiple trajectories is available and the number of second estimated lanes based on multiple trajectories is above a predetermined trajectory threshold” (equates to the geographic position traces of the vehicles that traversed the first road includes a first subset and a second subset as the first three quote show the subset of data for the first lane comprising trajectories of vehicles traversing a first lane by way of lane markings and the second lane data subset being multiple vehicle trajectories.)) the first subset includes the geographic position traces of the vehicles that traversed the first lane, (Pg. 9 – [76 ] – “At 210, if first preliminary lane estimates based on multiple lane markings are available and the number of first preliminary lane estimates based on multiple lane markings is above a predetermined lane marking threshold,” & See Also Pg. 8 – [65] – “At 104 , the location and type of lane markings may be determined based on sensor data 122 . Therefore, appropriate methods can be used, such as image recognition methods using neural networks or classical methods that are not based on machine learning. Sensor data 122 may be determined using cameras and/or LiDAR sensors or any other suitable sensors.” & See Also Pg. 8 – [68] – “In other words, the plurality of estimated lane markings 132 may be combined from multiple trips and/or from multiple trips of recorded vehicles recorded at the same location 136 to determine a combined, more accurate estimates” (equates to the first subset includes the geographic position traces of the vehicles that traversed the first lane as the quotes show the geographic position traces being the detected lane marking that are based on vehicle trajectories through the first lane.)) the second subset includes the geographic position traces of the vehicles that traversed the second lane, (Pg. 9 – [74] – “At 206 , if a second estimated lane based on multiple trajectories is available and the number of second estimated lanes based on multiple trajectories is above a predetermined trajectory threshold” (equates to the second subset includes the geographic position traces of the vehicles that traversed the second lane as the subset is shown to be vehicle trajectories which is compared to a threshold for later scenario understanding.)) the count of the geographic position traces of the vehicles that traversed the first road includes a first count subset and a second count subset, (Pg. 9 – [76 ] – “At 210, if first preliminary lane estimates based on multiple lane markings are available and the number of first preliminary lane estimates based on multiple lane markings is above a predetermined lane marking threshold,” & See Also Pg. 8 – [65] – “At 104 , the location and type of lane markings may be determined based on sensor data 122 . Therefore, appropriate methods can be used, such as image recognition methods using neural networks or classical methods that are not based on machine learning. Sensor data 122 may be determined using cameras and/or LiDAR sensors or any other suitable sensors.” & See Also Pg. 8 – [68] – “In other words, the plurality of estimated lane markings 132 may be combined from multiple trips and/or from multiple trips of recorded vehicles recorded at the same location 136 to determine a combined, more accurate estimates” & See Also Pg. 9 – [74] – “At 206 , if a second estimated lane based on multiple trajectories is available and the number of second estimated lanes based on multiple trajectories is above a predetermined trajectory threshold” (equates to the count of the geographic position traces of the vehicles that traversed the first road includes a first count subset and a second count subset as the first three quote show the subset of data for the first lane comprising trajectories of vehicles traversing a first lane by way of lane markings and includes a count by way of the number of the multiple trip, and the second lane data subset being multiple vehicle trajectories and the counts of the subset being represented by the multiple trajectories.)) the first count subset is a count of the geographic position traces of the vehicles that traversed the first lane, (Pg. 9 – [76 ] – “At 210, if first preliminary lane estimates based on multiple lane markings are available and the number of first preliminary lane estimates based on multiple lane markings is above a predetermined lane marking threshold,” & See Also Pg. 8 – [65] – “At 104 , the location and type of lane markings may be determined based on sensor data 122 . Therefore, appropriate methods can be used, such as image recognition methods using neural networks or classical methods that are not based on machine learning. Sensor data 122 may be determined using cameras and/or LiDAR sensors or any other suitable sensors.” & See Also Pg. 8 – [68] – “In other words, the plurality of estimated lane markings 132 may be combined from multiple trips and/or from multiple trips of recorded vehicles recorded at the same location 136 to determine a combined, more accurate estimates” (equates to the first count subset is a count of the geographic position traces of the vehicles that traversed the first lane, as the quote shows lane making captured by way of vehicle trajectories and the traces of the vehicles over a plurality of trips are the counts used to determine the first lane.)) the second count subset is a count of the geographic position traces of the vehicles that traversed the second lane, (Pg. 9 – [74] – “At 206 , if a second estimated lane based on multiple trajectories is available and the number of second estimated lanes based on multiple trajectories is above a predetermined trajectory threshold” (equates to the second count subset is a count of the geographic position traces of the vehicles that traversed the second lane as the quote shows the vehicles trajectories or object traces being used to determine a second lane. )) the count of the geographic position traces of the vehicles that traversed the first lane is greater than a third threshold count, (Pg. 9 – [76 ] – “At 210, if first preliminary lane estimates based on multiple lane markings are available and the number of first preliminary lane estimates based on multiple lane markings is above a predetermined lane marking threshold,” & See Also Pg. 8 – [65] – “At 104 , the location and type of lane markings may be determined based on sensor data 122 . Therefore, appropriate methods can be used, such as image recognition methods using neural networks or classical methods that are not based on machine learning. Sensor data 122 may be determined using cameras and/or LiDAR sensors or any other suitable sensors.” & See Also Pg. 8 – [68] – “In other words, the plurality of estimated lane markings 132 may be combined from multiple trips and/or from multiple trips of recorded vehicles recorded at the same location 136 to determine a combined, more accurate estimates” (equates to the count of the geographic position traces of the vehicles that traversed the first lane is greater than a third threshold count as the first quote shows a threshold being above a prescribed limit in terms of detecting lane markings for a first traveling lane and the second quote showing the lane markings being data as a data type corresponding to sensor data and third quote showing the vehicle traversing a region thus the lane marking data is a geographical position trace. )) the count of the geographic position traces of the vehicles that traversed the second lane is greater than a fourth threshold count (Pg. 9 – [74] – “At 206 , if a second estimated lane based on multiple trajectories is available and the number of second estimated lanes based on multiple trajectories is above a predetermined trajectory threshold” (equates to the count of the geographic position traces of the vehicles that traversed the second lane is greater than a fourth threshold count as the quote shows a second lane wherein a number or count of vehicle trajectories or geographic position traces are used to determine if the lane exists and does if above the prescribed threshold for the second lane.)). It would have been an advantageous addition to the system disclosed by Zhang- Shaojun to include the geographic position traces of the vehicles that traversed the first road includes a first subset and a second subset, the first subset includes the geographic position traces of the vehicles that traversed the first lane, the second subset includes the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first road includes a first count subset and a second count subset, the first count subset is a count of the geographic position traces of the vehicles that traversed the first lane, the second count subset is a count of the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first lane is greater than a third threshold count, and the count of the geographic position traces of the vehicles that traversed the second lane is greater than a fourth threshold count as these limitations allow for detection of lanes to be had within the system thus allowing an improved mapping experience that specifically takes into account other vehicle and host vehicle trajectories into account for determining lane existence of the vehicles traveling on the specified region.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include the geographic position traces of the vehicles that traversed the first road includes a first subset and a second subset, the first subset includes the geographic position traces of the vehicles that traversed the first lane, the second subset includes the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first road includes a first count subset and a second count subset, the first count subset is a count of the geographic position traces of the vehicles that traversed the first lane, the second count subset is a count of the geographic position traces of the vehicles that traversed the second lane, the count of the geographic position traces of the vehicles that traversed the first lane is greater than a third threshold count, and the count of the geographic position traces of the vehicles that traversed the second lane is greater than a fourth threshold count as these limitation allow for a simple measurable way of determining the existence of a multiple lane road based on the threshold of traces of surrounding or host vehicles traveling upon a given region thus allowing for multiple lanes to be mapped within the system allowing for better control of the host vehicle.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang- Shaojun in view of Yonglu ( CN112634320B), and in further view of Sun (US 2020/0134325 Al).
Regarding Claim 12 Zhang- Shaojun teaches The system of claim 8, wherein: the aspect further includes a road junction of the first road and a second road, (Pg. 67 – Col. 58 – lines 36 – 42 – “FIG. 34 illustrates an example map entry for a feature point of an object located on ground level view in extensible markup language. Examples of objects located on ground level view include broken white lines, solid white lines, double yellow solid lines, broken yellow lines, edge lines, HOV lanes, freeway entrances and exits, pedestrian crosswalks, stop lines, roundabouts, signalized intersections,” (equates to the aspect further includes a road junction of the first road and a second road as the object detected is shown to include an intersection which would comprise two or more lanes.)) the geographic position traces of the vehicles that traversed the first road includes a subset, (Pg. 66 – col. 56 – lines 66-67 and pg. 67 – Col. 57 – line 1 – “The autonomous vehicle 2905 is on a two-lane road in an urban environment with a car in front 3026 and a car at the back 3028” & See Also Pg. 67 – Col. 57 – lines 40-45 – “FIG. 32 illustrates detection of features points of non- 40 moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points” (equates to the geographic position traces of the vehicles that traversed the first road includes a subset as the quote show the vehicle travelling across a first road and collecting a subset of data to determine road features.)) the subset includes the geographic position traces of the vehicles that traversed the road junction, (Pg. 67 – [Col. 57 ] – lines 43-46 – “A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points” & See Also Pg. 67 – Col. 58 – lines 36 – 42 – “FIG. 34 illustrates an example map entry for a feature point of an object located on ground level view in extensible markup language. Examples of objects located on ground level view include broken white lines, solid white lines, double yellow solid lines, broken yellow lines, edge lines, HOV lanes, freeway entrances and exits, pedestrian crosswalks, stop lines, roundabouts, signalized intersections,” (equates to the subset includes the geographic position traces of the vehicles that traversed the road junction, as the quote shows the subset of data used for identifying feature points of the specified location wherein the second quote shows the feature point can include an intersection. ))
Yet Zhang- Shaojun fails to teach the count of the geographic position traces of the vehicles that traversed the first road includes a count subset, the count subset is a count of the geographic position traces of the vehicles that traversed the road junction, and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count.
Yonglu teaches the count of the geographic position traces of the vehicles that traversed the first road includes a count subset (Pg. 4 – [41] – “ One or more embodiments of the present application can determine traffic flow information based on the direction of travel of the vehicle, and the traffic flow information includes, but is not limited to, the direction of travel of vehicles at each intersection of the venue, the number of vehicles entering each intersection of the venue, the number of vehicles in each direction of travel at each intersection of the venue ” & See Also Pg. 4 – [44] – “The trajectory point determination module 104 may be used to determine the coordinate position of the target object in a plurality of images, and then obtain a plurality of trajectory points on the motion trajectory of the target object” (equates to the count of the geographic position traces of the vehicles that traversed the first road includes a count subset as the first quote shows the count or the number of vehicles traveling through an intersection which contains a road and the trace being denoted by way of determining trajectories in the second quote.)) the count subset is a count of the geographic position traces of the vehicles that traversed the road junction, (Pg. 4 – [41] – “ One or more embodiments of the present application can determine traffic flow information based on the direction of travel of the vehicle, and the traffic flow information includes, but is not limited to, the direction of travel of vehicles at each intersection of the venue, the number of vehicles entering each intersection of the venue, the number of vehicles in each direction of travel at each intersection of the venue ” & See Also Pg. 4 – [44] – “The trajectory point determination module 104 may be used to determine the coordinate position of the target object in a plurality of images, and then obtain a plurality of trajectory points on the motion trajectory of the target object” (equates to the count subset is a count of the geographic position traces of the vehicles that traversed the road junction as the quote shows a count of vehicles traversing an intersection or road junction. ))
Yet all fail to teach and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count.
Sun teaches and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count. (Pg. 21 – [0072] – “As shown in FIG. 9, the selected feature parameter(s) may include the number of the trajectories, the probability of stopping twice, the stop duration, the average value and the variance of stop distances, the average value and the variance of the speeds of passing through the intersection, and the delay time. In operation 903, a determination as to whether the number of trajectories is greater than a first threshold may be made” & See Also Pg. 10 – Fig. 9 (equates to and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count as the threshold count in this art is pertaining to a count of vehicle trajectories passing through an intersection and thus a second threshold is seen. Fig. 9 and the quote both further demonstrate how this trajectory threshold count is in relation to vehicles passing through an intersection. )) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu to include and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count as this limitation allows for certain number of vehicle trajectories to have to be taken in by the system for best estimating when the vehicle is within an intersection.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and the count of the geographic position traces of the vehicles that traversed the road junction is greater than a third threshold count as this limitation gives a certainty about the trajectories crossing through an intersection by ensuring the sample amount is of appropriate size when compared to the threshold value.
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang- Shaojun -Yonglu-Sun and in further view of Li (US 2023/0065341 Al)
Regarding Claim 13 Zhang- Shaojun -Yonglu- Sun teaches The system of claim 12, as previously mapped above wherein:
Yet Zhang- Shaojun -Sun fails to teach the aspect further includes: a first direction of travel through the road junction, and a second direction of travel through the road junction, the geographic position traces of the vehicles that traversed the road junction includes a first sub-subset and a second sub-subset, the first sub-subset includes the geographic position traces of the vehicles that traversed the first direction of travel through the road junction, the second sub-subset includes the geographic position traces of the vehicles that traversed the second direction of travel through the road junction, the count of the geographic position traces of the vehicles that traversed the road junction includes a first count sub-subset and a second count sub-subset, the first count sub-subset is a count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction, the second count sub-subset is a count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction, the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a fourth threshold count, and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth threshold count.
Yonglu teaches : a first direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 7 – [65] – “an algorithm for determining the target vehicle's driving direction, an algorithm for determining whether the target vehicle is going straight” (equates to a first direction of travel through the road junction, as the art cited generates predictions of object trajectories through intersections as seen within the first quote wherein the first direction determination will be straight. )) and a second direction of travel through the road junction (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 7 – [65] – “an algorithm for determining whether the target vehicle is turning left or right, and the thresholds involved in each algorithm.” (equates to and a second direction of travel through the road junction as the second direction considered is going right through the intersection.)) the geographic position traces of the vehicles that traversed the road junction includes a first sub-subset and a second sub-subset (Pg. 9 – [88] – “after determining that the target vehicle is going straight, the method further includes counting the number of vehicles going straight” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted” (equates to the geographic position traces of the vehicles that traversed the road junction includes a first sub-subset and a second sub-subset as the first quote shows a subset of data for the straight direction and the second quote showing a subset of data for the right turning direction. )) the first sub-subset includes the geographic position traces of the vehicles that traversed the first direction of travel through the road junction (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 9 – [88] – “after determining that the target vehicle is going straight, the method further includes counting the number of vehicles going straight” (equates to the first sub-subset includes the geographic position traces of the vehicles that traversed the first direction of travel through the road junction as the quote shows the subset being vehicles going straight wherein the art overall is about directionality of the vehicle through the intersection.)) the second sub-subset includes the geographic position traces of the vehicles that traversed the second direction of travel through the road junction (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the second sub-subset includes the geographic position traces of the vehicles that traversed the second direction of travel through the road junction as the quote shows the subset of data being included for the right turning vehicles as they are passing through the intersection. )) the count of the geographic position traces of the vehicles that traversed the road junction includes a first count sub-subset and a second count sub-subset, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 9 – [88] – “after determining that the target vehicle is going straight, the method further includes counting the number of vehicles going straight” & See Also Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the count of the geographic position traces of the vehicles that traversed the road junction includes a first count sub-subset and a second count sub-subset as we have two separate numbers for the count subset for each straight and right turning directions.)) the first count sub-subset is a count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 9 – [88] – “after determining that the target vehicle is going straight, the method further includes counting the number of vehicles going straight” (equates to the first count sub-subset is a count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction, as the quote shows the subset being vehicles going straight wherein the art overall is about directionality of the vehicle through the intersection. And the count is being done via the number of vehicles passing through heading straight)) the second count sub-subset is a count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the second count sub-subset is a count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction as the quote shows the subset of data being included for the right turning vehicles as they are passing through the intersection, wherein a count is taking place via a number of vehicles making the right turn.))
Yet all fail to teach the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a forth threshold count, and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth = threshold count.
Li teaches teach the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a forth threshold count, (Pg. 9 – [0054] – “The associated roads include a road that the vehicle enters from a current road and a road that the vehicle is on before entering the current road. In practical applications, a road has two ends. If each end has a crossroad, features such as turn left from Road 1 into the current road, turn right from Road 2 into the current road, go straight from Road 3 into the current road, turn around from Road 4 into the current road, turn left from the current road into Road 4, tum right from the current road into Road 5, go straight from the current road into Road 6, and turn around from the current road into Road 8 may be set. The trajectory linkage feature may also be identified by using a multi-dimensional vector. For example, a number of trajectories of the vehicle with the above features may be counted in the time window. In order to eliminate errors, when the number of trajectories is greater than a preset number threshold, it may be considered that a corresponding trajectory linkage feature exists at the corresponding position” (equates to teach the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a forth threshold count, as the quote shows a threshold of trajectories being looked at for determining a road feature and the forth threshold in this case can be the straight direction as each direction, straight, left and right would each have their associated threshold within the matrix to determine whether or not said road exists.)) and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth threshold count. (Pg. 9 – [0054] – “The associated roads include a road that the vehicle enters from a current road and a road that the vehicle is on before entering the current road. In practical applications, a road has two ends. If each end has a crossroad, features such as turn left from Road 1 into the current road, turn right from Road 2 into the current road, go straight from Road 3 into the current road, turn around from Road 4 into the current road, turn left from the current road into Road 4, tum right from the current road into Road 5, go straight from the current road into Road 6, and turn around from the current road into Road 8 may be set. The trajectory linkage feature may also be identified by using a multi-dimensional vector. For example, a number of trajectories of the vehicle with the above features may be counted in the time window. In order to eliminate errors, when the number of trajectories is greater than a preset number threshold, it may be considered that a corresponding trajectory linkage feature exists at the corresponding position” (equates to and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth threshold count as the fourth threshold would be the right turning threshold as each of the trajectories within matrix would have their own associated threshold value to determine if the road linkage were to be there.)) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu-Sun to include the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a forth threshold count, and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth threshold count as these limitations allow for a prescribed number of trajectories in multiple directions to be taken into account for best understanding the paths of travel available for mapping when passing through a designated area.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include the count of the geographic position traces of the vehicles that traversed the first direction of travel through the road junction is greater than a forth threshold count, and the count of the geographic position traces of the vehicles that traversed the second direction of travel through the road junction is greater than a fifth threshold count as this limitation allows for a plurality of directions to be considered for mapping the scene wherein a threshold in a plurality of directions allows for a higher level of certainty to be attained when mapping potential roads and actions the host vehicle can take based on other vehicle data.
Regarding Claim 14 Zhang- Shaojun -Yonglu-Sun-Li teaches The system of claim 13, wherein: as previously mapped above.
Yet Zhang- Shaojun -Sun fails to teach the aspect further includes a third direction of travel through the road junction, the geographic position traces of the vehicles that traversed the road junction further includes a third sub-subset, the third sub-subset includes the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, the count of the geographic position traces of the vehicles that traversed the road junction further includes a third count sub-subset, the third count sub-subset is a count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a sixth threshold count.
Yonglu teaches the aspect further includes a third direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 7 – [65] – “an algorithm for determining whether the target vehicle is turning left or right, and the thresholds involved in each algorithm.” (equates to the aspect further includes a third direction of travel through the road junction, as the quote shows the left turning direction being separate from straight and right turning previously mapped as first and second directions.)) , the geographic position traces of the vehicles that traversed the road junction further includes a third sub-subset, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to , the geographic position traces of the vehicles that traversed the road junction further includes a third sub-subset as the quote shows a subset of data for vehicles that are turning left to be counted and thus equates to a third subset as the previous quote mapped the subsets of right and straight respectively.) ) the third sub-subset includes the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the third sub-subset includes the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, as the quote shows the third direction comprising left turning vehicles and a number of these vehicles being stored into a third subset.) ) the count of the geographic position traces of the vehicles that traversed the road junction further includes a third count sub-subset, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the count of the geographic position traces of the vehicles that traversed the road junction further includes a third count sub-subset as the quote shows a number of vehicles tallied for their left turning and thus a count subset of another direction of vehicles is attained.)) the third count sub-subset is a count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, (Pg. 17 – [182] – “Based on the above method for identifying the driving direction of a vehicle at an intersection” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted”. (equates to the third count sub-subset is a count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction, as the quote shows a number of vehicles tallied for their left turning and thus a count subset of another direction of vehicles is attained))
Yet all fail to teach and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a sixth threshold count.
Li teaches and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a sixth threshold count. Pg. 9 – [0054] – “The associated roads include a road that the vehicle enters from a current road and a road that the vehicle is on before entering the current road. In practical applications, a road has two ends. If each end has a crossroad, features such as turn left from Road 1 into the current road, turn right from Road 2 into the current road, go straight from Road 3 into the current road, turn around from Road 4 into the current road, turn left from the current road into Road 4, tum right from the current road into Road 5, go straight from the current road into Road 6, and turn around from the current road into Road 8 may be set. The trajectory linkage feature may also be identified by using a multi-dimensional vector. For example, a number of trajectories of the vehicle with the above features may be counted in the time window. In order to eliminate errors, when the number of trajectories is greater than a preset number threshold, it may be considered that a corresponding trajectory linkage feature exists at the corresponding position” (equates to and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a fifth threshold count as the quote shows a trajectory matrix wherein each straight, right, and left direction are compared to their respectively set trajectory to determine the existence of a road and in this case the third direction is left with the fifth threshold being the left turning threshold within the matrix. )) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu-Sun to include and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a sixth threshold count as this allows for another direction to be included in the road mapping and specifically for certainty of ensuring the road link based on other vehicles trajectories exist when a prescribed number of car take a certain pathway.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and the count of the geographic position traces of the vehicles that traversed the third direction of travel through the road junction is greater than a sixth threshold count as this allows for a setting of individual threshold for each direction as some direction might be more advantageous to have their own threshold set as the other vehicles travelling on a road might be prone to travel illegally in a certain direction but said data can be filtered out with a certain threshold.
Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang-SHAOJUN as previously mapped above and in further view of Long II (US-20250052593-A1)
Regarding Claim 15 Zhang- SHAOJUN teaches The system of claim 1, wherein the instructions to determine the set of the geographic position traces to be used to produce the digital map include instructions to determine, (Pg. 40 – Col. 3 – lines 8-9 – “FIG. 32 illustrates detection of features points to build a sparse 3D mapping of object feature points.” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.”), the set of the geographic position traces to be used to produce the digital map. )Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006”)
Yet Zhang- Shaojun fails to teach using a greedy constraint optimization technique.
LONG, II teaches using a greedy constraint optimization technique. (Pg. 1 – Abstract – “Technique for map refinement (e.g., tracking data refinement) by an inside-out location tracking system may include computing a transform from a reference point to an epipolar line using an Essential Matrix derived from a reference frame camera motion in a live frame, computing several appearance errors between a feature associated with the reference point and other projected and optimized features, using a perpendicular projection hypothesis and an optimized point generated on the epipolar line, and evaluating the appearance errors using a greedy, ordered optimization” (equates to using a greedy constraint optimization technique as the quote shows an object tracking method wherein outlier/error points are not included in the final result by using greedy optimization as the constraint for keeping data points within the given set.)) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun to include a greedy constraint optimization technique as this optimization type allows for a grouping of data to be considered for determining the mapping of the region or aspect of interest wherein each trace is considered next to the last ensuring that no outlier points exist within a set of data being taken.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include a greedy constraint optimization technique as this allows for a locally optimized solution to be determined and then to be utilized for generating an overall solution of object traces.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang- Shaojun in view of Yonglu (CN112634320B), in view of Reagan (CN114995361A), and in further view of Shirabe (US 2015/0160838 Al)
Regarding Claim 16 Zhang- Shaojun teaches The system of claim 1, wherein: the aspect, in the region of interest, comprises a first aspect and a second aspect, (Pg. 55 – Col. 34 – lines – 37-44 – “Semantic perspective view: Objects in the surrounding scenery are identified in the semantic perspective view and is created with visual information from the visual data from the quadocular-auxiliary sensor. In one implementation, the object can be moving or nonmoving. Examples of non-moving objects include traffic light signals, sidewalks, traffic signs, benches, buildings, fire hydrants, etc.” (equates to wherein: the aspect, in the region of interest, comprises a first aspect and a second aspect, as the quote shows a plurality of aspects being determined within a region of interest being mapped.)) and the instructions to determine the set of the geographic position traces to be used to produce the digital map (Pg. 40 – Col. 3 – lines 8-9 – “FIG. 32 illustrates detection of features points to build a sparse 3D mapping of object feature points.” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.”), instructions to determine a first classification of a candidate geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, with respect to being affiliated with a traversal of a subject of the first aspect; (Pg. 45 – Col. 14 – lines 65-67 & Pg. 46 – Col. 15 – lines 1-4 – “feature detection with spatial binning in order to obtain features from regions to cover as much as possible for the full image. The feature extractor 402 uses IMU and the pose information received from sensor fusion tracker 411 in order to dynamically decide the regions to track and the parameters to use. Features are "interesting" parts of an image” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to instructions to determine a first classification of a candidate geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, with respect to being affiliated with a traversal of a subject of the first aspect as the quote shows a first classification of a region of interest being determined based on the features being extracted wherein the feature or aspect would be a lane divider in this case.)) instructions to determine a second classification of the candidate geographic position trace with respect to being affiliated with a traversal of a subject of the second aspect; (Pg. 45 – Col. 14 – lines 65-67 & Pg. 46 – Col. 15 – lines 1-4 – “feature detection with spatial binning in order to obtain features from regions to cover as much as possible for the full image. The feature extractor 402 uses IMU and the pose information received from sensor fusion tracker 411 in order to dynamically decide the regions to track and the parameters to use. Features are "interesting" parts of an image” & See Also Pg. 44 – Col. 11 – lines 64-66 – “A mapping process 392 generates a hybrid occupancy grid that maps the space and objects recognized by the feature 65 extractor 352.” & See Also Pg. 67 – Col. 57 – lines 4-20 – “Other obstacles situated near the autonomous vehicle 2905 include a pedestrian crossing the road 3012, a pedestrian on the sidewalk 3014, a curbside 3008, a mailbox 3016, and traffic lights/signs 3018, 3020, 3022, 3024. A set of keyrigs will be captured by the quadocular auxiliary sensor of autonomous vehicle 2905 at location 2910, each keyrig containing: (i) a timestamp where the images in the keyrig is captured; (ii) a pose (latitude, longitude position of the car, and orientation); (iii) a pair of 360-degrees images captured by the cameras, and (iv) a sequence of readings from the auxiliary sensors. The pair of360-degrees images will have visual information regarding the car in front 3026, the car at the back 3028, the lane divider 3006, the pedestrian crossing 3010, the pedestrian crossing the road 3012, the pedestrian on the sidewalk 3014, the curbside 3008, the mailbox 3016, and the traffic lights/signs 3018, 3020, 3022, 3024” (equates to instructions to determine a second classification of the candidate geographic position trace with respect to being affiliated with a traversal of a subject of the second aspect; as the first quote shows the use of the trace, for the sensor fusion, for feature recognition of interesting objects wherein the second quote shows the types of obstacles that are detected as well as a lane divider and thus a first and second aspect is determined within a region and a second classification determined via the second aspect determined within the region.) )
Yet Zhang- Shaojun fails to teach include: instructions to rank, among the geographic position traces of the vehicles that traversed the region of interest, the geographic position traces of the vehicles by length; and in an iterative manner starting with a geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, having a greatest length: instructions to include, in response to the first classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map; and instructions to include, in response to the second classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map.
Yonglu teaches instructions to include, in response to the second classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map. (Pg. 2 – [19] – “In some embodiments, obtaining multiple trajectory points on the target object's motion trajectory based on the target object's coordinate position in multiple images also includes: mapping the target object's coordinate position in multiple images to a target coordinate system to obtain the multiple trajectory points” & See Also Pg. 4 – [41] – “The venues to which the present application can be applied include, but are not limited to, road intersections, specific road areas, specific venue entrances and exits, etc. For example, crossroads, T-junctions, three-way intersections, multiple intersections, main road/auxiliary road entrances and exits, parking lot entrances and exits, service area entrances and exits, etc” & See Also Pg. 4 – [45] – “The processing module 106 may be configured to analyze the positional relationship of the plurality of trajectory points according to a preset algorithm to determine the direction of motion of the target object. In some embodiments, the direction of motion includes at least one of going straight, turning left, or turning right” (equates to instructions to include, in response to the second classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce a digital map as the first quote shows the trajectory being put into a coordinate system and thus a digital map is being created based on the object trajectory, and the second quote showing the second aspect including a road area or intersection in this art, and finally the last quote showing the classification being positive as the object is turning in a positive direction in this case right within the region of the second aspect being defined. ))
Yet Zhang- Shaojun -Yonglu fail to teach include: instructions to rank, among the geographic position traces of the vehicles that traversed the region of interest, the geographic position traces of the vehicles by length; and in an iterative manner starting with a geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, having a greatest length: instructions to include, in response to the first classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map;
Reagan teaches include: instructions to rank, (Pg. 7 – [58] – “In some examples, the route selector service may be configured to rank routes,” (equates to instructions to rank as the quote shows the ranking of routes)) among the geographic position traces of the vehicles that traversed the region of interest, (Pg. 7 – [58] – “The route selector service is configured to evaluate routes including alternative routes according to a cost metric that depends on the time or distance to the point of approach between the nearby mobile object and the robot” (equates to among the geographic position traces of the vehicles that traversed the region of interest as the quote shows the routes of a plurality of vehicles in a region of interest or point of approach being considered by the route selector.)) the geographic position traces of the vehicles by length; (“The route selector service is configured to evaluate routes including alternative routes according to a cost metric that depends on the time or distance to the point of approach between the nearby mobile object and the robot” (equates to the geographic position traces of the vehicles by length; as the quote shows the sector rank based on distance to the point or a length.)) and in an iterative manner starting with a geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, having a greatest length (Pg. 7 – [58] – “In some examples, the route selector service may be configured to rank routes, and the cost metric may allow the route selector service to evaluate routes, where the higher ranked routes are routes with longer time or distance to approach points” & See Also Pg. 7 – [58] – “The route selector service is configured to evaluate routes including alternative routes according to a cost metric that depends on the time or distance to the point of approach between the nearby mobile object and the robot” (equates to and in an iterative manner starting with a geographic position trace, of the geographic position traces of the vehicles that traversed the region of interest, having a greatest length as the first quote shows a region of interest traversed by multiple vehicles and the route sector ranking the trajectories based upon the distance to the approach point, and the greatest length ranking shown by the longer distance to the approach point being used for ranking trajectories.))
Yet Zhang- Shaojun -Yonglu-Reagan fails to teach instructions to include, in response to the first classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map;
Shirabe teaches instructions to include, in response to the first classification being positive, ( Pg. 20 – [0129] – “adding to the graph's path set all paths between any two adjacent floating points in the graph's point set” (equates to instructions to include, in response to the first classification being positive as the quote shows paths being within a region of interest and the classification is defined as positive in the specification when the traces are north of the starting point of the region of interest or within the region of interest to use the trace data for the mapping. )) the candidate geographic position trace in the set of geographic position traces to be used to produce the digital map; (Pg. 20 – [0129] – “adding to the graph's path set all paths between any two adjacent floating points in the graph's point set” (equates to the candidate geographic position trace in the set of geographic position traces to be used to produce a digital map; as the quote shows that any path within the region of interest is added to a graph to produce a digital map so long as the path is within the region and thus positive as the specification defines the positive value of the first classification being north of the region of the interest or within a region of interest to add data to the map.)) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu-Reagan to include instructions to include, in response to the first classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce a digital map; as these limitations allows for the building of the digital map to only concern traces of the vehicles to be within the region of interest and thus attain accurate data only when the detected vehicle is within the region needing to be mapped.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include instructions to include, in response to the first classification being positive, the candidate geographic position trace in the set of geographic position traces to be used to produce a digital map; as these limitations allow for an accurate digital map to be created based only on data from vehicles traversing a region of interest and thus ensure the computer is not dealing any vehicle traces outside a region needing to be mapped.
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang- Shaojun -Yonglu-Reagan-Shirabe as mapped above previously and in view of Mattelaer (US 2024/0071217 Al) and in further view of Taniguchi (US 2014/0029469 A1)
Regarding Claim 17 Zhang- Shaojun --Yonglu-Reagan-Shirabe teaches (Zhang discloses the following limitations:) The system of claim 16, wherein: the count of the geographic position traces in the set comprises: a first count for the first aspect, (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”(equates to the count of the geographic position traces in the set comprises: a first count for the first aspect, as the first quote shows the classification of objects in the mapping that is performed wherein the object detected in this cited art is equivalent to the application first aspect of basing the mapping upon. The last quote shows a count of traces as multiple poses and previous imaging is considered in the mapping. )) and a second count for the second aspect, Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,” & See Also Pg. 67 – Col. 58 – lines 46-49 – “illustrates an example map entry for a feature point of an object located above ground view in extensible markup language. Examples of objects above ground level view include traffic light signals, sidewalks, traffic signs, benches, buildings, fire hydrants, other vehicles, pedestrians, motorbikes, bicycles, trains, etc. (equates to and a second count for the second aspect as the first quote shows the classification of objects and the second quote showing the count of the object as multiple images are used to classify, and finally the second aspect being an object within the list in which the prior art can recognize from. ))
Yet Zhang- Shaojun --Reagan fails to teach and the iterative manner further comprises, in response to the candidate geographic position trace having been included in the set of geographic position traces: instructions to increment the first count in response to the first classification being positive, instructions to increment the second count in response to the second classification being positive, instructions to continue the instructions to determine the first classification in response to the first count being less than a third threshold count, and instructions to continue the instructions to determine the second classification in response to the second count being less than a fourth threshold count.
Yonglu teaches instructions to increment the second count in response to the second classification being positive, (Pg. 2 – [19] – “In some embodiments, obtaining multiple trajectory points on the target object's motion trajectory based on the target object's coordinate position in multiple images also includes: mapping the target object's coordinate position in multiple images to a target coordinate system to obtain the multiple trajectory points” & See Also Pg. 4 – [41] – “The venues to which the present application can be applied include, but are not limited to, road intersections, specific road areas, specific venue entrances and exits, etc. For example, crossroads, T-junctions, three-way intersections, multiple intersections, main road/auxiliary road entrances and exits, parking lot entrances and exits, service area entrances and exits, etc” & See Also Pg. 4 – [45] – “The processing module 106 may be configured to analyze the positional relationship of the plurality of trajectory points according to a preset algorithm to determine the direction of motion of the target object. In some embodiments, the direction of motion includes at least one of going straight, turning left, or turning right” & See Also Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted.” (equates to instructions to increment the second count in response to the second classification being positive as the first and second quotes show a traversal of a region of interest by other vehicles and a taken in of traces, and the third quote showing the determination of the vehicles going in a particular direction wherein the last quote shows the vehicles heading in a particular direction being counted in response to the direction determination within the region.)) determine the second classification (Pg. 10 – [98] – “after determining that the target vehicle is turning left or right, the number of vehicles turning left or right may be counted.” & See Also Pg. 4 – [41] – “The venues to which the present application can be applied include, but are not limited to, road intersections, specific road areas, specific venue entrances and exits, etc. For example, crossroads, T-junctions, three-way intersections, multiple intersections, main road/auxiliary road entrances and exits, parking lot entrances and exits, service area entrances and exits, etc” (equates to determine the second classification as the quote shows the determination of the vehicle having a direction within a target region as the second quote shows the regions in which the trajectory directions are determined.))
Yet Zhang- Shaojun- Reagan- Yonglu fail to teach and the iterative manner further comprises, in response to the candidate geographic position trace having been included in the set of geographic position traces: instructions to increment the first count in response to the first classification being positive, instructions to continue the instructions to determine the first classification in response to the first count being less than a third threshold count, and instructions to continue the instructions to in response to the second count being less than a fourth threshold count.
Shirabe teaches and the iterative manner further comprises, in response to the candidate geographic position trace having been included in the set of geographic position traces: instructions to increment the first count in response to the first classification being positive (Pg. 20 – [0129] – “adding to the graph's path set all paths between any two adjacent floating points in the graph's point set” & See Also Pg. 18 – [0085] – “FIG. 9 is a flowchart illustrating a method 900 included in one embodiment of the computer program instructions 113, which may be used at Step 820 of Method 800, for modifying one or more graphic objects affected by an initial modification on a graphic object or on a constraint. It does so by iteratively selecting and modifying connected graphs of points and paths that are components of the affected graphic objects.”(equates to iterative manner further comprises, in response to the candidate geographic position trace having been included in the set of geographic position traces: instructions to increment the first count in response to the first classification being positive as the first quote shows the paths or traces being added to the graph and thus those paths are being considered when building a map of a region of interest, and the last quote showing the iterative process of updating the map based on the additions of the graph.))
Yet Zhang- Shaojun- Reagan- Yonglu- Shirabe fail to teach instructions to continue the instructions to determine the first classification in response to the first count being less than a third threshold count, and instructions to continue the instructions to in response to the second count being less than a fourth threshold count.
Mattelaer teaches instructions to continue the instructions to determine the first classification in response to the first count being less than a third threshold count (Pg. 21 – [0241] – “triggering one or several of POI presence, importance verification, validation, update in an electronic map based on a net-instream of probe traces that end in an area ( e.g., a threshold count in a certain period, such as at least 300 or at least 3000” (equates to instructions to continue the instructions to determine the first classification in response to the first count being less than a third threshold count as the quote shows the map being updated based on traces within a specified area and thus a vehicle being within a region of interest triggering a positive first classification wherein the data is continued to be read for map updating as the count continues to increase until a threshold amount is reached ))
Yet all fail to teach and instructions to continue the instructions to in response to the second count being less than a forth threshold count.
Taniguchi teaches and instructions to continue the instructions to in response to the second count being less than a forth threshold count. (Pg. 1 – [Abstract] – “The node transmits the route search demand when the number of adjacent communication devices serving as confirmed routes is Smaller than a predetermined number and” (equates to and instructions to continue the instructions to in response to the second count being less than a second threshold count as the quote shows instructions being continued when a number of routes is less than a predetermined number.)) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu-Reagan-Shirabe-Mattlaer to include and instructions to continue the instructions to in response to the second count being less than a forth threshold count as this gives a set number of counts to be compared to ensure enough data is attained to understand the vehicles traversing the particular region.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include and instructions to continue the instructions to in response to the second count being less than a forth threshold count as this allows a collection of data to continue until enough points of data is collected to reach a value of certainty of the vehicle traversing through a region within a manor of interest.
Regarding Claim 18 Zhang- Shaojun --Yonglu-Reagan-Shirabe-Mattlaer-Taniguchi teaches (Zhang discloses the following limitations:) The system of claim 17, determine the first classification (Pg. 45 – Col. 14 – lines 65-67 & Pg. 46 – Col. 15 – lines 1-4 – “feature detection with spatial binning in order to obtain features from regions to cover as much as possible for the full image. The feature extractor 402 uses IMU and the pose information received from sensor fusion tracker 411 in order to dynamically decide the regions to track and the parameters to use. Features are "interesting" parts of an image” & See Also Pg. 67 – Col. 57 – lines 40-45 – “illustrates detection of features points of non-moving objects in the surrounding scenery near the autonomous vehicle 2905 at location 2910 to build a sparse 3D mapping 3200. A subset of keyrigs from the set of keyrigs captured by the quadocular-auxiliary sensor of autonomous vehicle 2905 at location 2910 will be used to determine a sparse mapping of features points of the lane divider 3006” (equates to determine the first classification as the quote shows a feature detection being done within a spatial binning thus equating to a trace being done within a designated region as specified by a first classification)) determine the second classification (Pg. 45 – Col. 14 – lines 65-67 & Pg. 46 – Col. 15 – lines 1-4 – “feature detection with spatial binning in order to obtain features from regions to cover as much as possible for the full image. The feature extractor 402 uses IMU and the pose information received from sensor fusion tracker 411 in order to dynamically decide the regions to track and the parameters to use. Features are "interesting" parts of an image” & See Also Pg. 44 – Col. 11 – lines 64-66 – “A mapping process 392 generates a hybrid occupancy grid that maps the space and objects recognized by the feature 65 extractor 352.” & See Also Pg. 67 – Col. 57 – lines 4-20 – “Other obstacles situated near the autonomous vehicle 2905 include a pedestrian crossing the road 3012, a pedestrian on the sidewalk 3014, a curbside 3008, a mailbox 3016, and traffic lights/signs 3018, 3020, 3022, 3024. A set of keyrigs will be captured by the quadocular auxiliary sensor of autonomous vehicle 2905 at location 2910, each keyrig containing: (i) a timestamp where the images in the keyrig is captured; (ii) a pose (latitude, longitude position of the car, and orientation); (iii) a pair of 360-degrees images captured by the cameras, and (iv) a sequence of readings from the auxiliary sensors. The pair of360-degrees images will have visual information regarding the car in front 3026, the car at the back 3028, the lane divider 3006, the pedestrian crossing 3010, the pedestrian crossing the road 3012, the pedestrian on the sidewalk 3014, the curbside 3008, the mailbox 3016, and the traffic lights/signs 3018, 3020, 3022, 3024” (equates to determine the second classification as second aspect is determined within a region and a second classification determined via the second aspect determined within the region.) )
Yet Zhang- Shaojun -Yonglu-Reagan-Shirabe-Mattlaer fails to teach wherein the iterative manner further comprises: instructions to cease the instructions to in response to the first count being equal to the third threshold count, and instructions to cease the instructions to determine in response to the second count being equal to the forth threshold count.
Taniguchi teaches wherein the iterative manner further comprises: instructions to cease the instructions to in response to the first count being equal to the third threshold count, (Pg. 1 – Abstract – “The node stops transmission of the route search demand when the number of the adjacent communication devices serving as the confirmed routes reaches the predetermined number.” & See Also Pg. 7 – Fig. 9 & See Also Pg. 19 – [0088] – “determines that the indefinite number (i) does not satisfy the SLA condition (NO at S107), the process is repeated from S104.”(equates to instructions to cease the instructions to in response to the first count being equal to the third threshold count, as the quote shows a predetermined number being reached and when the predetermined number of routes is reached the instruction to perform the operation is ceasing to happen. Also shows the iterative manner of the process as the steps are repeated until the number of route satisfies the predetermined threshold as seen in figure 9 and the s107 step in fig. 9.)) and instructions to cease the instructions to determine in response to the second count being equal to the forth threshold count. (Pg. 1 – Abstract – “The node stops transmission of the route search demand when the number of the adjacent communication devices serving as the confirmed routes reaches the predetermined number.” (equates to instructions to cease the instructions to determine in response to the second count being equal to the forth threshold count as the quote shows a predetermined number being reached and when the predetermined number of routes is reached the instruction to perform the operation is ceasing to happen.))) It would have been an advantageous addition to the system disclosed by Zhang- Shaojun -Yonglu-Reagan-Shirabe-Mattlaer to include wherein the iterative manner further comprises: instructions to cease the instructions to in response to the first count being equal to the third threshold count, and instructions to cease the instructions to determine in response to the second count being equal to the forth threshold count as these limitations ensure a stop point is reached in collecting data for the classifications ensuring the certainty of data collection meets a quota and an overconsumption of data is not had ensuring a saving of computational power for other tasks.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the iterative manner further comprises: instructions to cease the instructions to in response to the first count being equal to the third threshold count, and instructions to cease the instructions to determine in response to the second count being equal to the forth threshold count as these limitations ensure a finite amount of computational power is utilized when determining a classification ensuring a level of certainty about the region of interest while similarly not needlessly computing something of nonvalue.
Response to Arguments
Response to Double Patenting Rejection in regard to claims 1, 11, 19, and 20. Applicant amendments to the claims changes scope. Applicant’s arguments have been considered but are not persuasive.
Applicant Argues on page 1-2, “Claims 1, 11, 19, and 20 are provisionally rejected on the ground of obviousness- type double patenting as allegedly being unpatentable over claims 1, 10, 17, and 20 of U.S. Application No. 18/425,175 to Mendelowitz (Mendelowitz).
Reconsideration is respectfully requested.
Section 804.02 of the Manual of Patent Examining Procedure states, inter alia, "A rejection based on the statutory type of double patenting can be avoided by amending the conflicting claims so that they are not coextensive in scope."
None of the claims 1, 10, 17, and 20 of Mendelowitz recites at least "wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map, the count being between a first threshold count and a second threshold count" as recited in each of independent claims 1, 19, and 20 of the present patent application.
Accordingly, each of independent claims 1, 19, and 20 of the present patent application is patently distinct from claims 1, 17, and 20 of Mendelowitz.
Moreover, because claim 11 of the present patent application depends upon independent claim 1 of the present patent application, and because of the distinctive features recited in claim 11 of the present patent application, this claim is patently distinct from claims 1, 17, and 20 of Mendelowitz.
Therefore, Applicant respectfully requests that the Examiner reconsider and withdraw the provisional rejection of claims 1, 11, 19, and 20 on the ground of obviousness-type double patenting and pass these claims to allowance.” – As to point A The examiner respectfully disagrees. The applicant appears to argue the inclusion of “a wherein a count of the geographic position traces in the set is based on an aspect, in the region of interest, to be included in the digital map, the count being between a first threshold count and a second threshold count” takes the claim out of nonstatutory obviousness-type double patenting territory. During Patent Examination, nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). (see MPEP 804). The above definition for an nonstatutory obviousness-type double patenting rejection includes “examined application claim… would have been obvious over, the reference claim(s)”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The Broadest Reasonable Interpretation of the aforementioned quote is an inclusion of two thresholds to which a number of measurements can fall within. Zhang teaches the specific number of measurements as referred to by ‘the count;’ in claim 1 and Shaojun specifically teaches a two threshold values in which a count must within to trigger a SLAM mapping in their art. Therefor the examiner respectfully disagrees and asserts Zhang-Shaojun in combination with U.S. Application No. 18/425,175 teaches claim 1 as cited above. See at least: Zhang teaches the count (Pg. 66 – col. 56 – lines 11-13 – “The semantic information is used to classify the objects in visual data from the quadocular-auxiliary sensors moving or non-moving.” & See Also Pg. 66 – col. 56 lines – 46-51- “With the help of the captured keyrigs by autonomous units with quadocular-auxiliary sensors, a 3D map can be built for navigation with an accuracy in a range of 5 centimeters to 10 centimeters. In one implementation, the 3D map is built at the map server after the map server receives keyrigs from one or more autonomous units” & See Also Pg. 68 – Col. 59 – lines 24 – 26 – “each keyrig is a set of 360-degrees images with a pose generated 25 using combinations of GPS, IMU, and visual information of a scene by the autonomous unit.” & See Also Pg. 17 – Col. 17 – lines 6-7 – “In a multiple observation implementation, based on multiple previous image observation,”)
Yet Zhang fails to teach being between a first threshold count and a second threshold count.
SHAOJUN teaches being between a first threshold count and a second threshold count; (Pg. 5 – “Further preferably, the triggering, by the monocular SLAM positioning module, monocular SLAM positioning includes: when the matching number of the current frame and the local map points is smaller than a first threshold and larger than a second threshold, the positioning quality is poor” & See Also Pg. 7 – “Preferably, the first threshold value is one value in a range of 80 to 100, and the second threshold value is one value in a range of 20 to 40.” (equates to being between a first threshold count and a second threshold count; as the first quote shows the matching number in a SLAM method being less than a first threshold and more than a second threshold.)) It would have been an advantageous addition to the system disclosed by Zhang to include being between a first threshold count and a second threshold count as this would allow for the count to fall within a range that does not exceed the processing capabilities of the system or require extraneous processing power to work.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include being between a first threshold count and a second threshold count as this allows for a minimum number of counts to be included ensuring a large enough dataset while not overloading the processing abilities of the system with too much data.
Response to 35 U.S.C. § 101 rejection of claims 1-20 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered and are persuasive.
Applicant argues on pages 3-4, “As agreed during the March 4, 2026, interview, because each of independent claims 1, 19, and 20 recites at least "[transmitting] ... the digital map to a specific vehicle to control an operation of a vehicle system to control movement of the specific vehicle[,]" each of independent claims 1, 19, and 20 recites "a meaningful limitation in that it employs the information provided by the [alleged] judicial exceptions" (e.g., "[producing] ... the digital map" from "a set of geographic position traces" determined "from geographic position traces of vehicles that traversed a region") to "control an operation of a vehicle system to control movement of the specific vehicle." For at least these reasons, each of independent claims 1, 19, and 20 is directed to significantly more than an abstract idea and is directed to eligible subject matter. Moreover, because each of claims 2-18 depends upon independent claim 1, and because of the distinctive features recited in each of claims 2-18, each of these claims is directed to significantly more than an abstract idea and is directed to eligible subject matter. Therefore, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection of claims 1-20 under 35 U.S.C. § 101. "” – As to point B the examiner agrees with the applicant. The inclusion of the limitation “an operation of a vehicle system to control movement of the specific vehicle” adds an element of control to the claim that otherwise contains steps of data gathering, and generic computer components being incorporated to run an otherwise mental but the control of the vehicle cannot be practically performed in the human mind.
Response to 35 U.S.C. § 103 rejection of claims 1-20 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on page 5, “ Claims 1-10, 15, 19, and 20 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over U.S. Patent No. 10,395,117 to Zhang et al. (Zhang) in view of U.S. Publication No. 2025/005293 to Long (Long).
Claim 11 is rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Zhang and Long in view of Chinese Publication No. 115892017 to Collin et al. (Collin).
Claim 12 is rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Zhang and Long in view of Chinese Patent No. 112634320 to Shi et al. (Shi) and in further in view of U.S. Publication No. 2020/0134325 to Sun et al. (Sun).
Claims 13 and 14 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Zhang, Long, Shi, Sun and in further in view of U.S. Publication No. 2023/0065341 to Li et al. (Li).
Claim 16 is rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Zhang, Long, and Shi in view of Chinese Publication No. 114995361 to Reagan et al. (Reagan) and in further view of U.S. Publication No. 2015/0160838 to Shirabe (Shirabe).
Claims 17 and 18 are rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Zhang, Long, Shi, Reagan, Shirabe in view of U.S. Publication No. 2024/0071217 to Mattelaer (Mattelaer) and in further view of U.S. Publication No. 2014/0029469 to Taniguchi et al. (Taniguchi).
Reconsideration is respectfully requested.
Features of claims 8 and 9 have been incorporated into each of independent claims 1, 19, and 20.
As agreed during the March 4, 2026, interview, each of independent claims 1, 19, and 20 is patentable over Zhang, Long, Collin, Shi, Sun, Li, Reagan, Shirabe, Mattelaer, and Taniguchi.” –Applicant’s arguments with respect to claim(s) 1, 19, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
d. Applicant argues on page 5 – “Moreover, because each of claims 2-18 depends upon independent claim 1, and because of the distinctive features recited in each of claims 2-18, each of these claims is patentable over is patentable over Zhang, Long, Collin, Shi, Sun, Li, Reagan, Shirabe, Mattelaer, and Taniguchi. Therefore, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection of claims 1-20 under 35 U.S.C. § 103 and pass these claims to allowance. ” – As to point D see point C.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. King et al. (US 20200148201 A1).
Navigation and control of an autonomous vehicle is performed wherein predictive trajectories can be generated based on a variety of vehicle and environment sensed data ensuring a safe passage of the vehicle.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/R.A.W./ Examiner, Art Unit 3667
/Hitesh Patel/ Supervisory Patent Examiner, Art Unit 3667
5/26/26
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