DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
This Office Action is in response to the Application filed on May 20, 2025. Claims 1-5 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 20, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-2 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. US 12454270 . Although the claims at issue are not identical, they are not patentably distinct from each other because of the following:
Current application (19/213,486)
US Patent 12454270
An apparatus comprising:
A vehicle control system …
an acquiring unit configured to acquire environmental information regarding at
least one of environment around a current location of a moving object, and environment ahead of the moving object in a travel direction, the moving object being capable of moving by unmanned driving; and
comprising a peripheral information acquiring device including an imaging device configured to capture an image of an outside of a host vehicle to generate an image and a LiDAR device configured to measure an inter-vehicle distance to a preceding vehicle, and a control device, wherein the control device is configured to: determine a road type of a road on which the host vehicle is traveling by recognizing a specific object in the image; take, as a determination measure for determining the road type of the road on which the host vehicle is traveling, a measure of increasing an inter-vehicle distance between a preceding vehicle traveling in the same lane as the own vehicle or a preceding vehicle traveling in an adjacent lane than a current inter-vehicle distance, when the specific object cannot be recognized, determine whether a field of view of the imaging device is blocked by the preceding vehicle by measuring the inter-vehicle distance using the LiDAR device, wherein the field of view is determined to be blocked when the inter-vehicle distance is less than a predetermined threshold;
…
and perform autonomous driving in which a driving operation related to acceleration, steering, and braking is automatically performed in accordance with the driving plan;
a generating unit configured to generate value data using the acquired environmental information, the value data including at least one of a braking control value, a
braking correction value, a work control value, a work setting value, and a work correction value, the braking control value being a control value regarding braking of the moving object, the braking correction value being a correction value to correct the braking control value, the work control value being a control value for controlling a work device performing an operation on the moving object, the work setting value being a setting value of the work device, the work correction value being a correction value to correct the work control value.
determine whether the road type is an expressway; start platoon driving automatically by creating a driving plan of the host vehicle on the basis of vehicle peripheral information so that the host vehicle can change a lane in accordance with a lane change of the preceding vehicle while keeping the inter-vehicle distance to the preceding vehicle constant, and perform autonomous driving in which a driving operation related to acceleration, steering, and braking is automatically performed in accordance with the driving plan; and return the inter-vehicle distance to an original state when the specific object is subsequently recognized in the image.
In regards to claim 2, the claim is fully disclosed within at least claim 1 of U.S. Patent 12454270 and is therefore rejected.
Additionally, it is noted that claim 1 recites similar limitations as that claimed by claim 1 of US 11507917 and claim 1 of US 12503104 and therefore will be considered for future double patenting rejections. It is recommended to overcome these potential double patenting rejections.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-5 recite a method/process, therefore claims 1-10 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mathematical concepts and/or mental processes (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
An apparatus comprising:
an acquiring unit configured to acquire environmental information regarding at least one of environment around a current location of a moving object, and environment ahead of the moving object in a travel direction, the moving object being capable of moving by unmanned driving; and
a generating unit configured to generate value data using the acquired environmental information, the value data including at least one of a braking control value, a braking correction value, a work control value, a work setting value, and a work correction value, the braking control value being a control value regarding braking of the moving object, the braking correction value being a correction value to correct the braking control value, the work control value being a control value for controlling a work device performing an operation on the moving object, the work setting value being a setting value of the work device, the work correction value being a correction value to correct the work control value.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process. That is, nothing in the claim elements preclude the steps from practically being performed as mental process. For example, " generate value data…" encompass mental processes as a human can perform these limitations using observations, evaluations, judgments, and/or opinions. “generate value data…” involves a human judging and/or evaluating where a the environmental data to determine an action that a vehicle should perform. Thus, the claim recites at least a mental process.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
An apparatus comprising:
an acquiring unit configured to acquire environmental information regarding at least one of environment around a current location of a moving object, and environment ahead of the moving object in a travel direction, the moving object being capable of moving by unmanned driving; and
a generating unit configured to generate value data using the acquired environmental information, the value data including at least one of a braking control value, a braking correction value, a work control value, a work setting value, and a work correction value, the braking control value being a control value regarding braking of the moving object, the braking correction value being a correction value to correct the braking control value, the work control value being a control value for controlling a work device performing an operation on the moving object, the work setting value being a setting value of the work device, the work correction value being a correction value to correct the work control value.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of "An apparatus” and “the moving object capable of moving by unmanned driving” the examiner submits that this limitation characterizes the apparatus as being associated with an unmanned vehicle, which merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). Additionally, the claim limitation “acquire environmental information …” does not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting (MPEP § 2106.05(g)). The “acquiring unit” and the “generating unit” are merely generic computing components to apply the judicial exception. The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “acquire environmental information …" amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). With respect to “acquire environmental information …” it was ruled within Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), which are recited within MPEP 2106.05(d)(II) that mere data collection or receiving/obtaining and transmitting of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic matter, as it is here. Additionally, “acquiring unit” and the “generating unit” are each generic computing components that merely apply the judicial exception (See 2106.05(f)). Additionally, “"An apparatus” and “the moving object capable of moving by unmanned driving” is merely a technological environment or field of use as the limitations merely link the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Dependent claims 2-5 specify limitations that elaborate on the abstract idea of claim 1, and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to "significantly more" for similar reasons.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ponomarev et al. (US 20250026370; hereinafter Ponomarev).
In regards to claim 1, Ponomarev discloses of an apparatus (“A method and a system for planning motion of a Self-Driving Car (SDC) are provided. The method comprises: receiving sensed data representative of the surroundings of the SDC; feeding the sensed data to a plurality of machine-learning (ML) models, the feeding comprising: feeding the sensed data to a first ML model to generate a first prediction for a given object in the surroundings of the SDC; in response to a time for generating the first prediction being higher than a predetermined threshold: planning the motion of the SDC based on the first prediction; and in response to the time for generating the first prediction being lower than the first predetermined time period threshold: feeding the first prediction along with the sensed data to a second ML model to generate a second prediction for the given object; and planning the motion of the SDC based on the second prediction.” (Abstract)) comprising:
an acquiring unit configured to acquire environmental information regarding at least one of environment around a current location of a moving object, and environment ahead of the moving object in a travel direction, the moving object being capable of moving by unmanned driving (“It should be noted that a variety of sensors and systems may be used by the electronic device 210 for gathering information about surroundings 250 of the vehicle 220. As seen in FIG. 2, the vehicle 220 may be equipped with a plurality of sensor systems 280. It should be noted that different sensor systems from the plurality of sensor systems 280 may be used for gathering different types of data regarding the surroundings 250 of the vehicle 220.” (Para 0084), “In other non-limiting embodiments of the present technology, the camera sensor 290 is configured to capture an image (or a series of images) that represent approximately 180 degrees of the surroundings 250 around the vehicle 220 that are along a movement path of the vehicle 220. In yet other non-limiting embodiments of the present technology, the camera sensor 290 is configured to capture the image data that represent approximately 360 degrees of the surroundings 250 around the vehicle 220 that are along a movement path of the vehicle 220 (in other words, the entirety of the surrounding area around the vehicle 220).” (Para 0088), “One of the technical challenges associated with the SDC's is their ability to predict, or otherwise determine trajectories of other road users (other vehicles, for example) travelling in the surrounding area of the SDC, for example, in neighbouring lanes. When a given vehicle, travelling, for example, ahead of the SDC in a neighbouring lane, is about to perform a maneuver (such as turning left or right), its trajectory may overlap and/or intersect (at least partially) with the trajectory of the SDC, which may cause high risk of collision between the SDC and one of the other vehicles (including the given one) in the surrounding area. Consequently, this may require the SDC to take corrective measures, be it braking or otherwise active accelerating resulting in building the SDC trajectory ensuring minimal risk of an accident.” (Para 0005)); and
a generating unit configured to generate value data using the acquired environmental information, the value data including at least one of a braking control value, a braking correction value, a work control value, a work setting value, and a work correction value, the braking control value being a control value regarding braking of the moving object, the braking correction value being a correction value to correct the braking control value, the work control value being a control value for controlling a work device performing an operation on the moving object, the work setting value being a setting value of the work device, the work correction value being a correction value to correct the work control value (“One of the technical challenges associated with the SDC's is their ability to predict, or otherwise determine trajectories of other road users (other vehicles, for example) travelling in the surrounding area of the SDC, for example, in neighbouring lanes. When a given vehicle, travelling, for example, ahead of the SDC in a neighbouring lane, is about to perform a maneuver (such as turning left or right), its trajectory may overlap and/or intersect (at least partially) with the trajectory of the SDC, which may cause high risk of collision between the SDC and one of the other vehicles (including the given one) in the surrounding area. Consequently, this may require the SDC to take corrective measures, be it braking or otherwise active accelerating resulting in building the SDC trajectory ensuring minimal risk of an accident.” (Para 0005), “In at least some non-limiting embodiments of the present technology, the electronic device 210 is communicatively coupled to control systems of the vehicle 220. The electronic device 210 could be arranged and configured to control different operations systems of the vehicle 220, including but not limited to: an ECU (engine control unit), steering systems, braking systems, and signaling and illumination systems (i.e. headlights, brake lights, and/or turn signals). In such an embodiment, the vehicle 220 could be a self-driving vehicle.” (Para 0074), “As it can be appreciated from FIG. 4, as the vehicle 220 approaches an intersection in the given road section 402, it may be configured, according to a predetermined (prior) movement trajectory thereof, to make a right maneuver 404 to an intersecting road. However, to avoid collision with an upcoming vehicle 420 driving down the intersecting road in a straight direction 406, the vehicle 220 must be capable of (i) detecting the upcoming vehicle 420; and (ii) taking certain corrective measures with respect to the prior predetermined movement trajectory, such as one of slowing down, accelerating, or breaking, as an example, thereby re-determining the movement trajectory for the vehicle 220.” (Para 0112)).
In regards to claim 2, Ponomarev discloses of the apparatus according to claim 1, wherein the environmental information includes an image in which the environment ahead of the moving object is captured (“As it can be appreciated from FIG. 4, as the vehicle 220 approaches an intersection in the given road section 402, it may be configured, according to a predetermined (prior) movement trajectory thereof, to make a right maneuver 404 to an intersecting road. However, to avoid collision with an upcoming vehicle 420 driving down the intersecting road in a straight direction 406, the vehicle 220 must be capable of (i) detecting the upcoming vehicle 420; and (ii) taking certain corrective measures with respect to the prior predetermined movement trajectory, such as one of slowing down, accelerating, or breaking, as an example, thereby re-determining the movement trajectory for the vehicle 220.” (Para 0112), “One of the technical challenges associated with the SDC's is their ability to predict, or otherwise determine trajectories of other road users (other vehicles, for example) travelling in the surrounding area of the SDC, for example, in neighbouring lanes. When a given vehicle, travelling, for example, ahead of the SDC in a neighbouring lane, is about to perform a maneuver (such as turning left or right), its trajectory may overlap and/or intersect (at least partially) with the trajectory of the SDC, which may cause high risk of collision between the SDC and one of the other vehicles (including the given one) in the surrounding area. Consequently, this may require the SDC to take corrective measures, be it braking or otherwise active accelerating resulting in building the SDC trajectory ensuring minimal risk of an accident.” (Para 0005), “More specifically, for determining the object class of the given surrounding object, the MLA 260 can be configured to determine a plurality of object features describing the given surrounding object. Merely as an example, and in no way as a limitation, in case where the given surrounding object is the upcoming vehicle 420, the object features can include, without limitation: (i) a type of the given surrounding object, such as a movable object; (ii) a type of the movable object, such as an inanimate object; (iii) a type of the movable inanimate object, such as a vehicle; (iv) a brand and a model of the upcoming vehicle 420; (v) an issue year of the upcoming vehicle 420; (vi) a body type of the upcoming vehicle 420, such as a sedan, a hatchback, a wagon vehicle, a minivan and others; (vii) a control type of the upcoming vehicle 420, such as traditional (operated by a driver) or driverless (that is, an other SDC); (viii) a current speed of the upcoming vehicle 420 in the straight direction 406; (ix) a distance 408 from the vehicle 220 to the upcoming vehicle 420; and others.” (Para 0115) see also Para 0113 and Fig 4).
In regards to claim 3, Ponomarev discloses of the apparatus according to claim 2, wherein the generating unit acquires, from the image, a feature regarding at least one of brightness of the image, presence or absence of a predetermined object in the image, and a proportion of the image occupied by the predetermined object (“More specifically, for determining the object class of the given surrounding object, the MLA 260 can be configured to determine a plurality of object features describing the given surrounding object. Merely as an example, and in no way as a limitation, in case where the given surrounding object is the upcoming vehicle 420, the object features can include, without limitation: (i) a type of the given surrounding object, such as a movable object; (ii) a type of the movable object, such as an inanimate object; (iii) a type of the movable inanimate object, such as a vehicle; (iv) a brand and a model of the upcoming vehicle 420; (v) an issue year of the upcoming vehicle 420; (vi) a body type of the upcoming vehicle 420, such as a sedan, a hatchback, a wagon vehicle, a minivan and others; (vii) a control type of the upcoming vehicle 420, such as traditional (operated by a driver) or driverless (that is, an other SDC); (viii) a current speed of the upcoming vehicle 420 in the straight direction 406; (ix) a distance 408 from the vehicle 220 to the upcoming vehicle 420; and others.” (Para 0115), “Further, according to certain non-limiting embodiments of the present technology, the respective training feature vector for the training road section 702 can include a plurality of LiDAR features associated with LiDAR points of the training 3D point cloud representative of the training road section 702. According to certain non-limiting embodiments of the present technology, the plurality of LiDAR features can include, without limitation, (i) a total number of LiDAR points of the training point cloud; and (ii) light intensity values of each LiDAR point of the training 3D point cloud. However, it should be noted that the above LiDAR features are non-exhaustive, and in some non-limiting embodiments of the present technology, the plurality of LiDAR features of the training 3D point cloud may include other LiDAR features, such as a colour of a surface portion of the training road section 702 from which each LiDAR point has reflected; a distance, from the LiDAR sensor 300, to each LiDAR point of the training 3D point cloud, and the like.” (Para 0153), “As it can be appreciated from FIG. 4, as the vehicle 220 approaches an intersection in the given road section 402, it may be configured, according to a predetermined (prior) movement trajectory thereof, to make a right maneuver 404 to an intersecting road. However, to avoid collision with an upcoming vehicle 420 driving down the intersecting road in a straight direction 406, the vehicle 220 must be capable of (i) detecting the upcoming vehicle 420; and (ii) taking certain corrective measures with respect to the prior predetermined movement trajectory, such as one of slowing down, accelerating, or breaking, as an example, thereby re-determining the movement trajectory for the vehicle 220.” (Para 0112), see also Para 0116-0117, 0127, 0162), and
the generating unit generates the value data using the acquired at least one feature(“More specifically, for determining the object class of the given surrounding object, the MLA 260 can be configured to determine a plurality of object features describing the given surrounding object. Merely as an example, and in no way as a limitation, in case where the given surrounding object is the upcoming vehicle 420, the object features can include, without limitation: (i) a type of the given surrounding object, such as a movable object; (ii) a type of the movable object, such as an inanimate object; (iii) a type of the movable inanimate object, such as a vehicle; (iv) a brand and a model of the upcoming vehicle 420; (v) an issue year of the upcoming vehicle 420; (vi) a body type of the upcoming vehicle 420, such as a sedan, a hatchback, a wagon vehicle, a minivan and others; (vii) a control type of the upcoming vehicle 420, such as traditional (operated by a driver) or driverless (that is, an other SDC); (viii) a current speed of the upcoming vehicle 420 in the straight direction 406; (ix) a distance 408 from the vehicle 220 to the upcoming vehicle 420; and others.” (Para 0115), “Further, according to certain non-limiting embodiments of the present technology, the respective training feature vector for the training road section 702 can include a plurality of LiDAR features associated with LiDAR points of the training 3D point cloud representative of the training road section 702. According to certain non-limiting embodiments of the present technology, the plurality of LiDAR features can include, without limitation, (i) a total number of LiDAR points of the training point cloud; and (ii) light intensity values of each LiDAR point of the training 3D point cloud. However, it should be noted that the above LiDAR features are non-exhaustive, and in some non-limiting embodiments of the present technology, the plurality of LiDAR features of the training 3D point cloud may include other LiDAR features, such as a colour of a surface portion of the training road section 702 from which each LiDAR point has reflected; a distance, from the LiDAR sensor 300, to each LiDAR point of the training 3D point cloud, and the like.” (Para 0153), “As it can be appreciated from FIG. 4, as the vehicle 220 approaches an intersection in the given road section 402, it may be configured, according to a predetermined (prior) movement trajectory thereof, to make a right maneuver 404 to an intersecting road. However, to avoid collision with an upcoming vehicle 420 driving down the intersecting road in a straight direction 406, the vehicle 220 must be capable of (i) detecting the upcoming vehicle 420; and (ii) taking certain corrective measures with respect to the prior predetermined movement trajectory, such as one of slowing down, accelerating, or breaking, as an example, thereby re-determining the movement trajectory for the vehicle 220.” (Para 0112), see also Para 0116-0117, 0127, 0162).
In regards to claim 4, Ponomarev discloses of the apparatus according to claim 1, wherein the environmental information includes three-dimensional point cloud information of the environment ahead of the moving object (“More specifically, for determining the object class of the given surrounding object, the MLA 260 can be configured to determine a plurality of object features describing the given surrounding object. Merely as an example, and in no way as a limitation, in case where the given surrounding object is the upcoming vehicle 420, the object features can include, without limitation: (i) a type of the given surrounding object, such as a movable object; (ii) a type of the movable object, such as an inanimate object; (iii) a type of the movable inanimate object, such as a vehicle; (iv) a brand and a model of the upcoming vehicle 420; (v) an issue year of the upcoming vehicle 420; (vi) a body type of the upcoming vehicle 420, such as a sedan, a hatchback, a wagon vehicle, a minivan and others; (vii) a control type of the upcoming vehicle 420, such as traditional (operated by a driver) or driverless (that is, an other SDC); (viii) a current speed of the upcoming vehicle 420 in the straight direction 406; (ix) a distance 408 from the vehicle 220 to the upcoming vehicle 420; and others.” (Para 0115), “With reference to FIG. 3, there is depicted a schematic diagram of a LiDAR data acquisition procedure 302, executed by the processor 110 of the electronic device 210, for generating a 3D point cloud data 310 representative of surrounding object present in the surroundings 250 of the vehicle 220, in accordance with certain non-limiting embodiments of the present technology.” (Para 0105), “According to certain non-limiting embodiments of the present technology, to detect the surrounding objects of the vehicle 220, such as those in the given road section 402 depicted in FIG. 4, using the MLA 260 as described above, the processor 110 can be configured to: (i) receive, from the plurality of sensor systems 280 of the vehicle 220, the sensed data representative of the given road section 402, such as at least one of a respective image (not separately labelled) from the camera sensor 290 and the 3D point cloud 312 from the LiDAR sensor 300; (ii) generate, based on the sensed data of the given road section 402, a respective in-use feature vector; and (iii) feed the respective in-use feature vector to each one of the plurality of ML models.” (Para 0176), see also Figs 4 and 7).
In regards to claim 5, Ponomarev discloses of the apparatus according to claim 4, wherein the generating unit acquires, from the three-dimensional point cloud information, a feature regarding at least one of a number of points in a point cloud, density of the point cloud, and detection distance of the point cloud (“According to certain non-limiting embodiments of the present technology, the additional training features for the given training surrounding object 704 can include, without limitation, at least one of: a respective distance value form the vehicle 220 to the given training surrounding object 704, such as a respective distance value 710; a surface area of the respective bounding box 706 associated with the given training surrounding object 704; dimensions of the respective bounding box 706 a number of LiDAR points of the training 3D point cloud 604 having fallen within the respective bounding box 706—namely, a number of LiDAR points in the set of LiDAR points 708 associated contained therein; a density of the LiDAR points in the set of LiDAR points 708; light intensity values of the LiDAR in the set of LiDAR points 708; and average distance values from each of the LiDAR points in the set of LiDAR points 708.” (Para 0159-0166), “Further, according to certain non-limiting embodiments of the present technology, the respective training feature vector for the training road section 702 can include a plurality of LiDAR features associated with LiDAR points of the training 3D point cloud representative of the training road section 702. According to certain non-limiting embodiments of the present technology, the plurality of LiDAR features can include, without limitation, (i) a total number of LiDAR points of the training point cloud; and (ii) light intensity values of each LiDAR point of the training 3D point cloud. However, it should be noted that the above LiDAR features are non-exhaustive, and in some non-limiting embodiments of the present technology, the plurality of LiDAR features of the training 3D point cloud may include other LiDAR features, such as a colour of a surface portion of the training road section 702 from which each LiDAR point has reflected; a distance, from the LiDAR sensor 300, to each LiDAR point of the training 3D point cloud, and the like.” (Para 0153)), and
the generating unit generates the value data using the acquired at least one feature (“According to certain non-limiting embodiments of the present technology, the additional training features for the given training surrounding object 704 can include, without limitation, at least one of: a respective distance value form the vehicle 220 to the given training surrounding object 704, such as a respective distance value 710; a surface area of the respective bounding box 706 associated with the given training surrounding object 704; dimensions of the respective bounding box 706 a number of LiDAR points of the training 3D point cloud 604 having fallen within the respective bounding box 706—namely, a number of LiDAR points in the set of LiDAR points 708 associated contained therein; a density of the LiDAR points in the set of LiDAR points 708; light intensity values of the LiDAR in the set of LiDAR points 708; and average distance values from each of the LiDAR points in the set of LiDAR points 708.” (Para 0159-0166), “Further, according to certain non-limiting embodiments of the present technology, the respective training feature vector for the training road section 702 can include a plurality of LiDAR features associated with LiDAR points of the training 3D point cloud representative of the training road section 702. According to certain non-limiting embodiments of the present technology, the plurality of LiDAR features can include, without limitation, (i) a total number of LiDAR points of the training point cloud; and (ii) light intensity values of each LiDAR point of the training 3D point cloud. However, it should be noted that the above LiDAR features are non-exhaustive, and in some non-limiting embodiments of the present technology, the plurality of LiDAR features of the training 3D point cloud may include other LiDAR features, such as a colour of a surface portion of the training road section 702 from which each LiDAR point has reflected; a distance, from the LiDAR sensor 300, to each LiDAR point of the training 3D point cloud, and the like.” (Para 0153) “With reference to FIG. 3, there is depicted a schematic diagram of a LiDAR data acquisition procedure 302, executed by the processor 110 of the electronic device 210, for generating a 3D point cloud data 310 representative of surrounding object present in the surroundings 250 of the vehicle 220, in accordance with certain non-limiting embodiments of the present technology.” (Para 0105), “According to certain non-limiting embodiments of the present technology, to detect the surrounding objects of the vehicle 220, such as those in the given road section 402 depicted in FIG. 4, using the MLA 260 as described above, the processor 110 can be configured to: (i) receive, from the plurality of sensor systems 280 of the vehicle 220, the sensed data representative of the given road section 402, such as at least one of a respective image (not separately labelled) from the camera sensor 290 and the 3D point cloud 312 from the LiDAR sensor 300; (ii) generate, based on the sensed data of the given road section 402, a respective in-use feature vector; and (iii) feed the respective in-use feature vector to each one of the plurality of ML models.” (Para 0176), see also Figs 4 and 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ike et al. (US 20210155232) discloses of sensing vehicles in front of an autonomous vehicle and controlling the work device and brakes accordingly.
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/KYLE J KINGSLAND/ Primary Examiner, Art Unit 3663
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