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 .
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-9, 12, 14-17, & 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2022/0017122A1 (“Malla”).
As per claim 1 Malla discloses
A system for navigating a host vehicle relative to a road segment (see at least Malla, para. [0026]: Generally the environment 100 includes an ego vehicle 102 with an electronic control unit (ECU) 104 that executes one or more applications, operating systems, ego vehicle system and subsystem user interfaces, among others.), the system comprising:
at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to (see at least Malla, para. [0034]: In one or more embodiments, the ECU 104 may include a microprocessor, one or more application-specific integrated circuit(s) (ASIC), or other similar devices. The ECU 104 may also include internal processing memory, an interface circuit, and bus lines for transferring data, sending commands, and communicating with the plurality of components of the ego vehicle 102.) :
receive at least one captured image acquired by a camera onboard the host vehicle (see at least Malla, para. [0038]: With continued reference to FIG. 1, the vehicle camera system 122 may include one or more of the cameras (not shown) that may be positioned in one or more directions and at one or more areas to capture one or more images of the surrounding environment 200 of the ego vehicle 102 (e.g., images of the roadway on which the ego vehicle 102 is traveling)… In one embodiment, the one or more cameras may be configured as RGB cameras that may capture RGB bands that are configured to capture rich information about object appearance, as well as relationships and interactions between the ego vehicle 102 and objects within the surrounding environment 200 of the ego vehicle 102.);
generate a graph representative of one more semantic links between subjects represented in the at least one captured image (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-rfmkx,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z.);
provide the at least one captured image and the graph to a graph neural network configured to receive the at least one captured image and the graph (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z.), and
generate based on analysis of the graph and the at least one captured image, an output including an indicator of a predicted likelihood of occurrence of at least one predetermined event type (see at least Malla, para. [0055-0067]: Accordingly, the decoder 114 may thereby output the plurality of predicated trajectories 616 that are associated with each of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 in addition to the rankings pertaining to the probabilities of usage and overlap with respect to the travel path of the ego vehicle 102. As shown in FIG. 5, the neural network 108 may be configured to output the plurality of predicated trajectories with the rankings 512 pertaining to the probabilities to the multi-modal module 128 of the social-stage application 106. The multi-modal module 128 may thereby analyze the predicted multi-modal trajectories with a respective ranking 512 that have been output based on the consideration of both the motion and interactions using graph encoding and multi-attentions and may communicate respective data pertaining to the predicted multi-modal trajectories and respective rankings to the vehicle control module 130 of the social-stage application 106.); and
cause at least one navigational response of the host vehicle based on the indicator of the predicted likelihood of occurrence of the at least one predetermined event type (see at least Malla, para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 2 Malla discloses
wherein the at least one navigational response includes slowing the host vehicle (see at least Malla, para. [0033-0036]: Such autonomous control of the ego vehicle 102 may be provided by sending one or more commands to control one or more of the vehicle systems/control units 118 to operate (e.g., drive) the ego vehicle 102 during one or more circumstances ( e.g., when providing driver assist controls) and/or to fully control driving of the ego vehicle 102 during an entire trip of the ego vehicle 102. The one or more commands may be provided to one or more vehicle systems/control units 118 that include, but are not limited to an engine control unit, a braking control unit, a transmission control unit, a steering control unit, driver assistance systems, and the like to control the ego vehicle 102 to be autonomously driven and/or provide audio and/or visual alerts to an operator of the ego vehicle 102 in one or more circumstances.).
As per claim 3 Malla discloses
wherein the at least one navigational response includes changing a heading direction of the host vehicle (see at least Malla, para. [0033-0036]: Such autonomous control of the ego vehicle 102 may be provided by sending one or more commands to control one or more of the vehicle systems/control units 118 to operate (e.g., drive) the ego vehicle 102 during one or more circumstances ( e.g., when providing driver assist controls) and/or to fully control driving of the ego vehicle 102 during an entire trip of the ego vehicle 102. The one or more commands may be provided to one or more vehicle systems/control units 118 that include, but are not limited to an engine control unit, a braking control unit, a transmission control unit, a steering control unit, driver assistance systems, and the like to control the ego vehicle 102 to be autonomously driven and/or provide audio and/or visual alerts to an operator of the ego vehicle 102 in one or more circumstances. & para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 4 Malla discloses
wherein the graph neural network is trained on image data, links between subject nodes represented in the image data, and known event type outcomes associated with the image data (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z. & para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 5 Malla discloses
wherein the graph neural network generates the graph representative of the one more semantic links between subjects represented in the at least one captured image (see at least Malla, para. [0052-0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z.).
As per claim 6 Malla discloses
wherein the at least one predetermined event type includes a pedestrian moving into a path of the host vehicle (see at least Malla, para. [0027]: The agents 202 may also include, but may not be limited to, additional transportation vehicles, pedestrians, robots, motorized bicycles/scooters, automated shopping carts, automated suit cases, motorized wheel chairs, and the like. The surrounding environment 200 may include one or more pathways 204 on which the ego vehicle 102 and the agents 202 may be traveling. & para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
As per claim 8 Malla discloses
wherein the at least one predetermined event type includes a target vehicle moving into a path of the host vehicle (see at least Malla, para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
As per claim 9 Malla discloses
wherein the at least one predetermined event type includes a bicycle moving into a path of the host vehicle (see at least Malla, para. [0027]: The agents 202 may also include, but may not be limited to, additional transportation vehicles, pedestrians, robots, motorized bicycles/scooters, automated shopping carts, automated suit cases, motorized wheel chairs, and the like. The surrounding environment 200 may include one or more pathways 204 on which the ego vehicle 102 and the agents 202 may be traveling. & para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
As per claim 12 Malla discloses
wherein the at least one predetermined event type includes an object entering a path of the host vehicle (see at least Malla, para. [0027]: The agents 202 may also include, but may not be limited to, additional transportation vehicles, pedestrians, robots, motorized bicycles /scooters, automated shopping carts, automated suit cases, motorized wheel chairs, and the like. The surrounding environment 200 may include one or more pathways 204 on which the ego vehicle 102 and the agents 202 may be traveling. & para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
As per claim 14 Malla discloses
A method for navigating a host vehicle relative to a road segment (see at least Malla, para. [0068]: the method 400 may proceed to block 410, wherein the method 400 may include controlling one or more vehicle systems based on the predicted trajectories and rankings.), comprising:
receiving at least one captured image acquired by a camera onboard the host vehicle (see at least Malla, para. [0038]: With continued reference to FIG. 1, the vehicle camera system 122 may include one or more of the cameras (not shown) that may be positioned in one or more directions and at one or more areas to capture one or more images of the surrounding environment 200 of the ego vehicle 102 (e.g., images of the roadway on which the ego vehicle 102 is traveling)… In one embodiment, the one or more cameras may be configured as RGB cameras that may capture RGB bands that are configured to capture rich information about object appearance, as well as relationships and interactions between the ego vehicle 102 and objects within the surrounding environment 200 of the ego vehicle 102.);
generating a graph representative of one or more semantic links between subjects represented in the at least one captured image (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z.);
providing the at least one captured image to a graph neural network configured to receive the at least one captured image (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z.) and
generate, based on analysis of the graph and the at least one captured image, an output including an indicator of a predicted likelihood of occurrence of at least one predetermined event type (see at least Malla, para. [0055-0067]: Accordingly, the decoder 114 may thereby output the plurality of predicated trajectories 616 that are associated with each of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 in addition to the rankings pertaining to the probabilities of usage and overlap with respect to the travel path of the ego vehicle 102. As shown in FIG. 5, the neural network 108 may be configured to output the plurality of predicated trajectories with the rankings 512 pertaining to the probabilities to the multi-modal module 128 of the social-stage application 106. The multi-modal module 128 may thereby analyze the predicted multi-modal trajectories with a respective ranking 512 that have been output based on the consideration of both the motion and interactions using graph encoding and multi-attentions and may communicate respective data pertaining to the predicted multi-modal trajectories and respective rankings to the vehicle control module 130 of the social-stage application 106.); and
causing at least one navigational response of the host vehicle based on the indicator of the predicted likelihood of occurrence of the at least one predetermined event type (see at least Malla, para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 15 Malla discloses
wherein the graph neural network is trained on image data, links between subject nodes represented in the image data, and known event type outcomes associated with the image data (see at least Malla, para. [0056]: Referring again to FIG. 5, upon inputting the past trajectories 502 of each of the agents 202 to the neural network 108, the neural network 108 may create the graphic representation of the historic positions of the agents 202 by processing a spatio-temporal graph 504. In particular, the neural network 108 may create a graphical representation using all of the agents' positions x, at each time step t, G,=(E,, V,) where E, is edges and V, is vertices. Edges are also represented using a weighted matrix called an adjacency matrix A,. A representation of relative motion to represent a node/vertex may include: v/=llx/-x,/lb for agent k. Using this information, the adjacency matrix may be created for each element in adjacency matrix a/=1/llv,'vflb, where i, j are different agents, for same agents a"=O z. & para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 16 Malla discloses
wherein the at least one navigational response includes slowing the host vehicle or changing a heading direction of the host vehicle (see at least Malla, para. [0033-0036]: Such autonomous control of the ego vehicle 102 may be provided by sending one or more commands to control one or more of the vehicle systems/control units 118 to operate (e.g., drive) the ego vehicle 102 during one or more circumstances ( e.g., when providing driver assist controls) and/or to fully control driving of the ego vehicle 102 during an entire trip of the ego vehicle 102. The one or more commands may be provided to one or more vehicle systems/control units 118 that include, but are not limited to an engine control unit, a braking control unit, a transmission control unit, a steering control unit, driver assistance systems, and the like to control the ego vehicle 102 to be autonomously driven and/or provide audio and/or visual alerts to an operator of the ego vehicle 102 in one or more circumstances. & para. [0070]: As such, the vehicle control module 130 may be configured to output instructions to communicate the autonomous control parameters to the vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are ranked above a predetermined probability threshold of the agents 202 at one or more future time steps (t+l, t+2, t+n).).
As per claim 17 Malla discloses
wherein the at least one predetermined event type includes a target vehicle moving into a path of the host vehicle (see at least Malla, para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
As per claim 20 Malla discloses
wherein the at least one predetermined event type includes a pedestrian moving into a path of the host vehicle (see at least Malla, para. [0027]: The agents 202 may also include, but may not be limited to, additional transportation vehicles, pedestrians, robots, motorized bicycles/scooters, automated shopping carts, automated suit cases, motorized wheel chairs, and the like. The surrounding environment 200 may include one or more pathways 204 on which the ego vehicle 102 and the agents 202 may be traveling. & para. [0032]: In particular, the social-stage application 106 may be configured to analyze the predicted trajectories of the agents 202 that are located within the surrounding environment 200 of the ego vehicle 102 with respect to the highest ranked/most probable predicted trajectories. In one embodiment, the social-stage application 106 may be configured to output instructions to communicate autonomous control parameters to a vehicle autonomous controller 116 of the ego vehicle 102 to autonomously control the ego vehicle 102 to avoid overlap with the respective predicted trajectories that are respectively associated with each of the agents 202 for projected trajectories that include probabilities that are ranked above a predetermined probability threshold at one or more future time steps (t+l, t+2, t+n).).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malla, in view of US 9248834B1 (“Ferguson”).
As per claim 7 Malla does not explicitly disclose
wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle
Ferguson teaches
wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle (see at least Ferguson, col. 18 lines 1-10: In this example, the vehicle 800 is directly in front of vehicle 100 and also approaching intersection 502. The one or more computing devices may receive information from the perception system 172 and/or determine various characteristics of the vehicle 800. Additionally, the perception system 172 and/or one or more computing devices may track the location of the vehicle 800 over a brief period of time in order to determine a heading and speed, plot a past trajectory (not shown), and identify contextual information. For example, the one or more computing devices may detect brake lights and a right turn signal from the vehicle 800 as the vehicle 800 approaches intersection 502.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle of Ferguson, with a reasonable expectation of success, in order to improve safety and reduce traffic accidents (see at least Ferguson, col. 5 lines 50-51).
As per claim 19 Malla does not explicitly disclose
wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle
Ferguson teaches
wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle (see at least Ferguson, col. 18 lines 1-10: In this example, the vehicle 800 is directly in front of vehicle 100 and also approaching intersection 502. The one or more computing devices may receive information from the perception system 172 and/or determine various characteristics of the vehicle 800. Additionally, the perception system 172 and/or one or more computing devices may track the location of the vehicle 800 over a brief period of time in order to determine a heading and speed, plot a past trajectory (not shown), and identify contextual information. For example, the one or more computing devices may detect brake lights and a right turn signal from the vehicle 800 as the vehicle 800 approaches intersection 502.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes a target vehicle braking ahead of the host vehicle of Ferguson, with a reasonable expectation of success, in order to improve safety and reduce traffic accidents (see at least Ferguson, col. 5 lines 50-51).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malla, in view of US 2023/0159060A1 (“Garimella”).
As per claim 10 Malla does not explicitly disclose
wherein the at least one predetermined event type includes an animal moving into a path of the host vehicle
Garimella teaches
wherein the at least one predetermined event type includes an animal moving into a path of the host vehicle (see at least Garimella, para. [0187]: In some instances, the perception component 1222 may include functionality to perform object detection, segmentation, and/or classification. In some examples, the perception component 1222 may provide processed sensor data that indicates a presence of an object (e.g., entity) that is proximate to the vehicle 1202 and/or a classification of the object as an object type ( e.g., car, pedestrian, cyclist, animal, building, tree, road surface, curb, sidewalk, unknown, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes an animal moving into a path of the host vehicle of Garimella, with a reasonable expectation of success, in order to improve a functioning of a computing device as well as improve safety outcomes (see at least Garimella, para. [0033]).
Claim(s) 11 & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malla, in view of US 2017/0371344A1 (“Cohen”).
As per claim 11 Malla does not explicitly disclose
wherein the at least one predetermined event type includes cargo falling from a target vehicle
Cohen teaches
wherein the at least one predetermined event type includes cargo falling from a target vehicle (see at least Cohen, para. [0159-0160]: Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards may vary in structure, shape, size, and color, which may make detection of such hazards more challenging. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on the plurality of images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit 110 may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface…At step 530, processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 520 and the techniques as described above in connection with FIG. 4.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes cargo falling from a target vehicle of Cohen, with a reasonable expectation of success, in order to improve safety conditions (see at least Cohen, para. [0175]).
As per claim 18 Malla does not explicitly disclose
wherein the at least one predetermined event type includes cargo falling from a target vehicle
Cohen teaches
wherein the at least one predetermined event type includes cargo falling from a target vehicle (see at least Cohen, para. [0159-0160]: Processing unit 110 may also execute monocular image analysis module 402 to detect various road hazards at step 520, such as, for example, parts of a truck tire, fallen road signs, loose cargo, small animals, and the like. Road hazards may vary in structure, shape, size, and color, which may make detection of such hazards more challenging. In some embodiments, processing unit 110 may execute monocular image analysis module 402 to perform multi-frame analysis on the plurality of images to detect road hazards. For example, processing unit 110 may estimate camera motion between consecutive image frames and calculate the disparities in pixels between the frames to construct a 3D-map of the road. Processing unit 110 may then use the 3D-map to detect the road surface, as well as hazards existing above the road surface…At step 530, processing unit 110 may execute navigational response module 408 to cause one or more navigational responses in vehicle 200 based on the analysis performed at step 520 and the techniques as described above in connection with FIG. 4.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes cargo falling from a target vehicle of Cohen, with a reasonable expectation of success, in order to improve safety conditions (see at least Cohen, para. [0175]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Malla, in view of US 2022/0227367A1 (“Kario”).
As per claim 13 Malla does not explicitly disclose
wherein the at least one predetermined event type includes a loss of traction by the host vehicle on a road surface associated with the road segment.
Kario teaches
wherein the at least one predetermined event type includes a loss of traction by the host vehicle on a road surface associated with the road segment (see at least Kario, para. [0295-0296]: Where the presence of a navigational constraint augmentation factor is identified (e.g., at step 1507), a second navigational constraint may be determined or developed in response to detection of the constraint augmentation factor. This second navigational constraint may be different from the first navigational constraint and may include at least one characteristic augmented with respect to the first navigational constraint. The second navigational constraint may be more restrictive than the first navigational constraint, because detection of a constraint augmentation factor in the environment of the host vehicle or associated with the host vehicle may suggest that the host vehicle may have at least one navigational capability reduced with respect to normal operating conditions. Such reduced capabilities may include lowered road traction (e.g., ice, snow, or water on a roadway; reduced tire pressure; etc.); impaired vision (e.g., rain, snow, dust, smoke, fog etc. that reduces captured image quality); impaired detection capability (e.g., sensor failure or partial failure, reduced sensor performance, etc.), or any other reduction in capability of the host vehicle to navigate in response to a detected navigational state… Where at least one constraint augmentation factor is detected at step 1507, and at least one constraint has been augmented in step 1509, a navigational action for the host vehicle may be determined at step 1511. The navigational action for the host vehicle may be based on the identified navigational state and may satisfy the second navigational (i.e., augmented) constraint. The navigational action may be implemented at step 1513 by causing at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Malla to incorporate the teaching of wherein the at least one predetermined event type includes a loss of traction by the host vehicle on a road surface associated with the road segment of Kario, with a reasonable expectation of success, in order to optimize future objectives (see at least Kario, para. [0303]).
Conclusion
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/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668
