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 Objections
Claim 10 is objected to because of the following informalities: the word “circuity” appears to have a spelling error. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Boulton, US 2020/0079382 A1, in view of Sabatini, et al., WO 2022/242842 A1.
As per Claim 1, Boulton teaches a system (¶¶ 23-25), comprising:
a computer system (¶ 24; computing system 110 of Figure 1) constructing a knowledge context corresponding to knowledge (¶¶ 25-26), wherein the knowledge context and the knowledge are associated with a first driving environment during creation of the knowledge (¶¶ 52-54; e.g., as collected from “a camera”), and performing context-based knowledge forwarding for a knowledge query by determining that the knowledge context corresponds to a query context of a knowledge query (¶ 54-55; after an “identification”); and
a vehicle communicatively connected to the computer system (¶ 23), the vehicle comprising:
a processor device generating a query context corresponding to a knowledge query (¶ 24; processor 120 of Figure 1), wherein the query context is associated with a second driving environment during querying for the knowledge, and submitting the knowledge query with the query context (¶¶ 57-60).
Boulton does not expressly teach a controller device receiving the knowledge, in response to the knowledge query, and performing one or more safety operations approaching the second driving environment, wherein the received knowledge is routed to the vehicle using the context-based knowledge forwarding and the received knowledge has a knowledge context that corresponds to the query context of the second driving environment. Sabatini teaches a controller device receiving the knowledge, in response to the knowledge query (page 9, lines 36-37; page 10, lines 1-14), and performing one or more safety operations approaching the second driving environment, wherein the received knowledge is routed to the vehicle using the context-based knowledge forwarding and the received knowledge has a knowledge context that corresponds to the query context of the second driving environment (page 12, lines 21-37). At the time of the invention, a person of skill in the art would have thought it obvious to combine the processing system of Boulton with the controller device of Sabatini, in order to determine pathways that will most likely help a vehicle avoid perilous driving or accidents.
As per Claim 2, Boulton does not expressly teach that the computer system receives the knowledge context as knowledge contextual feature maps constructed by a plurality of connected vehicles at the first driving environment. Sabatini teaches that the computer system receives the knowledge context as knowledge contextual feature maps constructed by a plurality of connected vehicles at the first driving environment (page 11, lines 19-35). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 3, Boulton does not expressly teach that the knowledge contextual feature maps comprise static features and dynamic features relating to the first driving environment. Sabatini teach that the knowledge contextual feature maps comprise static features and dynamic features relating to the first driving environment (page 11, lines 19-22; “center lines, drivable areas and stop lines”). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 4, Boulton does not expressly teach that the computer system receives the query context as query contextual feature maps constructed by the vehicle at the second driving environment. Sabatini teaches that the computer system receives the query context as query contextual feature maps constructed by the vehicle at the second driving environment (page 11, lines 29-35; in a “heatmap”).
As per Claim 5, Boulton teaches that the computer system performs context-based knowledge forwarding using a context-based analysis function (¶¶ 14-16; with “contextual risk profiles”).
As per Claim 6, Boulton teaches that the computer system performs the context-based analysis function comprising longest contextual feature matching (¶¶ 16-17).
As per Claim 7, Boulton teaches that the computer performs longest contextual feature matching by determining whether a selected number of contextual features in the knowledge contextual feature maps match the contextual features in the query contextual feature maps (¶¶ 18-19; by measuring “environmental conditions such as the location of vehicles and stationary objects around the vehicle, road information including traffic lights, road conditions, traffic conditions, among other factors”).
As per Claim 8, Boulton teaches that the computer performs context-based knowledge forwarding by, in response to determining that the knowledge context and the query context fail the longest contextual feature matching, filtering the knowledge query such that the knowledge is not forwarded to the vehicle (¶ 91; based on “whether the computing device has stored information about the desired vehicle”).
As per Claim 9, Boulton teaches that the computer performs context-based knowledge forwarding by, in response to determining that the knowledge context and the query context pass the longest contextual feature matching, routing the knowledge query such that the knowledge is forwarded to the vehicle (¶¶ 92-93).
As per Claim 10, Boulton teaches that the vehicle further comprises circuity communicatively connected to a vehicular knowledge network (¶¶ 43, 47).
As per Claim 11, Boulton teaches that the vehicular knowledge network comprises one or more entities communicating knowledge within the vehicular knowledge network (¶¶ 38, 43, 47).
As per Claim 12, Boulton teach that the one or more entities comprise the computer system, and the vehicle receives the communicated knowledge forwarded from the computer system via the vehicular knowledge network (¶¶ 36-38).
As per Claim 13, Boulton does not expressly teach that the circuity receives knowledge forwarded from the computer system vehicles via vehicle-to-cloud (V2C) communication. Sabatini teaches that the circuity receives knowledge forwarded from the computer system vehicles via vehicle-to-cloud (V2C) communication (page 9, lines 35-37; page 10, lines 1-10).
As per Claim 14, Boulton teaches that the knowledge is associated with risk reasoning of the second driving environment (¶¶ 17-18, 58, 66).
As per Claim 15, Boulton teaches that the one or more safety operations comprises autonomous safety maneuvers based on the risk reasoning of the second driving environment (¶ 44; e.g., “sudden decelerations”).
As per Claim 16, Boulton teaches a system (¶¶ 23-25) comprising:
at least one memory storing machine-executable instructions (¶¶ 28-29; memory 140 of Figure 1); and
at least one processor configured to access the at least one memory and execute the machine-executable instructions (¶¶ 28-30; processor 120 of Figure 1) to:
construct knowledge contextual feature maps corresponding to knowledge (¶¶ 25-26), wherein the knowledge contextual feature maps and the knowledge are associated with a first driving environment during creating the knowledge (¶¶ 52-54; e.g., as collected from “a camera”); receive a knowledge query (¶¶ 54-55) with query contextual feature maps corresponding to the query, wherein the query contextual feature maps and the knowledge query are associated with a second driving environment during querying the knowledge (¶¶ 57-60); and
perform a context-based routing for the knowledge query based on performing a context analysis function on the knowledge feature maps and the query feature maps (¶¶ 98-99).
Boulton does not expressly teach retrieving and forwarding the knowledge in response to determining a contextual relationship between the knowledge feature maps and the query feature maps based on the performed context analysis function, wherein the knowledge is forwarded via a vehicular knowledge network. Sabatini teaches retrieving and forwarding the knowledge in response to determining a contextual relationship between the knowledge feature maps and the query feature maps based on the performed context analysis function, wherein the knowledge is forwarded via a vehicular knowledge network (page 10, lines 21-31). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
As per Claim 17, Boulton teaches that the at least one processor configured to access the at least one memory further executes the machine-executable instructions to:
perform a knowledge cycle creating the knowledge and concurrently constructing the knowledge contextual feature maps corresponding to the knowledge (¶¶ 28-29);
perform a context analysis function using longest contextual feature matching (¶¶ 43-44);
determine that there is a contextual relationship between the query contextual features and the knowledge contextual features in response to determining a longest contextual feature match in a comparison of one or more features of the query contextual feature map and one or more contextual features of the knowledge contextual feature map (¶ 56; by matching a license plate number); and
determine that there is no contextual relationship between the query contextual features and the knowledge contextual features in response to determining no contextual feature match in a comparison of one or more contextual features of the query contextual feature map and one or more contextual features of the knowledge contextual feature map (¶¶ 59-60; if a license plate number or a VIN does not match).
As per Claim 18, Boulton teach that the at least one processor configured to access the at least one memory further executes the machine-executable instructions to: perform a context-based routing for the knowledge query by forwarding the knowledge request to a destination location for the knowledge associated with the knowledge contextual feature maps, in response to the determination that there is a contextual relationship between the query contextual features and the knowledge contextual features.
As per Claim 19, Boulton teaches that the at least one processor configured to access the at least one memory further executes the machine-executable instructions to: perform a context-based routing for the knowledge query by filtering the destination location for the knowledge associated with the knowledge contextual feature maps such that the knowledge query is not forwarded to the destination location and the knowledge is not retrieved and forwarded, in response to the determination that there is no contextual relationship between the query contextual features and the knowledge contextual features (¶¶ 89-91; based on risk profile determinations).
As per Claim 20, Boulton teaches a method comprising:
constructing, by at least one processor, knowledge contextual feature maps corresponding to knowledge (¶¶ 25-26), wherein the knowledge contextual feature maps and the knowledge are associated with a first driving environment during creating the knowledge (¶¶ 52-54; e.g., as collected from “a camera”);
receiving, by at least one processor, a knowledge query (¶¶ 54-55) with query contextual feature maps corresponding to the query, wherein the query contextual feature maps and the knowledge query are associated with a second driving environment during querying the knowledge (¶¶ 57-60); and
performing, by at least one processor, a context-based routing for the knowledge query based on performing a context analysis function on the knowledge feature maps and the query feature maps (¶¶ 98-99).
Boulton does not expressly teach: retrieving and forwarding, by at least one processor, the knowledge in response to determining a contextual relationship between the knowledge feature maps and the query feature maps based on the performed context analysis function, wherein the knowledge is forwarded via a vehicular knowledge network. Sabatini teaches: retrieving and forwarding, by at least one processor, the knowledge in response to determining a contextual relationship between the knowledge feature maps and the query feature maps based on the performed context analysis function, wherein the knowledge is forwarded via a vehicular knowledge network (page 10, lines 21-31). See Claim 1 above for the rationale based on obviousness, motivations and reasons to combine.
Conclusion
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ATUL TRIVEDI
Primary Examiner
Art Unit 3661
/ATUL TRIVEDI/Primary Examiner, Art Unit 3661
Prosecution Insights