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 .
Information Disclosure Statement
The two information disclosure statement (IDS) documents submitted on January 25, 2022 and June 23, 2022 are in compliance with the provisions of 37 CFR 1.97 and are being considered by the examiner.
Claim Objections
Claims 8 and 15 objected to because of the following informalities: “an autonomous vehicle (AV), comprising: an AV, comprising:…”
How is an AV comprising an AV?
Second instance of “an AV” should be “the AV comprising…”
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A method for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising: using a computing device, comprising a processor and a memory: determining a location of an AV; determining whether a pick-up is required at the location; when a pick-up is required at the location, collecting data pertaining to a user and a destination; based on the data, calculating a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required; determining whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, triggering the pick-up, causing the AV to perform the pick-up.”
In step 2A prong 1 of the 101- analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“determining a location of an AV; determining whether a pick-up is required at the location”, (mental process, a person can determine whether a pick-up is necessary by looking and judging, see MPEP 2106.04(a)(2)(III)),
“based on the data, calculating a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required;”, (mental process, a person can mentally process, calculate and evaluate the trigger value to determine whether a pick-up is required, see MPEP 2106.04(a)(2)(III)),
“determining whether the trigger value is greater than a threshold value;” (mental process, a person can mentally calculate, evaluate the trigger value and determine if the value is greater than a threshold value, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“when a pick-up is required at the location, collecting data pertaining to a user and a destination;”, (This limitation is an insignificant extra solution activity of mere data gathering, see MPEP 2106.05(g))
“using a computing device, comprising a processor and a memory”, (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f))).
“when the trigger value is greater than the threshold value, triggering the pick-up, causing the AV to perform the pick-up”, (these are considered mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For element iv, this insignificant extra solution activity is well understood routine and conventional activity. See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
As discussed above, addition elements v and vi are either generic computer components or recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2:
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites the following additional elements:
“The method of claim 1, wherein the determining the location of the AV comprises determining whether the AV is parked.”, (In step 2A, Prong 1, a mental process, a person can mentally evaluate where the AV is located and determine if it is parked, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites the following additional elements:
“The method of claim 1, wherein the determining whether a pick-up is required at the location comprises determining whether the parking location meets predefined criteria.”, (In step 2A, Prong 1, a mental process, a human can mentally determine whether a pick-up would be required at a location with knowing the predefined criteria, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites the following additional elements:
“The method of claim 1, wherein the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.”, (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g) , In step 2B, this insignificant extra solution activity is well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 5 recites the following additional elements:
“The method of claim 1, wherein the data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.” (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g), In step 2B, these insignificant extra solution activities are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites the following additional elements:
“The method of claim 1, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”, (In step 2A, Prong 1, a mental process, a person can mentally evaluate one or more parameters, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites the following additional elements:
“The method of claim 1, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.”, (In step 2A, Prong 1, a mental process, a person can mentally recalculate trigger value by evaluating that the trigger value is not greater than the threshold, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising: an AV, comprising: a processor; a memory; and one or more location sensors, wherein the processor is configured to: determine, using the one or more location sensors, a location of the AV; determine whether a pick-up is required at the location; when a pick-up is required at the location, collect data pertaining to a user and a destination; based on the data, calculate a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required; determine whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, trigger the pick-up, causing the AV to perform the pick-up.” A system is considered a process, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101- analysis set forth in the MPEP 2106, the examiner has determined that the following limitation recites a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“determining a location of an AV; determining whether a pick-up is required at the location”, (mental process, a person can determine whether a pick-up is necessary by looking and judging, see MPEP 2106.04(a)(2)(III)),
“based on the data, calculating a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required;”, (mental process, a person can mentally process, calculate and evaluate the trigger value to determine whether a pick-up is required, see MPEP 2106.04(a)(2)(III)),
“determining whether the trigger value is greater than a threshold value;” (mental process, a person can mentally calculate, evaluate the trigger value and determine if the value is greater than a threshold value, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“comprising: a processor”, (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“a memory”, (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“one or more location sensors”, (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“determine, using the one or more location sensors, a location of the AV”, (this is considered mere instructions to apply an exception using generic computer components, see MPEP 2106.05(f)).
“when a pick-up is required at the location, collect data pertaining to a user and a destination;” (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g).
“when the trigger value is greater than the threshold value, trigger the pick-up, causing the AV to perform the pick-up” (these are considered mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f)).
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For element viii, this insignificant extra solution activity is well understood routine and conventional activity. See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
For elements iv, v, and vi, these are generic computer components being used as a tool to perform the judicial exception.
As discussed above, addition elements vii and ix recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 9:
Regarding claim 9, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 9 recites the following additional elements:
“The method of claim 8, wherein the determining the location of the AV comprises determining whether the AV is parked.”, (In step 2A, Prong 1, a mental process, a person can mentally evaluate where the AV is located and determine if it is parked, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 10:
Regarding claim 10, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 10 recites the following additional elements:
“The method of claim 8, wherein the determining whether a pick-up is required at the location comprises determining whether the parking location meets predefined criteria.”, (In step 2A, Prong 1, a mental process, a human can mentally determine whether a pick-up would be required at a location with knowing the predefined criteria, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 11:
Regarding claim 11, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 11 recites the following additional elements:
“The method of claim 8, wherein the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.”, (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g) , In step 2B, these insignificant extra solution activities are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 12:
Regarding claim 12, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 12 recites the following additional elements:
“The method of claim 8, wherein the data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.” (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g), In step 2B, these insignificant extra solution activities are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 13:
Regarding claim 13, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 13 recites the following additional elements:
“The method of claim 8, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”, (In step 2A, Prong 1, a mental process, a person can mentally evaluate one or more parameters, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 14:
Regarding claim 14, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 14 recites the following additional elements:
“The method of claim 8, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.”, (In step 2A, Prong 1, a mental process, a person can mentally recalculate trigger value by evaluating that the trigger value is not greater than the threshold, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 15:
Regarding claim 15, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising: an AV, comprising one or more location sensors; and a computing device, comprising a processor and a memory, configured to store programming instructions that, when executed by the processor, cause the processor to: determine, using the one or more location sensors, a location of the AV; determine whether a pick-up is required at the location; when a pick-up is required at the location, collect data pertaining to a user and a destination; based on the data, calculate a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required; determine whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, trigger the pick-up, causing the AV to perform the pick-up.” A system is considered a process, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101- analysis set forth in the MPEP 2106, the examiner has determined that the following limitation recites a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“determine whether a pick-up is required at the location; (mental process, a person can mentally process, calculate and evaluate the trigger value to determine whether a pick-up is required, see MPEP 2106.04(a)(2)(III)),
based on the data, calculate a trigger value, wherein the trigger is indicative of a percent likelihood that a pick-up request is required; (mental process, a person can mentally process, calculate and evaluate the trigger value to determine whether a pick-up is required, see MPEP 2106.04(a)(2)(III)),
determine whether the trigger value is greater than a threshold value;”, (mental process, a person can mentally calculate, evaluate the trigger value and determine if the value is greater than a threshold value, see MPEP 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“comprising: an AV,” (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“comprising one or more location sensors;” (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“a computing device,” (generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.05(f)).
“comprising a processor and a memory, configured to store programming instructions that, when executed by the processor, cause the processor to:”, (A processor, memory, a computing device and location sensors are generic computer components being used as a tool to perform the judicial exception, see MPEP 2106.04(a)(2)(III)).
when a pick-up is required at the location, collect data pertaining to a user and a destination; (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g).
“determine, using the one or more location sensors, a location of the AV;” (this is considered mere instructions to apply an exception using generic computer components, see MPEP 2106.05(f)).
“when the trigger value is greater than the threshold value, trigger the pick-up, causing the AV to perform the pick-up” (these are considered mere instructions to apply the judicial exception using generic computer components, see MPEP 2106.05(f)).
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element ix and x recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more. The additional element viii recites mere data gathering, and is considered insignificant extra-solution activity. In step 2B, this insignificant extra-solution activity is well understood routine and conventional activity which includes receiving or transmitting data over a network from court case Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), see MPEP 2106.05(d) (II)(i)),
For elements iv, v, vi, and vii, these are generic computer components being used as a tool to perform the judicial exception which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 16:
Regarding claim 16, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 16 recites the following additional elements:
“The method of claim 15, wherein the determining the location of the AV comprises determining whether the AV is parked.”, (In step 2A, Prong 1, a mental process, a person can mentally evaluate where the AV is located and determine if it is parked, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 17:
Regarding claim 17, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 17 recites the following additional elements:
“The method of claim 15, wherein the determining whether a pick-up is required at the location comprises determining whether the parking location meets predefined criteria.”, (In step 2A, Prong 1, a mental process, a human can mentally determine whether a pick-up would be required at a location with knowing the predefined criteria, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 18:
Regarding claim 18, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 18 recites the following additional elements:
“The system of claim 15, wherein:the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data” (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g) , In step 2B, these insignificant extra solution activities are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))),
The data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.” (In step 2A, Prong 2, this recites mere data gathering, which is considered insignificant extra-solution activity, see MPEP 2106.05(g), In step 2B, these insignificant extra solution activities are well understood routine and conventional activities, see Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 see MPEP 2106.05(d(2))),
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 19:
Regarding claim 19, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 19 recites the following additional elements:
“The method of claim 15, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”, (In step 2A, Prong 1, a human can perform the mere task of performing one or more calculations and dynamically adjusting the trigger value based on these calculations, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 20:
Regarding claim 20, it is dependent upon claim 15, and thereby incorporates the limitations of, and corresponding analysis applied to claim 15. Further, claim 20 recites the following additional elements:
“The method of claim 15, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.”, (In step 2A, Prong 1, a mental process, a person can mentally recalculate trigger value by evaluating that the trigger value is not greater than the threshold, see MPEP 2106.04(a)(2)(III)).
Since the claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
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, 2, 6, 7, 8, 9, 13, 14, 15, 16, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Beaurepaire, et al. “Providing Access to an Autonomous Vehicle Based on User’s Detected Interest” (US PG Pub No. US 20220198196A1), available in the May 17,2023 IDS, (hereinafter, BEAUREPAIRE).
Claim 1:
Regarding claim 1, BEAUREPAIRE teaches “A method for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising…”, see BEAUREPAIRE, paragraph [0022], “The autonomous vehicles may also park themselves or move cargo or passengers between locations without a human operator” and “For level 4, the vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. For level 5, the vehicle includes humans only as passengers, no human interaction is needed or possible. Vehicles classified under Levels 4 and 5 are considered highly and fully autonomous respectively as they can engage in all the driving tasks without human intervention.”. Beaurepaire teaches that at levels 4 and 5 their vehicular invention can be fully autonomous.
Further BEAUREPAIRE teaches “comprising: using a computing device, comprising a processor and a memory: determining a location of an AV “, in paragraph [0006] “The apparatus includes at least one processor and at least one memory including computer program code for one or more programs; the at least one memory configured to store the computer program code configured to, with the at least one processor, cause the at least one processor to: monitor an area around a shared vehicle;” and in paragraph [0021] “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.).”. Which the first quote shows it is comprising of computing device, processor and memory and the second quote explaining at least one of the processors is used to monitor an area around a shared vehicle calling it spatial awareness.
Further BEAUREPAIRE teaches “determining whether a pick-up is required at the location”
See BEAUREPAIRE, paragraph [0036], “They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door. With this system, similar to a personal vehicle, no mobile application is needed, no hailing gestures or intricate dances are required needed to hail or reserve the shared vehicle 124”. Beaurepaire uses a method which identifies a passenger, and a passenger in this context is someone who requires a pick-up.
Further BEAUREPAIRE teaches “when a pick-up is required at the location, collecting data pertaining to a user and a destination”
See BEAUREPAIRE, paragraph [0021], “The shared vehicle may provide access to different features based on the computed interest index for a nearby pedestrian. Feedback may also be provided by the shared vehicle to the pedestrian when a threshold of the interest index is reached, for example when the shared vehicle acknowledges the pedestrian's detected interest. Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.)” and paragraph [0007], “shared autonomous vehicle is providing including one or more sensors, a geographic database, a processor, and an automatic door locking mechanism. The one or more sensors are configured to acquire image data for one or more pedestrian candidates. The geographic database is configured to store mapping data. The processor is configured to predict destinations for the one or more pedestrian candidates based on the acquired image data and the mapping data. The processor is further configured to calculate an interest index value for each of the one or more pedestrians based on the predicted destinations.”. Beaurepaire explains that after the threshold is reached at a location, the vehicle provides spatial awareness for other shared vehicles. This is saying the vehicle is collecting data such as map data and nearby event data at a destination. The car has also already collected user data since the interest index value is always updating based on each user.
Further BEAUREPAIRE teaches, “based on the data, calculating a trigger value”
See BEAUREPAIRE, paragraph [0005], “calculating, by the processor in real time, an interest index value for each pedestrian candidate based on at least one trajectory for each pedestrian candidate; acknowledging, by the processor, an interest of a respective pedestrian candidate based on a respective interest index value passing a threshold value; and performing, by the processor, an action relating to access to the one or more features in the shared vehicle for the respective pedestrian candidate.”. Beaurepaire is talking about a value being calculated based on the pedestrian data calculated.
Further BEAUREPAIRE teaches “wherein the trigger is indicative of a percent likelihood that a pick-up request is required“,
See BEAUREPAIRE in paragraph [0062],“FIGS. 9 and 10 depict interest values for a candidate in the example described above over time in two different scenarios. In FIG. 9, the candidate is attempting to reserve and hail the shared vehicle. In FIG 10, the candidate is not attempting to reserve the shared vehicle. FIG. 9 depicts six (6) different time periods T1-T6 of the candidate as they make their way to the shared vehicle. The interest index values are listed below each location, for example, at T1 the interest index value is 10%. At T4, the interest index value is 30%. As can be seen, the interest index value increases over time as the candidate approaches the shared vehicle indicating that the candidate is more and more likely going to use the shared vehicle. The two biggest jumps in the values are when the candidate crosses the roads to make their way to the shared vehicle. AT T5 and T6, the shared vehicle may acknowledge the candidate's interest by generating and transmitting a visual or audio signal. Similarly, at both these points, the shared vehicle, depending on predetermined or dynamic thresholds, may provide some or all access to certain features. For example, at T6, with the interest index value at 90%, the shared vehicle may unlock all of the doors for the candidate.”. BEAUREPAIRE mentions using interest index values which change overtime based on the many different calculation parameters used. Demonstrates the use of a percent likelihood in order to determine whether a pick-up is necessary.
Further BEAUREPAIRE teaches, “determining whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, triggering the pick-up, causing the AV to perform the pick-up.”
See BEAUREPAIRE, paragraph [0036], “The mapping features are used to compute the interest index as a goal of the index is to determine the likelihood that a given user is interested in boarding a specific vehicle. The use of the map data allows a shared vehicle 124 to predict with high accuracy a trajectory and destination of a given user/candidate. When the trajectory of the candidate is predicted to end at the shared vehicle 124 (or with a high probability) the shared vehicle 124 provides access to one or more features to the candidate. The result is a seamless integration of shared vehicles 124. An individual who was used to owning a vehicle may be able to operate and use shared vehicles 124 as if the shared vehicles 124 were their own. A user with their own vehicle may be accustomed to walking up to their vehicle, entering, and operating the vehicle. No mobile application needed, no hailing gestures, or intricate dance needed to hail or reserve a personal vehicle. With a shared vehicle 124, the user may have the same experience. They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door”. Also see paragraph [0065], “At Act A140, the shared vehicle 124 acknowledges an interest of a candidate 135 based on a respective interest index value passing a threshold value. In an embodiment, a signal is generated that lets the candidate 135 know that the shared vehicle 124 is monitoring them. The signal may change over time as the interest index value increases, for example, increasing an intensity of a light or sound. A different or unique signal may be used when the interest index value passes the threshold value. There may be multiple different threshold values or reflection points on the interest index scale. As an example, when a “certainty threshold” is crossed (e.g., >80%, >60%, >50%), the shared vehicle 124 might unlock or, if that is not the case (between 50%, and 80%), the shared vehicle 124 may decide to wait and keep monitoring the user until it is able to decide whether the particular user 135 will want to use that vehicle or not. In an embodiment, the shared vehicle 124 may only be sure of the user's intent when the user physically touches the car to open the door/trunk.” Beaurepaire here explains how the index value is the trigger value and the threshold value is the predicted to end value. After this level is reached the user gets access to one of the features such as unlocking or opening a door.
Claim 2:
Regarding claim 2, BEAUREPAIRE teaches the limitations in claim 1.
Further, BEAUREPAIRE teaches “The method of claim 1, wherein the determining the location of the AV comprises determining whether the AV is parked.”
See BEAUREPAIRE, paragraph [0021], “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.)”. This demonstrates the idea of the shared vehicle sending out its location data while its parked in order for its spatial awareness to be accurate.
Claim 6:
Regarding claim 6, BEAUREPAIRE teaches the limitations in claim 1.
Further, BEAUREPAIRE teaches “The method of claim 1, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”
See BEAUREPAIRE, paragraph [0064], “In an embodiment, each of these examples, snapshots, and values may be used as feedback to improve the predictive capabilities of the shared vehicle and to improve the calculation of the interest index value. In an embodiment, the interest index may be calculated using a trained neural network. The neural network/machine learning techniques may each be or include a trainable algorithm, an, for example deep, i.e., multilayer, artificial neural network, a Support Vector Machine (SVM), a decision tree and/or the like. The machine-learning facilities may be based on k-means clustering, Temporal Difference (TD) learning, for example Q learning, a genetic algorithm and/or association rules or an association analysis. Rather than pre-programming the features and trying to relate the features to attributes, the deep architecture is defined to learn the features at different levels of abstraction based on the input data. Alternatively, a singular network may be trained using a large swath of training data and thus may be configured to handle each scenario. Collection of the training data and feedback may include acquiring image data, trajectory data, and result data over a period of time for various candidates”. Fully teaches the ideology of using multiple calculation parameters through a trained neural network that are based on the data retrieved, whether that be location data or user data. As well as the process of dynamically adjusting the trigger value or in this case the “interest index” value using the data acquired.
Claim 7:
Regarding claim 7, BEAUREPAIRE teaches the limitations in claim 1.
Further, BEAUREPAIRE teaches “The method of claim 1, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.”
See BEAUREPAIRE paragraph [0063], “Figure 10 depicts a similar candidate 135 as Figure 9. However, this candidate 135 does not end up using the shard vehicle. The interest index value can be seen increasing over time (T1-T7) as the candidate 135 makes their way towards and past the shared vehicle 124. At T1 the interest index value is 10%, at T4 when the candidate 135 crosses the street, the interest index value is 50%. However, the candidate 135 does not wish to use the shared vehicle 124 and passes by the shared vehicle 124 at T6 which leads to the drop in the interest index value to 10% at T7.”. Shows and explains that the interest index gets recalculated based on factors such as how close the candidate is to the car and the direction they are going.
Claim 8:
Regarding claim 8, BEAUREPAIRE teaches “A method for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising…”, see BEAUREPAIRE, paragraph [0022], “The autonomous vehicles may also park themselves or move cargo or passengers between locations without a human operator” and “For level 4, the vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. For level 5, the vehicle includes humans only as passengers, no human interaction is needed or possible. Vehicles classified under Levels 4 and 5 are considered highly and fully autonomous respectively as they can engage in all the driving tasks without human intervention.”. Beaurepaire teaches that at levels 4 and 5 their vehicular invention can be fully autonomous.
Further BEAUREPAIRE teaches “comprising: using a computing device, comprising a processor and a memory; and one or more location sensors, determining a location of an AV “,
See BEAUREPAIRE, in paragraph [0006] “The apparatus includes at least one processor and at least one memory including computer program code for one or more programs; the at least one memory configured to store the computer program code configured to, with the at least one processor, cause the at least one processor to: monitor an area around a shared vehicle;” and in paragraph [0021] “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.).”. Which the first quote shows it is comprising of computing device, processor and memory and the second quote explaining at least one of the processors is used to monitor an area around a shared vehicle calling it spatial awareness.
Further BEAUREPAIRE teaches “determining whether a pick-up is required at the location”
See BEAUREPAIRE, paragraph [0036], “They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door. With this system, similar to a personal vehicle, no mobile application is needed, no hailing gestures or intricate dances are required needed to hail or reserve the shared vehicle 124”. Beaurepaire uses a method which identifies a passenger, and a passenger in this context is someone who requires a pick-up.
Further BEAUREPAIRE teaches “when a pick-up is required at the location, collecting data pertaining to a user and a destination”
See BEAUREPAIRE, paragraph [0021], “The shared vehicle may provide access to different features based on the computed interest index for a nearby pedestrian. Feedback may also be provided by the shared vehicle to the pedestrian when a threshold of the interest index is reached, for example when the shared vehicle acknowledges the pedestrian's detected interest. Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.)” and paragraph [0007], “shared autonomous vehicle is providing including one or more sensors, a geographic database, a processor, and an automatic door locking mechanism. The one or more sensors are configured to acquire image data for one or more pedestrian candidates. The geographic database is configured to store mapping data. The processor is configured to predict destinations for the one or more pedestrian candidates based on the acquired image data and the mapping data. The processor is further configured to calculate an interest index value for each of the one or more pedestrians based on the predicted destinations.”. Beaurepaire explains that after the threshold is reached at a location, the vehicle provides spatial awareness for other shared vehicles. This is saying the vehicle is collecting data such as map data and nearby event data at a destination. The car has also already collected user data since the interest index value is always updating based on each user.
Further BEAUREPAIRE teaches, “based on the data, calculating a trigger value”
See BEAUREPAIRE, paragraph [0005], “calculating, by the processor in real time, an interest index value for each pedestrian candidate based on at least one trajectory for each pedestrian candidate; acknowledging, by the processor, an interest of a respective pedestrian candidate based on a respective interest index value passing a threshold value; and performing, by the processor, an action relating to access to the one or more features in the shared vehicle for the respective pedestrian candidate.”. Beaurepaire is talking about a value being calculated based on the pedestrian data calculated.
Further BEAUREPAIRE teaches “wherein the trigger is indicative of a percent likelihood that a pick-up request is required“,
See BEAUREPAIRE in paragraph [0062],“FIGS. 9 and 10 depict interest values for a candidate in the example described above over time in two different scenarios. In FIG. 9, the candidate is attempting to reserve and hail the shared vehicle. In FIG 10, the candidate is not attempting to reserve the shared vehicle. FIG. 9 depicts six (6) different time periods T1-T6 of the candidate as they make their way to the shared vehicle. The interest index values are listed below each location, for example, at T1 the interest index value is 10%. At T4, the interest index value is 30%. As can be seen, the interest index value increases over time as the candidate approaches the shared vehicle indicating that the candidate is more and more likely going to use the shared vehicle. The two biggest jumps in the values are when the candidate crosses the roads to make their way to the shared vehicle. AT T5 and T6, the shared vehicle may acknowledge the candidate's interest by generating and transmitting a visual or audio signal. Similarly, at both these points, the shared vehicle, depending on predetermined or dynamic thresholds, may provide some or all access to certain features. For example, at T6, with the interest index value at 90%, the shared vehicle may unlock all of the doors for the candidate.”. BEAUREPAIRE mentions using interest index values which change overtime based on the many different calculation parameters used. Demonstrates the use of a percent likelihood in order to determine whether a pick-up is necessary.
Further BEAUREPAIRE teaches, “determining whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, triggering the pick-up, causing the AV to perform the pick-up.”
See BEAUREPAIRE, paragraph [0036], “The mapping features are used to compute the interest index as a goal of the index is to determine the likelihood that a given user is interested in boarding a specific vehicle. The use of the map data allows a shared vehicle 124 to predict with high accuracy a trajectory and destination of a given user/candidate. When the trajectory of the candidate is predicted to end at the shared vehicle 124 (or with a high probability) the shared vehicle 124 provides access to one or more features to the candidate. The result is a seamless integration of shared vehicles 124. An individual who was used to owning a vehicle may be able to operate and use shared vehicles 124 as if the shared vehicles 124 were their own. A user with their own vehicle may be accustomed to walking up to their vehicle, entering, and operating the vehicle. No mobile application needed, no hailing gestures, or intricate dance needed to hail or reserve a personal vehicle. With a shared vehicle 124, the user may have the same experience. They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door”. Also see, paragraph [0065], “At Act A140, the shared vehicle 124 acknowledges an interest of a candidate 135 based on a respective interest index value passing a threshold value. In an embodiment, a signal is generated that lets the candidate 135 know that the shared vehicle 124 is monitoring them. The signal may change over time as the interest index value increases, for example, increasing an intensity of a light or sound. A different or unique signal may be used when the interest index value passes the threshold value. There may be multiple different threshold values or reflection points on the interest index scale. As an example, when a “certainty threshold” is crossed (e.g., >80%, >60%, >50%), the shared vehicle 124 might unlock or, if that is not the case (between 50%, and 80%), the shared vehicle 124 may decide to wait and keep monitoring the user until it is able to decide whether the particular user 135 will want to use that vehicle or not. In an embodiment, the shared vehicle 124 may only be sure of the user's intent when the user physically touches the car to open the door/trunk.” Beaurepaire here explains how the index value is the trigger value and the threshold value is the predicted to end value. After this level is reached the user gets access to one of the features such as unlocking or opening a door.
Claim 9:
Regarding claim 9, BEAUREPAIRE teaches the limitations in claim 8.
Further, BEAUREPAIRE teaches “The method of claim 8, wherein the determining the location of the AV comprises determining whether the AV is parked.”
See BEAUREPAIRE, paragraph [0021], “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.).”. This demonstrates the idea of the shared vehicle sending out its location data while its parked in order for its spatial awareness to be accurate.
Claim 13:
Regarding claim 13, BEAUREPAIRE teaches the limitations in claim 8.
Further, BEAUREPAIRE teaches “The method of claim 1, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”
See BEAUREPAIRE, paragraph [0064], “In an embodiment, each of these examples, snapshots, and values may be used as feedback to improve the predictive capabilities of the shared vehicle and to improve the calculation of the interest index value. In an embodiment, the interest index may be calculated using a trained neural network. The neural network/machine learning techniques may each be or include a trainable algorithm, an, for example deep, i.e., multilayer, artificial neural network, a Support Vector Machine (SVM), a decision tree and/or the like. The machine-learning facilities may be based on k-means clustering, Temporal Difference (TD) learning, for example Q learning, a genetic algorithm and/or association rules or an association analysis. Rather than pre-programming the features and trying to relate the features to attributes, the deep architecture is defined to learn the features at different levels of abstraction based on the input data. Alternatively, a singular network may be trained using a large swath of training data and thus may be configured to handle each scenario. Collection of the training data and feedback may include acquiring image data, trajectory data, and result data over a period of time for various candidates”. Fully teaches the ideology of using multiple calculation parameters through a trained neural network that are based on the data retrieved, whether that be location data or user data. As well as the process of dynamically adjusting the trigger value or in this case the “interest index” value using the data acquired.
Claim 14:
Regarding claim 14, BEAUREPAIRE teaches the limitations in claim 8.
Further, BEAUREPAIRE teaches “The method of claim 1, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.”
See BEAUREPAIRE, paragraph [0063], “Figure 10 depicts a similar candidate 135 as Figure 9. However, this candidate 135 does not end up using the shard vehicle. The interest index value can be seen increasing over time (T1-T7) as the candidate 135 makes their way towards and past the shared vehicle 124. At T1 the interest index value is 10%, at T4 when the candidate 135 crosses the street, the interest index value is 50%. However, the candidate 135 does not wish to use the shared vehicle 124 and passes by the shared vehicle 124 at T6 which leads to the drop in the interest index value to 10% at T7.” Beaurepaire shows and explains that the interest index gets recalculated based on factors such as how close the candidate is to the car and the direction they are going.
Claim 15:
Regarding claim 15, BEAUREPAIRE teaches “A method for performing anticipatory detection of a user's need to be picked up by an autonomous vehicle (AV), comprising…”, see BEAUREPAIRE, paragraph [0022], “The autonomous vehicles may also park themselves or move cargo or passengers between locations without a human operator” and “For level 4, the vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. For level 5, the vehicle includes humans only as passengers, no human interaction is needed or possible. Vehicles classified under Levels 4 and 5 are considered highly and fully autonomous respectively as they can engage in all the driving tasks without human intervention.”. Beaurepaire teaches that at levels 4 and 5 their vehicular invention can be fully autonomous.
Further BEAUREPAIRE teaches “comprising: an AV, comprising one or more location sensors; and a computing device, comprising a processor and a memory, configured to store programming instructions that, when executed by the processor, cause the processor to: determine, using the one or more location sensors, a location of the AV; “
See BEAUREPAIRE, in paragraph [0006] “The apparatus includes at least one processor and at least one memory including computer program code for one or more programs; the at least one memory configured to store the computer program code configured to, with the at least one processor, cause the at least one processor to: monitor an area around a shared vehicle;” and in paragraph [0021] “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.).”. Which the first quote shows it is comprising of computing device, processor and memory used to store code and the second quote explaining at least one of the processors is used to monitor an area around a shared vehicle calling it spatial awareness.
Further BEAUREPAIRE teaches “determining whether a pick-up is required at the location”
See BEAUREPAIRE, paragraph [0036], “They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door. With this system, similar to a personal vehicle, no mobile application is needed, no hailing gestures or intricate dances are required needed to hail or reserve the shared vehicle 124”. Beaurepaire uses a method which identifies a passenger, and a passenger in this context is someone who requires a pick-up.
Further BEAUREPAIRE teaches “when a pick-up is required at the location, collecting data pertaining to a user and a destination”
See BEAUREPAIRE, paragraph [0021], “The shared vehicle may provide access to different features based on the computed interest index for a nearby pedestrian. Feedback may also be provided by the shared vehicle to the pedestrian when a threshold of the interest index is reached, for example when the shared vehicle acknowledges the pedestrian's detected interest. Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.)” and paragraph [0007], “shared autonomous vehicle is providing including one or more sensors, a geographic database, a processor, and an automatic door locking mechanism. The one or more sensors are configured to acquire image data for one or more pedestrian candidates. The geographic database is configured to store mapping data. The processor is configured to predict destinations for the one or more pedestrian candidates based on the acquired image data and the mapping data. The processor is further configured to calculate an interest index value for each of the one or more pedestrians based on the predicted destinations.”. Beaurepaire explains that after the threshold is reached at a location, the vehicle provides spatial awareness for other shared vehicles. This is saying the vehicle is collecting data such as map data and nearby event data at a destination. The car has also already collected user data since the interest index value is always updating based on each user.
Further BEAUREPAIRE teaches, “based on the data, calculating a trigger value”
See BEAUREPAIRE, paragraph [0005], “calculating, by the processor in real time, an interest index value for each pedestrian candidate based on at least one trajectory for each pedestrian candidate; acknowledging, by the processor, an interest of a respective pedestrian candidate based on a respective interest index value passing a threshold value; and performing, by the processor, an action relating to access to the one or more features in the shared vehicle for the respective pedestrian candidate.”. Beaurepaire is talking about a value being calculated based on the pedestrian data calculated.
Further BEAUREPAIRE teaches “wherein the trigger is indicative of a percent likelihood that a pick-up request is required“,
See BEAUREPAIRE in paragraph [0062],“FIGS. 9 and 10 depict interest values for a candidate in the example described above over time in two different scenarios. In FIG. 9, the candidate is attempting to reserve and hail the shared vehicle. In FIG 10, the candidate is not attempting to reserve the shared vehicle. FIG. 9 depicts six (6) different time periods T1-T6 of the candidate as they make their way to the shared vehicle. The interest index values are listed below each location, for example, at T1 the interest index value is 10%. At T4, the interest index value is 30%. As can be seen, the interest index value increases over time as the candidate approaches the shared vehicle indicating that the candidate is more and more likely going to use the shared vehicle. The two biggest jumps in the values are when the candidate crosses the roads to make their way to the shared vehicle. AT T5 and T6, the shared vehicle may acknowledge the candidate's interest by generating and transmitting a visual or audio signal. Similarly, at both these points, the shared vehicle, depending on predetermined or dynamic thresholds, may provide some or all access to certain features. For example, at T6, with the interest index value at 90%, the shared vehicle may unlock all of the doors for the candidate.”. BEAUREPAIRE mentions using interest index values which change overtime based on the many different calculation parameters used. Demonstrates the use of a percent likelihood in order to determine whether a pick-up is necessary.
Further BEAUREPAIRE teaches, “determining whether the trigger value is greater than a threshold value; and when the trigger value is greater than the threshold value, triggering the pick-up, causing the AV to perform the pick-up.”
See BEAUREPAIRE, paragraph [0036], “The mapping features are used to compute the interest index as a goal of the index is to determine the likelihood that a given user is interested in boarding a specific vehicle. The use of the map data allows a shared vehicle 124 to predict with high accuracy a trajectory and destination of a given user/candidate. When the trajectory of the candidate is predicted to end at the shared vehicle 124 (or with a high probability) the shared vehicle 124 provides access to one or more features to the candidate. The result is a seamless integration of shared vehicles 124. An individual who was used to owning a vehicle may be able to operate and use shared vehicles 124 as if the shared vehicles 124 were their own. A user with their own vehicle may be accustomed to walking up to their vehicle, entering, and operating the vehicle. No mobile application needed, no hailing gestures, or intricate dance needed to hail or reserve a personal vehicle. With a shared vehicle 124, the user may have the same experience. They may be able to walk up to a shared vehicle 124 that is configured to identify that the user is a passenger, reserve the shared vehicle 124, and access one or more features such as unlocking or opening a door”. Also see, paragraph [0065], “At Act A140, the shared vehicle 124 acknowledges an interest of a candidate 135 based on a respective interest index value passing a threshold value. In an embodiment, a signal is generated that lets the candidate 135 know that the shared vehicle 124 is monitoring them. The signal may change over time as the interest index value increases, for example, increasing an intensity of a light or sound. A different or unique signal may be used when the interest index value passes the threshold value. There may be multiple different threshold values or reflection points on the interest index scale. As an example, when a “certainty threshold” is crossed (e.g., >80%, >60%, >50%), the shared vehicle 124 might unlock or, if that is not the case (between 50%, and 80%), the shared vehicle 124 may decide to wait and keep monitoring the user until it is able to decide whether the particular user 135 will want to use that vehicle or not. In an embodiment, the shared vehicle 124 may only be sure of the user's intent when the user physically touches the car to open the door/trunk.” Beaurepaire here explains how the index value is the trigger value and the threshold value is the predicted to end value. After this level is reached the user gets access to one of the features such as unlocking or opening a door.
Claim 16:
Regarding claim 16, BEAUREPAIRE teaches the limitations in claim 15.
Further, BEAUREPAIRE teaches “The method of claim 15, wherein the determining the location of the AV comprises determining whether the AV is parked.”
See BEAUREPAIRE, paragraph [0021], “Additionally, embodiments provide a spatial awareness for shared vehicles at their respective parking locations (for example based on map data, sensor data, dynamic information such as nearby events, number of people around etc.).”. This demonstrates the idea of the shared vehicle sending out its location data while its parked in order for its spatial awareness to be accurate.
Claim 19:
Regarding claim 19, BEAUREPAIRE teaches the limitations in claim 15.
Further, BEAUREPAIRE teaches “The method of claim 1, wherein the calculating the trigger value comprises: performing one or more calculation parameters based on the data to dynamically adjust the trigger value.”
See BEAUREPAIRE, paragraph [0064], “In an embodiment, each of these examples, snapshots, and values may be used as feedback to improve the predictive capabilities of the shared vehicle and to improve the calculation of the interest index value. In an embodiment, the interest index may be calculated using a trained neural network. The neural network/machine learning techniques may each be or include a trainable algorithm, an, for example deep, i.e., multilayer, artificial neural network, a Support Vector Machine (SVM), a decision tree and/or the like. The machine-learning facilities may be based on k-means clustering, Temporal Difference (TD) learning, for example Q learning, a genetic algorithm and/or association rules or an association analysis. Rather than pre-programming the features and trying to relate the features to attributes, the deep architecture is defined to learn the features at different levels of abstraction based on the input data. Alternatively, a singular network may be trained using a large swath of training data and thus may be configured to handle each scenario. Collection of the training data and feedback may include acquiring image data, trajectory data, and result data over a period of time for various candidates”. Fully teaches the ideology of using multiple calculation parameters through a trained neural network that are based on the data retrieved, whether that be location data or user data. As well as the process of dynamically adjusting the trigger value or in this case the “interest index” value using the data acquired.
Claim 20:
Regarding claim 20, BEAUREPAIRE teaches the limitations in claim 15.
Further, BEAUREPAIRE teaches “The method of claim 1, further comprising, when the trigger value is not greater than the threshold value, recalculating the trigger value.” by use of a figure and an explanation.
See BEAUREPAIRE, paragraph [0063], “Figure 10 depicts a similar candidate 135 as Figure 9. However, this candidate 135 does not end up using the shard vehicle. The interest index value can be seen increasing over time (T1-T7) as the candidate 135 makes their way towards and past the shared vehicle 124. At T1 the interest index value is 10%, at T4 when the candidate 135 crosses the street, the interest index value is 50%. However, the candidate 135 does not wish to use the shared vehicle 124 and passes by the shared vehicle 124 at T6 which leads to the drop in the interest index value to 10% at T7.”. Beaurepaire shows and explains that the interest index gets recalculated based on factors such as how close the candidate is to the car and the direction they are going.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over BEAUREPAIRE, further in view of HURLEY et al., “Systems and methods for matching transportation requestor devices with autonomous vehicles” (U.S. Patent No 11232375) published on January, 25, 2022, provided in the May 17,2023 IDS (hereafter, HURLEY).
Claim 3:
Regarding claim 3, BEAUREPAIRE teaches the limitations in claim 1.
BEAUREPAIRE does not explicitly disclose “The method of claim 1, wherein the determining whether a pick-up is required at the location comprises determining whether the parking location meets predefined criteria.
However, HURLEY teaches “The method of claim 1, wherein the determining whether a pick-up is required at the location comprises determining whether the parking location meets predefined criteria.”
See HURLEY, Column 5 Lines 5-15, “while in other examples pickup zones may change based on factors such as time of day, day of week, traffic, weather, and/or other relevant conditions. In some embodiments, AV may be constrained to only drop off transportation requestors in drop-off zone and/or drop-off zone. The term "drop-off zone," in some examples, may refer to an area where an AV is able to drop off a transportation requestor. In some examples, drop-off zones may vary in size and/or may be static and/or dynamic, similar to the description of pickup zones provided above. In some embodiments, pickup and drop-off zones for an autonomous vehicle may be the same”
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of HURLEY et al. into the method of BEAUREPAIRE to determine whether the parking location meets predefined criteria. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of HURLEY et al. as all the references are in the field of explaining and demonstrating determining whether pick-up is required based on predefined criteria such as the factors listed (time of day, day of week, traffic, weather, and/or other relevant conditions). A person of ordinary skill of the art would have been motivated to perform the combination being able to combine HURLEY’s method of predefined criteria with BEAUREPAIRE’s self-pick-up AV because it provides advantages to the field of transportation by increasing the utilization of autonomous transportation provider vehicles and the efficient allocation of non- autonomous transportation provider vehicles (e.g., to transportation tasks that autonomous vehicles are not able or well-suited to perform). More efficient and/or better allocated available transportation resources throughout a region may lead to fewer canceled requests, higher throughput of successful transportation matches, and lower wait times for requestors across a region (Column 3 Lines 19-28).
Claims 10 and 17 recite similar limitations as recited in claim 3, except that they set forth the claimed invention as a system and are rejected for the same reasons as applied hereinabove.
Claims 4, and 11 is rejected under 35 U.S.C. 103 as being unpatentable over BEAUREPAIRE, further in view of Penilla, Angel A. et al.” Methods and vehicles for processing voice commands and moderating vehicle response” (US20170140757A1) published on May, 18, 2017 (hereafter, Penilla, Angel A.).
Claim 4:
Regarding claim 4, BEAUREPAIRE teaches the limitations in claim 1.
BEAUREPAIRE does not explicitly disclose “wherein the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.
However, Penilla, Angel A. teaches “wherein the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.”
See Penilla, Angel A., paragraph [0012], “The method acts to access, by electronics of the vehicle, data for learned behavior of the user. The learned behavior is associated to the profile for the user. The method processes the voice command to identify a type command. The type of command is one of an instruction to make a change to a setting associated with the vehicle or a request for interfacing with a remote service over the network.”. It explains using voice recognition data and learned behavior data in order to control a remote service associated with the vehicle.
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Penilla, Angel A. into the method of BEAUREPAIRE to teach user digital behavior data and user voice recognition data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Penilla, Angel A. as all the references are in the field of self-driving cars, making these references analogous. A person of ordinary skill of the art would have been motivated to perform the combination for being able to easily control and manipulate your vehicle with a learned behavioral system associated to the user profiles for easier implemented voice commands and processing (Column 3 Lines 3-7) and its features such as making a setting, finding a map, finding directions, setting entertainment functions, looking up information, selecting a communication tool, making a call, sending a message, looking up a contact, looking up a calendar event, performing an Internet search, controlling a system of the vehicle, etc. for ease of access (Column 2 Lines 30-35).
Claim 11 recite similar limitations as recited in claim 4, except that it sets forth the claimed invention as a system and is rejected for the same reasons as applied hereinabove
Claims 5 and 12 is rejected under 35 U.S.C. 103 as being unpatentable over BEAUREPAIRE, further in view of Brito, Vilosh et al.” Navigation System” (US20170138747A1) published on May, 18, 2017 (hereafter, Brito, Vilosh).
Claim 5:
Regarding claim 5, BEAUREPAIRE teaches the limitations in claim 1.
BEAUREPAIRE does not explicitly disclose “wherein the data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment”.
However Brito, Vilosh teaches “wherein the data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment”
See Brito, Vilosh, paragraph [0201], “For each historical journey, the historical data may include GPS data for the journey. The GPS data may include latitude, longitude, velocity, time, heading etc. The GPS data may be a sequence of values measured at points along the journey. The measurements may have been made periodically. For example, the GPS data may include a sequence of velocity values corresponding to the journey. In the velocity example therefore, the velocity can be determined along the journey. The primary database may also include a delivery ID foreign key, and/or a GPS entry ID primary key for use in a relational database. The delivery ID may be used to prioritise end locations where the end locations visited more frequently are optimised more accurately.” Talks about using a location graph in order to track past movements of users, which correlates to historical travel data” and paragraph [0226] “A particular address stored in the historical database may have a measured location associated with it. If a user requests to go to go that destination again, the database may instruct the user to go to the measured location.” and paragraph [0234], “Advantageously, the journey information includes a note, wherein the note includes information about the destination.”. Vilosh talks about a historical database that records a user’s past locations. The database can instruct the user to go to that location if requested. These locations include a note which includes information about the destination.
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Brito, Vilosh et al. into the method of BEAUREPAIRE to whether a user has been to a certain location, data pertaining to that location and the most optimal route to the location. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Brito, Vilosh et al. as all the references are analogous in the field of a historical database that records a user’s past locations. A person of ordinary skill of the art would have been motivated to perform the combination for being able to add a database that can instruct the user to go to a location if requested. These locations include a note which includes information about the destination. Combining this teaching of Vilosh with BEAUREPAIRE’s AV provides an increasing amount of location data and history data to more effectively optimize the journey (Paragraph [0184]) also providing a tool necessary to get and store all the data that is being claimed by this limitation while also optimizing the more frequently visited past locations (Paragraph [0201]).
Claim 12 recite similar limitations as recited in claim 5, except that it sets forth the claimed invention as a system and is rejected for the same reasons as applied hereinabove.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over BEAUREPAIRE, in view of Penilla, Angel A. et al., further in view of Brito, Vilosh.
Claim 18:
Regarding claim 18, BEAUREPAIRE teaches the limitations in claim 15.
BEAUREPAIRE does not explicitly disclose “The system of claim 15, wherein: the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.
The data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.”
However, Penilla, Angel A. teaches “The system of claim 15, wherein: the data pertaining to the user comprises one or more of the following: user digital behavior data; user physiological data; user voice recognition data; user payment histories; and user historical travel data.”
See Penilla, Angel A., paragraph [0012], “The method acts to access, by electronics of the vehicle, data for learned behavior of the user. The learned behavior is associated to the profile for the user. The method processes the voice command to identify a type command. The type of command is one of an instruction to make a change to a setting associated with the vehicle or a request for interfacing with a remote service over the network.”. It explains using voice recognition data and learned behavior data in order to control a remote service associated with the vehicle.
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Penilla, Angel A. into the method of BEAUREPAIRE to teach user digital behavior data and user voice recognition data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Penilla, Angel A. as all the references are in the field of self-driving cars, making these references analogous. A person of ordinary skill of the art would have been motivated to perform the combination for being able to easily control and manipulate your vehicle with a learned behavioral system associated to the user profiles for easier implemented voice commands and processing (Column 3 Lines 3-7) and its features such as making a setting, finding a map, finding directions, setting entertainment functions, looking up information, selecting a communication tool, making a call, sending a message, looking up a contact, looking up a calendar event, performing an Internet search, controlling a system of the vehicle, etc. for ease of access (Column 2 Lines 30-35).
BEAUREPAIRE and Penilla, Angel A. do not explicitly disclose “The data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.”
However Brito, Vilosh teaches “wherein the data pertaining to the destination comprises one or more of the following: historical data comprising: whether the user has been to the destination before; an average timeframe of how long the user has visited the destination during a trip to the destination; and an average timeframe of how long an average customer visits the destination during a trip to the destination; data pertaining to whether the destination is an establishment; and when the destination is an establishment, hours of operation of the establishment.”
See Brito, Vilosh, paragraph [0201], “For each historical journey, the historical data may include GPS data for the journey. The GPS data may include latitude, longitude, velocity, time, heading etc. The GPS data may be a sequence of values measured at points along the journey. The measurements may have been made periodically. For example, the GPS data may include a sequence of velocity values corresponding to the journey. In the velocity example therefore, the velocity can be determined along the journey. The primary database may also include a delivery ID foreign key, and/or a GPS entry ID primary key for use in a relational database. The delivery ID may be used to prioritise end locations where the end locations visited more frequently are optimised more accurately.” Talks about using a location graph in order to track past movements of users, which correlates to historical travel data” and paragraph [0226] “A particular address stored in the historical database may have a measured location associated with it. If a user requests to go to go that destination again, the database may instruct the user to go to the measured location.” and paragraph [0234], “Advantageously, the journey information includes a note, wherein the note includes information about the destination.”. Vilosh talks about a historical database that records a user’s past locations. The database can instruct the user to go to that location if requested. These locations include a note which includes information about the destination.
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Brito, Vilosh et al. into the method of BEAUREPAIRE and Penilla, Angel A. to whether a user has been to a certain location, data pertaining to that location and the most optimal route to the location. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Brito, Vilosh et al. as all the references are analogous in the field autonomous vehicles that records a user’s past locations. A person of ordinary skill of the art would have been motivated to perform the combination for being able to add a database that can instruct the user to go to a location if requested. These locations include a note which includes information about the destination. Combining this teaching of Brito, Vilosh with BEAUREPAIRE and Penilla, Angel A., provides an increasing amount of location data and history data to more effectively optimize the journey (Paragraph [0184]) also providing a tool necessary to get and store all the data that is being claimed by this limitation while also optimizing the more frequently visited past locations (Paragraph [0201]).
Conclusion
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