DETAILED ACTION
This office action is in response to the communication filed on January 16, 2026. Claims 1-9, 12-17, 19, and 27-30 are currently pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/16/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/28/25 has been considered by the examiner.
Response to Arguments
Applicant's arguments filed on January 16, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Page 15 of the Remarks disagrees that the amended claims 1-9, 12-17, 19, and 27-30 are directed to non-statutory subject matter.
Examiner respectfully disagrees.
Amended independent claims 1, 17, and 29 covers several steps, such as the comparing and withholding steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, discussed in detail in the current 101 rejection below.
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, and are recognized as a well understood, routine, and conventional activity within the field of computer functions, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Applicant in Page 11-15 of the Remarks argues that Harvey, Anastassov, and Isaac do not teach or even suggest the features “responsive to the comparing, withholding, by the vehicle access platform, prior to transmitting of search results to the search query, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results to the search query”, as recited in amended independent claim 17 and similarly recited in amended independent claims 17 and 29.
Examiner respectfully disagrees. The cited prior art alone and/or in combination discloses the argued features.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Harvey in Column 1 lines 23-39 and Column 13 lines 45-58 discloses renters access a vehicle sharing platform to search for a vehicle to rent according to their criteria, such as the time period of need, the type, price, etc., user providing trip information for vehicle rental, pick up and return.
Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences.
Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed.
Harvey in Column 5 line 55 – Column 6 lines 36 discloses storing historical data that describes usage patterns of users, and which are collected before, during, and/or after a trip. Harvey discloses that the historical data includes various vehicle information related to damage, injuries, accidents, dates and times of use etc..
Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server.
Harvey in Column 7 line 55 – Column 8 line 21 discloses retrieve information from one or more databases to learn owner preferences to predict preferences.
Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, such as a number of times a particular type of driving operation occurred during a predetermined amount of time, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter.
Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page.
Therefore, Harvey discloses responsive to the comparing, withholding, by the vehicle access platform, prior to transmitting of search results to the search query, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results.
Harvey discloses a vehicle access platform identifying one or more of available vehicles to prevent from being returned as search results using user, vehicle, or trip information and also discloses predicting based at least in part on the pre-trip information about the upcoming trip, however, Harvey does not explicitly disclose:
receiving…from one or more machine learning models, a severity amount occurring during the upcoming trip…;
responsive to receiving the severity amount occurring during the upcoming trip…comparing…the severity amount…;
responsive to the comparing, withholding…one or more vehicles of the available vehicles corresponding to…the severity amount occurring during the upcoming trip…;
Isaac in [0002], [0003], and [0053] discloses a user can rent a vehicle for visiting a new city or for a vacation, user can browse a listing of available vehicles for travel, a user can request to rent a vehicle, listing vehicles available for rent; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles.
Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics.
Isaac in [0021] and [0023] discloses user can use a web browser to interact and obtain data, user can provide input such as queries which are processed by the system.
Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like.
Therefore, Isaac discloses receiving from one or more machine learning models a severity amount occurring during the upcoming trip, responsive to receiving the severity amount occurring during the upcoming trip comparing the severity amount, and responsive to the comparing, withholding one or more vehicles of the available vehicles corresponding to the severity amount occurring during the upcoming trip.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey and Isaac, to have combined Harvey and Isaac. The motivation to combine Harvey and Isaac would be to prevent a user from renting particular vehicles based on a prediction that the user tends to mishandle rented vehicles (Isaac: [0017]).
Therefore, Harvey in view of Isaac discloses “responsive to the comparing, withholding, by the vehicle access platform, prior to transmitting of search results to the search query, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results to the search query”.
For the above reasons, Examiner states that rejection of the current Office action is proper.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 16 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 recites the limitation "the predicted severity amount" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
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 1-9, 12-17, 19, and 27-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1, 17, and 29 respectively recite a computer-implemented method, a system, and a computer-implemented method, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 recites the limitations:
“responsive to receiving the probability of an adverse outcome occurring during the upcoming trip based on the pre-trip information about the upcoming trip, comparing…the probability of the adverse outcome to a plurality of thresholds”;
A person can mentally or using a pen and paper compare a probability of an adverse outcome to a plurality of thresholds responsive to receiving the probability of an adverse outcome occurring during an upcoming trip based on pre-trip information about the upcoming trip.
“responsive to the comparing, withholding…prior to transmission, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the probability of the adverse outcome occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted…as search results to the search query”;
A person can mentally or using a pen and paper withhold, prior to a transmission, one or more vehicles of available vehicles corresponding to thresholds exceeded by a probability of adverse outcome occurring during an upcoming trip based on pre-trip information about the upcoming trip, to prevent from being transmitted as search results to the search query.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, covers steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 17 recites the limitations:
“compare…the severity amount to a plurality of threshold amounts corresponding to the available vehicles”;
A person can mentally or using a pen and paper compare a severity amount to a plurality of threshold amounts corresponding to available vehicles.
"withhold…prior to transmission, one or more available vehicles of the available vehicles corresponding to threshold amounts exceeded by the severity amount”;
A person can mentally or using a pen and paper withhold one or more available vehicles of available vehicles corresponding to threshold amounts exceeded by a severity amount prior to a transmission.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, covers steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 29 recites the limitations:
“responsive to receiving the severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, comparing…the severity amount to a plurality of threshold amounts”;
A person can mentally or using a pen and paper compare a severity amount to a plurality of threshold amounts responsive to receiving the severity amount occurring during an upcoming trip based on pre-trip information about the upcoming trip.
“responsive to the comparing, withholding…prior to transmitting of search results to the search query, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results”;
A person can mentally or using a pen and paper withhold, prior to transmitting of search results to the search query, one or more vehicles of available vehicles corresponding to thresholds exceeded by a severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, covers steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 1 recites the limitations:
“receiving, by the vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip”, which is a step for receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving, by the vehicle access platform, via one or more machine learning models, a probability of an adverse outcome occurring during the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles prevented from being transmitted via the server as the search results”, which is a step for controlling or manipulating data. The step is recited at a high level of generality, and amounts to mere data manipulation, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “by a vehicle access platform”, “from a client device”, “via one or more machine learning models to”, and “via a server” in the steps are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 17 recites the limitations:
“a vehicle access platform implemented at least partially in hardware of the computing device to receive a search query from a client device to view vehicles for an upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“an information identification module implemented at least partially in the hardware of the computing device to receive, based on the search query, pre-trip information about the upcoming trip, including user information, vehicle information, and trip information”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“a vehicle availability engine implemented at least partially in the hardware of the computing device to receive available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receive, via one or more machine learning models, a severity amount for an outcome occurring during the upcoming trip by using the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“an access controller implemented at least partially in the hardware of the computing device to control access to the available vehicles for the upcoming trip by transmitting, to the client device, search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more available vehicles withheld from being transmitted via the server”, which is a step of controlling or manipulating data. The step is recited at a high level of generality, and amounts to mere data manipulation, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a system comprising:”, “a vehicle access platform implemented at least partially in hardware of the computing device to”, “from a client device”, “an information identification module implemented at least partially in the hardware of the computing device to”, “a vehicle availability engine implemented at least partially in the hardware of the computing device to”, “a severity prediction engine implemented at least partially in the hardware of the computing device to”, “via one or more machine learning models”, “at a server”, “an access controller implemented at least partially in the hardware of the computing device to”, and “to the client device” in the steps in claim 17 are recited at a high-level of generality, such as a generic platform and device for returning search results, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 29 recites the limitations:
“receiving, by a vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receiving, by the vehicle access platform, from one or more machine learning models, a severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results including a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles withheld from being transmitted as part of the search results”, which is a step of controlling or manipulating data. The step is recited at a high level of generality, and amounts to mere data manipulation, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a computer-implemented method”, “by a vehicle access platform”, “from a client device”, “and from one or more machine learning models” in the steps in claim 29 are recited at a high-level of generality, such as a generic platform and device for returning search results, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1, 17, and 29 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 1 recites the limitations:
“receiving, by a vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip”, which is a step for receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receiving, by the vehicle access platform, via one or more machine learning models, a probability of an adverse outcome occurring during the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles prevented from being transmitted via the server as the search results”, which is a step for controlling or manipulating access to data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, these additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Independent claim 17 recites the limitations:
“a vehicle access platform implemented at least partially in hardware of a computing device to receive a search query from a client device to display vehicles for an upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“an information identification module implemented at least partially in the hardware of the computing device to receive, based on the search query, pre-trip information about the upcoming trip, including user information, vehicle information, and trip information”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“a vehicle availability engine implemented at least partially in the hardware of the computing device to receive available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receive, via one or more machine learning models, a severity amount for an outcome occurring during the upcoming trip by using the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“an access controller implemented at least partially in the hardware of the computing device to control access to the available vehicles for the upcoming trip by transmitting, to the client device, search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more available vehicles withheld from being transmitted via the server”, which is a step for controlling or manipulating access to data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, these additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Independent claim 24 recites the limitations:
“receiving, by a vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receiving, by the vehicle access platform, from one or more machine learning models, a severity amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results including a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles withheld from being transmitted as part of the search results”, which is a step for controlling or manipulating access to data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, these additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 2 recites additional limitations, such as:
“wherein the search query is received via user input to a user interface displayed at the client device”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
The additional elements “a user interface displayed at the client device” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 recites additional limitations, such as:
“wherein the pre-trip information includes a location of the upcoming trip, a start time of the upcoming trip, and an end time of the upcoming trip”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 recites additional limitations, such as:
“calculating, by the vehicle access platform, an amount of time between a time at which the search query is received and the start time of the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper calculate an amount of time between a time at which a search query is received and a start time of an upcoming trip, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “by the vehicle access platform” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 recites additional limitations, such as:
“wherein the probability of the adverse outcome occurring during the upcoming trip is based at least in part on the amount of time between the time at which the search query is received and the start time of the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper base a probability of an adverse outcome occurring during an upcoming trip at least in part on an amount of time between a time at which a search query is received and a start time of the upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 recites additional limitations, such as:
“wherein the probability of the adverse outcome occurring during the upcoming trip is based at least in part on the start time of the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper base a probability of an adverse outcome occurring during an upcoming trip at least in part on a start time of the upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitations, such as:
“wherein the probability of the adverse outcome occurring during the upcoming trip is generated based at least in part on the location of the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate a probability of an adverse outcome occurring during an upcoming trip based at least in part on a location of the upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 recites additional limitations, such as:
“wherein the pre-trip information includes a trip history with the vehicle access platform”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 9 recites additional limitations, such as:
“wherein the trip history indicates no previous trips with the vehicle access platform”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 recites additional limitations such as:
“receiving, by the vehicle access platform, via the one or more machine learning models, a prediction of one or more adverse outcomes occurring during the upcoming trip based at least in part on the pre-trip information about the upcoming trip”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“and wherein the withholding of the one or more of the available vehicles to prevent from being returned as the search results is based at least in part on the prediction of the one or more adverse outcomes occurring during the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper withhold one or more of available vehicles to prevent from being returned as search results based at least in part on a prediction of the one or more adverse outcomes occurring during the upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “by the vehicle access platform” and “via the one or more machine learning models” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 recites additional limitations, such as:
“wherein the receiving of the prediction of the one or more adverse outcomes occurring during the upcoming trip further comprises: receiving, by the vehicle access platform, via at least one machine learning model of the one or more machine learning models, a severity amount corresponding to the one or more adverse outcomes occurring during the upcoming trip”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“responsive to receiving the severity amount, comparing…the severity amount to a plurality of thresholds corresponding to the available vehicles”;
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper compare a severity amount to a plurality of thresholds corresponding to available vehicles, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
“and responsive to the comparing of the severity amount, withholding… prior to transmission, one or more vehicles of the available vehicles corresponding to the thresholds exceeded by the severity amount, to prevent from being returned as part of the search results to the search query”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper withhold, prior to a transmission, one or more vehicles of available vehicles corresponding to thresholds exceeded by a severity amount, to prevent from being returned as part of a search results to a search query, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “by the vehicle access platform”, “via at least one machine learning model of the one or more machine learning models”, “server-side”, and ”at the server” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 14 recites additional limitations, such as:
“wherein the severity amount includes a predicted damage amount for the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper calculate a predicted severity amount including a predicted damage amount for an upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 15 recites additional limitations, such as:
“wherein the severity amount includes a predicted incidental amount for the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper calculate a severity amount including a predicted incidental amount for an upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 recites additional limitations, such as:
“identifying, by the vehicle access platform, at least one vehicle of the available vehicles that the client device is ineligible for based on the severity amount exceeding a threshold amount, and wherein the at least one vehicle is included as the one or more of the available vehicles to prevent from being returned as search results based on the predicted severity amount being higher than the threshold severity amount”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper identify at least one vehicle that a client device is ineligible for based on a severity amount exceeding a threshold amount, and wherein the at least one vehicle is included as one or more of available vehicles to prevent from being returned as search results based on a predicted severity amount being higher than the threshold severity amount, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “by the vehicle access platform” and “client device” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 19 recites additional limitations, such as:
“an outcome predictor implemented at least partially in the hardware of the computing device to receive, from at least one machine learning model, an adverse outcome for the upcoming trip based at least in part on the severity amount”, which is a step of receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“and in which the withholding of the one or more available vehicles is further based on the adverse outcome for the upcoming trip”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 17, because a person can mentally or using a pen and paper withhold one or more available vehicles based on an adverse outcome for an upcoming trip, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “an outcome predictor implemented at least partially in the hardware of the computing device to” and “from the at least one machine learning model” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 27 recites additional limitations, such as:
“training, by a vehicle access platform, one or more machine learning models to generate, based on search queries to view vehicles for upcoming trips, probabilities of adverse outcomes occurring during upcoming trips corresponding to the search queries, in which the training includes training at least one of the one or more machine learning models to identify one or more patterns in pre-trip information for the upcoming trips, that correspond to an increased probability of the adverse outcomes occurring during the upcoming trips”, which is a step of training one or more machine learning models to generate probabilities of adverse outcomes occurring, and amounts to no more than mere instructions to apply an exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (MPEP 2106.05(f)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 28 recites additional limitations, such as:
“wherein the plurality of thresholds includes one or more fleet-level thresholds and one or more per-vehicle thresholds”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 17, because a person can mentally or using a pen and paper mentally or using a pen and paper compare a severity amount to a plurality of threshold amounts including one or more fleet-level thresholds and one or more per-vehicle thresholds, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 30 recites additional limitations, such as:
“wherein the plurality of threshold amounts includes per-vehicle threshold amounts”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 17, because a person can mentally or using a pen and paper mentally or using a pen and paper compare a severity amount to a plurality of threshold amounts including per-vehicle threshold amounts, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 2-9, 12-16, 19, 27, 28, and 30 are also directed to an abstract idea without significantly more and are not patent eligible.
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 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.
Claim(s) 1-9, 12-16, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harvey (US Pat 10,703,379) in view of Isaac (US Pub 2022/0055649) and in further view of Anastassov (US Pub 2016/0379485).
With respect to claim 1, Harvey discloses a computer-implemented method (Harvey in Column 2 lines 8-62 and Column 11 line 46 - Column 12 line 3 and in Figure 3 discloses a computer implemented method) comprising:
receiving, by a vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip (Harvey in Column 12 line 63 – Column 13 line 8 discloses displaying a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; Harvey in Column 1 lines 23-39 and Column 13 lines 45-58 discloses renters access a vehicle sharing platform to search for a vehicle to rent according to their criteria, such as the time period of need, the type, price, etc., user providing trip information for vehicle rental, pick up and return; Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
receiving, by the vehicle access platform…based on the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; here Harvey does not explicitly disclose receiving, via the one or more machine learning models, a probability of an adverse outcome occurring during the upcoming trip, but the Isaac and Anastassov references disclose the features, as discussed below);
responsive to receiving…the upcoming trip based on the pre-trip information about the upcoming trip, comparing, by the vehicle access platform… to a plurality of thresholds (Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; here Harvey does not explicitly disclose receiving the probability of an adverse outcome occurring during the upcoming trip, but the Isaac and Anastassov references disclose the features, as discussed below);
responsive to the comparing, withholding, by the vehicle access platform, via a server prior to transmission, one or more vehicles of the available vehicles corresponding to thresholds exceeded by…the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted via the server as search results to the search query (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; here Harvey does not explicitly disclose based at least in part on the probability of the adverse outcome occurring during the upcoming trip, but the Isaac and Anastassov references disclose the features as discussed below); and
controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles prevented from being transmitted via the server as the search results (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page).
Harvey discloses a vehicle access platform identifying one or more of available vehicles to prevent from being returned as search results using user, vehicle, or trip information and also discloses predicting based at least in part on the pre-trip information about the upcoming trip, however, Harvey does not explicitly disclose:
receiving…via one or more machine learning models…an adverse outcome occurring during the upcoming trip…;
responsive to receiving the…adverse outcome occurring during the upcoming trip…comparing…the adverse outcome…;
responsive to the comparing, withholding…one or more vehicles of the available vehicles corresponding to…the adverse outcome occurring during the upcoming trip…;
The Isaac reference discloses receiving via one or more machine learning models, an adverse outcome occurring during an upcoming trip, responsive to receiving the adverse outcome occurring during the upcoming trip, comparing the adverse outcome, responsive to the comparing, withholding one or more vehicles of available vehicles corresponding to the adverse outcome occurring during the upcoming trip (Isaac in [0002], [0003], and [0053] discloses a user can rent a vehicle for visiting a new city or for a vacation, user can browse a listing of available vehicles for travel, a user can request to rent a vehicle, listing vehicles available for rent; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0021] and [0023] discloses user can use a web browser to interact and obtain data, user can provide input such as queries which are processed by the system; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like; here Isaac does not explicitly disclose generate probabilities of adverse outcomes occurring during trips, but the Anastassov reference discloses the feature, as discussed below);
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey and Isaac, to have combined Harvey and Isaac. The motivation to combine Harvey and Isaac would be to prevent a user from renting particular vehicles based on a prediction that the user tends to mishandle rented vehicles (Isaac: [0017]).
Harvey discloses predicting by a vehicle access platform and preventing vehicles from being returned as a search result based on the prediction, and Isaac discloses using a trained machine learning model to predict an adverse outcome occurring during an upcoming trip, and based at least in part on an adverse outcome occurring during the upcoming trip, such as mishandling of a vehicle during the trip, identifying one or more of available vehicles to prevent from being returned, however, Harvey and Isaac do not explicitly disclose:
receiving…via one or more machine learning models, a probability of an adverse outcome occurring during…trip…;
The Anastassov reference discloses receiving, via one or more machine learning models, a probability of an adverse outcome occurring during a trip (Anastassov in [0034] and [0039] discloses determine probability of a vehicle crashing while traveling along a path, determine accident predictions and accident probability for at least one vehicle; Anastassov in [0116], [0117], and [0120] discloses predicting accident probability for at least one vehicle using a prediction model, determine accident probability for one or more vehicles; Anastassov in [0035], [0111], and [0117] discloses using machine learning models; Anastassov in [0061], [0071], and [0073] discloses historical accident data used to train the model, training of predictor model based on historical data).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey, Isaac, and Anastassov, to have combined Harvey, Isaac, and Anastassov. The motivation to combine Harvey, Isaac, and Anastassov would be to determine accident probability for a vehicle using a prediction model (Anastassov: [0116] and [0117]).
With respect to claim 2, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 1, wherein the search query is received via user input to a user interface displayed at the client device (Harvey in Column 12 line 63 – Column 13 line 8 discloses displaying a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page).
With respect to claim 3, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 1, wherein the pre-trip information includes a location of the upcoming trip, a start time of the upcoming trip, and an end time of the upcoming trip (Harvey in Column 12 line 63 – Column 13 line 8 discloses displaying a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page).
With respect to claim 4, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 3, further comprising calculating, by the vehicle access platform, an amount of time between a time at which the search query is received and the start time of the upcoming trip (Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 13 lines 45-58 discloses providing trip information and pickup time).
With respect to claim 5, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 4, wherein the probability of adverse outcome occurring during the upcoming trip is based at least in part on the amount of time between the time at which the search query is received and the start time of the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 13 lines 45-58 discloses providing trip information).
With respect to claim 6, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 3, wherein the probability of adverse outcome occurring during the upcoming trip is based at least in part on the start time of the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 13 lines 45-58 discloses providing trip information).
With respect to claim 7, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 3, wherein the probability of adverse outcome occurring during the upcoming trip is based at least in part on the location of the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 13 lines 45-58 discloses providing trip information).
With respect to claim 8, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 3, wherein the pre-trip information includes a trip history with the vehicle access platform (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017] and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars).
With respect to claim 9, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 8, wherein the trip history indicates no previous trips with the vehicle access platform (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017] and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars; here new or first time user will not have any history).
With respect to claim 12, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 1, further comprising receiving, by the vehicle access platform, via the one or more machine learning models, a prediction of one or more adverse outcomes occurring during the upcoming trip based at least in part on the pre-trip information about the upcoming trip, and wherein the withholding of the one or more of the available vehicles to prevent from being returned as the search results is based at least in part on the prediction of the one or more adverse outcomes occurring during the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017] and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, prevent the user from renting high powered sports cars, using machine learning model to predict).
With respect to claim 13, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 12, wherein the receiving of the prediction of the one or more adverse outcomes occurring during the upcoming trip further comprises:
receiving, by the vehicle access platform, via at least one machine learning model of the one or more machine learning models, a severity amount corresponding to the one or more adverse outcomes occurring during the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like);
responsive to receiving the severity amount, comparing, by the vehicle access platform, server-side, the severity amount to a plurality of thresholds corresponding to the available vehicles (Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like);
responsive to the comparing of the severity amount, withholding, by the vehicle access platform, at the server prior to transmission, one or more vehicles of the available vehicles corresponding to the thresholds exceeded by the severity amount, to prevent from being returned as part of the search results to the search query (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics).
With respect to claim 14, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 13, wherein the severity amount includes a predicted damage amount for the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0042], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict, vehicle differences determined based on properties of vehicles, such as cost).
With respect to claim 15, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 13, wherein the severity amount includes a predicted incidental amount for the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0042], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict, vehicle differences determined based on properties of vehicles, such as cost).
With respect to claim 16, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 13, further comprising identifying, by the vehicle access platform, at least one vehicle of the available vehicles that the client device is ineligible for based on the severity amount exceeding a threshold severity amount, and wherein the at least one vehicle is included as the one or more available vehicles to prevent from being returned as search results based on the predicted severity amount being higher than the threshold severity amount (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict).
With respect to claim 27, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 1, further comprising training, by a vehicle access platform, the one or more machine learning models to generate, based on search queries to view vehicles for upcoming trips, probabilities of adverse outcomes occurring during upcoming trips corresponding to the search queries, in which the training includes training at least one of the one or more machine learning models to identify one or more patterns in pre-trip information for the upcoming trips, that correspond to an increased probability of the adverse outcomes occurring during the upcoming trips (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like; Anastassov in [0034] and [0039] discloses determine probability of a vehicle crashing while traveling along a path, determine accident predictions and accident probability for at least one vehicle; Anastassov in [0116], [0117], and [0120] discloses predicting accident probability for at least one vehicle using a prediction model, determine accident probability for one or more vehicles; Anastassov in [0035], [0111], and [0117] discloses using machine learning models; Anastassov in [0061], [0071], and [0073] discloses historical accident data used to train the model, training of predictor model based on historical data).
Claim(s) 17, 19, 29, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harvey (US Pat 10,703,379) in view of Isaac (US Pub 2022/0055649).
With respect to claim 17, Harvey discloses a system comprising:
a vehicle access platform implemented at least partially in hardware of a computing device to receive a search query from a client device to display vehicles for an upcoming trip (Harvey in Column 12 line 63 – Column 13 line 8 discloses displaying a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request from the renter by querying a database against the input details and presenting any available vehicles in a results page; Harvey in Column 15 line 30 – Column 16 line 26 discloses hardware modules of a computing system comprising memory and processor performing operations; Harvey in Column 5 lines 3-13 and Figure 3 discloses computing device implemented with hardware and software components);
an information identification module implemented at least partially in the hardware of the computing device to receive, based on the search query, pre-trip information about the upcoming trip, including user information, vehicle information, and trip information (Harvey in Column 1 lines 23-39 and Column 13 lines 45-58 discloses renters access a vehicle sharing platform to search for a vehicle to rent according to their criteria, such as the time period of need, the type, price, etc., user providing trip information for vehicle rental, pick up and return; Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
a vehicle availability engine implemented at least partially in the hardware of the computing device to receive available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
a…prediction engine implemented at least partially in the hardware of the computing device (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; here Harvey does not explicitly disclose severity amount, but the Isaacs reference discloses the feature, as discussed below) to:
receive…a…amount for…the upcoming trip by using the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; here Harvey does not explicitly disclose receive, via one or more machine learning models, a severity amount for an outcome occurring during the upcoming trip, but the Isaacs reference discloses the feature, as discussed below);
compare, at a server, the…amount to a plurality of threshold amounts corresponding to the available vehicles; and
withholding, at the server prior to transmission, one or more available vehicles of the available vehicles corresponding to threshold amounts exceeded by the…amount (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; here Harvey does not explicitly disclose withhold one or more available vehicles corresponding to amounts exceeded by the severity amount, but the Isaacs reference discloses the feature, as discussed below);
an access controller implemented at least partially in the hardware of the computing device to control access to the available vehicles for the upcoming trip by transmitting to the client device search results for a subset of the available vehicles in which the subset of the available vehicles excludes the one or more available vehicles withheld from being transmitted via the server (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page).
Harvey discloses a vehicle access platform identifying one or more of available vehicles to prevent from being returned as search results using user, vehicle, or trip information and also discloses predicting based at least in part on the information about the upcoming trip, however, Harvey does not explicitly disclose:
a severity prediction engine…to…receive, via one or more machine learning models, a severity amount for an outcome occurring during the upcoming trip…;
withhold…one or more available vehicles…corresponding to…amounts exceeded by the severity amount;
The Isaac reference discloses a severity prediction engine to receive, via one or more machine learning models, a severity amount for an outcome occurring during an upcoming trip and withhold one or more available vehicles corresponding to amounts exceeded by the severity amount (Isaac in [0002], [0003], and [0053] discloses a user can rent a vehicle for visiting a new city or for a vacation, user can browse a listing of available vehicles for travel, a user can request to rent a vehicle, listing vehicles available for rent; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0021] and [0023] discloses user can use a web browser to interact and obtain data, user can provide input such as queries which are processed by the system; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like);
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey and Isaac, to have combined Harvey and Isaac. The motivation to combine Harvey and Isaac would be to prevent a user from renting particular vehicles based on a prediction that the user tends to mishandle rented vehicles (Isaac: [0017]).
With respect to claim 19, Harvey in view of Isaac discloses the system of claim 17 further comprising:
an outcome predictor implemented at least partially in the hardware of the computing device to predict an adverse outcome for the upcoming trip based at least in part on the severity prediction (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict); and
in which the filter out of the one or more available vehicles to prevent from being returned as the search results ins further based on predicting the adverse outcome for the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage; Harvey in Column 13 lines 45-58 discloses providing trip information; Isaac in [0017], [0047], and [0051] discloses predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars, using machine learning model to predict).
With respect to claim 29, Harvey discloses a computer-implemented method (Harvey in Column 2 lines 8-62 and Column 11 line 46 - Column 12 line 3 and in Figure 3 discloses a computer implemented method) comprising:
receiving, by a vehicle access platform, a search query from a client device to view vehicles for an upcoming trip, in which the search query includes pre-trip information about the upcoming trip (Harvey in Column 12 line 63 – Column 13 line 8 discloses displaying a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; Harvey in Column 1 lines 23-39 and Column 13 lines 45-58 discloses renters access a vehicle sharing platform to search for a vehicle to rent according to their criteria, such as the time period of need, the type, price, etc., user providing trip information for vehicle rental, pick up and return; Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
receiving, by the vehicle access platform, available vehicles for the upcoming trip based on the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page);
receiving, by the vehicle access platform…based on the pre-trip information about the upcoming trip (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 8 line 47 – Column 9 line 29 discloses analyzing collected data to determine driving behavior of a renter, such as a number of times a particular type of driving operation occurred during a predetermined amount of time, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; here Harvey does not explicitly disclose receiving, via the one or more machine learning models, a severity amount occurring during the upcoming trip, but the Isaac reference discloses the features, as discussed below);
responsive to receiving…the upcoming trip based on the pre-trip information about the upcoming trip, comparing, by the vehicle access platform, the…amount to a plurality of threshold amounts (Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, such as a number of times a particular type of driving operation occurred during a predetermined amount of time, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; here Harvey does not explicitly disclose receiving a severity amount occurring during the upcoming trip, but the Isaac reference discloses the feature, as discussed below);
responsive to the comparing, withholding, by the vehicle access platform, prior to transmitting of search results to the search query, one or more vehicles of the available vehicles corresponding to thresholds exceeded by the…amount occurring during the upcoming trip based on the pre-trip information about the upcoming trip, to prevent from being transmitted as part of the search results (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, such as a number of times a particular type of driving operation occurred during a predetermined amount of time, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page; here Harvey does not explicitly disclose severity amount, but the Isaac reference discloses the features as discussed below); and
controlling, by the vehicle access platform, access to the available vehicles for the upcoming trip by transmitting to the client device the search results including a subset of the available vehicles in which the subset of the available vehicles excludes the one or more of the available vehicles withheld from being transmitted as part of the search results (Harvey in Column 2 lines 8-26 discloses predicting user preference, identifying one or more driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 5 line 55 – Column 7 line 33 discloses providing details for a trip for rental request, historical data of driving citations, claim history, accident, damage accessed; Harvey in Column 6 line 57 – Column 7 line 54 discloses data communicated via a server; Harvey in Column 12 line 63 – Column 13 line 8 discloses display a search portal on a renter’s device to input details about type of desired vehicle, pick up location, day and time, drop off location etc., searching for available vehicles based upon the rental vehicle request by querying a database against the input details and presenting any available vehicles in a results page).
Harvey discloses a vehicle access platform identifying one or more of available vehicles to prevent from being returned as search results using user, vehicle, or trip information and also discloses predicting based at least in part on the pre-trip information about the upcoming trip, however, Harvey does not explicitly disclose:
receiving…from one or more machine learning models, a severity amount occurring during the upcoming trip…;
responsive to receiving the severity amount occurring during the upcoming trip…comparing…the severity amount…;
responsive to the comparing, withholding…one or more vehicles of the available vehicles corresponding to…the severity amount occurring during the upcoming trip…;
The Isaac reference discloses receiving from one or more machine learning models a severity amount occurring during the upcoming trip, responsive to receiving the severity amount occurring during the upcoming trip comparing the severity amount, and responsive to the comparing, withholding one or more vehicles of the available vehicles corresponding to the severity amount occurring during the upcoming trip (Isaac in [0002], [0003], and [0053] discloses a user can rent a vehicle for visiting a new city or for a vacation, user can browse a listing of available vehicles for travel, a user can request to rent a vehicle, listing vehicles available for rent; Isaac in [0007] and [0050] discloses tracking and predicting driver behavior across different vehicles, predicting how a user will drive a vehicle based on performance metrics of driving other vehicles; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, predicting future driving activity by a user for a vehicle the user plans to rent in the future, prediction based on time, location, and use of the vehicle, driver history, predicting that vehicle will be user in poor weather conditions, at night, or the like, predicting that a user has a tendency of mishandling rented cars, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0021] and [0023] discloses user can use a web browser to interact and obtain data, user can provide input such as queries which are processed by the system; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like);
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey and Isaac, to have combined Harvey and Isaac. The motivation to combine Harvey and Isaac would be to prevent a user from renting particular vehicles based on a prediction that the user tends to mishandle rented vehicles (Isaac: [0017]).
With respect to claim 30, Harvey in view of Isaac discloses the computer-implemented method of claim 29, wherein the plurality of threshold amounts includes per-vehicle threshold amounts (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like).
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harvey (US Pat 10,703,379) in view of Isaac (US Pub 2022/0055649) in view of Anastassov (US Pub 2016/0379485) and in further view of Prasad (US Pat 10,630,723).
With respect to claim 28, Harvey in view of Isaac and in further view of Anastassov discloses the computer-implemented method of claim 1, wherein the plurality of thresholds includes…one or more per-vehicle thresholds (Harvey in Column 2 lines 8-26 and Column 13 lines 9-58 discloses predicting user preference, identifying one or more user driving behaviors, predicting owner preference, cause display of only vehicles the owner approves to the renter based on applying different criteria and preferences; Harvey in Column 8 line 47 – Column 9 line 19 discloses analyzing collected data to determine driving behavior of a renter, predict owner preference, predict desired renters by the owner based on identified behavior data, applying preference to a threshold value, preference value defining criteria for renters, compare renter’s data with owner’s preference or threshold values to determine whether the vehicle-sharing platform will permit owner’s vehicle to be displayed to the potential renter; Isaac in [0017], [0052], and [0055] discloses predicting future driving behavior to determine whether to provide a user access to a new type of vehicle, prevent the user from renting high powered sports cars based on prediction, prediction based on determined performance metrics; Isaac in [0047], [0050], and [0063] discloses using a machine learning algorithm to evaluate and/or compare performance metrics, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like, based on comparing performance metrics determine another performance metrics which predicts how a driver would drive a vehicle, the machine learning model trained to predict performance metrics corresponding to a third vehicle based on performance metrics corresponding to a first vehicle and a second vehicle, machine learning model trained based on training sets of performance data indicating safe or unsafe driving, proficient or unskilled driving, or the like; Anastassov in [0034] and [0039] discloses determine probability of a vehicle crashing while traveling along a path, determine accident predictions and accident probability for at least one vehicle; Anastassov in [0116], [0117], and [0120] discloses predicting accident probability for at least one vehicle using a prediction model, determine accident probability for one or more vehicles).
Harvey discloses predictions based on thresholds including one or more per vehicle thresholds, Isaac discloses comparing performance metrics corresponding to one or more vehicles to driver data, and Anastassov discloses predicting accident probability for one or more vehicles, however, Harvey, Isaac, and Anastassov do not explicitly disclose:
…one or more fleet-level thresholds…;
The Prasad reference discloses one or more fleet-level thresholds (Prasad in Column 9 lines 12-37 and in Column 11 lines 24-42 discloses if a driver uses a recommended route for a threshold number of trips or for a threshold amount of time, the driver is rewarded with low insurance rates, exception data characterizing safety events or other unusual driving behavior of a driver determined based on various thresholds; Prasad in Column 15 lines 33-54 and in Column 17 lines 37-48 discloses insurance policy characteristics determined based on risk of a trip or based on similarity metrics, plurality of previous trips of a driver of a vehicle analyzed to determine similarity between pairs of trips, if previous trips exhibit a higher than threshold aggregate similarity the insurance policy characteristics are set lower for the driver, policy priced lower for drivers exhibiting similar driving habits along similar routes, determining insurance policy characteristics or route recommendations for a fleet of vehicles maintained and operated by a business, agency, or other organization, complexity and/or similarity determinations made across a fleet of vehicles instead of, or in addition to, being determined for individual vehicles).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Harvey, Isaac, Anastassov, and Prasad, to have combined Harvey, Isaac, Anastassov, and Prasad. The motivation to combine Harvey, Isaac, Anastassov, and Prasad would be to adjusting characteristics of an insurance policy based on analysis of dynamic data collected during trips taken by a fleet of vehicles (Prasad: Column 1 lines 21-49 and Column 17 lines 37-48).
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/R.M/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159