DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been examined.
P = paragraph e.g. P[0001] = paragraph[0001]
Response to Arguments
Applicant's arguments filed 06/30/2026 have been fully considered but they are not persuasive.
Regarding the rejections under 35 U.S.C. 101, the Applicant argues
“Applicant respectfully submits that proposed amended claim 1 solves a data
interoperability problem specific to search functions. That is, in proposed amended claim 1, the search input may be received in OEM-specific terminology, which an ontology model translates to OEM-agnostic terminology, thereby enabling the search feature to function correctly” and
“Applicant respectfully submits that this is a technological improvement to how heterogeneous vehicle feature data from multiple manufacturers is unified and made searchable. More specifically, the claimed ontology-based translation addresses a technical challenge in aggregating vehicle feature data from multiple OEMs-namely, that different manufacturers use disparate, proprietary terminology to describe functionally equivalent features. Without the ontology model, a search system would be unable to correctly retrieve relevant results when a user enters OEM-specific terminology, because the underlying database stores information in OEM-agnostic form. The claimed solution-receiving search input in OEM-specific terminology and translating it via an ontology model into OEM-agnostic terminology-improves the functioning of the search system itself by enabling it to correctly process and respond to queries that would otherwise fail due to terminology mismatches. This is not merely using a computer as a tool to perform an abstract idea; rather, it is a specific technical solution to a specific technical problem arising from data heterogeneity in multi-manufacturer vehicle ecosystems. Accordingly, amended claim 1 integrates any alleged judicial exception into a practical application”.
It has been determined that the amended claims remain directed to an abstract idea, and that the abstract idea is not integrated into a practical application. The additional elements remain directed to merely using a generic computer to implement an abstract idea and adding insignificant extra solution activity, which does not integrate the judicial exception into a “practical application.” Regarding the “translate” limitation of the amendments to Claim 1, a user may mentally translate a search input into OEM-agnostic terminology using an ontology model. For example, the Examiner notes that an “ontology model” encompasses simply a reference or rule that relates a first type of terminology to a second type of terminology, where a user could mentally use such an “ontology model”, such as by simply mentally determining how a particular input should be translated using a reference or rule, which could be merely a word substitution process, which is a trivial process to perform mentally. Therefore, the arguments are not persuasive.
The Applicant further argues
“Even assuming, arguendo, that amended claim 1 is directed to a judicial exception, the claim recites additional elements that amount to significantly more than any alleged judicial exception. Applicant respectfully submits that the claimed combination of elements-receiving search input in OEM-specific terminology via a search feature and translating that input into OEM-agnostic terminology using an ontology model to enable retrieval of effectiveness scores for vehicle feature updates from a database storing heterogeneous multi-manufacturer vehicle data-is not well-understood, routine or conventional in the relevant field. Applicant therefore respectfully submits that amended claim 1 is subject matter eligible at Step 2B as well”.
It has been determined that the additional elements do not amount to significantly more than the abstract idea, as the additional elements remain directed to merely using a generic computer to implement the abstract idea and adding insignificant extra solution activity, as explained above and in the new grounds of rejection. See the new grounds of rejection.
Regarding the rejections under 35 U.S.C. 103, the arguments are moot in view of the new grounds of rejection. See the new grounds of rejection in view of new prior art James et al. (WO2015132582A2).
Therefore, the arguments are not persuasive for the reasons given above and in view of the new grounds of rejection.
All claims are rejected. See the new grounds of rejection.
Claim Interpretation
The Claim 1 limitations “for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs)” are directed to an intended use that does not further limit the claim.
The Claim 10 limitations “for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs)” are directed to an intended use that does not further limit the claim.
The Claim 16 limitations “for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs)” are directed to an intended use that does not further limit 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. See below.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer-implemented method comprising:
receiving, by one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
constructing, by the one or more processors, the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
constructing, by the one or more processors, the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculating, by the one or more processors, an effectiveness score of the update based upon both the first dataset and the second dataset, wherein the calculating the effectiveness score comprises inputting the first dataset and the second dataset into a trained machine learning algorithm;
displaying, by the one or more processors, via a user interface, a search feature;
receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology;
translating, via the one or more processors, the search input into OEM-agnostic terminology using an ontology model;
receiving, by the one or more processors, via the user interface, an indication of a vehicle;
retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
displaying, via the one or more processors, via the user interface, the effectiveness score.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “constructing…the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “constructing, by the one or more processors, the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “calculating an effectiveness score of the update based upon both the first dataset and the second dataset” limitation, a user may mentally calculate an effectiveness score of the update based upon both the first dataset and the second dataset. Regarding the “translating…the search input into OEM-agnostic terminology using an ontology model” limitation, a user may mentally translate the search input into OEM-agnostic terminology using an ontology model. Regarding the “retrieving,…from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle” limitation, a user may mentally retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle, such as by simply looking at information contained in the database and performing a basic mental activity of identifying the effectiveness score based upon a mental association of the effectiveness score with the vehicle. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented method for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer-implemented method comprising:
receiving, by one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
constructing, by the one or more processors, the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
constructing, by the one or more processors, the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculating, by the one or more processors, an effectiveness score of the update based upon both the first dataset and the second dataset, wherein the calculating the effectiveness score comprises inputting the first dataset and the second dataset into a trained machine learning algorithm;
displaying, by the one or more processors, via a user interface, a search feature;
receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology;
translating, via the one or more processors, the search input into OEM-agnostic terminology using an ontology model;
receiving, by the one or more processors, via the user interface, an indication of a vehicle;
retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
displaying, via the one or more processors, via the user interface, the effectiveness score.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation “computer-implemented”, the claim does not recite a specific computer that corresponds to “computer-implemented” other than reciting “by one or more processors”, where the “one or more processors” are addressed as follows. Regarding the additional limitation “receiving, by one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature”, the “one or more processors” are recited at a high level of generality and amount to nothing more than a generic computer used to apply the exception. Furthermore, the “receiving” limitation amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the additional limitation “by the one or more processors” of “constructing, by the one or more processors, the first dataset”, the “one or more processors” are recited at a high level of generality and merely automate the “constructing” the “first dataset” step, therefore acting as a generic computer to perform the abstract idea.
Regarding the additional limitation “by the one or more processors” of “constructing, by the one or more processors, the second dataset”, the “one or more processors” are recited at a high level of generality and merely automate the “constructing” the “second dataset” step, therefore acting as a generic computer to perform the abstract idea.
Regarding the additional limitation “by the one or more processors” of “calculating, by the one or more processors”, the “one or more processors” are recited at a high level of generality and merely automate the “calculating” step, therefore acting as a generic computer to perform the abstract idea.
Regarding the additional limitation “wherein the calculating the effectiveness score comprises inputting the first dataset and the second dataset into a trained machine learning algorithm”, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception.
The additional limitation “displaying, by the one or more processors, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
The additional limitation “receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the additional limitation “via the one or more processors” of “translating, via the one or more processors”, the “one or more processors” are recited at a high level of generality and merely automate the “translating” step, therefore acting as a generic computer to perform the abstract idea.
The additional limitation “receiving, by the one or more processors, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the additional limitation “by the one or more processors” of “retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle”, the “one or more processors” are recited at a high level of generality and merely automate the “retrieving” step, therefore acting as a generic computer to perform the abstract idea, and additionally, retrieving the “effectiveness score” by the “one or more processors” amounts to mere data gathering, which is a form of insignificant extra-solution activity.
The additional limitation “displaying, via the one or more processors, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite a specific computer that corresponds to “computer-implemented” other than reciting “by one or more processors” where the “one or more processors” amount to nothing more than a generic computer used to apply the exception, the additional limitation “receiving, by one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “by the one or more processors” of “constructing, by the one or more processors, the first dataset” are recited at a high level of generality and merely automate the “constructing” the “first dataset” step, therefore acting as a generic computer to perform the abstract idea, the additional limitation “by the one or more processors” of “constructing, by the one or more processors, the second dataset” are recited at a high level of generality and merely automate the “constructing” the “second dataset” step, therefore acting as a generic computer to perform the abstract idea, the additional limitation “by the one or more processors” of “calculating, by the one or more processors” are recited at a high level of generality and merely automate the “calculating” step, therefore acting as a generic computer to perform the abstract idea, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception, the additional limitation “displaying, by the one or more processors, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity, the additional limitation “receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “via the one or more processors” of “translating, via the one or more processors” are recited at a high level of generality and merely automate the “translating” step, therefore acting as a generic computer to perform the abstract idea, the additional limitation “receiving, by the one or more processors, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “by the one or more processors” of “retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle” are recited at a high level of generality and merely automate the “retrieving” step, therefore acting as a generic computer to perform the abstract idea, and additionally, retrieving the “effectiveness score” by the “one or more processors” amounts to mere data gathering, which is a form of insignificant extra-solution activity, and the additional limitation “displaying, via the one or more processors, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 2-9 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 2-9 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-9 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 2-9 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 2, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes a feature, which does not amount to significantly more than the judicial exception.
As per Claim 3, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes a feature and an update, where the limitation “the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive” is not directed to a step of making a “sensitivity metric of the forward collision warning system more sensitive”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 4, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally determine vehicles that the update has been implemented in, and may mentally construct the first dataset from the data from the vehicles having the vehicle feature from before the update was implemented, and may mentally construct the second dataset from the data from the vehicles having the vehicle feature from after the update was implemented. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 5, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim describes an update, where the limitation “the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system” is not directed to a step of changing a “button placement on a vehicle infotainment system”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 6, said claim is rejected as it fails to correct the deficiency of Claim 1. The “receiving” step amounts to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, a user may mentally receive the insurance claims data and a user may mentally calculate an increase or decrease in insurance premiums for vehicles having implemented the update, wherein the calculating of the increase or decrease is based upon: (i) the insurance claims data, and (ii) the effectiveness score of the update. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 7, said claim is rejected as it fails to correct the deficiency of Claim 1. The “receiving” step amounts to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, a user may mentally receive the insurance claims data and a user may mentally calculate an impact on: (i) cost of insurance claims, or (ii) amount of insurance claims for vehicles having implemented the update; and wherein the calculating or the impact is based upon: (i) the insurance claims data, and (ii) the effectiveness score of the update. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 8, said claim is rejected as it fails to correct the deficiency of Claim 1. The claim is directed to mere data gathering, which is a form of insignificant extra-solution activity. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 9, said claim is rejected as it fails to correct the deficiency of Claim 1. A user may mentally calculate the effectiveness score comprises comparing the first dataset to the second dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 10 is directed to a computer system (i.e., a machine). Therefore, claim 10 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 10 includes limitations that recite an abstract idea (emphasized below). Claim 10 recites:
A computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising one or more processors configured to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm;
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “calculate an effectiveness score of the update” limitation, a user may mentally calculate an effectiveness score of the update. Regarding the “translate, the search input into OEM-agnostic terminology using an ontology model” limitation, a user may mentally translate, the search input into OEM-agnostic terminology using an ontology model. Regarding the “retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle” limitation, a user may mentally retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle, such as by simply looking at information contained in the database and performing a basic mental activity of identifying the effectiveness score based upon a mental association of the effectiveness score with the vehicle. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising one or more processors configured to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm;
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation “computer system” and “the computer system comprising one or more processors configured to”, the “one or more processors” are recited at a high level of generality and merely automate the steps of the claim, therefore acting as a generic computer to perform the abstract idea. The additional limitation “receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitation “by inputting both the first dataset and the second dataset into a trained machine learning algorithm”, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception. The additional limitation “display, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. The additional limitation “receive, via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “receive, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “display, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, independent claim 10 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the “one or more processors” are recited at a high level of generality and merely automate the steps of the claim, therefore acting as a generic computer to perform the abstract idea, the additional limitation “receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception, the additional limitation “display, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity, the additional limitation “receive, via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “receive, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “display, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 11-15 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 11-15 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 11-15 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 10-15 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 11, said claim is rejected as it fails to correct the deficiency of Claim 10. The claim describes a feature, which does not amount to significantly more than the judicial exception.
As per Claim 12, said claim is rejected as it fails to correct the deficiency of Claim 10. The claim describes a feature and an update, where the limitation “the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive” is not directed to a step of making a “sensitivity metric of the forward collision warning system more sensitive”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 13, said claim is rejected as it fails to correct the deficiency of Claim 10. A user may mentally determine vehicles that the update has been implemented in, and many mentally construct the first dataset from the data from the vehicles having the vehicle feature from before the update was implemented, and may mentally construct the second dataset from the data from the vehicles having the vehicle feature from after the update was implemented. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 14, said claim is rejected as it fails to correct the deficiency of Claim 10. The claim describes an update, where the limitation “the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system” is not directed to a step of changing a “button placement on a vehicle infotainment system”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 15, said claim is rejected as it fails to correct the deficiency of Claim 10. The “receive” step amounts to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, a user may mentally receive the insurance claims data and a user may mentally calculate an increase or decrease in insurance premiums for vehicles having implemented the update, wherein the calculation of the increase or decrease is based upon: (i) the insurance claims data, and (ii) the effectiveness score of the update. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 16 is directed to a computer system (i.e., a machine). Therefore, claim 16 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 16 includes limitations that recite an abstract idea (emphasized below). Claim 16 recites:
A computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising:
one or more processors; and
a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm;
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, regarding the “construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” limitation, a user may mentally construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature. Regarding the “calculate an effectiveness score of the update” limitation, a user may mentally calculate an effectiveness score of the update. Regarding the “translate, the search input into OEM-agnostic terminology using an ontology model” limitation, a user may mentally translate, the search input into OEM-agnostic terminology using an ontology model. Regarding the “retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle” limitation, a user may mentally retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle, such as by simply looking at information contained in the database and performing a basic mental activity of identifying the effectiveness score based upon a mental association of the effectiveness score with the vehicle. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising:
one or more processors; and
a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;
construct the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;
construct the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature;
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm;
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation “A computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising: one or more processors”, the “one or more processors” are recited at a high level of generality and merely automate the steps of the claim, therefore acting as a generic computer to perform the abstract idea. Regarding the additional limitation “a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to”, the “non-transitory program memory” is recited at a high level of generality such that it amounts to no more than merely a generic computer component, and this additional limitation amounts to mere instructions to apply an exception using a generic computer component. The additional limitation “receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature” amounts to mere data gathering, which is a form of insignificant extra-solution activity. Regarding the additional limitation “by inputting both the first dataset and the second dataset into a trained machine learning algorithm”, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception. The additional limitation “display, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. The additional limitation “receive, via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “receive, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation “display, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, independent claim 16 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the “one or more processors” are recited at a high level of generality and merely automate the steps of the claim, therefore acting as a generic computer to perform the abstract idea, the additional limitation “a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to” amounts to mere instructions to apply an exception using a generic computer component, the additional limitation “receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the “trained machine learning algorithm” is recited at a high level of generality and amounts to mere instructions to apply the exception, the additional limitation “display, via a user interface, a search feature” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity, the additional limitation “receive, via the search feature, a search input in OEM-specific terminology” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “receive, via the user interface, an indication of a vehicle” amounts to mere data gathering, which is a form of insignificant extra-solution activity, the additional limitation “display, via the user interface, the effectiveness score” amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claim(s) 17-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims 17-20 are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 17-20 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claim(s) 16-20 are ineligible under 35 USC §101.
See below regarding the dependent claims.
As per Claim 17, said claim is rejected as it fails to correct the deficiency of Claim 16. The claim describes a feature, which does not amount to significantly more than the judicial exception.
As per Claim 18, said claim is rejected as it fails to correct the deficiency of Claim 16. The claim describes a feature and an update, where the limitation “the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive” is not directed to a step of making a “sensitivity metric of the forward collision warning system more sensitive”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 19, said claim is rejected as it fails to correct the deficiency of Claim 16. A user may mentally determine vehicles that the update has been implemented in; and wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented; and wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented. Therefore, the claim does not amount to significantly more than the judicial exception.
As per Claim 20, said claim is rejected as it fails to correct the deficiency of Claim 16. The claim describes an update, where the limitation “the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system” is not directed to a step of changing a “button placement on a vehicle infotainment system”, but describes the effect of the update, where the “second dataset” that is “from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim remains simply a dataset. Therefore, the claim does not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4, 6-11, 13, 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Konrardy et al. (9,858,621) in view of Walther et al. (10,599,546) further in view of James et al. (WO2015132582A2).
Regarding Claim 1, Konrardy et al. teaches the claimed computer-implemented method for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer-implemented method comprising:
receiving, by one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature (“At block 702, the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters. In some embodiments, the rest result data may be received from the on-board computer 114 or from the database 146. In addition, in some embodiments, the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704, such as test result data and corresponding actual loss or operating data for the other autonomous operation features”, see col.28, particularly lines 59-67 and col.29, particularly lines 1-7 and “…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3);
constructing, by the one or more processors, [[a]] the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version” (emphasis added), see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
constructing, by the one or more processors, [[a]] the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
calculating, by the one or more processors, an effectiveness score of the update based upon both the first dataset and the second dataset, wherein the calculating the effectiveness score comprises inputting the first dataset and the second dataset into a trained machine learning algorithm (“At block 610, the observed responses of the autonomous operation feature are recorded for use in determining effectiveness of the feature”, see col.27, particularly lines 4-11 and “The server 140 may determine the expected actual loss or operating data using known techniques, such as regression analysis or machine learning tools (e.g., neural network algorithms or support vector machines)” and “…the server 140 may further determine a risk level …the risk level is defined as an effectiveness rating score…”, see col.29, particularly lines 7-33, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update).
Konrardy et al. does not expressly recite the claimed
displaying, by the one or more processors, via a user interface, a search feature;
receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology;
translating, via the one or more processors, the search input into OEM-agnostic terminology using an ontology model;
receiving, by the one or more processors, via the user interface, an indication of a vehicle;
retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
displaying, via the one or more processors, via the user interface, the effectiveness score.
However, Walther et al. (10,599,546) teaches displaying, by one or more processors, via a user interface, a search feature (Walther et al.; see FIGS. 4-5, particularly the elements that show “SEARCH”), receiving, by the one or more processors, via the user interface, an indication of a vehicle (Walther et al.; “The test result data 132 can be filtered and/or searched via the user interface 600”, see col.23, particularly lines 30-51 and “…a user 108 can provide (e.g., via user input to a user interface) a search query indicative of at least one of the test 106, the testing scenario 124, or the one or more autonomous vehicle capabilities 116”, see col.23, particularly lines 52-67 and col.24, particularly lines 1-16), retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle (Walther et al.; “Moreover, the testing system 100 can store (e.g., in the accessible memory 128) the data indicative of the test 106 associated with the test result data 132”, see col.25, particularly lines 65-67 and col.26, particularly lines 1-11, and “…the testing system can be configured to determine a performance metric associated with the autonomous vehicle computing system…the performance metric can be indicative of a score of the performance of the autonomous vehicle computing system with respect to a test and/or testing scenario…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52), and displaying, via the one or more processors, via the user interface, the effectiveness score (Walther et al.; “…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52).
Furthermore, regarding the limitations “receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology; translating, via the one or more processors, the search input into OEM-agnostic terminology using an ontology model”, James et al. (WO2015132582A2) teaches translating input terms “into several different terms with the same meaning across various protocols” (James et al.; see page 21-22) and translating OEM terms into a another protocol of a specific user (James et al.; “The input terms 1040 represent terms from an OEM, however such terms are unintelligible to an end user, for example a parts dealer, retail customers and partners such as Fleet Operators, who may all have their own protocol”, see page 25, and see page 26), and identifying the meaning of input terms “relating to a description of a component for a vehicle” (James et al.; see page 19), and also teaches searching for specific terms (James et al.; “…searching for specific terms and/or information in line with end user requests…”, see page 34, also see “The language mapping system 1000 is adapted to allow searching of Lexicons 1900 by type 1940”, see page 33, and page 34), which renders obvious the claimed receiving of a “search input”, and “translating” the “search input” into any terminology, including an “OEM-agnostic terminology”, where an “OEM-agnostic terminology” encompasses any terminology different from that used by an OEM. Also, the claimed “ontology model” encompasses any instructions or program to perform the associated steps, which is equivalent to the “computer program and a computer program product for carrying out any of the methods described herein and/or for embodying any of the apparatus features described herein” of James et al. (James et al.; see page 7).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Walther et al. and James et al., and displaying, by the one or more processors, via a user interface, a search feature; receiving, via the one or more processors and via the search feature, a search input in OEM-specific terminology; translating, via the one or more processors, the search input into OEM-agnostic terminology using an ontology model; receiving, by the one or more processors, via the user interface, an indication of a vehicle; retrieving, by the one or more processors, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and displaying, via the one or more processors, via the user interface, the effectiveness score, as rendered obvious by Walther et al. and James et al. as described above, so that a “user can more readily determine performance progress, as well as search, retrieve, review, and/or re-run the tests to verify autonomy software (e.g., newer versions of the software)” (Walther et al.; see col.3, lines 57-64), and in order to provide for “converting a first term…in a first language to a second term…in a second language” (James et al.; see Abstract).
Regarding Claim 2, Konrardy et al. teaches the claimed computer-implemented method of claim 1, wherein the vehicle feature is a vehicle safety feature (“…determining the indicator of effectiveness may include determining a change in accident avoidance and/or a change in mitigation effectiveness of the autonomous or semi-autonomous vehicle technology based upon the results of the testing of the updated computer-readable instructions involved in the implementation of part or all of the autonomous or semi-autonomous vehicle technology”, see col.5, particularly lines 18-31, and “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43).
Regarding Claim 4, Konrardy et al. teaches the claimed computer-implemented method of claim 1, further comprising:
determining, by the one or more processors, vehicles that the update has been implemented in (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3).
Regarding Claim 6, Konrardy et al. teaches the claimed computer-implemented method of claim 1, further comprising:
receiving, by the one or more processors, insurance claims data (“At block 508, the server 140 may receive data regarding actual losses on autonomous vehicles that included the autonomous operation feature. This information may include claims filed pursuant to insurance policies, claims paid pursuant to insurance policies, accident reports filed with government agencies, or data from the sensors 120 regarding incidents (e.g., collisions, alerts presented, etc.)”, see col.29, particularly lines 64-67 and col.30, particularly lines 1-12); and
calculating, by the one or more processors, an increase or decrease in insurance premiums for vehicles having implemented the update (“…a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided”, see col.57, particularly lines 46-67 and col.58, particularly lines 1-18), wherein the calculating of the increase or decrease is based upon: (i) the insurance claims data (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56), and (ii) the effectiveness score of the update (“…determining part or all of the insurance policy associated with the vehicle may include determining the insurance policy based upon information regarding the impact on the effectiveness of the autonomous or semi-autonomous vehicle technology of actual operating conditions and/or actual operating behavior of a vehicle operator associated with the vehicle”, see col.4, particularly lines 39-54).
Regarding Claim 7, Konrardy et al. teaches the claimed computer-implemented method of claim 1, further comprising:
receiving, by the one or more processors, insurance claims data (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56); and
calculating, by the one or more processors, an impact on: (i) cost of insurance claims, or (ii) amount of insurance claims for vehicles having implemented the update (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56); and
wherein the calculating or the impact is based upon: (i) the insurance claims data (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56), and (ii) the effectiveness score of the update (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56).
Regarding Claim 8, Konrardy et al. teaches the claimed computer-implemented method of claim 1, wherein the information indicating the update to the vehicle is received from a vehicle manufacturer, a third party aggregator, an application of a computing device of vehicle operators, or a vehicle data repository (“At block 702, the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters. In some embodiments, the rest result data may be received from the on-board computer 114 or from the database 146. In addition, in some embodiments, the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704, such as test result data and corresponding actual loss or operating data for the other autonomous operation features. The reference data received at block 704 may be limited to data for other autonomous operation features having sufficient similarity to the autonomous operation feature being evaluated, such as those performing a similar function, those with similar test result data, or those meeting a minimum threshold level of actual loss or operating data”, see col.28, particularly lines 59-67 and col.29, particularly lines 1-7).
Regarding Claim 9, Konrardy et al. teaches the claimed computer-implemented method of claim 1, wherein the calculating the effectiveness score comprises comparing the first dataset to the second dataset (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67).
Regarding Claim 10, Konrardy et al. teaches the claimed computer system for use in determining effectiveness of an update to a vehicle feature (“Each server 140 may include one or more computer processors…”, see col.14, particularly lines 24-56) based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising one or more processors configured to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature (“At block 702, the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters. In some embodiments, the rest result data may be received from the on-board computer 114 or from the database 146. In addition, in some embodiments, the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704, such as test result data and corresponding actual loss or operating data for the other autonomous operation features”, see col.28, particularly lines 59-67 and col.29, particularly lines 1-7 and “…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3);
construct [[a]] the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version” (emphasis added), see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
construct [[a]] the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm (“At block 610, the observed responses of the autonomous operation feature are recorded for use in determining effectiveness of the feature”, see col.27, particularly lines 4-11 and “The server 140 may determine the expected actual loss or operating data using known techniques, such as regression analysis or machine learning tools (e.g., neural network algorithms or support vector machines)” and “…the server 140 may further determine a risk level …the risk level is defined as an effectiveness rating score…”, see col.29, particularly lines 7-33, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update).
Konrardy et al. does not expressly recite the claimed
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
However, Walther et al. (10,599,546) teaches display, via a user interface, a search feature (Walther et al.; see FIGS. 4-5, particularly the elements that show “SEARCH”), receive, via the user interface, an indication of a vehicle (Walther et al.; “The test result data 132 can be filtered and/or searched via the user interface 600”, see col.23, particularly lines 30-51 and “…a user 108 can provide (e.g., via user input to a user interface) a search query indicative of at least one of the test 106, the testing scenario 124, or the one or more autonomous vehicle capabilities 116”, see col.23, particularly lines 52-67 and col.24, particularly lines 1-16), retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle (Walther et al.; “Moreover, the testing system 100 can store (e.g., in the accessible memory 128) the data indicative of the test 106 associated with the test result data 132”, see col.25, particularly lines 65-67 and col.26, particularly lines 1-11, and “…the testing system can be configured to determine a performance metric associated with the autonomous vehicle computing system…the performance metric can be indicative of a score of the performance of the autonomous vehicle computing system with respect to a test and/or testing scenario…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52), and display, via the user interface, the effectiveness score (Walther et al.; “…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52).
Furthermore, regarding the limitations “receive, via the search feature, a search input in OEM-specific terminology; translate, the search input into OEM-agnostic terminology using an ontology model”, James et al. (WO2015132582A2) teaches translating input terms “into several different terms with the same meaning across various protocols” (James et al.; see page 21-22) and translating OEM terms into a another protocol of a specific user (James et al.; “The input terms 1040 represent terms from an OEM, however such terms are unintelligible to an end user, for example a parts dealer, retail customers and partners such as Fleet Operators, who may all have their own protocol”, see page 25, and see page 26), and identifying the meaning of input terms “relating to a description of a component for a vehicle” (James et al.; see page 19), and also teaches searching for specific terms (James et al.; “…searching for specific terms and/or information in line with end user requests…”, see page 34, also see “The language mapping system 1000 is adapted to allow searching of Lexicons 1900 by type 1940”, see page 33, and page 34), which renders obvious the claimed receiving of a “search input”, and to “translate” the “search input” into any terminology, including an “OEM-agnostic terminology”, where an “OEM-agnostic terminology” encompasses any terminology different from that used by an OEM. Also, the claimed “ontology model” encompasses any instructions or program to perform the associated steps, which is equivalent to the “computer program and a computer program product for carrying out any of the methods described herein and/or for embodying any of the apparatus features described herein” of James et al. (James et al.; see page 7).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Walther et al. and James et al., and display, via a user interface, a search feature; receive, via the search feature, a search input in OEM-specific terminology; translate, the search input into OEM-agnostic terminology using an ontology model; receive, via the user interface, an indication of a vehicle; retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and display, via the user interface, the effectiveness score, as rendered obvious by Walther et al. and James et al. as described above, so that a “user can more readily determine performance progress, as well as search, retrieve, review, and/or re-run the tests to verify autonomy software (e.g., newer versions of the software)” (Walther et al.; see col.3, lines 57-64), and in order to provide for “converting a first term…in a first language to a second term…in a second language” (James et al.; see Abstract).
As per Claim 11, Konrardy et al. teaches the claimed computer system of claim 10, wherein the vehicle feature is a vehicle safety feature (“…determining the indicator of effectiveness may include determining a change in accident avoidance and/or a change in mitigation effectiveness of the autonomous or semi-autonomous vehicle technology based upon the results of the testing of the updated computer-readable instructions involved in the implementation of part or all of the autonomous or semi-autonomous vehicle technology”, see col.5, particularly lines 18-31, and “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43).
As per Claim 13, Konrardy et al. teaches the claimed computer system of claim 10, wherein the one or more processors are further configured to:
determine vehicles that the update has been implemented in (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3).
As per Claim 15, Konrardy et al. teaches the claimed computer system of claim 10, wherein the one or more processors are further configured to:
receive insurance claims data (“At block 508, the server 140 may receive data regarding actual losses on autonomous vehicles that included the autonomous operation feature. This information may include claims filed pursuant to insurance policies, claims paid pursuant to insurance policies, accident reports filed with government agencies, or data from the sensors 120 regarding incidents (e.g., collisions, alerts presented, etc.)”, see col.29, particularly lines 64-67 and col.30, particularly lines 1-12); and
calculate an increase or decrease in insurance premiums for vehicles having implemented the update (“…a computer-implemented method of updating, adjusting, and/or generating an insurance policy, premium, rate, and/or discount may be provided”, see col.57, particularly lines 46-67 and col.58, particularly lines 1-18), wherein the calculation of the increase or decrease is based upon: (i) the insurance claims data (“The risk level may be a metric indicating the risk of collision, malfunction, or other incident leading to a loss or claim against a vehicle insurance policy covering a vehicle in which the autonomous operation feature is functioning”, see col.20, particularly lines 20-33 and “The risk profiles or risk levels associated with one or more autonomous operation features determined above may be further used to determine risk categories or premiums for vehicle insurance policies covering autonomous vehicles”, see col.30, particularly lines 34-56), and (ii) the effectiveness score of the update (“…determining part or all of the insurance policy associated with the vehicle may include determining the insurance policy based upon information regarding the impact on the effectiveness of the autonomous or semi-autonomous vehicle technology of actual operating conditions and/or actual operating behavior of a vehicle operator associated with the vehicle”, see col.4, particularly lines 39-54).
As per Claim 16, Konrardy et al. teaches the claimed computer system for use in determining effectiveness of an update to a vehicle feature based upon a first dataset and a second dataset, and for searching vehicle feature data across multiple original equipment manufacturers (OEMs), the computer system comprising:
one or more processors (“Each server 140 may include one or more computer processors…”, see col.14, particularly lines 24-56); and
a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors (“The controller 155 may include a program memory 160, a processor 162 (which may be called a microcontroller or a microprocessor), a random-access memory (RAM) 164…the memory of the controller 155 may include multiple RAMs 164 and multiple program memories 160” and “The server 140 may further include a number of software applications stored in a program memory 160”, see col.15, particularly lines 8-49), cause the computer system to:
receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature (“At block 702, the server 140 receives the test result data observed and recorded in block 502 for the autonomous operation feature in conjunction with a set of parameters. In some embodiments, the rest result data may be received from the on-board computer 114 or from the database 146. In addition, in some embodiments, the server 140 may receive reference data for other autonomous operation features in use on insured autonomous vehicles at block 704, such as test result data and corresponding actual loss or operating data for the other autonomous operation features”, see col.28, particularly lines 59-67 and col.29, particularly lines 1-7 and “…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3);
construct [[a]] the first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version” (emphasis added), see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
construct [[a]] the second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature (“…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “At block 602, the server 140 may determine the scope of the testing based upon the autonomous operation feature and the availability of test results for related or similar autonomous operation features (e.g., previous versions of the feature)”, see col.26, particularly lines 5-22, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update);
calculate an effectiveness score of the update by inputting both the first dataset and the second dataset into a trained machine learning algorithm (“At block 610, the observed responses of the autonomous operation feature are recorded for use in determining effectiveness of the feature”, see col.27, particularly lines 4-11 and “The server 140 may determine the expected actual loss or operating data using known techniques, such as regression analysis or machine learning tools (e.g., neural network algorithms or support vector machines)” and “…the server 140 may further determine a risk level …the risk level is defined as an effectiveness rating score…”, see col.29, particularly lines 7-33, also see “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43, where determining an increase in effectiveness implies a comparison of a new effectiveness to a previous effectiveness prior to the upgrade or update).
Konrardy et al. does not expressly recite the claimed
display, via a user interface, a search feature;
receive, via the search feature, a search input in OEM-specific terminology;
translate, the search input into OEM-agnostic terminology using an ontology model;
receive, via the user interface, an indication of a vehicle;
retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and
display, via the user interface, the effectiveness score.
However, Walther et al. (10,599,546) teaches display, via a user interface, a search feature (Walther et al.; see FIGS. 4-5, particularly the elements that show “SEARCH”), receive, via the user interface, an indication of a vehicle (Walther et al.; “The test result data 132 can be filtered and/or searched via the user interface 600”, see col.23, particularly lines 30-51 and “…a user 108 can provide (e.g., via user input to a user interface) a search query indicative of at least one of the test 106, the testing scenario 124, or the one or more autonomous vehicle capabilities 116”, see col.23, particularly lines 52-67 and col.24, particularly lines 1-16), retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle (Walther et al.; “Moreover, the testing system 100 can store (e.g., in the accessible memory 128) the data indicative of the test 106 associated with the test result data 132”, see col.25, particularly lines 65-67 and col.26, particularly lines 1-11, and “…the testing system can be configured to determine a performance metric associated with the autonomous vehicle computing system…the performance metric can be indicative of a score of the performance of the autonomous vehicle computing system with respect to a test and/or testing scenario…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52), display, via the user interface, the effectiveness score (Walther et al.; “…the testing system can provide for display data indicative of the performance metric via a user interface of a display device (e.g., for viewing by a user)”, see col.9, particularly lines 28-52).
Furthermore, regarding the limitations “receive, via the search feature, a search input in OEM-specific terminology; translate, the search input into OEM-agnostic terminology using an ontology model”, James et al. (WO2015132582A2) teaches translating input terms “into several different terms with the same meaning across various protocols” (James et al.; see page 21-22) and translating OEM terms into a another protocol of a specific user (James et al.; “The input terms 1040 represent terms from an OEM, however such terms are unintelligible to an end user, for example a parts dealer, retail customers and partners such as Fleet Operators, who may all have their own protocol”, see page 25, and see page 26), and identifying the meaning of input terms “relating to a description of a component for a vehicle” (James et al.; see page 19), and also teaches searching for specific terms (James et al.; “…searching for specific terms and/or information in line with end user requests…”, see page 34, also see “The language mapping system 1000 is adapted to allow searching of Lexicons 1900 by type 1940”, see page 33, and page 34), which renders obvious the claimed receiving of a “search input”, and to “translate” the “search input” into any terminology, including an “OEM-agnostic terminology”, where an “OEM-agnostic terminology” encompasses any terminology different from that used by an OEM. Also, the claimed “ontology model” encompasses any instructions or program to perform the associated steps, which is equivalent to the “computer program and a computer program product for carrying out any of the methods described herein and/or for embodying any of the apparatus features described herein” of James et al. (James et al.; see page 7).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Walther et al. and James et al., and display, via a user interface, a search feature; receive, via the search feature, a search input in OEM-specific terminology; translate, the search input into OEM-agnostic terminology using an ontology model; receive, via the user interface, an indication of a vehicle; retrieve, from a database, the effectiveness score based upon an association of the effectiveness score with the vehicle; and display, via the user interface, the effectiveness score, as rendered obvious by Walther et al. and James et al. as described above, so that a “user can more readily determine performance progress, as well as search, retrieve, review, and/or re-run the tests to verify autonomy software (e.g., newer versions of the software)” (Walther et al.; see col.3, lines 57-64), and in order to provide for “converting a first term…in a first language to a second term…in a second language” (James et al.; see Abstract).
As per Claim 17, Konrardy et al. teaches the claimed computer system of claim 16, wherein the vehicle feature is a vehicle safety feature (“…determining the indicator of effectiveness may include determining a change in accident avoidance and/or a change in mitigation effectiveness of the autonomous or semi-autonomous vehicle technology based upon the results of the testing of the updated computer-readable instructions involved in the implementation of part or all of the autonomous or semi-autonomous vehicle technology”, see col.5, particularly lines 18-31, and “…(1) testing an upgrade or update to computer or processor instructions that direct and/or control one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment (and that are stored on a non-transitory computer readable media or medium); (2) determining an increase in accident avoidance or mitigation effectiveness based upon the upgraded or updated computer or processor instructions that direct and/or control the one or more autonomous or semi-autonomous vehicle technologies, functionalities, systems, and/or pieces of equipment)…”, see col.61, particularly lines 21-43).
As per Claim 19, Konrardy et al. teaches the claimed computer system of claim 16, wherein the executable instructions, when executed by the one or more processors, cause the computer system to:
determine vehicles that the update has been implemented in (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3); and
wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented (“…the indicator of effectiveness is determined based upon information regarding actual vehicle accidents involving vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update and/or information regarding the results of physical testing of vehicles equipped with the autonomous or semi-autonomous vehicle technology having the update”, see col.5, particularly lines 18-31 and “The scope of the testing may include parameters such as configurations, settings, vehicles 108, sensors 120, communication units 122, on-board computers 114, control software, other autonomous operation features, or combinations of these parameters to be tested” and “…the autonomous operation feature is enabled within a test system with a set of parameters determined in block 602”, see col.26, particularly lines 5-33 and “…a limited test of the new version of the autonomous operation feature may be performed and compared to the test results of the previous version, such that additional testing may not be performed when the limited test results of the new version are within a predetermined range based upon the test results of the previous version”, see col.25, particularly lines 55-67 and col.26, particularly lines 1-4 and “…additional vehicles may be used to test the responses of the autonomous operation feature to moving obstacles”, see col.26, particularly lines 52-67 and col.27, particularly lines 1-3).
Claims 3, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Konrardy et al. (9,858,621) in view of Walther et al. (10,599,546) further in view of James et al. (WO2015132582A2), further in view of Iwasaki (JP2011150578A).
As per Claim 3, Konrardy et al. teaches the claimed computer-implemented method of claim 1, wherein:
the vehicle feature is a forward collision warning system (“…autonomous operation feature risk levels for features that assist the vehicle operator in safely controlling the vehicle. Such features may include alerts, warnings, automatic braking for collision avoidance, and/or similar features that may provide information to the vehicle operator or take control of the vehicle from the vehicle operator under some conditions”, see col.34, particularly lines 26-53).
Konrardy et al. does not expressly recite the claimed
and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive.
However, the nature and effect of the “update” is a design choice, and Claim 3 is not directed to a step of making “a sensitivity metric of the forward collision warning system more sensitive”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Iwasaki (JP2011150578A) teaches updating an alarm determination threshold which allows for updating the “sensitivity” of the alarm by the updating of the threshold (Iwasaki; see P[0021], P[0025] and P[0042]), which renders obvious the design choice of the claimed update that “makes a sensitivity metric of the forward collision warning system more sensitive”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Iwasaki, and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive, as rendered obvious by Iwasaki, in order to provide a system that “updates the threshold value held by” an “alarm determination unit” (Iwasaki; see P[0025]) and so that a “driver can change the threshold value used for the determination of the necessity of the alarm according to the danger felt by the driver, the alarm can be output in accordance with the driver's feeling” which “also leads to prevention of issuance of an alarm unnecessary for the driver” (Iwasaki; see P[0042]).
As per Claim 12, Konrardy et al. teaches the claimed computer system of claim 10, wherein:
the vehicle feature is a forward collision warning system (“…autonomous operation feature risk levels for features that assist the vehicle operator in safely controlling the vehicle. Such features may include alerts, warnings, automatic braking for collision avoidance, and/or similar features that may provide information to the vehicle operator or take control of the vehicle from the vehicle operator under some conditions”, see col.34, particularly lines 26-53).
Konrardy et al. does not expressly recite the claimed
and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive.
However, the nature and effect of the “update” is a design choice, and Claim 12 is not directed to a step of making “a sensitivity metric of the forward collision warning system more sensitive”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Iwasaki (JP2011150578A) teaches updating an alarm determination threshold which allows for updating the “sensitivity” of the alarm by the updating of the threshold (Iwasaki; see P[0021], P[0025] and P[0042]), which renders obvious the design choice of the claimed update that “makes a sensitivity metric of the forward collision warning system more sensitive”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Iwasaki, and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive, as rendered obvious by Iwasaki, in order to provide a system that “updates the threshold value held by” an “alarm determination unit” (Iwasaki; see P[0025]) and so that a “driver can change the threshold value used for the determination of the necessity of the alarm according to the danger felt by the driver, the alarm can be output in accordance with the driver's feeling” which “also leads to prevention of issuance of an alarm unnecessary for the driver” (Iwasaki; see P[0042]).
As per Claim 18, Konrardy et al. teaches the claimed computer system of claim 16, wherein:
the vehicle feature is a forward collision warning system (“…autonomous operation feature risk levels for features that assist the vehicle operator in safely controlling the vehicle. Such features may include alerts, warnings, automatic braking for collision avoidance, and/or similar features that may provide information to the vehicle operator or take control of the vehicle from the vehicle operator under some conditions”, see col.34, particularly lines 26-53).
Konrardy et al. does not expressly recite the claimed
and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive.
However, the nature and effect of the “update” is a design choice, and Claim 18 is not directed to a step of making “a sensitivity metric of the forward collision warning system more sensitive”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Iwasaki (JP2011150578A) teaches updating an alarm determination threshold which allows for updating the “sensitivity” of the alarm by the updating of the threshold (Iwasaki; see P[0021], P[0025] and P[0042]), which renders obvious the design choice of the claimed update that “makes a sensitivity metric of the forward collision warning system more sensitive”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Iwasaki, and the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive, as rendered obvious by Iwasaki, in order to provide a system that “updates the threshold value held by” an “alarm determination unit” (Iwasaki; see P[0025]) and so that a “driver can change the threshold value used for the determination of the necessity of the alarm according to the danger felt by the driver, the alarm can be output in accordance with the driver's feeling” which “also leads to prevention of issuance of an alarm unnecessary for the driver” (Iwasaki; see P[0042]).
Claims 5, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Konrardy et al. (9,858,621) in view of Walther et al. (10,599,546) further in view of James et al. (WO2015132582A2), further in view of Kim et al. (2019/0138184).
As per Claim 5, Konrardy et al. does not expressly recite the claimed computer-implemented method of claim 1, wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system.
However, the nature and effect of the “update” is a design choice, and Claim 5 is not directed to a step of changing “button placement on a vehicle infotainment system”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Kim et al. (2019/0138184) teaches a server that selects a size and arrangement of a button icon on a UI for a display device of a vehicle (Kim et al.; see P[0087]-P[0088]) and “transmits the selected button icon and information about the size and arrangement of the button icon to the vehicle” (Kim et al.; see P[0089]), which renders obvious the design choice of the claimed update that comprises “changes to button placement on a vehicle infotainment system”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Kim et al., and wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system, as rendered obvious by Kim et al., in order to “determine the size and arrangement of the button icon using the UI configuration information in consideration of the specification information about the display device from the vehicle” (Kim et al.; see P[0087]).
As per Claim 14, Konrardy et al. does not expressly recite the claimed computer system of claim 10, wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system.
However, the nature and effect of the “update” is a design choice, and Claim 14 is not directed to a step of changing “button placement on a vehicle infotainment system”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Kim et al. (2019/0138184) teaches a server that selects a size and arrangement of a button icon on a UI for a display device of a vehicle (Kim et al.; see P[0087]-P[0088]) and “transmits the selected button icon and information about the size and arrangement of the button icon to the vehicle” (Kim et al.; see P[0089]), which renders obvious the design choice of the claimed update that comprises “changes to button placement on a vehicle infotainment system”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Kim et al., and wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system, as rendered obvious by Kim et al., in order to “determine the size and arrangement of the button icon using the UI configuration information in consideration of the specification information about the display device from the vehicle” (Kim et al.; see P[0087]).
As per Claim 20, Konrardy et al. does not expressly recite the claimed computer system of claim 16, wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system.
However, the nature and effect of the “update” is a design choice, and Claim 20 is not directed to a step of changing “button placement on a vehicle infotainment system”, but is merely directed to using a “second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature” as in the parent claim, where the exact effect of the update in terms of how a “vehicle feature” is altered by the update is non-critical to the invention, as the invention is only concerned with the “second dataset” and the “effectiveness score” would be analyzed in the same manner in the parent claim regardless of how a specific “vehicle feature” was altered to result in the “second dataset”. In view of this, Kim et al. (2019/0138184) teaches a server that selects a size and arrangement of a button icon on a UI for a display device of a vehicle (Kim et al.; see P[0087]-P[0088]) and “transmits the selected button icon and information about the size and arrangement of the button icon to the vehicle” (Kim et al.; see P[0089]), which renders obvious the design choice of the claimed update that comprises “changes to button placement on a vehicle infotainment system”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Konrardy et al. with the teachings of Kim et al., and wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system, as rendered obvious by Kim et al., in order to “determine the size and arrangement of the button icon using the UI configuration information in consideration of the specification information about the display device from the vehicle” (Kim et al.; see P[0087]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ISAAC G SMITH/ Primary Examiner, Art Unit 3662
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