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 .
This is the Non-Final Office Action in response to the Application No. 18/911,637 filed on October 10, 2024, title: “Autonomous Vehicle Operation Feature Monitoring And Evaluation Of Effectiveness”.
Status of The Claims
Claims 1-20 are pending in this application and have been examined.
Priority
This application is a CON of US Application No. 18/149,488 filed on 01/03/2023 (Patented No. 12,140,959) which is a CON of US Application No. 16/817,845 filed on 03/13/2020 (Patented No. 11,580,604) which is a CON of US Application No. 15/421,521 filed on 02/01/2017 (Patented No. 10,599,155) which claims the benefit of US Provisional Application No. 62/291,789 filed on 02/05/2016 and is a CIP of US Application No. 14/713,249 filed on 05/15/2015 (Patented No. 10,529,027) which claims benefit of US Provisional Application No. 62/056,893 filed on 09/29/2014 and claims benefit of US Provisional Application No. 62/047,307 filed on 09/08/2014 and claims benefit of US Provisional Application No. 62/035,867 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/036,090 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,983 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,980 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,878 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,859 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,660 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,723 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,669 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,729 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,832 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,780 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/035,769 filed on 08/11/2014 and claims benefit of US Provisional Application No. 62/018,169 filed on 06/27/2014 and claims benefit of US Provisional Application No. 62/000,878 filed on 05/20/2014.
For the purpose of examination, the 05/20/2014 is considered to be the effective filing date.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/10/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDSs are being considered by the examiner and copies of the PTO-1449 forms with the examiner’s initials are attached to this Office Action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of US Patent No. 12,140,959, Claims 1-20 of US Patent No. 11,580,604, Claims 1-21 of US Patent No. 10,599,155, and Claims 1-21 of US Patent No. 10,529,027. Although the claims at issue are not identical, they are not patentably distinct from each other because the examined claims are broader than the reference claims in the patents and anticipated by the reference claims. The examined claims recite substantially the same limitations as the reference claims in the patents with minor variations that would have been obvious to one of ordinary skills in the art. Also, the Application and Patents are directed to the same invention of autonomous vehicle operation feature monitoring and evaluation of effectiveness and are commonly owned.
Application No. 18/911,637
Patent No. 12,140,959
Claim 1, A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
Claim 1, A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
one or more processors; and
one or more processors; and
a non-transitory program memory 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:
a non-transitory program memory 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 indications of a plurality of vehicle incidents involving a plurality of vehicles having the autonomous operation feature; for each vehicle incident of the plurality of vehicle incidents involving a respective vehicle of the plurality of vehicles:
receive indications of a plurality of vehicle collisions involving a plurality of vehicles having the autonomous operation feature; for each vehicle collision of the plurality of vehicle collisions involving a respective vehicle of the plurality of vehicles:
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle incident occurred, (ii) one or more movements of the vehicle at the time of the vehicle incident, and (iii) one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle incident;
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle collision occurred, (ii) a person positioned within the vehicle to operate the vehicle at the time of the vehicle collision, and (iii) one or more capabilities or features of the autonomous operation feature of the vehicle;
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of incident or mitigate an effect of the vehicle incident immediately before or during the vehicle incident based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data; and
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of collision or mitigate an effect of the vehicle collision immediately before or during the vehicle collision based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions;
receive control decision data indicating one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle collision; and
assign a degree of fault for the vehicle incident to the autonomous operation feature based upon an extent of consistency or inconsistency between the one or more preferred control decisions and the one or more actual control decisions; and
assign a degree of fault for the vehicle collision to the autonomous operation feature based upon the one or more preferred control decisions and the one or more actual control decisions; and
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle incidents.
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle collisions.
Application No. 18/911,637
Patent No. 11,580,604
Claim 1, A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
Claim 1, A computer system for evaluating operation of a vehicle having an autonomous system, comprising:
one or more processors; and
one or more processors; and
a non-transitory program memory 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:
a non-transitory program memory 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 indications of a plurality of vehicle incidents involving a plurality of vehicles having the autonomous operation feature; for each vehicle incident of the plurality of vehicle incidents involving a respective vehicle of the plurality of vehicles:
receive an indication of occurrence of a vehicle collision involving the vehicle;
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle incident occurred, (ii) one or more movements of the vehicle at the time of the vehicle incident, and (iii) one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle incident;
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle collision occurred, (ii) a person positioned within the vehicle to operate the vehicle at the time of the vehicle collision, and (iii) one or more capabilities or features of the autonomous system;
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of incident or mitigate an effect of the vehicle incident immediately before or during the vehicle incident based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data; and
determine one or more preferred control decisions the autonomous system could have made to control the vehicle to reduce a risk of collision or mitigate an effect of the vehicle collision immediately before or during the vehicle collision based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions by identifying patterns in a training data set comprising sample sensor data and sample control data;
receive control decision data indicating one or more actual control decisions the autonomous system made to control the vehicle immediately before or during the vehicle collision;
determine a degree of similarity between the one or more preferred control decisions and the one or more actual control decisions; and
assign a degree of fault for the vehicle incident to the autonomous operation feature based upon an extent of consistency or inconsistency between the one or more preferred control decisions and the one or more actual control decisions; and
assign a degree of fault for the vehicle collision to the autonomous system based upon the determined degree of similarity between the one or more preferred control decisions and the one or more actual control decisions.
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle incidents.
Application No. 18/911,637
Patent No. 10,599,155
Claim 1, A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
Claim 1, A computer system for monitoring an autonomous vehicle having an autonomous system, comprising:
one or more processors; and
one or more processors; and
a non-transitory program memory 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:
a non-transitory program memory 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 indications of a plurality of vehicle incidents involving a plurality of vehicles having the autonomous operation feature; for each vehicle incident of the plurality of vehicle incidents involving a respective vehicle of the plurality of vehicles:
receive, via wireless communication or data transmission over one or more radio links, initial sensor data indicating the occurrence of a vehicle collision involving the autonomous vehicle, wherein the initial sensor data includes information from at least one vehicle-mounted sensor or mobile device sensor;
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle incident occurred, (ii) one or more movements of the vehicle at the time of the vehicle incident, and (iii) one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle incident;
receive, via wireless communication or data transmission over the one or more radio links, additional sensor data indicating (i) one or more environmental conditions in which the vehicle collision occurred, (ii) an identification of a person positioned within the autonomous vehicle to operate the autonomous vehicle at the time of the vehicle collision, and (iii) an identification of one or more capabilities or features of the autonomous system, wherein the additional sensor data includes information from at least one vehicle-mounted sensor, autonomous system sensor, or mobile device sensor;
process the additional sensor data using a trained machine learning program to determine one or more preferred control decisions the autonomous system should have made to control the autonomous vehicle immediately before or during the vehicle collision;
receive control decision data indicating one or more actual control decisions the autonomous system made to control the autonomous vehicle immediately before or during the vehicle collision;
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of incident or mitigate an effect of the vehicle incident immediately before or during the vehicle incident based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data; and
determine a degree of similarity between the one or more preferred control decisions that should have been made by the autonomous system to control the autonomous vehicle and the one or more actual control decisions made by the autonomous system to control the autonomous vehicle; and
assign a degree of fault for the vehicle incident to the autonomous operation feature based upon an extent of consistency or inconsistency between the one or more preferred control decisions and the one or more actual control decisions; and
assign a percentage of fault for the vehicle collision to the autonomous system based upon the determined degree of similarity between the one or more preferred control decisions and the one or more actual control decisions.
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle incidents.
Application No. 18/911,637
Patent No. 10,529,027
Claim 1, A computer system for evaluating operation of an autonomous operation feature for controlling vehicle operation, comprising:
Claim 6, A computer system for monitoring and evaluating a vehicle having one or more autonomous operation features for controlling the vehicle, comprising:
one or more processors; and
one or more processors; and
a non-transitory program memory 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:
a non-transitory program memory 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 indications of a plurality of vehicle incidents involving a plurality of vehicles having the autonomous operation feature; for each vehicle incident of the plurality of vehicle incidents involving a respective vehicle of the plurality of vehicles:
receive a baseline effectiveness profile indicating effectiveness of the one or more autonomous operation features in controlling operation of vehicles;
receive sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle incident occurred, (ii) one or more movements of the vehicle at the time of the vehicle incident, and (iii) one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle incident;
generate operating data regarding operation of the vehicle by an on-board computer configured to control operation of the vehicle, wherein the operating data includes (i) information from one or more sensors disposed within the vehicle, (ii) information regarding the one or more autonomous operation features, and (iii) a plurality of control decisions generated by the one or more autonomous operation features in response to sensor data from the one or more sensors, wherein the plurality of control decisions includes, for each of a plurality of times during vehicle operation: (i) an implemented control decision implemented to control the vehicle and (ii) one or more unimplemented control decisions generated by the one or more autonomous operation features but not implemented to control the vehicle, the unimplemented control decisions indicating alternative control actions for controlling the vehicle;
record a log of the generated operating data, including the plurality of control decisions;
receive actual loss data regarding losses associated with insurance policies covering a plurality of other vehicles having the one or more autonomous operation features;
determine one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of incident or mitigate an effect of the vehicle incident immediately before or during the vehicle incident based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data; and
determine at least one risk level indicative of effectiveness of operation of the one or more autonomous operation features in controlling the vehicle based at least in part upon the baseline effectiveness profile and the recorded log of the operating data, including an indication of operating conditions of the vehicle in the information from the one or more sensors and the information regarding the one or more autonomous operation features;
determine responses of the vehicle to the implemented control decisions and predicted responses of the vehicle to the unimplemented control decisions; and
assign a degree of fault for the vehicle incident to the autonomous operation feature based upon an extent of consistency or inconsistency between the one or more preferred control decisions and the one or more actual control decisions; and
adjust the at least one risk level based upon the received actual loss data, the responses of the vehicle to the implemented control decisions, and the predicted responses of the vehicle to the unimplemented control decisions.
determine a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle incidents.
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.
Step 1:
Under the Step 1 analysis, the claims are reviewed to determine whether they fall within the four statutory categories of patentable subject matter (i.e., process, machine, manufacture, or combination of matter).
Claims 1-20 recite a computer system, program, and method for evaluating of an autonomous operation feature for controlling vehicle operation. Therefore, the claims are directed to a machine, manufacture, and process which fall within the four statutory categories of invention (Step 1-Yes, the claims are statutory).
Step 2A, Prong 1:
Under the Step 2A, Prong 1 analysis, the claims are reviewed to determine whether they recite a judicial exception by identifying if the claim limitations fall in one of the enumerated abstract idea groupings (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 15, A computer-implemented method of evaluating operation of an autonomous operation feature for controlling vehicle operation, the method comprising:
receiving, at one or more processors, indications of a plurality of vehicle incidents involving a plurality of vehicles having the autonomous operation feature;
for each vehicle incident of the plurality of vehicle incidents involving a respective vehicle of the plurality of vehicles:
receiving, at the one or more processors, sensor data from one or more sensors within the vehicle indicating (i) one or more environmental conditions in which the vehicle incident occurred, (ii) one or more movements of the vehicle at the time of the vehicle incident, and (iii) one or more actual control decisions the autonomous operation feature of the vehicle made to control the vehicle immediately before or during the vehicle incident;
determining, by the one or more processors, one or more preferred control decisions the autonomous operation feature could have made to control the vehicle to reduce a risk of incident or mitigate an effect of the vehicle incident immediately before or during the vehicle incident based upon analysis of the sensor data using a trained machine learning program that has been previously trained to predict preferred control decisions under a plurality of operating conditions associated with corresponding sets of training sensor data; and
assigning, by the one or more processors, a degree of fault for the vehicle incident to the autonomous operation feature based upon an extent of consistency or inconsistency between the one or more preferred control decisions and the one or more actual control decisions; and
determining, by the one or more processors, a risk level for the autonomous operation feature based upon the respective degrees of fault for the plurality of vehicle incidents.
The above limitations (underlined), as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity but for the recitation of generic computer components (e.g., computer, processors, non-transitory program memory with stored executable instructions). Monitoring and evaluating of effectiveness of an autonomous operation feature for controlling vehicle operation for accident avoidance is a fundamental economic practice because it relates to insurance (e.g., hedging, insurance, mitigating risk).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Therefore, the claim recites an abstract idea.
Claim 1 recites a computer system comprising processors, memory, wireless communication, and sensors. Claim 8 recites a tangible, non-transitory computer-readable medium with stored executable instructions. The claims recite the similar elements and limitations as discussed in claim 15. The mere nominal recitation of the generic computer components and instructions do not take the claims out of the methods of organizing human activity grouping. Therefore, these claims also recite an abstract idea (Step 2A Prong 1-Yes, the claims recite an abstract idea).
Step 2A, Prong 2:
Under the Step 2A, Prong 2 analysis, the claims are reviewed to determine whether the judicial exception (i.e., abstract idea) is integrated into a practical application. In order to make this determination, the additional element(s), or combination of elements, are analyzed to determine if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
The judicial exception is not integrated into a practical application. In particular, the claims (1, 8, and 15) recite the additional elements of: computer system including processors, memory, wireless communication, and sensors (see Claim 1). The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. In addition, the machine learning is performed at a high level of generality and merely shows an end result. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claims are directed to an abstract idea (Step 2A-Prong 2-No, the claims are not integrated into a practical application).
Step 2B:
Under the Step 2B analysis, the claims are reviewed to determine whether the claims provide an inventive concept (i.e., whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware and/or software amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, the instant application’s Specification states, “The artificial intelligence pricing model may be combined with traditional methods for semi-autonomous vehicles.” [0064]; “a general-use personal computer … a general- use on-board computer capable of performing many functions” [0067]; [0072-3]; “plurality of general purpose or mobile platforms” [0076]; “general-purpose computer” [107]; and [0141]. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the independent claims are not patent eligible (Step 2B-No, the claims are not significantly more than the abstract idea).
Dependent claims 2-7, 9-14, and 16-20 depend on claims 1, 8, and 15 and thus include all the limitations and features of claims 1, 8, and 15. Therefore, the dependent claims also are directed the same abstract idea of claims 1, 8, and 15.
Claims 2, 9, and 16 include more detailed instructions “wherein each of the one or more preferred control decisions and the one or more actual control decisions are virtually time-stamped for comparison of such controlled and actual control decisions based upon matching virtual time stamps.” (Additional detailed instructions for comparing the preferred control decisions and the actual control decisions. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
Claims 3, 10, and 17 include more detailed instructions “wherein the risk level is associated with one or more sets of parameters indicating configurations or settings of the autonomous operation feature.” (Additional detailed instructions for the risk level associated with sets of parameters. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
Claims 4, 11, and 18 include more detailed instructions “wherein the risk level is associated with a weighted average of a plurality of risk levels associated with operation of the autonomous operation feature under a plurality of sets of conditions comprising one or more of the following conditions: environmental conditions, road conditions, construction conditions, or traffic conditions.” (Additional detailed instructions for the risk level associated with sets of conditions. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
Claims 5, 12, and 19 include more detailed instructions “wherein the executable instructions that cause the computer system to determine the risk level for the autonomous operation feature cause the computer system to adjust an initial risk level determined based upon testing the autonomous operation feature in a test environment.” (Additional detailed instructions determining the risk level comprises adjusting an initial risk level. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
Claims 6, 13, and 19 include more detailed instructions “wherein the test environment is a virtual test environment configured to present a plurality of sets of virtual environmental conditions to the autonomous operation feature in a plurality of virtual test scenarios.” (Additional detailed instructions about the test environment. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
Claims 7, 14, and 20 include more detailed instructions “wherein each of the plurality of vehicle incidents comprises at least one of the following: a collision, a hard braking event, a hard acceleration event, an evasive maneuvering event, a loss of traction event, or a detection of one or more objects within a threshold distance from the vehicle.” (Additional detailed instructions about the vehicle incidents. The claims individually or in combination with others do not integrate the abstract idea into a practical application or provide an inventive concept to the abstract idea).
The dependent claims do no more than providing additional detailed instructions and administrative requirements for the functional steps already recited in the independent claims. Every recited combination between the recited computing hardware and the recited computing functions has been considered. No inventive concept is found in the claims. The claims further describe the business relations of the certain method of organizing human activity (abstract idea) and do not include additional elements other than those of claims 1, 8, and 15 to provide a practical application or significantly more than the judicial exception. Therefore, the dependent claims also are not patent eligible.
The focus of the claims is on a method of monitoring and evaluating of effectiveness of an autonomous operation feature for controlling vehicle operation for accident avoidance. The claims are not directed to a new type of processor, a computer network, or a system memory, nor do they provide a method for processing data that improves existing technological processes. The focus of the claims is not on improving computer-related technology, but on an independent abstract idea that uses computers as tools. Accordingly, when viewed as a whole, the claims do no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims do not add significantly more (i.e., an inventive concept) to the abstract idea (Step 2B-No, the claims are not significantly more than the abstract idea).
Applicant’s claimed invention is basically a “business solution” to a “business problem”, especially in determination of insurance premium. This is supported in Applicant’s Specification in paragraphs 2-5:
[0003] The present disclosure generally relates to systems and methods for determining risk, pricing, and offering vehicle insurance policies, specifically vehicle insurance policies where vehicle operation is partially or fully automated.
[0004] Vehicle or automobile insurance exists to provide financial protection against physical damage and/or bodily injury resulting from traffic accidents and against liability that could arise therefrom. Typically, a customer purchases a vehicle insurance policy for a policy rate having a specified term. In exchange for payments from the insured customer, the insurer pays for damages to the insured which are caused by covered perils, acts, or events as specified by the language of the insurance policy. The payments from the insured are generally referred to as “premiums,” and typically are paid on behalf of the insured over time at periodic intervals. An insurance policy may remain “in-force” while premium payments are made during the term or length of coverage of the policy as indicated in the policy. An insurance policy may “lapse” (or have a status or state of “lapsed”), for example, when premium payments are not being paid or if the insured or the insurer cancels the policy.
[0005] Premiums may be typically determined based upon a selected level of insurance coverage, location of vehicle operation, vehicle model, and characteristics or demographics of the vehicle operator. The characteristics of a vehicle operator that affect premiums may include age, years operating vehicles of the same class, prior incidents involving vehicle operation, and losses reported by the vehicle operator to the insurer or a previous insurer. Past and current premium determination methods do not, however, account for use of autonomous vehicle operating features. The present embodiments may, inter alia, alleviate this and/or other drawbacks associated with conventional techniques.
Claim Rejections - 35 USC § 102/103
A review of the parent cases 18/149,488, 16/817,845, 15/421,521, and 14/713,249 and noted that extensive prior art searches have been performed. An updated prior art search did not identify any art that teaches each and every elements of the claims at this time.
Conclusion
Claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAI TRAN whose telephone number is (571)272-7364. The examiner can normally be reached Monday-Friday, 9-5.
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HAI TRAN
Primary Examiner
Art Unit 3695
/HAI TRAN/Primary Examiner, Art Unit 3695