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 .
Status of Claims
This action is in reply to the communications filed on 11/27/2024.
Claims 1-20 are currently pending and have been examined.
This action is made Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/27/2024 was filed before the mailing date of a first Office Action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Objection
Claims 1-9 are objected to because of the following informalities:
Claims 1-9 repeatedly recites the limitation “via one or more processors.” “Via one or more processors” is initially and previously recited in Claim 1: line 3. Is the ‘one or more processors’ repeatedly recited in Claims 1-9 different than ‘one or more processors’ initially and previously recited in Independent Claim 1: line 3? It appears there is a typographical mistake since the specification only points to single one or more processors. For compact examination purposes, Examiner interpreted the repeated instances recited in Claims 1-9, after the initial recitation in Claim 1: line 3, as “via the one or more processors.” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-12, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of analyzing an auto loss report without significantly more.
Examiner has identified claim 12 as the claim that represents the claimed invention presented in independent claims 1, 12, and 20.
Claim 1 is directed to a method which is one of the statutory categories of invention; Claim 12 is directed to a system which is one of the statutory categories of invention; and Claim 20 is directed to a non-transitory computer readable medium which is one of the statutory categories of invention. (Step 1: YES).
Claim 12 is directed to computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to: train, via the one or more processors, a machine learning model including a plurality of training parameters, the training including: encoding historical claim information as labels in a labeled data set, wherein the historical claim information includes historical user characteristics, historical vehicle characteristics, and at least one of historical shop volume or historical claim volume, and the historical claim information is associated with at least one of known loss time to repair or known loss cost to repair; initializing the plurality of training parameters to be random values; and iteratively modifying the plurality of training parameters such that at least one of a predicted loss time to repair or a predicted loss cost to repair determined by the machine learning model using the plurality of training parameters and the historical claim information gradually converges to the at least one of known loss time to repair or known loss cost to repair; receive, via the one or more processors, an auto loss report; extract, via the one or more processors, machine learning parameters from the auto loss report; analyze, via the one or more processors and using the machine learning model, the machine learning parameters and volume data to determine at least one of (i) a loss time to repair and (ii) a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair indicating a wait time the user is predicted to require a rental vehicle; and output, via the one or more processors and by the machine learning model, the at least one of (i) the loss time to repair and (ii) the loss cost to repair. These series of steps describe the abstract idea of analyzing an auto loss report (with the exception of the italicized and bolded terms above), which is mitigating risk of improperly preparing vehicle insurance claim estimates by accurately assessing vehicle damage, identifying and communicating repairability issues and vehicle condition on potential total loss claims, communicate repair estimate information to claim leadership, claim handlers, customers, agents, and repair facilities; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing and determining of a customer’s out-of-pocket costs to repair the customer’s vehicle and rent a vehicle while the vehicle is being repaired, based on the customer’s vehicle insurance coverage, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a computing system, one or more processors, one or more memories, and machine learning model do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 12 is directed to an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional limitations of a computing system, one or more processors, one or more memories, and machine learning model are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence do not integrate the abstract idea into a practical application. Thus, claim 12 is directed to an abstract idea (Step 2A-Prong 2: NO).
Claim 12 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, a computing system, one or more processors, one or more memories, and machine learning model limitations are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 12 is not patent eligible.
Similar arguments can be extended to the other independent claims, claims 1 and 20; and hence claims 1 and 20 are rejected on similar grounds as claim 12.
Dependent claims 2-11 and 14-19 are directed to a method and a system, respectively, which perform the steps that describe the abstract idea of analyzing an auto loss report. Furthermore, dependent claims 4, 5, 9, 15, and 19 are directed to a method and a system, respectively, which recite the steps: wherein analyzing the auto loss report to identify the vehicle rental coverage associated with the user includes querying, via one or more processors, customer insurance policy information in an electronic database; wherein the method is implemented in at least one of (i) a call center computing system accessible by a customer service user and (ii) an application executing in a mobile device of the user; and wherein determining the set of vehicle rental branches includes searching, via one or more processors, for rental branches via a search interface. These series of steps describe the abstract idea of analyzing an auto loss report (with the exception of the italicized and bolded terms above), which is mitigating risk of improperly preparing vehicle insurance claim estimates by accurately assessing vehicle damage, identifying and communicating repairability issues and vehicle condition on potential total loss claims, communicate repair estimate information to claim leadership, claim handlers, customers, agents, and repair facilities; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing and determining of a customer’s out-of-pocket costs to repair the customer’s vehicle and rent a vehicle while the vehicle is being repaired, based on the customer’s vehicle insurance coverage, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-11 and 14-19 are directed to an abstract idea. The additional limitations of a computing system, one or more processors, one or more memories, machine learning model, electronic database, call center computing system, application, mobile device, and search interface are no more than simply applying the abstract idea using generic computer elements. Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence, do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a computing system, one or more processors, one or more memories, machine learning model, electronic database, call center computing system, application, mobile device, and search interface, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-11 and 14-19 have further defined the abstract idea that is present in their respective independent claims 1, 12, and 20; and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract in nature for the reason presented above. The dependent claims 2-11 and 14-19 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 2-11 and 14-19 are directed to an abstract idea.
Thus, claims 1-20 are not patent-eligible.
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 AlA. 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 1-6, 8-9, and 11-20 of U.S. Patent No. 11,710,185 and claims 1, 3-12, and 14-20 of U.S. Patent No. 12,190,388. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are anticipated by patent claims 1-6, 8-9, and 11-20 of U.S. Patent No. 11,710,185 and patent claims 1, 3-12, and 14-20 of U.S. Patent No. 12,190,388. See chart below:
Instant Application No. 18/962,872
US Patent No. 11,710,185
For ease of comprehension, below contains only relevant and redacted claim language from the allowed patented application.
US Patent No. 12,190,388
For ease of comprehension, below contains only relevant and redacted claim language from the allowed patented application.
Claims 1, 12, and 20:
A computer-implemented method for providing smart auto rental estimatics to a user, the method comprising:
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to:
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:
training, via one or more processors, a machine learning model including a plurality of training parameters, the training including:
encoding historical claim information as labels in a labeled data set, wherein the historical claim information includes historical user characteristics, historical vehicle characteristics, and at least one of historical shop volume or historical claim volume, and the historical claim information is associated with at least one of known loss time to repair or known loss cost to repair;
initializing the plurality of training parameters to be random values; and iteratively modifying the plurality of training parameters such that at least one of a predicted loss time to repair or a predicted loss cost to repair determined by the machine learning model using the plurality of training parameters and the historical claim information gradually converges to the at least one of known loss time to repair or known loss cost to repair;
receiving, via one or more processors, an auto loss report;
extracting, via one or more processors, machine learning parameters from the auto loss report;
analyzing, via one or more processors and using the machine learning model, the machine learning parameters and volume data to determine at least one of (i) a loss time to repair and (ii) a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair indicating a wait time the user is predicted to require a rental vehicle; and
outputting, via one or more processors and by the machine learning model, the at least one of (i) the loss time to repair and (ii) the loss cost to repair.
Claims 1, 11, and 16:
A computer-implemented method for providing smart auto rental estimatics to a user, the method comprising:
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to:
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:
the trained machine learning model associated with a plurality of training parameters
the trained machine learning model associated with a plurality of training parameters and trained to predict a repair time to repair and a cost to repair using historical claim information, including historical user characteristics and historical vehicle characteristics, and one or both of (i) historical shop volume and (ii) historical claim volume, wherein at least a portion of the historical claim information is encoded as labels in a labeled data set that is used to train the machine learning model,
the trained machine learning model associated with a plurality of training parameters and trained to predict a repair time to repair and a cost to repair using historical claim information, including historical user characteristics and historical vehicle characteristics, analyzing, using a trained machine learning model, the machine learning parameters and a volume data to determine a loss time to repair and a loss cost to repair
receiving, via a processor, an auto loss report,
extracting, via one or more processors, machine learning parameters from the auto loss report,
analyzing, using a trained machine learning model, the machine learning parameters and a volume data to determine a loss time to repair and a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair indicating a wait time the user is predicted to require a rental vehicle,
calculating, based on the loss time to repair, the loss cost to repair, and the vehicle rental coverage a set of estimated out-of-pockets; transmitting, via a processor, the set of estimated out-of-pockets
Claims 1, 12, and 20:
A computer-implemented method for providing smart auto rental estimatics to a user, the method comprising:
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to:
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to:
training, via one or more processors, a machine learning model including a plurality of training parameters, the training including:
encoding historical claim information as labels in a labeled data set, wherein the historical claim information includes historical user characteristics, historical vehicle characteristics, and at least one of historical shop volume or historical claim volume, and the historical claim information is associated with at least one of known loss time to repair or known loss cost to repair;
initializing the plurality of training parameters to be random values; and iteratively modifying the plurality of training parameters such that at least one of a predicted loss time to repair or a predicted loss cost to repair determined by the machine learning model using the plurality of training parameters and the historical claim information gradually converges to the at least one of known loss time to repair or known loss cost to repair;
receiving, via one or more processors, an auto loss report;
extracting, via one or more processors, machine learning parameters from the auto loss report,
analyzing, via one or more processors and using the machine learning model, the machine learning parameters and volume data to determine at least one of (i) a loss time to repair and (ii) a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair indicating a wait time the user is predicted to require a rental vehicle
calculating, via one or more processors, based on the at least one of (i) the loss time to repair and (ii) the loss cost to repair; transmitting, via one or more processors, the set of estimated out-of-pockets.
Claims 2 and 13:
further comprising: analyzing, via one or more processors, the machine learning parameters to identify a vehicle rental coverage associated with the user; calculating, via one or more processors, based on the at least one of (i) the loss time to repair and (ii) the loss cost to repair, and the vehicle rental coverage, a set of estimated out-of-pockets each corresponding to a respective vehicle class; and transmitting, via one or more processors, the set of estimated out-of-pockets.
Claims 1, 11, and 16:
analyzing the
Claims 1, 12, and 20:
analyzing, via one or more processors, the machine learning parameters to identify a vehicle rental coverage associated with the user; calculating, via one or more processors, based on the at least one of (i) the loss time to repair and (ii) the loss cost to repair, and the vehicle rental coverage, a set of estimated out-of- pockets each corresponding to a respective vehicle class; and transmitting, via one or more processors, the set of estimated out-of-pockets.
Claims 3 and 14:
wherein calculating set of estimated out-of-pockets includes calculating, via one or more processors, the maximum of zero and a per-day cost, less the vehicle rental coverage associated with the user.
Claims 2, 12, and 17:
wherein calculating set of estimated out-of-pockets each corresponding to a respective vehicle class includes calculating the maximum of zero and a per-day cost, less the customer's rental coverage.
Claims 3 and 14:
wherein calculating set of estimated out-of-pockets includes calculating, via one or more processors, the maximum of zero and a per-day cost, less the vehicle rental coverage.
Claims 4 and 15:
wherein analyzing the auto loss report to identify the vehicle rental coverage associated with the user includes querying, via one or more processors, customer insurance policy information in an electronic database.
Claims 4, 13, and 18:
wherein analyzing the
Claims 4 and 15:
wherein analyzing the auto loss report to identify the vehicle rental coverage associated with the user includes querying, via one or more processors, customer insurance policy information in an electronic database.
Claim 5:
wherein the method is implemented in at least one of (i) a call center computing system accessible by a customer service user and (ii) an application executing in a mobile device of the user.
Claim 3:
wherein the method is implemented in one or both of (i) a call center computing system accessible by a customer service user and (ii) an application executing in a mobile device of the user.
Claim 5:
wherein the method is implemented in at least one of (i) a call center computing system accessible by a customer service user and (ii) an application executing in a mobile device of the user.
Claims 6 and 16:
further comprising: determining, via one or more processors, a candidate set of vehicle rental vendors; and receiving, via one or more processors, an indication of a chosen vehicle rental vendor, the chosen vendor included in the candidate set of vehicle rental vendors.
Claims 1, 11, and 16:
determining, via a processor, a candidate set of vehicle rental vendors, receiving, via a processor, an indication of a chosen vehicle rental vendor, the chosen vendor included in the candidate set of vehicle rental vendors,
Claims 6 and 16:
further comprising: determining, via one or more processors, a candidate set of vehicle rental vendors; and receiving, via one or more processors, an indication of a chosen vehicle rental vendor, the chosen rental vendor included in the candidate set of vehicle rental vendors.
Claims 7 and 17:
the method further comprising: after receiving the indication of the chosen vehicle rental vendor, displaying, via one or more processors, a default word track to the user.
Claims 5, 14, and 19:
wherein receiving, via a processor, the indication of the chosen vehicle rental vendor, the chosen vendor included in the candidate set of vehicle rental vendors includes displaying a default word track to the user.
Claims 7 and 17:
the method further comprising: after receiving the indication of the chosen vehicle rental vendor, displaying, via one or more processors, a default word track to the user.
Claims 8 and 18:
further comprising: determining, via one or more processors and based on the chosen vehicle rental vendor and a location of the user, a set of vehicle rental branches; and receiving, via one or more processors, an indication of a chosen vehicle rental branch, the chosen vehicle rental branch included in the set of vehicle rental branches.
Claims 1, 11, and 16:
determining, based on the chosen vehicle rental vendor and the location of the user, a set of vehicle rental branches, receiving via a processor, an indication of a chosen vehicle rental branch, the chosen vehicle rental branch included in the set of vehicle rental branches,
Claims 8 and 18:
further comprising: determining, via one or more processors and based on the chosen vehicle rental vendor and a location of the user, a set of vehicle rental branches; and receiving, via one or more processors, an indication of a chosen vehicle rental branch, the chosen vehicle rental branch included in the set of vehicle rental branches.
Claims 9 and 19:
wherein determining the set of vehicle rental branches includes searching, via one or more processors, for rental branches via a search interface.
Claims 6, 15, and 20:
wherein determining, based on the chosen vehicle rental vendor and the location of the user, the set of vehicle rental branches includes searching for rental branches via a search interface.
Claims 9 and 19:
wherein determining the set of vehicle rental branches includes searching, via one or more processors, for rental branches via a search interface.
Claim 10:
wherein the trained machine learning model is trained further using vehicle feature information relating to years, makes, and/or models.
Claim 8:
wherein the trained machine learning model is further trained by analyzing vehicle feature information relating to a year, a make, and/or a model.
Claim 10:
wherein the trained machine learning model is trained further using vehicle feature information relating to years, makes, and/or models.
Claim 11:
wherein the trained machine learning model is trained further using vehicle classes.
Claim 9:
wherein the trained machine learning model is further trained by analyzing the vehicle class.
Claim 11:
wherein the trained machine learning model is trained further using vehicle classes.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 rejected under 35 U.S.C. 103 as being unpatentable over Franke (U.S. Patent Application Publication No. US 2017/0148101 A1 hereinafter “Franke”), in view of Renwick (U.S. Patent Application Publication No. US 2002/0188479 A1; hereinafter “Renwick”).
Regarding Claims 1, 12, and 20:
Franke teaches:
A computer-implemented method for providing smart auto rental estimatics to a user, the method comprising: (Franke, See, Para. 19-21);
A computing system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to: (Franke, See, Para. 6-11, 96);
A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: (Franke, See, Para. 16, 18, 21, 97);
training, via one or more processors, a machine learning model including a plurality of training parameters, (Franke, preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; (See, Para. 8 and 56); a support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 56, 139, 175-176));
the training including: encoding historical claim information as labels in a labeled data set, wherein the historical claim information includes historical user characteristics, historical vehicle characteristics, and at least one of historical shop volume or historical claim volume, and the historical claim information is associated with at least one of known loss time to repair or known loss cost to repair; (Franke, the models 128 may include currently scanned models such as any of the digital surface representations described above, previously generated models, previously scanned models (See, Para. 8, 83, and 84); with objective data available from one or more vehicles, statistical analysis may also be usefully performed in a variety of different ways regardless of the particular vehicle repair process (historical shop volume and (ii) historical claim volume)(See, Para. 118 and 121); preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; (See, Para. 8, 56, 139, 175-176); This analysis may include a physical inference based on, e.g. a physical model of a vehicle or a statistical or historical inference based on data concerning similar, previous repairs resulting from a particular vehicle condition. (See, Para. 143));
initializing the plurality of training parameters to be random values; (Franke, preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage; analyze the various data sets for damage assessment including, e.g., machine vision, machine learning, artificial intelligence, expert systems, rules or heuristics, and so forth, as well as techniques for requesting manual, human intervention under certain circumstances. The report 146 may usefully document the source for any identified damage, such as by identifying the specific digital surface representation, three-dimensional location within the digital surface representation where data indicated a defect or damage, and so forth. (See, Para. 8, 56, 63, 68, 75, 81-83));
and iteratively modifying the plurality of training parameters such that at least one of a predicted loss time to repair or a predicted loss cost to repair determined by the machine learning model using the plurality of training parameters and the historical claim information [gradually converges to the at least one of known loss time to repair or known loss cost to repair]; (Franke, preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage; report 146 may thus include a damage report for a scanned vehicle based on any number of independent digital surface representations, along with vehicle identification information (e.g., a vehicle identification number, model, make, etc.), a scan of a vehicle (which may include multiple independent digital surface representations acquired using different scanning techniques), and an analysis of the scan covering, e.g., locations and sizes of defects. (See, Para. 8, 56, 63); support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 14, 15, 18, 111, 166, 175));
receiving, via one or more processors, an auto loss report; (Franke, Analyzing the scan may include estimating an impairment to vehicle value and including the impairment to vehicle value in the damage report. (See, Para. 20, 185));
extracting, via one or more processors, machine learning parameters from the auto loss report; (Franke, preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage (See, Para. 8, 56, 63); The item 306 may include one or more panels 320. The panels 320 may generally include sections of an item 306, e.g., sections of a vehicle. The sections may correspond to a particular vehicle type (e.g., automobile) and classification (e.g., sedan, station wagon, sport utility vehicle, convertible, pickup truck, van, and so on); the method 700 may include analyzing the scan to detect defects in each one of the plurality of panels. As described herein, a variety of techniques may be used to computationally extract damage information about a vehicle, such as information characterizing dents or other defects (See, Para.113, 173); a support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 56, 139, 175-176));
analyzing, via one or more processors and using the machine learning model, the machine learning parameters and volume data to determine at least one of (i) a loss time to repair and (ii) a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair [indicating a wait time the user is predicted to require a rental vehicle]; (Franke, processor may be configured to obtain automatic approval of the estimate by a remote claim processing system. The processor may be configured to automatically determine whether the owner is entitled to a rental during repairs, and wherein the user interface includes an interface for the owner to schedule the rental during repairs. (See, Para. 11); the server 122 may provide a centralized point of contact among various other participating entities (e.g., a user 105, the insurer platform 110, the rental provider platform 112, the repair provider platform 114, other service providers 116, and so on). For example, the user interface 138 may include an interface for an owner of the item being scanned (e.g., a vehicle) to schedule a rental during repairs, e.g., from the rental provider platform; (See, Para. 51); The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71); preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage (See, Para. 8, 56, 63); a support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 56, 139, 175-176));
and outputting, via one or more processors and by the machine learning model, the at least one of (i) the loss time to repair and (ii) the loss cost to repair. (Franke, processor may be configured to obtain automatic approval of the estimate by a remote claim processing system.(See, Para. 11); preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage (See, Para. 8, 56, 63); a support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 56, 139, 175-176); The user interface may include an interface for providing status updates to the owner on the damage assessment and repair process (See, Para. 14, 18-21)).
However, Franke does not specifically teach indicating a wait time the user is predicted to require a rental vehicle.
Renwick teaches the following limitations:
indicating a wait time the user is predicted to require a rental vehicle; (Renwick, A rental vehicle may be pre-arranged by the insurer and picked up by the claimant at the insurer's facility at the same time the damaged vehicle is dropped off. Alternatively, the insurer can drop the rental vehicle off at the claimant's residence (or other location) and pick up the damaged vehicle. A repair estimate is prepared at the insurer's facility by a representative of the insurer. processing vehicle damage claims minimize the involvement and time input required by the claimant during the vehicle repair process. The inputs of the claimant are limited to, for example, the delivery and pickup of the damaged vehicle and rental vehicle at the insurer's facility. (See, Para.15, 37, 41, and 43)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Franke with the features of Renwick’s system because “this system processes vehicle damage claims for use by insurers; a rental vehicle may be pre-arranged by the insurer and picked up by the claimant at the insurer's facility at the same time the damaged vehicle is dropped off; and the vehicle is then repaired at the selected repair facility and returned to the insurer's facility. The insurer inspects the repaired vehicle to confirm that the required repairs have been properly completed.” (Renwick, Para. 15).
Claims 2 and 13:
Franke discloses:
further comprising: analyzing, via one or more processors, the machine learning parameters to identify a vehicle rental coverage associated with the user; (Franke, preparing an estimate for repairing the item based on the digital surface representation using one or more local cost estimating rules and at least one remote repair cost tool accessible through the data network; the report 146 may simply document the condition of a vehicle, regardless of the presence or extent of damage (See, Para. 8, 56, 63); processor may be configured to obtain automatic approval of the estimate by a remote claim processing system. The processor may be configured to automatically determine whether the owner is entitled to a rental during repairs, and wherein the user interface includes an interface for the owner to schedule the rental during repairs. (See, Para. 11); a support vector machine or other classifier or other algorithm may be trained with training data to recognize damage based on these feature vectors or other descriptors so that dent detection can be automatically performed. (See, Para. 56, 139, 175-176)).
However, Franke does not specifically teach calculating, via one or more processors, based on the at least one of (i) the loss time to repair and (ii) the loss cost to repair, and the vehicle rental coverage, a set of estimated out-of-pockets each corresponding to a respective vehicle class; and transmitting, via one or more processors, the set of estimated out-of-pockets.
Renwick discloses the following limitations:
calculating, via one or more processors, based on the at least one of (i) the loss time to repair and (ii) the loss cost to repair, and the vehicle rental coverage, a set of estimated out-of-pockets each corresponding to a respective vehicle class; (Renwick, A rental vehicle may be pre-arranged by the insurer and picked up by the claimant at the insurer's facility at the same time the damaged vehicle is dropped off. Alternatively, the insurer can drop the rental vehicle off at the claimant's residence (or other location) and pick up the damaged vehicle. A repair estimate is prepared at the insurer's facility by a representative of the insurer. (See, Para.15); Upon selection of the repair facility, the insurance provider representative and the selected repair facility agree [0041] 520 on repair price and time for repair of the vehicle… the insured may be required to pay an insurance deductible or other charges not covered by the insurance policy; (See, Para. 37 and 41));
transmitting, via one or more processors, the set of estimated out-of-pockets. (Renwick, Upon selection of the repair facility, the insurance provider representative and the selected repair facility agree [0041] 520 on repair price and time for repair of the vehicle… insured may be required to pay an insurance deductible or other charges not covered by the insurance policy; (See, Para. 37, 41); This feature may be usefully augmented by providing concurrent payment processing capabilities for the owner of a vehicle, e.g., to permit receipt of reimbursement from the insurer, or to forward reimbursement to a repair facility, or to pay a deductible or other uninsured costs incurred by the repair service provider; (See, Para. 90)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Franke with the features of Renwick’s system because “this system processes vehicle damage claims for use by insurers; a rental vehicle may be pre-arranged by the insurer and picked up by the claimant at the insurer's facility at the same time the damaged vehicle is dropped off; and the vehicle is then repaired at the selected repair facility and returned to the insurer's facility. The insurer inspects the repaired vehicle to confirm that the required repairs have been properly completed.” (Renwick, Para. 15).
Claims 3 and 14:
Franke teaches:
wherein calculating set of estimated out-of-pockets includes calculating, via one or more processors, the maximum of zero and a per-day cost, less the vehicle rental coverage associated with the user. (Franke, processor may be configured to obtain automatic approval of the estimate by a remote claim processing system. The processor may be configured to automatically determine whether the owner is entitled to a rental during repairs, and wherein the user interface includes an interface for the owner to schedule the rental during repairs. (See, Para. 11); the server 122 may provide a centralized point of contact among various other participating entities (e.g., a user 105, the insurer platform 110, the rental provider platform 112, the repair provider platform 114, other service providers 116, and so on). For example, the user interface 138 may include an interface for an owner of the item being scanned (e.g., a vehicle) to schedule a rental during repairs, e.g., from the rental provider platform; (See, Para. 51); The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71); This feature may be usefully augmented by providing concurrent payment processing capabilities for the owner of a vehicle, e.g., to permit receipt of reimbursement from the insurer, or to forward reimbursement to a repair facility, or to pay a deductible or other uninsured costs incurred by the repair service provider; (See, Para. 90)).
Claims 4 and 15:
Franke teaches:
wherein analyzing the auto loss report to identify the vehicle rental coverage associated with the user includes querying, via one or more processors, customer insurance policy information in an electronic database. (Franke, The data 132 in the database 120 may include without limitation any data referenced herein, including insurance carrier information, adjustment report criteria and formatting, supplier information (e.g., cost of repairs and parts), three-dimensional models for makes and models of vehicles, and so forth. The rules 136 may include any cost estimating rules, insurance policy or coverage rules, damage assessment or analysis rules, approval rules for any participating entities, or any other rules or the like useful for facilitating a damage assessment and repair system; a client 106 may support an API connection or web connection (or any other form of connection) to remote resources such as a database 120 (e.g., a damage assessment costs database), an insurer platform 110 or insurance adjuster, a rental provider platform 112 or leasing companies, and so forth. The system 100 may also or instead support connections between adjusters, estimators, insurer platforms 110, parts databases, and so forth. (See, Para. 38, 50, 74-75, 86)).
Claim 5:
Franke teaches:
wherein the method is implemented in at least one of (i) a call center computing system accessible by a customer service user and (ii) an application executing in a mobile device of the user. (Franke, the user interface 800 provides a platform for case management in an insurance/repair context. In particular, a case manager, may have access to a pool of assignments 802 that have been submitted to the insurer through a customer-facing web site, call center, or other resource or combination of resources. (See, Para. 182)).
Claims 6 and 16:
Franke teaches:
further comprising: determining, via one or more processors, a candidate set of vehicle rental vendors; and (Franke, the server 122 may provide a centralized point of contact among various other participating entities (e.g., a user 105, the insurer platform 110, the rental provider platform 112, the repair provider platform 114, other service providers 116, and so on). For example, the user interface 138 may include an interface for an owner of the item being scanned (e.g., a vehicle) to schedule a rental during repairs, e.g., from the rental provider platform; (See, Para. 51));
receiving, via one or more processors, an indication of a chosen vehicle rental vendor, the chosen vendor included in the candidate set of vehicle rental vendors. (Franke, The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71)).
Claims 7 and 17:
Franke teaches:
the method further comprising: after receiving the indication of the chosen vehicle rental vendor, displaying, via one or more processors, a default word track to the user. ((Franke, The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71)).
Claims 8 and 18:
Franke teaches:
further comprising: determining, via one or more processors and based on the chosen vehicle rental vendor and a location of the user, a set of vehicle rental branches; and (Franke, the server 122 may provide a centralized point of contact among various other participating entities (e.g., a user 105, the insurer platform 110, the rental provider platform 112, the repair provider platform 114, other service providers 116, and so on). For example, the user interface 138 may include an interface for an owner of the item being scanned (e.g., a vehicle) to schedule a rental during repairs, e.g., from the rental provider platform; (See, Para. 51));
receiving, via one or more processors, an indication of a chosen vehicle rental branch, the chosen vehicle rental branch included in the set of vehicle rental branches. (Franke, the server 122 may provide a centralized point of contact among various other participating entities (e.g., a user 105, the insurer platform 110, the rental provider platform 112, the repair provider platform 114, other service providers 116, and so on). For example, the user interface 138 may include an interface for an owner of the item being scanned (e.g., a vehicle) to schedule a rental during repairs, e.g., from the rental provider platform; (See, Para. 51)); The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71)).
Claims 9 and 19:
Franke teaches:
wherein determining the set of vehicle rental branches includes searching, via one or more processors, for rental branches via a search interface. (Franke, The rental provider platform 112 may be operated by a vehicle rental service or rental car agency that uses the system 100. This may provide an interactive interface for scheduling a car rental while a vehicle is being repaired; (See, Para. 71)).
Claim 10:
Franke teaches:
wherein the trained machine learning model is trained further using vehicle feature information relating to years, makes, and/or models. (Franke, report 146 may thus include a damage report for a scanned vehicle based on any number of independent digital surface representations, along with vehicle identification information (e.g., a vehicle identification number, model, make, etc.), a scan of a vehicle (which may include multiple independent digital surface representations acquired using different scanning techniques), and an analysis of the scan covering, e.g., locations and sizes of defects. (See, Para. 8, 56, 139, 175-176)); The item 306 may include one or more panels 320. The panels 320 may generally include sections of an item 306, e.g., sections of a vehicle. The sections may correspond to a particular vehicle type (e.g., automobile) and classification (e.g., sedan, station wagon, sport utility vehicle, convertible, pickup truck, van, and so on) (See, Para.113)).
Claim 11:
Franke teaches:
wherein the trained machine learning model is trained further using vehicle classes. (Franke, The item 306 may include one or more panels 320. The panels 320 may generally include sections of an item 306, e.g., sections of a vehicle. The sections may correspond to a particular vehicle type (e.g., automobile) and classification (e.g., sedan, station wagon, sport utility vehicle, convertible, pickup truck, van, and so on) (See, Para. 56, 113, 139, 175-176)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Weinstock (U.S. Patent Pub. No. US-2014/0052478-A1) “Method and System for Marketing Vehicles for Sale or Lease to Replace Totaled Vehicles”
Hanson (U.S. Patent No. US-10,354,230-B1) “Automatic determination of rental car term associated with a vehicle collision repair incident”
Ethington (U.S. Patent Application Publication No. US-2019/0156298-A1) “Machine learning based repair forecasting”
Hanson (U.S. Patent No. US-10,176,532-B1) “Insurance claim capitation and predictive payment modeling”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693
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