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 .
Prior art cited in this office action:
Westhues et al. (US 20210027387 A1, hereinafter “Westhues”)
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 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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Westhues et al. (US 20210027387 A1, hereinafter “Westhues”).
Regarding claims 1, 8 and 15:
Westhues teaches A system (Westhues Abstract, [0003], [0006]-[0009] where Westhues teaches system, method and non-transitory machine-readable storage media), comprising:
one or more hardware processors (Westhues Abstract, [0003], [0006]-[0009], [0045], fig. 2 where Westhues teaches corresponding hardware processor to perform the method; and
one or more non-transitory machine-readable storage media encoded with instructions
that, when executed by the one or more hardware processors (Westhues Abstract, [0003], [0006]-[0009], [0045], fig. 2), cause the system to perform
operations comprising:
obtaining images and attributes of a damaged vehicle that has been damaged in a
collision event (Westhues [0034], where Westhues teaches in an embodiment, retrieving/receiving information from the historical data 208 may include analyzing image and/or video data to extract information (e.g., to identify damage and/or injury). For example, in an embodiment, instructions executing in claim analysis engine 204 may analyze a claim in claim 210-1 thorough 210-n to determine whether the claim includes an image of a damaged vehicle);
providing the obtained images and attributes of the damaged vehicle as first inference
input to a trained computer vision machine learning model, wherein responsive to the first
inference input, the computer vision machine learning model provides a first output comprising
a predicted severity class indicating a severity of the physical damage sustained by the
damaged vehicle during the collision event, wherein the predicted class of severity is one of
multiple possible predicted classes of severity, wherein each of the multiple possible predicted
classes of severity indicates a respective severity of physical damage sustained by the damaged
vehicle during the collision event, wherein the trained computer vision machine learning model
has been trained with first training data comprising historical correspondences between
examples of the first inference input and corresponding examples of the first output (Westhues [0023]-[0025], [0034], [0039]fig. 2, where Westhues teaches This set of tiers may be an injury segment corresponding to one or more levels of customer service handling, wherein each level is responsible for handling progressively more severe claims. For example, a first tier may handle claims limited to minor property damage, a second tier may handle claims including property damage with an estimated repair value less than $1000, a third tier including property damage with an estimated repair value greater than $100 but less than $5000, and so forth. A tier in a-z may handle claims that include any combination of estimated repair values, and personal injury wherein the injured person was able to be treated on an outpatient basis. Another tier in a-z may handle injury claims wherein the injured person was treated on an inpatient basis for one day or less. Another tier may handle an injury wherein the injured person was treated for a week or less. Another tier may handle an injury in which a dismemberment, paralysis, and/or death occurred, and so on);
generating second inference input for a trained regression machine learning model, the
second inference input comprising the predicted severity class, damaged vehicle physical
damage claim data related to the damaged vehicle and the collision event, and bodily injury
claim data related to an occupant of the damaged vehicle during the collision event (Westhues [0023]-[0025], [0034], [0039], [0049], fig. 2, where Westhues teaches for example, the operator of a 2018 Chevrolet Camaro may access the client device 302 to submit a loss report under the driver's collision insurance policy related to damage to the vehicle sustained when the driver was rear-ended at a red light;
providing the second inference input to the trained regression machine learning model,
wherein responsive to the second inference input, the trained regression machine learning
model provides a second output comprising a predicted frequency and/or duration of medical
treatments to treat bodily injury sustained by the occupant during the collision event, wherein
the trained regression machine learning model has been trained with second training data
comprising historical correspondences between examples of the second inference input and
corresponding examples of the second output (Westhues [0023]-[0025], [0034], [0039], [0049], fig. 2, where Westhues teaches the trained ML model may output an indication of severity which may include a flat and/or hierarchical set of tiers and/or sub-tiers, or a numeric representation of severity. For example, the ML model may output a set of tiers a-z wherein tiers a-z may include any number of tiers, and wherein each tier represents a tier in tier set 106. This set of tiers may be an injury segment corresponding to one or more levels of customer service handling, wherein each level is responsible for handling progressively more severe claims. For example, a first tier may handle claims limited to minor property damage, a second tier may handle claims including property damage with an estimated repair value less than $1000, a third tier including property damage with an estimated repair value greater than $100 but less than $5000, and so forth. A tier in a-z may handle claims that include any combination of estimated repair values, and personal injury wherein the injured person was able to be treated on an outpatient basis. Another tier in a-z may handle injury claims wherein the injured person was treated on an inpatient basis for one day or less. Another tier may handle an injury wherein the injured person was treated for a week or less. Another tier may handle an injury in which a dismemberment, paralysis, and/or death occurred, and so on.; and
providing the predicted frequency and/or duration of medical treatments to treat the
bodily injury sustained by the occupant during the collision event to an analyst for use in
evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision
event (Westhues [0023]-[0025], [0034], [0039], [0049], fig. 2, where Westhues teaches A tier in a-z may handle claims that include any combination of estimated repair values, and personal injury wherein the injured person was able to be treated on an outpatient basis. Another tier in a-z may handle injury claims wherein the injured person was treated on an inpatient basis for one day or less. Another tier may handle an injury wherein the injured person was treated for a week or less. Another tier may handle an injury in which a dismemberment, paralysis, and/or death occurred, and so on).
Westhues fails to explicitly teach a first learning model providing a first output and a second learning model receiving the first output and other input such as claim data related to the vehicle damage to determine the duration of treatment.
However, Westhues teaches the system is to analyze the severity of an injury claim and assigning and/or routing the claim to a set of tiers. a machine learning model using historical claim data to determine an injury claim severity; receiving, via a processor, an auto accident loss report; analyzing the loss report using the trained machine learning model to determine a severity of an injury; determining, based on the severity of the injury, an injury segment; and storing, via a processor, an indication of the injury segment (Westhues [0007]-[0009], [0024]-[0025]). In other words, the machine learning is trained using historical data and use the output data to determine the accuracy of the claim. using the predicted information generated to assess the severity of the injury and expected time of recovery by including age information whether they are passenger or driver and/or whether the airbag was deployed or not.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to use a first machine learning to predict the severity of the damage caused by an accident and using the output of the severity of the damage with extra information (age or status, etc.) to predict the duration of medical treatment, in order to control cost that needs to be spend for treatment.
Regarding claims 2, 9 and 16:
Westhues teaches wherein:
the second output of the trained regression machine learning model includes a
correlation indicator indicating a degree of correlation between the predicted class of severity
of physical damage sustained by the damaged vehicle during the collision event and the
predicted frequency and/or duration of medical treatments to treat the bodily injury sustained
by the occupant during the collision event (Westhues [0025], [0049], [0058]).
Regarding claims 3, 10 and 17:
Westhues teaches wherein the class of severity of physical damage sustained by the damaged vehicle during the collision event indicates at least one of:
a type of the physical damage sustained by the damaged vehicle during the collision
event;
whether the damage sustained by the damaged vehicle during the collision event
represents a partial loss of the damaged vehicle or a total loss of the damaged vehicle (Westhues [0025], [0049], [0058]).
Regarding claims 4, 11 and 18:
Westhues teaches wherein the attributes of the damaged vehicle comprise
at least one of:
a vehicle identification number (VIN) of the damaged vehicle;
make of the damaged vehicle;
submodel of the damaged vehicle;
model of the damaged vehicle;
year or age of the damaged vehicle;
mileage of the damaged vehicle;
transmission parameters of a transmission of the damaged vehicle; and engine and/or motor parameters of an engine and/or motor of the damaged vehicle (Westhues [0024], [0028], [0055], fig. 4).
Regarding claims 5, 12 and 19:
Westhues teaches the operations further comprising:
providing occupant metadata as part of the second inference input to the trained regression machine learning model, wherein the occupant metadata comprises at least one of:
an age of the occupant of the damaged vehicle;
a height of the occupant of the damaged vehicle;
a weight of the occupant of the damaged vehicle;
a gender of the occupant of the damaged vehicle; and
a role of the occupant of the damaged vehicle in operating the damaged vehicle (Westhues [0020],[0024], [0028], [0030],[0055]-[0056], fig. 4).
Regarding claims 6, 13 and 20:
Westhues teaches the operations further comprising:
providing collision metadata as part of the second inference input to the trained regression machine learning model, wherein the collision metadata comprises at least one of:
an indicator of the seat in which the occupant was seated in the damaged vehicle during
the collision event;
an indicator of seatbelt usage for the seat in which the occupant was seated in the
damaged vehicle during the collision event;
airbag status for the seat in which the occupant was seated in the damaged vehicle
during the collision event; and
a change in velocity of the damaged vehicle during the collision event (Westhues [0025], [0049], [0058]).
Regarding claims 7, 14 and 21:
Westhues teaches wherein:
the second output further comprises a predicted type of the medical treatments; and
the operations further comprise: providing the predicted type of the medical treatments
to the adjuster (Westhues [0022], [0051], [0063]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WEDNEL CADEAU whose telephone number is (571)270-7843. The examiner can normally be reached Mon-Fri 9:00-5:00.
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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 August 5, 2026
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