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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/05/2024 have being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 9-12, and 14-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Franke (US 2017/0148102).
As per claims 1 and 17, Franke teaches, a damage assessment method and system, comprising: recognizing, by processing circuitry, a region of interest in an image that has a target object displayed therein, wherein the region of interest corresponds to a portion of the target object in the image that is inflicted with a visible damage (Franke, ¶[0010] “ The user interface may include one or more of an interface for providing status updates to the owner on the damage assessment and repair process and an interface for the vehicle owner to upload data and images.” And ¶[0162] “As shown in step 606, the method 600 may include analyzing the scan to detect defects in each one of the plurality of panels.” Each panel is a region of interest and detects defects), and wherein the image is captured by an imaging device at a first-time instance (Franke, ¶ [0159] “As shown in step 601, the method 600 may include pre-scanning a vehicle before a user leases or rents the vehicle using any of the scanning techniques described above. This step can usefully establish a baseline for existing damage at the time the rental is initiated.” This would be the first time instance scan); determining, by the processing circuitry, a first plurality of feature values for a plurality of image features based on the recognized region of interest (Franke, ¶ [0153] “As shown in step 504, the method 500 may include analyzing the scan to detect defects in each one of the plurality of panels. This may, for example, include detection of hail damage or other types of vehicle damage, as well as characterization of the size, location, depth, and other features of such dents.” features of such dents are the features); retrieving, by the processing circuitry, from a memory, time-series information that indicates a usage pattern of the target object (Franke, ¶[007] “A scanning system creates an objective damage assessment of an item such as an automotive vehicle that can be used in subsequent assessment,” this would represent being able to have the usage pattern of the object which would be the vehicle in fig.3, and the first instance would be the first since there are subsequent assessments and ¶[007] “repair, audit, and insurance processes. In general, the scanning system may scan any relevant portions of an item” relevant portions of the item are the region of interest and fig.6 is a time series 601 scan and then 604 returned vehicle is the time series); determining, by the processing circuitry, a second plurality of feature values for a plurality of usage features based on the retrieved time-series information (Franke, ¶[0036] “Further, although the following description emphasizes devices, systems, and methods for damage assessment in vehicles (and then further applying damage information), the implementations may also or instead be used for obtaining a current state of a vehicle. This may be utilized for establishing a baseline, for pre-scanning purposes, for detecting damage after an accident or other event as described herein, for detecting the quality of a repair, for detecting mechanical flaws, for detecting cosmetic flaws, for detecting structural flaws, for detecting manufacturing flaws, and so forth. Thus, unless explicitly stated to the contrary or otherwise clear from the context, the term “damage” when used in the context of detection, identification, and classification, shall also or instead be referring to a state of the vehicle being assessed.” And fig.6 and description thereof, the usage features would be the second scan as the vehicle has been used ); providing, by the processing circuitry, the first plurality of feature values and the second plurality of feature values as input to a trained first classifier (Franke, ¶[0174] “[0174] Features of one or more surfaces (e.g., from a single vehicle or from a plurality of vehicles) may be gathered for use in the creation of feature vectors that describe a surface. In an aspect, 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.” Therefore the images are using to train to be able to identify the first and second features ); and predicting, by the processing circuitry, a true age of the visible damage based on a first classification output of the trained first classifier for the first plurality of feature values and the second plurality of feature values (Franke, ¶[0174] “For example, a hail dent is generally a circle or an ellipsis, and information about the shape may correspondingly be used in the classification process. In an aspect, certain geometric features can be aggregated to reach a determination. Thus, the system may use the geometry, surface normals, or any other derived, descriptive information about a surface in order to classify surface features as dents.” And fig.6 detect defects 606 and this would be the true age of when then the vehicle comes back of those feature damages), wherein the true age indicates a time duration between the first-time instance and a historical time instance at which the target object was inflicted with the visible damage (Franke, fig.6 by comparing 601 pre scan and then after receiving being able to detect and train with these defects, then it is the first time instance and a historical instance at which the target object the vehicle was inflicted with visible damage).
As per claim 2, Franke teaches, the damage assessment method of claim 1, further comprising receiving, by the processing circuitry, the image of the target object from a memory or over a communication network (Franke, ¶[0053] “The analysis facility 108 may include a processor 140 and a memory 142 configured to apply algorithms 144 to perform the functions described herein.” This represents a memory and ¶[008] “and a remote resource coupled in a communicating relationship with the scanning system through a data network” represents having a network ).
As per claims 3 and 18, Franke teaches, the damage assessment method of claim 1, further comprising: providing, by the processing circuitry, the first plurality of feature values as input to a trained second classifier; and classifying, by the processing circuitry, the region of interest into a first material category of a plurality of material categories based on a second classification output of the trained second classifier for the first plurality of feature values, wherein the plurality of material categories includes metal, plastic, and fabric (Franke, ¶ [0084] In one aspect, the models 128 may include a three-dimensional model of a vehicle arranged, for example, into various vehicle parts. For example, different surface types may include paint material, plastic trim, headlights, vehicle foils, etc. Because these various surfaces might respond differently to various scanning techniques, an awareness of the location and optical properties of these different regions may be usefully incorporated into a three-dimensional model 128 for use in the scanning systems 104 contemplated herein. Similarly, properties of these regions, such as material and the like, may be included in the models 128, e.g., for use in calculating repair costs or defining a repair method. During a physical scan, an identifier such as a tag or the like may be added to various panels to assist in discriminating among various panels and parts during downstream processing.” The different materials represent metal, plastic and/or fabric depending on the damage type).
As per claim 4, Franke teaches, the damage assessment method of claim 1, further comprising: providing, by the processing circuitry, the first plurality of feature values as input to a trained third classifier; and classifying, by the processing circuitry, the region of interest into a first damage category of a plurality of damage categories based on a third classification output of the trained third classifier for the first plurality of feature values, wherein the plurality of damage categories includes a crack, a scratch, and a dent (Franke, ¶[004] “Typically, the inspectors will document any previous damage, perform quantifying steps such as counting and classifying current damaged areas (e.g., dents and scratches),” represents scratches and dents, ¶[0112] “alternative assessment techniques such as analysis for cracks, fractures, “ represents cracks and would all be classified ¶[0036] “and classification, shall also or instead be referring to a state of the vehicle being assessed.” Represents the classification thereof).
As per claim 5, Franke teaches, the damage assessment method of claim 1, further comprising: providing, by the processing circuitry, the first plurality of feature values as input to a trained fourth classifier; and classifying, by the processing circuitry, the region of interest into a first intensity category of a plurality of intensity categories based on a fourth classification output of the trained fourth classifier for the first plurality of feature values, wherein the plurality of intensity categories includes a high intensity and a low intensity (Franke, ¶[0054] “ The data concerning damage may include information regarding the detection and classification of the damage, including without limitation, location, size, repair cost, and so forth.” The multiple classifications represent at least 4 different classifications and well as different intensities in terms of costs ect).
As per claims 9 and 16, Franke teaches, the damage assessment method of claim 1, wherein the usage pattern of the target object indicates one or more external and environmental conditions to which the target object has been exposed during a use of the target object and one or more object handling attributes of the target object, and wherein the time-series information includes time-series values of each of the one or more external and environmental conditions and the one or more object handling attributes (Franke, ¶[006] “A scanning system captures a digital surface representation of an automotive vehicle that can be used to objectively assess hail damage and estimate repair costs.” The hail is the environmental conditions).
As per claim 10, Franke teaches, the damage assessment method of claim 1, wherein the plurality of usage features includes a temperature, humidity, rain, an altitude, and a friction coefficient to which the target object has been exposed (Franke, ¶[004] “Inspections are thus very subjective (i.e., based on the particular inspector or other factors such as the environment in which the damage is viewed),” and the environment would include temperature, humidity, rain, an altitude, and a friction coefficient based on the hail for example).
As per claim 11, Franke teaches, the damage assessment method of claim 1, wherein the plurality of usage features includes a count of different users that have used the target object, a count of washing incidents associated with the target object, a count of maintenance and repair incidents of the target object, and a frequency of breakdown of the target object (Franke, fig.6 the pre-scan before lease and then coming back would then keep track of all these things).
As per claim 12, Franke teaches, the damage assessment method of claim 1, wherein the target object is a vehicle (Franke, fig.6 601 pre-scan of a vehicle).
As per claim 14, Franke teaches, the damage assessment method of claim 1, wherein the plurality of usage features further includes a count of historical visible damages inflicted on the target object, a position of each historical visible damage, and a true age of each historical visible damage (Franke, ¶ [0013] “The scan may capture three-dimensional surface data using at least one of optical techniques, mechanical techniques, and acoustic techniques. The status report may include one or more of a vehicle mileage, a repair history, an accident history, and a visual inspection report.” This represents a position of each historical visible damage, and a true age of each historical visible damage).
As per claim 15, Franke teaches, a method, comprising: receiving, by processing circuitry, time-series image data of at least one test vehicle, wherein each image in the time-series image data targets a portion of the test vehicle that is inflicted with a visible damage (Franke, ¶[0010] “ The user interface may include one or more of an interface for providing status updates to the owner on the damage assessment and repair process and an interface for the vehicle owner to upload data and images.” And ¶[0162] “As shown in step 606, the method 600 may include analyzing the scan to detect defects in each one of the plurality of panels.” Each panel is a region of interest and detects defects, and fig.6 shows before and after scans which would represent time-series and there if a test vehicle at some point in the method/system since ¶ [0174] “Features of one or more surfaces (e.g., from a single vehicle or from a plurality of vehicles) may be gathered for use in the creation of feature vectors that describe a surface. In an aspect, 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.” Therefore the images are using to train to be able to identify the first and second features” and there is training), and wherein the time-series image data is received for a first-time duration that begins from a time instance of infliction of the visible damage on the test vehicle (Franke, ¶[0159] “As shown in step 601, the method 600 may include pre-scanning a vehicle before a user leases or rents the vehicle using any of the scanning techniques described above. This step can usefully establish a baseline for existing damage at the time the rental is initiated.” This would be the first time instance scan and it is a test vehicle because there are instances); determining, by the processing circuitry, for each image in the time-series image data, a first plurality of feature values for a plurality of image features (Franke, ¶ [0153] “As shown in step 504, the method 500 may include analyzing the scan to detect defects in each one of the plurality of panels. This may, for example, include detection of hail damage or other types of vehicle damage, as well as characterization of the size, location, depth, and other features of such dents.” features of such dents are the features); retrieving, by the processing circuitry, from a memory, first time-series information that indicates a usage pattern of the test vehicle during the first-time duration ((Franke, ¶[007] “A scanning system creates an objective damage assessment of an item such as an automotive vehicle that can be used in subsequent assessment,” this would represent being able to have the usage pattern of the object which would be the vehicle in fig.3, and the first instance would be the first since there are subsequent assessments and ¶[007] “repair, audit, and insurance processes. In general, the scanning system may scan any relevant portions of an item” relevant portions of the item are the region of interest and fig.6 is a time series 601 scan and then 604 returned vehicle is the time series); determining, by the processing circuitry, a second plurality of feature values for a plurality of usage features based on the retrieved first time-series information, wherein the second plurality of feature values is determined with respect to each image in the time-series image data(Franke, ¶[0174] “[0174] Features of one or more surfaces (e.g., from a single vehicle or from a plurality of vehicles) may be gathered for use in the creation of feature vectors that describe a surface. In an aspect, 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.” Therefore, the images are using to train to be able to identify the first and second features. And ¶ [0174] “For example, a hail dent is generally a circle or an ellipsis, and information about the shape may correspondingly be used in the classification process. In an aspect, certain geometric features can be aggregated to reach a determination.” Represents usage features); and training, by the processing circuitry, a classifier using the first plurality of feature values and the second plurality of feature values to learn a relationship between a true age of the visible damage (Franke, ¶ [0174] “For example, a hail dent is generally a circle or an ellipsis, and information about the shape may correspondingly be used in the classification process. In an aspect, certain geometric features can be aggregated to reach a determination. Thus, the system may use the geometry, surface normals, or any other derived, descriptive information about a surface in order to classify surface features as dents.” And fig.6 detect defects 606 and this would be the true age of when then the vehicle comes back of those feature damages), the first plurality of feature values, and the second plurality of feature values, wherein the trained classifier is used to predict a true age of a visible damage inflicted on a target vehicle based on an image that captures the visible damage and second time-series information that indicates a usage pattern of the target vehicle (Franke, ¶[0142] “In one aspect, predictive analytics may be applied to infer damage that is not externally visible, but that is implied by an externally observable condition of the vehicle. For example, if the fender of a car is displaced by ten inches, then there are likely other parts of the vehicle that are also damaged. The externally observable physical state may be measured based on an aggregate three-dimensional scan, explicit linear measurements of a vehicle (e.g., length, width, etc.), or any other suitable scan or digital surface representation. Similarly, the system need not look behind a panel, but can predict damage based on a history of similar symptoms.” This represents the predicting).
Allowable Subject Matter
Claims 6-8, 13, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim 6 the remainder useful life of the vehicle was not found in the prior art taking into account the rest of the factors of claim 5 for example. Claim 7 all the elements combined such as texture of the region of interest at pixel level was not found in the prior art. Claim 13 all of the features being predicted was not found in the prior art. Claims 19-20 have similar subject matter addressed above.
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/