DETAILED ACTION
1. The present application is being examined under the pre-AIA first to invent provisions.
This Office action is in response to Applicant’s communication (RCE) filed on July 13, 2026. Amendments to claims 1 and 10, cancellation of claims 9 and 18-20 and addition of new claims 21-24 have been entered. Claims 1-8, 10-17 and 21-24 are pending, and have been examined. The Examiner would like to note that the Status identifier of claim 10 is incorrect. The Double Patenting rejections are withdrawn in view of the cancelled claims. The statement of reasons for the indication of allowable subject matter (over prior art) was already discussed in the Office action mailed on November 12, 2025 and hence to repeated here. The rejections and response to arguments are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-8, 10-17 and 21-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) a computing system and a non-transitory computer readable medium for determining a repair estimate for the damage to the vehicle (in the context of insurance), which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice such as insurance, and also commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, claim 1 is directed to a computing system.
Step 2A – Prong One: The limitations of “A computing system comprising:
one or more processors; and
one or more storage devices that store instruction code that, when executed by the one or more processors, causes the computing system to perform operations comprising:
causing a remote user device to display instructions that indicate how a plurality of acceptable images of a vehicle, and for determining by a machine learning algorithm configured to determine a severity of damage to a vehicle and a cost to repair damage, a severity of damage to a vehicle and a cost to repair damage to the vehicle, should be captured, wherein receiving the plurality of acceptable images comprises iteratively, and until a sufficient quantity of acceptable images is received:
receiving, via the remote user device, one or more images, wherein at least one of the one or more images corresponds to a three-dimensional image;
automatically processing image data of each of the one or more images to determine a blurriness of each image, and automatically determining, based on the determined blurriness, whether each of the one or more images is acceptable for input to the machine learning algorithm;
adding acceptable images of the one or more images to the plurality of acceptable images; and
after determining that one or more of the one or more images are unacceptable images, causing the remote user device to display instructions that indicate how to capture images related to damage to the vehicle;
after receiving the sufficient quantity of acceptable images:
applying object recognition algorithms to image data of the plurality of acceptable images to compare coordinate data of points on the vehicle surface depicted in the plurality of acceptable images against reference image data of an undamaged vehicle of a same make and model;
based on the comparison, identifying and locating one or more damaged areas on the vehicle surface; and
determining damage information comprising a location of damage on the vehicle and an indication of severity of damage on the vehicle based on the identified and located one or more damaged areas; and
determining a repair estimate for the damage to the vehicle” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the category of “Certain Methods of organizing human activity” such as fundamental economic practice such as insurance, as well as commercial or legal interactions including agreements.
Determining a repair estimate for the damage to the vehicle (in the context of insurance) is a fundamental economic practice such as insurance. Determining a repair estimate for the damage to the vehicle is also a form of fulfilling/resolving agreements in the context of an insurance policy and hence a form of commercial or legal interactions.
That is, other than, a computing system comprising one or more processors one or more storage devices; a remote user device, machine learning algorithm, and object recognition algorithms, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of a computing system comprising one or more processors one or more storage devices; a remote user device, a machine learning algorithm, and object recognition algorithms to perform all the steps. A plain reading of Figures 1-8 and descriptions in the associated paragraphs of the Specification reveals that the computing system comprising one or more processors one or more storage devices may be a suitably programmed generic system with suitably programmed generic processors and suitably programmed generic storage devices. The remote user device may be a generic user device such as personal computers, server computers, hand-held or laptop devices etc. The machine learning algorithm and the object recognition algorithms are broadly interpreted to include generic software suitably programmed to perform their respective functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claim 1 is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claims 1, 10 and 19 are not patent eligible.
Dependent claims 2-8, 11-17 and 21-24 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2-4, and 11-13 the steps “wherein the instructions indicating how to capture the plurality of images comprise an instruction to capture an image of a portion of the vehicle”, “wherein the instructions indicating how to capture the plurality of images comprise an instruction to capture an image of damage to the vehicle”, and “wherein the instructions indicating how to capture the plurality of images comprise an instruction to capture an image of a vehicle identification number (VIN) of the vehicle” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 5 and 14, the step “further comprising determining, based on the damage information and using machine learning, whether an area adjacent to the location of damage on the vehicle will require refinishing” under the broadest reasonable interpretation, is a further refinement of methods of organizing human activity because this step describes an intermediate step of the underlying process.
In claims 6 and 15, the step “wherein the instruction code causes the computing system to perform operations comprising:
receiving a text description of damage to the vehicle” under the broadest reasonable interpretation, is a further refinement of methods of organizing human activity because this step describes the data used in the underlying process.
In claims 7-8 and 16-17, the steps “wherein determining the repair estimate comprises comparing the determined location of damage and severity of damage to a database of prior vehicle repair costs”, and “further comprising determining a confidence factor for the comparison of the determined location of damage and severity of damage to the database of prior vehicle repair costs” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 21 and 23, the steps of “processing the at least one three-dimensional image of the one or more images to generate depth data representing a depth of points on the surface of the vehicle depicted in the three-dimensional image; and
wherein the damage information is further determined based on the generated depth data” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 22 and 24, the steps “wherein the instruction code further causes the computing system to perform operations comprising:
analyzing the generated depth data to determine a slope of depth values at one or more identified points of interest on the vehicle surface; and
classifying a type of damage at each identified point of interest based on the determined slope, wherein a high slope value indicates a deep rounded damage type and a low slope value indicates a shallow damage type” are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Response to Arguments
4. In response to Applicants arguments on pages 8-26 of the Applicant’s remarks (filed on July 1, 2026) that the claims are patent-eligible under 35 USC 101 when considered under MPEP 2106, the Examiner respectfully disagrees.
The fact that the claims are Patent-Ineligible when considered under the MPEP 2106 has already been addressed in the rejection and hence not all the details of the rejection are repeated here.
Response to Applicants’ arguments regarding Step 2A – Prong one and Prong two:
The claim(s) recite(s) a computing system and a computer readable medium for determining a repair estimate for the damage to the vehicle (in the context of insurance), which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice such as insurance, and also commercial or legal interactions including agreements as discussed in the rejection.
Abstract ideas can be characterized at different levels of abstraction. (See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) (“An abstract idea can generally be described at different levels of abstraction.”)). The Examiner does not see the parallel between the Applicant’s claims and those in Enfish and/or McRO. In the instant case, all the steps of the claim have been considered in arriving at the overall abstract idea of a computing system and a computer readable medium for determining a repair estimate for the damage to the vehicle (in the context of insurance).
According to MPEP 2106, limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The claims only recite the additional elements of a computing system comprising one or more processors one or more storage devices; a remote user device, a machine learning algorithm, and object recognition algorithms to perform all the steps. A plain reading of Figures 1-8 and descriptions in the associated paragraphs of the Specification reveals that the computing system comprising one or more processors one or more storage devices may be a suitably programmed generic system with suitably programmed generic processors and suitably programmed generic storage devices. The remote user device may be a generic user device such as personal computers, server computers, hand-held or laptop devices etc. The machine learning algorithm and the object recognition algorithms are broadly interpreted to include generic software suitably programmed to perform their respective functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims are directed to an abstract idea.
The claimed steps including those recited on pages 12-14 of the remarks such as “automatically processing image data of each received image to determine a blurriness of each image; automatically determining based on the determined blurriness whether each image is acceptable for input to the machine learning algorithm; dynamically generating corrective image capture instructions when images fail the automated acceptability determination; iterating this pipeline until a sufficient quantity of acceptable images is received; applying object recognition algorithms to the image data of the acceptable images to compare coordinate data of points on the vehicle surface against reference image data of an undamaged vehicle of the same make and model; identifying and locating one or more damaged areas based on that comparison; and determining damage information based on the identified and located damaged areas….. image data is automatically processed to generate a blurriness determination, that determination is automatically compared against a predefined criterion to assess acceptability for machine learning algorithm input, corrective instructions are dynamically generated based on the automated determination, and object recognition algorithms are applied to coordinate data of the acceptable images compared against reference vehicle data to identify and locate damaged areas ….. automated image data processing for blurriness determination, automated acceptability assessment for machine learning input, dynamic corrective feedback instruction generation, iterative pipeline operation until a defined quality threshold is met, object recognition algorithm application to coordinate data, coordinate comparison against reference vehicle image data, and damaged area identification and localization” are the intermediate steps in arriving at the overall abstract of determining a repair estimate for the damage to the vehicle (in the context of insurance), using the additional elements as tools in their normal capacity.
The claimed limitations of “automated image data processing for blurriness determination, automated acceptability assessment for machine learning input, dynamic corrective feedback instruction generation, iterative pipeline operation until a defined quality threshold is met, object recognition algorithm application to coordinate data, coordinate comparison against reference vehicle image data, and damaged area identification and localization”, may be characterized as an improvement in the abstract idea of determining a repair estimate for the damage to the vehicle (in the context of insurance) using the additional elements as tools in their ordinary capacity. An improvement in abstract idea is still abstract (SAP America v. Investpic *2-3 (“We may assume that the techniques claimed are “groundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Association for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); accord buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“A claim for a new abstract idea is still an abstract idea). As discussed in the rejection, the additional elements (identified in the rejection) are suitably programmed generic computer components used to apply the abstract idea. It does not involve any improvements to another technology, technical field, or improvements to the functioning of the computer itself. The Examiner does not see the parallel between the Applicant’s claims and those of BASCOM. Therefore, the Applicant’s arguments are not persuasive.
Response to Applicants’ arguments regarding Step 2B:
As discussed in the rejection, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified in the rejection) to perform the claimed steps, amount to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible.
The claimed steps including those recited on pages 24-26 such as “receiving image data from a remote user device including at least one three-dimensional image; automatically processing image data to determine blurriness; automatically determining acceptability for machine learning algorithm input based on the determined blurriness; dynamically generating corrective feedback instructions when images fail the automated acceptability determination; iterating the pipeline until a sufficient quantity of acceptable images is received; applying object recognition algorithms to coordinate data of vehicle surface points in the acceptable images; comparing that coordinate data against reference image data of an undamaged vehicle of the same make and model; identifying and locating one or more damaged areas from the comparison; determining damage information based on the identified and located damaged areas; and determining a repair estimate from the damage information ….. specific ordered combination (automated blurriness-based image quality filtering as a prerequisite gate for machine learning algorithm input, followed by object recognition algorithm application to coordinate data compared against reference vehicle image data for damage localization ….. specific automated image quality control pipeline that filters image data against defined blurriness criteria before it enters the machine learning algorithm, and by reciting a specific coordinate-based object recognition mechanism for damage localization from comparison against reference vehicle image data” may at best be characterized as an improvement in the abstract idea of determining a repair estimate for the damage to the vehicle (in the context of insurance) using the combination of additional elements as tools in their ordinary capacity. Hence, the claims do not recite significantly more than an abstract idea. Therefore, the Applicant’s arguments are not persuasive.
For these reasons and those discussed in the rejection, the rejections under 35 USC § 101 are maintained.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Konrardy; Blake et al. (US Pub. 2023/0267491 A1) discloses methods and systems for providing vehicle insurance discounts. For example, the method includes presenting, by a computing device, one or more questions to a user; receiving, from the user by the computing device, one or more responses to the one or more questions; determining, by the computing device, a first discount value for an insurance policy of a vehicle based at least in part upon the one or more responses; applying, by the computing device, the first discount value to the insurance policy of the vehicle for a predetermined period of time; collecting, by the computing device, driving data associated with one or more trips made by the vehicle during the predetermined period of time; analyzing, by the computing device, the driving data and the one or more responses; and determining, by the computing device, a first weight and a second weight based at least in part upon the driving data.
6. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
August 31, 2026
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