Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination under 37 CFR §1.114
2. A request for continued examination under 37 CFR §1.114, including the fee set forth in 37 CFR §1.17(e), was filed on March 23, 2026 in this application after final rejection. Since this application is eligible for continued examination under 37 CFR §1.114 and the fee set forth in 37 CFR §1.17(e) has been timely paid, the finality of the previous Office action dated October 23, 2025 has been withdrawn pursuant to 37 CFR §1.114 and the submission filed on March 23, 2026 has been entered. Claim X was cancelled by applicant. Claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1, 2, 4, 5, 7-12, 14, 15, and 17-20 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claim 20), a machine (claims 1, 2, 4, 5, and 7-10) and a manufacture (claims 11, 12, 14, 15, and 17-19). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
processing incident data corresponding to an incident involving a property of a user by guiding the user through a walk-through of the property, the user being prompted to capture and upload images of damage to the property during the walk-through;
automatically analyzing the images captured by [[the computing device of the user]];
based on the incident data, (i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for executing [[a learning model of the one or more learning models]], [[the learning model]] generating a loss prediction for the damage resulting from the incident involving the property of the user; and
communicating the total loss prediction to a policy provider.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., insurance -- here, determining a total loss prediction corresponding to an insurance claim).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., a computing system comprising: a network communication interface, one or more processors, and a memory storing instructions, a computing device of the user, an interface, and one or more learning models). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)).
Additionally, claim 1 recites the limitation, “training one or more learning models to make total loss predictions for properties damaged by incident, the training including (i) determining discrepancies in historical determinations between predicted loss amounts of properties involved in prior incidents and actual payout for those incidents; and (ii) tuning the one or more learning models based on the determined discrepancies.” This limitation simply states that a learning model is trained using discrepancies in historical predicted loss amounts of properties involved in prior incidents and actual payout for those incidents. However, the claims do not provide significant technical detail regarding how the model is trained and/or implemented to provide the desired output. Therefore, these limitations amount to no more than merely applying a generic machine learning model to implement the abstract idea on a computer.
Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Claim 1 also recites the following limitation:
wherein processing the incident data includes… (ii) receiving, using the interface, user inputs that identify damage to the property, the user inputs including images captured by the computing device of the user during the walkthrough;
This limitation merely states that the system receives input from the user including images captured during the walkthrough. However, the claims do not provide significant technical data regarding how the input is received from the user, and/or how the walkthrough is implemented to assist the user in the information gathering process. Therefore, this limitation amounts to no more than mere data gathering, which is a form of insignificant extra-solution activity (See MPEP 2106.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2024)).
Claim 1 also recites the following limitation:
wherein processing the incident data includes (i) providing, on a computing device of the user, an interface that includes a representation of the property involved in the incident.
This limitation merely states that the system presents an interface to the user in order to receive damage information. However, the claims do not provide significant technical data regarding how the interface is structured and/or how the damage information is input via the interface. Therefore, this limitation amounts to no more than merely outputting/displaying, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a computing system comprising: a network communication interface, one or more processors, and a memory storing instructions, a computing device of the user, a three-dimensional damage interface, and a machine learning model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP §2106.05 I.A.); (See also e.g., applicant’s Specification at least Paragraphs 244-250).
Additionally, the following limitation identified above as insignificant extra-solution activity (mere data gathering) has been revaluated in Step 2B:
wherein processing the incident data includes… (ii) receiving, using the interface, user inputs that identify damage to the property, the user inputs including images captured by the computing device of the user during the walk-through;
As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of mere data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Additionally, the following limitation identified above as insignificant extra-solution activity (merely outputting/displaying data) has been reevaluated under Step 2B:
wherein processing the incident data includes (i) providing, on a computing device of the user, an interface that includes a representation of the property involved in the incident.
In view of the requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of mere data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 11 and 20 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 11 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 11 and 1 is that claim 11 is drafted as a computer-readable medium rather than as a system. Similarly, as described above regarding claim 1, claim 11 recites generic computer components (e.g., a non-transitory computer readable medium storing instructions, one or more processors of a computing system, a computing device of the user, an interface, and one or more learning models) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 11, claim 11 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 20 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 20 and 1 is that claim 20 is drafted as a method rather than as a system. Similarly, as described above regarding claim 1, claim 20 recites generic computer components (e.g., one or more processors, a computing device of the user, an interface, and one or more learning models) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 20, claim 20 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 2, 4, 5, 7-10, 12, 14, 15, and 17-19 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claims 2 and 12 simply provides further definition to the “incident data” recited in claims 1 and 11. Simply stating that the incident data is received via a claim process and includes contextual data provided by a plurality of individuals does not provide any indication of an improvement to any technology or technological field. Rather, this merely defines the type of data collected by the system.
Claims 4 and 14 simply provides further definition to the “incident data” recited in claims 1 and 11. Simply stating that the incident data is received via a damage interface via a guided process does not provide any indication of an improvement to any technology or technological field. Rather, this amounts to no more than simply applying a generic user interface to facilitate the collection of the incident data.
Claims 5 and 15 simply provides further definition to the “guided content capture process” recited in claims 4 and 14. Simply stating that the walkthrough includes providing an outline to facilitate image alignment and capture does not provide any indication of an improvement to any technology or technological field. Rather, this amounts to no more than simply applying generic image capture technology. The claims do not provide significant technical detail regarding how the outline is implemented to facilitate the image capture process.
Claims 7 and 17 simply refine the abstract idea because they recite process steps (e.g., generating a ranked list of service providers based on various parameters) that falls under the category of organizing human activity as described above regarding claim 1.
Claims 8 and 18 simply refine the abstract idea because they recite process steps (e.g., providing the ranked list of service providers to the user) that falls under the category of organizing human activity as described above regarding claim 1. Additionally, merely stating that the list is received via a computing device of the user amounts to no more than applying generic computer components to implement the abstract idea on a computer.
Claims 9, 10, and 19 simply refine the abstract idea because they recite process steps (e.g., scheduling a repair service for the property) that falls under the category of organizing human activity as described above regarding claim 1. Additionally, simply stating that the scheduling is performed “automatically” does not amount to an improvement to any technology or technological field. The claims do not provide any technical detail regarding how the scheduling is automated.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Claim Rejections - 35 USC § 103
11. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
12. Claims 1, 4, 5, 11, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tomlinson (U.S. Patent No. 11120574) in view of Fields (U.S. Pre-Grant Publication No. 20220284484) and Cook (U.S. Pre-Grant Publication No. 20150356686).
Claim 1
Regarding Claim 1, Tomlinson teaches:
A computing system comprising: a network communication interface; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to perform operations comprising (See at least Col. 6, Line 27 0 Col. 7, Line 29: Describes a system for obtaining image data of a vehicle for automatic assessment of vehicle damage. The system comprises a communication network, a processing element, and a memory element):
processing incident data corresponding to an incident involving a property of a user by guiding the user through a walkthrough of the property, the user being prompted to capture and upload images of damage to the property during the walkthrough (See at least Col. 10, Line 31 – Col. 11, Line 19: The mobile application may guide the user through a process for capturing image data corresponding to damage done to their vehicle [i.e., incident data]. The mobile application may transmit the image data to the damage estimator computing device. Examiner's Note: While the embodiments described by Tomlinson relate to damage done to a vehicle, Tomlinson states that the mobile application may be applied to any kind of real or personal property [See Col. 4, Lines 34-45]);
wherein processing the incident data includes (i) providing, on a computing device of the user, an interface that includes a representation of the property involved in the incident, and (ii) receiving, using the interface, user inputs that identify damage to the property, the user inputs including images captured by the computing device of the user during the walk-through (See at least Col. 10, Line 31 – Col. 11, Line 19: The process of guiding the user in capturing the requested images of the vehicle includes displaying a series of interfaces which allow the user to input the damage information [See Figures 6-16]. The interfaces may display a "representation" of the user's vehicle which provides guidance regarding how to capture information associated with the vehicle. For example, the interface may display a representation of the vehicle indicating how to capture an image of the vehicle's VIN [See Figure 12]);
automatically analyzing the images captured by the computing device of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may analyze the images collected from the user to determine whether the images are acceptable for the damage assessment); and
based on the incident data, [[(i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for]] executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may determine whether the vehicle is repairable or not repairable [i.e., a total loss] and process a claim based on the determined level of damage. The damage estimator computing device may utilize a machine learning model to analyze the image data and estimate the repair costs [See Col. 21, Line 49 – Col. 22, Line 31]. Examiner's Note: Tomlinson does not explicitly teach that the machine learning model filters prior incidents which have similar damage in order to determine the loss prediction. However, this limitation is taught by Fields, as described below).
Regarding claim 1, Tomlinson does not explicitly teach, but Fields, however, does teach:
training one or more learning models to make total loss predictions for properties damaged by incident, the training including (i) determining discrepancies in historical determinations between predicted loss amounts of properties involved in prior incidents and actual payout for those incidents; and (ii) tuning the one or more learning models based on the determined discrepancies (See at least the Abstract: Describes a system for incorporating machine learning to assess damage to vehicles. The system may access actual damage information for the vehicle; determine differences between the actual damage and the assessed damage; and iterate the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy. The calculated damage may be in the form of a cost to repair the vehicle, and may be used to determine an insurance payout associated with the damage [See Paragraph 40]); and
based on the incident data, (i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Paragraphs 38 and 39: The system may receive data regarding vehicles with similar types of damage, and utilize a machine learning model to calculate damages based on the received data. In other words, the machine learning model "filters" instances of prior damage to identify incidents having similar damage to the vehicle currently being analyzed [Also see Paragraphs 45 and 46]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson and Fields in order to assist inspectors in measuring damage done to vehicles, and provide a system that provides an accurate evaluation of the damage (Fields: Paragraphs 4 and 5). The machine learning techniques described by Fields executes calculations more accurately and efficiently than a generic computing environment (Fields: Paragraph 29).
Regarding claim 1, the combination of Tomlinson and Fields does not explicitly teach, but Cook, however, does teach:
communicating the total loss prediction to a policy provider (See at least Paragraph 18: Describes a system for processing data associated with potential damage to a property. The property may correspond to a vehicle [See Paragraph 27]. The system may process sensor data to estimate an amount of damage that occurred to the property [i.e., a loss prediction], and then communicate damage data to an insurance provider. Examiner's Note: While Tomlinson teaches a process for determining a loss estimate, as described above, Tomlinson does not explicitly teach that the loss prediction is communicated to an insurance provider. Rather, the loss prediction is determined by the insurance provider. However, as described above, Cook describes a process for utilizing a third-party to determine the loss estimate, and provide the loss estimate to the insurance provider).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, and Cook in order to enable effective processing of any insurance-related functionalities (Cook: Paragraph 18).
Claim 4
Regarding Claim 4, Tomlinson teaches:
wherein processing the incident data includes implementing a guided content capture process to receive the images captured by the user during the walkthrough (See at least Col. 10, Lines 31-48: The user may launch the mobile application and view on the display a series of screens configured to guide the user in capturing the requested images of his/her vehicle).
Claim 5
Regarding Claim 5, Tomlinson teaches:
wherein the operations include: during the walkthrough, including, with the interface provided on the computing device of the user, an outline for at least one portion of the property to facilitate image alignment and capture (See at least Col. 10, Lines 31-48: The user may launch the mobile application and view on the display a series of screens configured to guide the user in capturing the requested images of his/her vehicle. The series of screens walk the user through a process for collection each of the required images [See Figures 5-16]. The mobile application may ask the user to capture one or more close up images of the damage to his/her vehicle [i.e., an outline for at least a portion of the property to facilitate image alignment and capture; See Col. 8, Line 55 – Col. 9, Line 2]).
Claim 11
Regarding Claim 11, Tomlinson teaches:
A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising (See at least Col. 6, Line 65 – Col. 7, Line 12: Describes a system for obtaining image data of a vehicle for automatic assessment of vehicle damage. The system comprises a computer-readable medium comprising instructions that are executed by a processing element):
processing incident data corresponding to an incident involving a property of a user by guiding the user through a walkthrough of the property, the user being prompted to capture and upload images of damage to the property during the walkthrough (See at least Col. 10, Line 31 – Col. 11, Line 19: The mobile application may guide the user through a process for capturing image data corresponding to damage done to their vehicle [i.e., incident data]. The mobile application may transmit the image data to the damage estimator computing device. Examiner's Note: While the embodiments described by Tomlinson relate to damage done to a vehicle, Tomlinson states that the mobile application may be applied to any kind of real or personal property [See Col. 4, Lines 34-45]);
wherein processing the incident data includes (i) providing, on a computing device of the user, an interface that includes a representation of the property involved in the incident, and (ii) receiving, using the interface, user inputs that identify damage to the property, the user inputs including images captured by the computing device of the user during the walkthrough (See at least Col. 10, Line 31 – Col. 11, Line 19: The process of guiding the user in capturing the requested images of the vehicle includes displaying a series of interfaces which allow the user to input the damage information [See Figures 6-16]. The interfaces may display a "representation" of the user's vehicle which provides guidance regarding how to capture information associated with the vehicle. For example, the interface may display a representation of the vehicle indicating how to capture an image of the vehicle's VIN [See Figure 12]);
automatically analyzing the images captured by the computing device of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may analyze the images collected from the user to determine whether the images are acceptable for the damage assessment); and
based on the incident data, [[(i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for]] executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may determine whether the vehicle is repairable or not repairable [i.e., a total loss] and process a claim based on the determined level of damage. The damage estimator computing device may utilize a machine learning model to analyze the image data and estimate the repair costs [See Col. 21, Line 49 – Col. 22, Line 31]. Examiner's Note: Tomlinson does not explicitly teach that the machine learning model filters prior incidents which have similar damage in order to determine the loss prediction. However, this limitation is taught by Fields, as described below).
Regarding claim 11, Tomlinson does not explicitly teach, but Fields, however, does teach:
training one or more learning models to make total loss predictions for properties damaged by incident, the training including (i) determining discrepancies in historical determinations between predicted loss amounts of properties involved in prior incidents and actual payout for those incidents; and (ii) tuning the one or more learning models based on the determined discrepancies (See at least the Abstract: Describes a system for incorporating machine learning to assess damage to vehicles. The system may access actual damage information for the vehicle; determine differences between the actual damage and the assessed damage; and iterate the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy. The calculated damage may be in the form of a cost to repair the vehicle, and may be used to determine an insurance payout associated with the damage [See Paragraph 40]); and
based on the incident data, (i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Paragraphs 38 and 39: The system may receive data regarding vehicles with similar types of damage, and utilize a machine learning model to calculate damages based on the received data. In other words, the machine learning model "filters" instances of prior damage to identify incidents having similar damage to the vehicle currently being analyzed [Also see Paragraphs 45 and 46]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson and Fields in order to assist inspectors in measuring damage done to vehicles, and provide a system that provides an accurate evaluation of the damage (Fields: Paragraphs 4 and 5). The machine learning techniques described by Fields executes calculations more accurately and efficiently than a generic computing environment (Fields: Paragraph 29).
Regarding claim 11, the combination of Tomlinson and Fields does not explicitly teach, but Cook, however, does teach:
communicating the total loss prediction to a policy provider (See at least Paragraph 18: Describes a system for processing data associated with potential damage to a property. The property may correspond to a vehicle [See Paragraph 27]. The system may process sensor data to estimate an amount of damage that occurred to the property [i.e., a loss prediction], and then communicate damage data to an insurance provider. Examiner's Note: While Tomlinson teaches a process for determining a loss estimate, as described above, Tomlinson does not explicitly teach that the loss prediction is communicated to an insurance provider. Rather, the loss prediction is determined by the insurance provider. However, as described above, Cook describes a process for utilizing a third-party to determine the loss estimate, and provide the loss estimate to the insurance provider).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, and Cook in order to enable effective processing of any insurance-related functionalities (Cook: Paragraph 18).
Claim 14
Regarding Claim 14, Tomlinson teaches:
wherein processing the incident data includes implementing a guided content capture process to receive the images captured by the user during the walkthrough (See at least Col. 10, Lines 31-48: The user may launch the mobile application and view on the display a series of screens configured to guide the user in capturing the requested images of his/her vehicle).
Claim 15
Regarding Claim 15, Tomlinson teaches:
wherein the operations include: during the walkthrough, including, with the interface provided on the computing device of the user, an outline for at least one portion of the property to facilitate image alignment and capture (See at least Col. 10, Lines 31-48: The user may launch the mobile application and view on the display a series of screens configured to guide the user in capturing the requested images of his/her vehicle. The series of screens walk the user through a process for collection each of the required images [See Figures 5-16]. The mobile application may ask the user to capture one or more close up images of the damage to his/her vehicle [i.e., an outline for at least a portion of the property to facilitate image alignment and capture; See Col. 8, Line 55 – Col. 9, Line 2]).
Claim 20
Regarding Claim 20, Tomlinson teaches:
A computer-implemented method of generating loss prediction, the method being performed by one or more processors and comprising (See at least Col. 6, Line 27 – Col. 7, Line 29: Describes a system for obtaining image data of a vehicle for automatic assessment of vehicle damage. The method may be implemented by a processing element):
processing incident data corresponding to an incident involving a property of a user by guiding the user through a walkthrough of the property, the user being prompted to capture and upload images of damage to the property during the walkthrough (See at least Col. 10, Line 31 – Col. 11, Line 19: The mobile application may guide the user through a process for capturing image data corresponding to damage done to their vehicle [i.e., incident data]. The mobile application may transmit the image data to the damage estimator computing device. Examiner's Note: While the embodiments described by Tomlinson relate to damage done to a vehicle, Tomlinson states that the mobile application may be applied to any kind of real or personal property [See Col. 4, Lines 34-45]);
wherein processing the incident data includes (i) providing, on a computing device of the user, an interface that includes a representation of the property involved in the incident, and (ii) receiving, using the interface, user inputs that identify damage to the property, the user inputs including images captured by the computing device of the user during the walkthrough (See at least Col. 10, Line 31 – Col. 11, Line 19: The process of guiding the user in capturing the requested images of the vehicle includes displaying a series of interfaces which allow the user to input the damage information [See Figures 6-16]. The interfaces may display a "representation" of the user's vehicle which provides guidance regarding how to capture information associated with the vehicle. For example, the interface may display a representation of the vehicle indicating how to capture an image of the vehicle's VIN [See Figure 12]);
automatically analyzing the images captured by the computing device of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may analyze the images collected from the user to determine whether the images are acceptable for the damage assessment); and
based on the incident data, [[(i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for]] executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Col. 11, Lines 4-43: The damage estimator computing device may determine whether the vehicle is repairable or not repairable [i.e., a total loss] and process a claim based on the determined level of damage. The damage estimator computing device may utilize a machine learning model to analyze the image data and estimate the repair costs [See Col. 21, Line 49 – Col. 22, Line 31]. Examiner's Note: Tomlinson does not explicitly teach that the machine learning model filters prior incidents which have similar damage in order to determine the loss prediction. However, this limitation is taught by Fields, as described below).
Regarding claim 20, Tomlinson does not explicitly teach, but Fields, however, does teach:
training one or more learning models to make total loss predictions for properties damaged by incident, the training including (i) determining discrepancies in historical determinations between predicted loss amounts of properties involved in prior incidents and actual payout for those incidents; and (ii) tuning the one or more learning models based on the determined discrepancies (See at least the Abstract: Describes a system for incorporating machine learning to assess damage to vehicles. The system may access actual damage information for the vehicle; determine differences between the actual damage and the assessed damage; and iterate the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy. The calculated damage may be in the form of a cost to repair the vehicle, and may be used to determine an insurance payout associated with the damage [See Paragraph 40]); and
based on the incident data, (i) filtering the prior incidents for a set of incidents in which properties involved in incidents had a similar type of damage as property of the user involving the incident, and (ii) using the similar prior incidents as inputs for executing a learning model of the one or more learning models, the learning model generating a loss prediction for the damage resulting from the incident involving the property of the user (See at least Paragraphs 38 and 39: The system may receive data regarding vehicles with similar types of damage, and utilize a machine learning model to calculate damages based on the received data. In other words, the machine learning model "filters" instances of prior damage to identify incidents having similar damage to the vehicle currently being analyzed [Also see Paragraphs 45 and 46]).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson and Fields in order to assist inspectors in measuring damage done to vehicles, and provide a system that provides an accurate evaluation of the damage (Fields: Paragraphs 4 and 5). The machine learning techniques described by Fields executes calculations more accurately and efficiently than a generic computing environment (Fields: Paragraph 29).
Regarding claim 20, the combination of Tomlinson and Fields does not explicitly teach, but Cook, however, does teach:
communicating the total loss prediction to a policy provider (See at least Paragraph 18: Describes a system for processing data associated with potential damage to a property. The property may correspond to a vehicle [See Paragraph 27]. The system may process sensor data to estimate an amount of damage that occurred to the property [i.e., a loss prediction], and then communicate damage data to an insurance provider. Examiner's Note: While Tomlinson teaches a process for determining a loss estimate, as described above, Tomlinson does not explicitly teach that the loss prediction is communicated to an insurance provider. Rather, the loss prediction is determined by the insurance provider. However, as described above, Cook describes a process for utilizing a third-party to determine the loss estimate, and provide the loss estimate to the insurance provider).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, and Cook in order to enable effective processing of any insurance-related functionalities (Cook: Paragraph 18).
13. Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Tomlinson (U.S. Patent No. 11120574) in view of Fields (U.S. Pre-Grant Publication No. 20220284484) and Cook (U.S. Pre-Grant Publication No. 20150356686), and in further view of Patt (U.S. Pre-Grant Publication No. 20230110710).
Claim 2
Regarding Claim 2, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Patt, however, does teach:
wherein receiving the incident data includes receiving the incident data via a claim process in which a plurality of individuals involved in the incident provide additional contextual information corresponding to the incident (See at least Paragraph 32: Describes a process for processing information corresponding to an insurance claim. The system may identify a plurality of other parties to the claim event or parties that may have relevant contextual information related to the claimant. Upon identifying each of the relevant individuals, the system can utilize various contact methods to remotely engage with the individuals to receive the relevant contextual information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Patt in order to improve the investigative process associated with processing insurance claim filings which are often manual and inefficient (Patt: Paragraphs 1 and 2).
Claim 12
Regarding Claim 12, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Patt, however, does teach:
wherein receiving the incident data includes receiving the incident data via a claim process in which a plurality of individuals involved in the incident provide additional contextual information corresponding to the incident (See at least Paragraph 32: Describes a process for processing information corresponding to an insurance claim. The system may identify a plurality of other parties to the claim event or parties that may have relevant contextual information related to the claimant. Upon identifying each of the relevant individuals, the system can utilize various contact methods to remotely engage with the individuals to receive the relevant contextual information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Patt in order to improve the investigative process associated with processing insurance claim filings which are often manual and inefficient (Patt: Paragraphs 1 and 2).
14. Claims 7-10 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tomlinson (U.S. Patent No. 11120574) in view of Fields (U.S. Pre-Grant Publication No. 20220284484) and Cook (U.S. Pre-Grant Publication No. 20150356686), and in further view of Wasserman (U.S. Pre-Grant Publication No. 20160092962).
Claim 7
Regarding Claim 7, Tomlinson teaches:
wherein the operations further comprise: based on a set of parameters and the total loss prediction, generating one or more [[ranked]] lists of service providers to repair the property for the user (See at least Col. 15, Line 59 – Col. 16, Line 2: The damage estimator computing device may submit a list of vehicle repair facilities to the user. Examiner's Note: Tomlinson does not explicitly teach that the list of vehicle repair facilities is "ranked" based on a set of parameters. However, this limitation is disclosed by Wasserman as described below).
Regarding Claim 7, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Wasserman, however, does teach:
wherein the operations further comprise: based on a set of parameters and the total loss prediction, generating one or more ranked lists of service providers to repair the property for the user (See at least Paragraph 64: Describes a system for providing automated roadside assistance. The system may determine a ranked list of service providers based on various information [Also See Paragraph 56]),
the set of parameters comprising at least one of: user-specific information of the user, a location-based optimization, service provider ratings, or service provider costs (See at least Paragraph 64: The ranking of the service providers may be based on various information including price, proximity to the user's home, best ratings/reviews, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Wasserman in order to provide a system that produces an enhanced system for coordinating towing facilities and roadside assistance providers and their available capacity to tow and provide roadside assistance, and matching users with those towing facilities and roadside assistance providers (Wasserman: See at least the Abstract and Paragraph 2).
Claim 8
Regarding Claim 8, Tomlinson teaches:
wherein the operations further comprise: providing, over one or more networks, the one or more [[ranked]] lists of service providers to the computing device of the user (See at least Col. 15, Line 59 – Col. 16, Line 2: The damage estimator computing device may present the list of vehicle repair facilities to the policyholder via the mobile application. Examiner's Note: Tomlinson does not explicitly teach that the list of vehicle repair facilities is "ranked" based on a set of parameters. However, this limitation is disclosed by Wasserman as described above).
Claim 9
Regarding Claim 9, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Wasserman, however, does teach:
wherein the operations further comprise: based on an authorization from the user, automatically coordinating and scheduling repair service for the property of the user using the one or more ranked lists of service providers (See at least Paragraph 80: The roadside assistance system may automatically select the tow truck and/or roadside assistance order and send the tow truck with the highest score based on the rating factors and rank. The service provider selected by the system may be based on preset preferences provided by the user, such as a preferred service provider [See Paragraph 74]. In other words, the scheduling of the particular service provider may be "authorized" in advance of the incident).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Wasserman in order to provide a system that produces an enhanced system for coordinating towing facilities and roadside assistance providers and their available capacity to tow and provide roadside assistance, and matching users with those towing facilities and roadside assistance providers (Wasserman: See at least the Abstract and Paragraph 2).
Claim 10
Regarding Claim 10, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Wasserman, however, does teach:
wherein the operations further comprise: selecting and scheduling one or more service providers from the one or more ranked lists using the total loss prediction (See at least Paragraph 80: The roadside assistance system may automatically select the tow truck and/or roadside assistance order and send the tow truck with the highest score based on the rating factors and rank).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Wasserman in order to provide a system that produces an enhanced system for coordinating towing facilities and roadside assistance providers and their available capacity to tow and provide roadside assistance, and matching users with those towing facilities and roadside assistance providers (Wasserman: See at least the Abstract and Paragraph 2).
Claim 17
Regarding Claim 17, Tomlinson teaches:
wherein the operations further comprise: based on a set of parameters, generating one or more [[ranked]] lists of service providers to repair the property for the user (See at least Col. 15, Line 59 – Col. 16, Line 2: The damage estimator computing device may submit a list of vehicle repair facilities to the user. Examiner's Note: Tomlinson does not explicitly teach that the list of vehicle repair facilities is "ranked" based on a set of parameters. However, this limitation is disclosed by Wasserman as described below).
Regarding Claim 17, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Wasserman, however, does teach:
wherein the operations further comprise: based on a set of parameters, generating one or more ranked lists of service providers to repair the property for the user (See at least Paragraph 64: Describes a system for providing automated roadside assistance. The system may determine a ranked list of service providers based on various information [Also See Paragraph 56]),
the set of parameters comprising at least one of: user-specific information of the user, a location-based optimization, service provider ratings, or service provider costs (See at least Paragraph 64: The ranking of the service providers may be based on various information including price, proximity to the user's home, best ratings/reviews, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Wasserman in order to provide a system that produces an enhanced system for coordinating towing facilities and roadside assistance providers and their available capacity to tow and provide roadside assistance, and matching users with those towing facilities and roadside assistance providers (Wasserman: See at least the Abstract and Paragraph 2).
Claim 18
Regarding Claim 18, Tomlinson teaches:
wherein the operations further comprise: providing, over one or more networks, the one or more [[ranked]] lists of service providers to the computing device of the user (See at least Col. 15, Line 59 – Col. 16, Line 2: The damage estimator computing device may present the list of vehicle repair facilities to the policyholder via the mobile application. Examiner's Note: Tomlinson does not explicitly teach that the list of vehicle repair facilities is "ranked" based on a set of parameters. However, this limitation is disclosed by Wasserman as described above).
Claim 19
Regarding Claim 19, the combination of Tomlinson, Fields, and Cook does not explicitly teach, but Kelley, however, does teach:
wherein the operations further comprise: based on an authorization from the user, automatically coordinating and scheduling repair service for the property of the user using the one or more ranked lists of service providers (See at least Paragraph 80: The roadside assistance system may automatically select the tow truck and/or roadside assistance order and send the tow truck with the highest score based on the rating factors and rank. The service provider selected by the system may be based on preset preferences provided by the user, such as a preferred service provider [See Paragraph 74]. In other words, the scheduling of the particular service provider may be "authorized" in advance of the incident).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to combine the teachings of Tomlinson, Fields, Cook, and Wasserman in order to increase efficiency in scheduling a service appointment (Kelley: Col. 2, Line 37 – Col. 3, Line 3).
Response to Arguments
15. Applicant’s arguments filed March 23, 2026 have been fully considered.
Arguments Regarding Double Patenting
16. All prior rejections of the claims under Double Patenting have been withdrawn in response to the applicant’s claim amendments. The claims of the instant application have been sufficiently differentiated from the claims of the co-pending application.
Arguments Regarding 35 U.S.C. 101
17. Applicant’s arguments (Amendment, pages 9-12) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on page 11 of their remarks, the applicant argues, “The aforementioned steps improves upon a workflow of the claim evaluation process, by enabling accurate estimates of damage to be determined early on. By enabling the accurate determinations, the resources needed to process the claim can be more efficiently planned and allocated. These determinations, therefore, improve the operations of conventional systems which make the same evaluations without the benefit of the recited claims. Under the holding of Recentive, Claim 1 is patent eligible because the recited features delineate how the learned models are used to achieve the recited improvement.” Similarly, on page 12 of their remarks, the applicant argues, “The "additional elements" integrate the alleged abstract idea into a practical application, and as such, confer subject matter eligibility onto the claims.” The examiner respectfully disagrees. Specifically, the examiner notes that the claims do not provide an indication of an improvement to machine learning technology. While the claims recited the user of machine learning models, the claims do not provide significant technical detail regarding how the models are trained and/or implemented to facilitate the claimed functions. Therefore, the use of machine learning technology in the claims amounts to no more than merely applying generic machine learning technology to implement the abstract idea. Similarly, the claims do not provide significant technical detail regarding how the user is guided through capturing the incident data. Simply stating that user is guided through this process does not provide any indication of an improvement to any technology or technological field. Rather, this simply further refines the abstract idea.
Additionally, on page 12 of their remarks, the applicant argues, “Given the specificity and technological solution provided by the claims, the claims should also be deemed subject matter eligible under Step 2B.” The examiner respectfully disagrees. As noted above, the claims do not provide any indication of an improvement to any technology or technological field. Rather, the claims simply recite the use of generic computer-related technologies to implement the abstract idea on a computer. Additionally, the examiner notes that the degree of specificity of the claims in not necessarily relevant to the subject matter eligibility determination. In other words, a specific abstract idea is still an abstract idea.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Arguments Regarding 35 U.S.C. 102/103
18. Applicant’s arguments regarding the prior art rejections are moot in view of the new grounds of rejection necessitated by applicant’s claim amendments.
Citation of Pertinent Prior Art
19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hayward (U.S. Pre-Grant Publication No. 20210312567): Describes a system for detecting damage, loss, injury and/or other conditions associated with an automobile using a computer system using an automobile monitoring system; and for processing, estimating, and optimizing loss reserves and financial reporting.
Sanchez (U.S. Patent No. 12412218): Describes a method for determining flat-rate insurance payouts after loss-events, and more specifically, the network-based systems and methods for determining personalized loss valuations associated with loss events based at least in part upon the event and policyholder information.
Conclusion
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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