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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 26 March 2026 has been entered.
Status of Claims
This is a non-final office action in response to the request for continued examination filed 26 March 2026. Claims 1, 4, 8, 11, 15, and 18 have been amended. Claims 1-20 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 4, 8, 11, 15, and 18 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Applicant’s amendment is sufficient to overcome the pending 35 U.S.C. 103 rejection. The rejection is respectfully withdrawn.
Response to Arguments
Applicant’s arguments regarding the pending 35 U.S.C. 103 rejection have been fully considered and are persuasive. Applicant asserts that the prior art of record, individually and in combination, fails to teach or suggest each and every limitation of the amended independent claims. Specifically, the prior art fails to teach, disclose, or otherwise suggest using an additional neural network machine classifier to process post-repair image data to verify task completion or automatically approving an invoice based on a probabilistic likelihood of exceeding a threshold value. Examiner agrees. Examiner analyzed amended independent claims 1, 8 and 15 in view of the prior art of record and an updated prior art search and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below. Therefore, the 35 U.S.C. 103 rejection is respectfully withdrawn.
Applicant’s arguments regarding the pending 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the amended claims are not directed to a judicial exception because the human mind is not equipped to train a neural network machine classifier, and further, processing image data using a neural network machine classifier to determine a probabilistic likelihood of task completion cannot practically be performed in the human mind. Applicant asserts that claim 1 recites training a neural network machine classifier and processing image data using an additional neural network machine learning classifier in a manner that is not directed to a mental process or a mathematical concept, and that is not analogous to Claim 2 of Example 47. Examiner respectfully disagrees.
While the amended claims include limitations for training a neural network machine classifier to identify rail car components from images and to determine damage severity, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). The recitation of a trained neural network machine classifier does not negate the mental nature of these limitations because the claim here merely uses the trained neural network as a tool to perform the otherwise mental process of classifying image data and making a damage severity determination. See MPEP 2106.04(a)(2), subsection III.C.
The method claim 1 recites receiving rail car damage data, processing the data using a neural network machine classifier, determining whether additional damage data is required, providing repair tasks based on user input, obtaining post repair image data, processing the post repair image data using an additional neural network machine classifier to determine likelihood that the repair is complete, approving an invoice associated with the repair, and generating a notification. Rail car damage assessment could be performed mentally by a damage adjuster or repair service worker without the aid of a computer or machine learning classifier. Receiving data, obtaining data, providing data, and generating a notification is transmitting data – insignificant extra-solution or post solution activity related to data gathering for use in the data processing steps or output of a result of the data processing steps. Determining result data is generic data analysis. The use of a trained neural network machine classifier as a tool to process the data does not transform an otherwise abstract concept of analyzing image data to make a determination into patent eligible subject matter. The amended claims therefore recite a mental process and mathematical concept and are directed to an abstract idea.
The amended claims are additionally directed to fundamental economic practices or principles related to contractual repair (legal/contractual obligations) of rail car damage and approval of an invoice. The Specification at [para. 0043] states: “variety of task tracking processes include viewing, editing, and maintaining contracts with a variety of service centers. A contracts user interface allows users to view, maintain, and/or edit the contracts that determine which service centers are allowed to submit billing and/or perform work for given rail cars.” Managing the completion of a task is a form of organizing human activity identified in MPEP 2106. Therefore the data collection, analysis, and output determinations of the amended claims recite a fundamental economic practice in the form of data repair task completion and invoice approval, and the claims are directed to an abstract idea.
Applicant asserts that the amended claims recite a specific technical solution for rail car damage assessment that when considered as a whole, integrate any alleged abstract idea into the practical application of automated rail car repair verification. Applicant further asserts that the independent claims do not merely recite generic computer components performing abstract functions, but rather recite a specific technical solution where neural network machine classifiers are used both to assess damage and to verify that repairs have been completed, withy invoice automatic approval tied to a probabilistic threshold determination. Applicant also asserts that the amended claim limitations are analogous to eligible claim 3 of Example 47, and are directed to patent eligible subjection matter under Step 2B. Examiner respectfully disagrees.
The amended limitations include neural network machine classifiers to process the gathered image data, while the neural network classifier is training using “a plurality of images of rail car components, one or more indications of features within the plurality of images, and one or more labels indicating a severity associated with the one or more indications of features,” the additional elements are used as data processing tools to implement the recited abstract idea. The claimed invention does not improve how the machine learning model operates. The neural network machine classifier is not improved such that a technical solution to a technical problem is implemented in a manner that amounts to a practical application of the recited data processing abstract idea. The claimed subject matter does not improve the computer system or another technology. See Enfish LLC v. Microsoft Corporation, 822 F.3d 1327, at 1335 (Fed. Cir. 2016). The amended claim limitations merely gather requested data using known technologies, received user input via selection of search option elements to output (provide) a list of repair tasks, and performing further data input, processing, output, and transmission steps, without significantly more. The “interactive user interface” itself is not an improvement or solution to an identified technological problem, nor do the claims recite an inventive concept that improves a computer’s ability to receive, process, or display information and interact with a user. See Core Wireless Licensing S.A.R.L. v. LG Elecs., Inc., 880 F.3d 1356 (Fed. Cir. 2018). Therefore, the steps recited in independent claims 1, 8, and 15 do not add meaningful limitations beyond generally linking the abstract idea to the particular technological environment. Because the amended claims are directed to a patent-ineligible abstract concept and do not recite something “significantly more” under the second prong of the Alice analysis, independent claims 1, 8, 15, and the claims that depend therefrom are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of collecting data, analyzing it, and presenting certain results of the collection and analysis, without significantly more. Independent claim 1 is directed to a process, independent claim 8 is directed to a system, and independent claim 15 is directed to a product for task distribution and tracking.
Independent claim 1 recites at least the following limitations:
training, using a plurality of images of components of rail cars, one or more indications of features within the plurality of images, and one or more labels indicating a severity associated with the one or more indications of features;
a neural network machine classifier trained to determine damage severity levels for the rail car components;
presenting, using a user interface associated with a task tracking server system, an upload dialogue for damage data;
receiving, via the user interface, the damage data indicating damage to a component of a rail car;
determining, based on the neural network machine classifier determining that a threshold likelihood of determining damage from the damage data is not satisfied, to capture additional damage data;
determining, based on the additional damage data, data indicating the damage and the component, wherein the data indicates a type of repair to the component to correct the indicated damage;
providing an interactive user interface comprising search option elements listed in a user- selectable drop down; and
providing, in response to a user selection of a search option element listed in the user- selectable drop down, and based on the type of repair, a plurality of tasks for a repair of the rail car;
obtaining post-repair image data of the component of the rail car;
processing the post-repair image data using an additional neural network machine classifier to determine a probabilistic likelihood that the plurality of tasks were completed;
automatically approving an invoice associated with the repair based on the probabilistic likelihood exceeding a threshold value; and
generating, based on the automatically approving, a notification indicating the invoice is approved.
Independent claim 8, recites at least the following limitations:
train, using damage data associated with rail cars, one or more indications of features, and one or more labels associated with the one or more indications of features;
a neural network machine classifier trained to determine damage severity levels for the rail car components;
present a data interface comprising an upload portal for damage data;
determine a service center that is proximate with a current geographic location of a rail car;
determine, based on the neural network machine classifier determining that a threshold likelihood of determining damage from the damage data is not satisfied, to capture additional damage data;
determine the damage data indicates a type of repair to a component to correct indicated damage;
provide a data interface;
provide, in response to the data interface, and based on the type of repair, a plurality of tasks for a repair of the rail car;
obtain post-repair image data of the component of the rail car;
process the post-repair image data using an additional neural network machine classifier to determine a probabilistic likelihood that the plurality of tasks were completed;
automatically approve an invoice associated with the repair based on the probabilistic likelihood exceeding a threshold value; and
generating, based on the automatically approving, a notification indicating the invoice is approved.
Independent claim 15 recites at least the following limitations:
training, using severity data associated with components of rail cars, one or more indications of features and one or more labels indicating a severity associated with the one or more indications of features;
a neural network machine classifier trained to determine damage severity levels for the rail car components;
determining, based on the neural network machine classifier determining that a threshold likelihood of determining damage is not satisfied, to capture additional data;
presenting, using a user interface, an upload dialogue for the additional data;
determining data associated with a component;
providing an interactive user interface comprising search option elements listed in a user- selectable drop down;
providing, in response to a user selection of a search option element listed in the user- selectable drop down, and based on a type of repair, a plurality of tasks for the component.;
obtaining post-repair image data of the component;
processing the post-repair image data using an additional neural network machine classifier to determine a probabilistic likelihood that the plurality of tasks were completed;
automatically approving an invoice associated with a repair based on the probabilistic likelihood exceeding a threshold value; and
generating, based on the automatically approving, a notification indicating the invoice is approved.
Under Step 1, independent claims 1, 8, and 15 recite at least one step or act, including training one or more indications of features. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One, the limitations for training a neural network machine classifier, presenting an upload dialogue for damage data, receiving damage data indicating damage to a component, determining to capture additional data, determining data indicating the damage and the component, providing an interactive user interface, provide a data interface, providing a plurality of tasks for a repair of the rail car, determine a service center that is proximate with a current geographic location of a rail car, determining a status, determining data associated with a component, obtaining post-repair image data, processing the post-repair image data, automatically approving an invoice, and generating a notification of approval, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (observations, evaluations, judgments, and opinions) because nothing in the claim elements precludes the steps from practically being performed in the human mind, or by a human using a pen and paper. An insurance adjuster or repair technician could mentally and manually perform the recited steps for detecting and identifying damage to a component, detailing steps to repair the damage, determining that the repair tasks have been completed, approving a repair invoice, and communicating the approval. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea.
The limitations for receiving and capturing damage data are recited broadly and amount to data gathering steps because the data obtained is merely used as input for the recited data processing steps, and are considered insignificant extra-solution activity (see MPEP 2106.05(g)).
The claim limitations are additionally directed to fundamental economic practices or principles related to contractual repair (legal/contractual obligations) of rail car damage and approval of an invoice. The Specification at [para. 0043] states: “variety of task tracking processes include viewing, editing, and maintaining contracts with a variety of service centers. A contracts user interface allows users to view, maintain, and/or edit the contracts that determine which service centers are allowed to submit billing and/or perform work for given rail cars.” Managing the completion of a task is a form of organizing human activity identified in MPEP 2106. Therefore the data collection, analysis, and output determinations of the amended claims recite a fundamental economic practice in the form of data repair task completion and invoice approval, and the claims are directed to an abstract idea.
Under Step 2A Prong Two, the judicial exception is not integrated into a practical application. In particular, the claims only recite a processor and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0041] states: “… the systems and methods described herein can be performed utilizing both general-purpose computing hardware and by single-purpose devices.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(d). Accordingly, the 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.
The presence of a machine learning algorithm or computer implementations do not necessarily restrict the claim from reciting an abstract idea. While the claim limitations include limitations for training … a first neural network machine classifier and a second neural network machine classifier, the claims fail to go beyond processing data characteristics to make a determination and merely “apply” the neural network machine learning classifier to classify or label the obtained damage data. The machine learning algorithm and the computer limitation simply process the data through inputting and outputting data.
While the claims include limitations for training a neural network machine classifier to identify rail car components from images and to determine damage severity, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). The recitation of a trained neural network machine classifier does not negate the mental nature of these limitations because the claim here merely uses the trained neural network as a tool to perform the otherwise mental process of classifying image data and making a damage severity determination. See MPEP 2106.04(a)(2), subsection III.C.
The claims are analogous to ineligible Claim 2 of Example 47, where under Step 2A, Prong One, it recites abstract ideas including, for example, mental concepts (e.g., rounding data values) that can be performed in the human mind, as well as mathematical concepts (e.g., a backpropagation algorithm and a gradient descent algorithm for training of the ANN). Further, even when considered as a whole under Step 2A, Prong Two, claim 2 of Example 47 fails to include a “practical application” because it recites generic computer hardware that simply recites the abstract ideas with the words “apply it” (or an equivalent), that amount to nothing more than mere instructions to implement an abstract idea on a computer without placing any limits on how such steps are performed. For example, even though Claim 2 includes AI-related elements such as “detecting one or more anomalies in a data set using the trained ANN” and “using a trained ANN,” such elements merely recite the outcome and fail to describe any details about how the elements are accomplished.
The limitations include neural network machine classifiers to process the gathered image data, while the neural network classifier is training using “a plurality of images of rail car components, one or more indications of features within the plurality of images, and one or more labels indicating a severity associated with the one or more indications of features,” the additional elements are used as data processing tools to implement the recited abstract idea. The claimed invention does not improve how the machine learning model operates. The neural network machine classifier is not improved such that a technical solution to a technical problem is implemented in a manner that amounts to a practical application of the recited data processing abstract idea. The claimed subject matter does not improve the computer system or another technology, but merely gather requested data using known technologies, received user input via selection of search option elements to output (provide) a list of repair tasks, and performing further data input, processing, output, and transmission steps. Therefore, the steps recited in independent claims 1, 8, and 15 do not add meaningful limitations beyond generally linking the abstract idea to the particular technological environment.
The claim limitations additionally recite “providing an interactive user interface comprising search option elements listed in a user- selectable drop down; and providing, in response to a user selection of a search option element listed in the user- selectable drop down, and based on the type of repair, a plurality of tasks for a repair of the rail car.” User interaction with an interface to receive, transmit, display, or otherwise input or filter data is insufficient to confer patent subject matter eligibility. User interaction with a user-selectable drop down is merely generic use of interface technology to receive user input used for data processing, without significantly more. That the interface includes search option elements performing filtering functions used to process data for display or human decision making, is also insufficient to confer subject matter eligibility because the user interaction with the interface does not improve the functioning of the interface or improve graphical user interface technology. The interface related limitations recited herein are analogous to those of Claim 2 of Example 40 wherein the claims are directed to mere data gathering steps that automate the comparison of data without significantly more than the recited insignificant extra solution activity and mere instructions to apply the exception using generic computer components.
Under Step 2B, 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 the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amounts to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2 - 7, 9 - 14, and 16- 20 include the abstract ideas of the independent claims. The dependent claims merely narrow the mental process by describing the type pf data used to provide the plurality of tasks and how the data is manipulated to generate the output of the data processing steps. The limitations of the dependent claims are not integrated into a practical application because no additional elements set forth any limitations that meaningfully limit the abstract idea implementation, therefore the claims are directed to an abstract idea. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Therefore claims 1-20 are ineligible under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 are rejected under 35 U.S.C. 101, but the claims would be allowable if the aforementioned rejections are overcome.
Examiner analyzed amended independent claims 1, 8 and 15 in view of the prior art of record and an updated prior art search and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success. Regarding the prior art of record, Chen et al. (US 10,740,891) and McQuown et al. (US 2005/0144183) combined disclose an image processing system that can be used to detect changes in objects, such as to detect damage, including neural network classifiers to detect or classify damage or changes to particular target object components, an indication of one or more types of damage detected, and an indication of damage severity. The system may use the damage determination to determine each of the parts of the target vehicle that needs to be replaced, each of the parts that need to be repaired (and potentially the type of repair), and may identify particular types of work that need to be performed.
However, the prior art fails to teach, disclose, or otherwise suggest using an additional neural network machine classifier to process post-repair image data to verify task completion or automatically approving an invoice based on a probabilistic likelihood of exceeding a threshold value. Since the specific ordered combined sequence of claim elements recited in claims 1, 8, and 15 can only be found as recited in Applicant’s specification, any combination of the cited references and/or additional references to teach all the claim elements, including the features discussed above, would be the result of impermissible hindsight reconstruction. Accordingly the prior art rejections set forth in the previous action are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Adegan (US 10,360,601) - Repair estimate software executable on a hardware platform is provided. A database of repair estimates is provided, where the database consists of data and images. A set of prior accidents is analyzed to assign a layer number and multiple values in a form of vectors that are utilized by a predictive model to determine which parts are damaged. A predictive model is applied to determine details of parts, refinish, and labor hours necessary for a given repair. Results are returned. An estimate that contains the details of all parts, the refinish and the labor hours necessary is created to repair the current vehicle. A crash level is selected through an extent-of-damage level slider. Parts-list, refinish and labor hour details and total cost of repair (1400) are generated in real-time.
Aquila et al. (US 2002/0035488) - Claim data accessible via Deskview 200 include the severity of claims, vehicle damage, cycle time for claim processing, repair status updates, estimates, basic management reports, attached digital images, and transaction logs, once the APS approves a payment request to pay an invoice for certain work completed (e.g., automobile repair), the APS requires confirmation of satisfaction from the policy holder or consumer who made the claim.
Brandmaier et al. (US 8,712,893) - automated system for analyzing damage to process claims associated with an insured item, such as a vehicle. An enhanced claims processing server may analyze damage associated with the insured item using photos/video transmitted to the server from a user device (e.g., a mobile device). The enhanced claims processing server may submit a signal that locks one or more portions of an application on the mobile device used to submit the photos/videos. The mobile device may receive feedback from the server regarding the acceptability of submitted photos/video. The photos may further be annotated using, for example, a touch screen display. An estimate, such as a cost estimate, may be determined for the damage associated with the insured item based on the photos and/or annotations.
Hart et al. (US 2007/0136106) - processing system manages the storage and retrieval of insurance claim information for the repair of automotive glass. Claim information is entered into the processing system whereby insurance claims are automatically processed for approval or disapproval of the repair based upon the information stored in the processing system. The processing system analyzes claim data against policy information and the current NAGS pricing specification. Work orders are automatically dispatched to the glass repair shop. When the network receives an invoice from the AGR shop, it verifies that the invoice meets the discount pricing agreement. Upon completion of the glass repair, the repair entity 23 may submit an invoice to the processing system 1 for payment by the insurance company 21.
Franke et al. (US 2017/0147991) - A vehicle damage report is constructed from a scan of vehicle surfaces. Objective information derived from the surface scan is augmented with other information from other sources, such as vehicle identification information or repair cost estimates that can be used to facilitate and administer a multi-party downstream repair process.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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