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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Status of Claims
This is in reply to the filing of 10/21/2024.
Claim 1 was amended by Applicant.
Claims 2 – 20 are new.
Claims 1 – 20 are currently pending and have been examined.
This action is made non-final.
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 pursuant to 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 – 7 are directed to a method (process), claims 8 – 14 are directed to a device (machine), and claims 15 – 20 are directed to a non-transitory computer-readable medium (composition). The claims therefore constitute eligible statutory categories of an invention. (Step 1: YES).
Independent claim 1 (and mirrored independent claims 8 and 15) recites the limitations of:
A method for providing data processing from a crowdsourced group, comprising: communicating, by a data processing server system and to a particular adjuster device (and including the generic computer related gear of claims 8 and 15 … a computing device, processors, storage devices, processors that store executable instruction code and a non-transitory computer readable medium) job request data that comprises anonymized item data associated with one or more item features; receiving, by the data processing server system and from the particular adjuster device, first annotation data associated with a first set of item features associated with the anonymized item data; generating, by a machine learning model, second annotation data associated with a second set of features associated with the anonymized item data; determining, based on a comparison of the first set of item features received from the particular adjuster device and the second set of item features generated by the machine learning model, one or more performance metrics associated with the particular adjuster device; and updating, by the data processing server system, an adjuster score associated with the particular adjuster device based on the one or more performance metrics.
The claims recite the abstract idea of:
generating … second annotation data associated with a second set of features associated with the anonymized item data; determining, based on a comparison of the first set of item features received from the particular adjuster device and the second set of item features generated … , one or more performance metrics,
The above abstract idea recites a fundamental economic practice and/or commercial interaction, i.e, comparing generated loss data which is typically expected at a particular loss site, with an insurance adjuster’s actual derived loss data at that same site, and thereby judging an adjuster’s performance.
Moreover, the same also constitutes the abstract idea of a mental process, namely, a process that one easily keeps in the mind. An experienced adjuster may well be familiar with typical items for a given insurance loss, and have the ability to compare those items mentally with the actual on-site loss item measurements recorded, thereby determining a measure of the adjuster’s performance score. That noted, this analysis proceeds forward primarily on the fundamental economic practice and/or commercial interaction components the above bulleted abstract idea.
The above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice and/or commercial interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, independent claim 1 (and 8 / 15) recites an abstract idea.
The above bolded terms of the mirrored independent claims 1, 8, and 15 recite a data processing server system, adjuster device, computing device, processors, storage devices, processors that store executable instruction code, a non-transitory computer readable medium, and machine learning models. Said bolded independent claim terms are just applying generic computer components / computer driven systems to perform the above noted abstract idea limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Any other potential computer related generic components claimed only serve to generally link the above noted abstract idea to them, without more. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
As to the dependent claims 2 - 7, 9 – 14, and 16 – 20, they further refine the above noted abstract idea set forth by the independent claims as follows:
further comprising: communicating, by the data processing server system and to the particular adjuster device, the updated adjuster score. (claims 2, 9, and 16); routing, by the data processing server system, second job request data to the particular adjuster device based at least on the updated adjuster score. (claims 3, 10, and 17); obtaining, by the data processing server system and from a third-party server system, certification data that specifies one or more certifications associated with respective adjuster devices of a plurality of adjuster devices; and determining, by the data processing server system and based on the certification data, the particular adjuster device to correspond to an adjuster device of the plurality of adjuster devices associated with one or more particular certifications. (claims 4, 11, and 18); determining the one or more particular certifications based on an item described in the anonymized item data. (claims 5, 12, and 19); generating, by the data processing server system, feedback data based on the one or more performance metrics; and communicating, by the data processing server system, the feedback data to the particular adjuster device. (claims 6, 13, and 20); wherein determining the one or more performance metrics associated with the particular adjuster device comprises at least one of: determining a completeness of the first annotation data based on comparing a number of features identified in the first annotation data to a number of features identified in the second annotation data; and determining an accuracy of the first annotation data based on comparing the set of features described in the first annotation data to features described in the second annotation data. (claims 7 and 14).
Examiner notes that the above bolded computer / computer driven terms set forth in the dependent claims: data processing server system, adjuster device, third-party server system, and adjuster devices of a plurality of adjuster devices are all also being applied as tools to the abstract idea, without more. And while the just mentioned bolded terms do constitute additional elements, those additional elements only generally link the abstract idea articulated herein to generic computer technology, without more.
The computer hardware/software above bolded are recited at a high-level of generality (i.e., generic processors, computer systems, and memories all performing generic computer functions), and the same amounts to no more than mere instructions to apply the exception using a generic computer component(s). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. That said, the claims are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additionally claimed elements in the claims do not integrate the abstract idea into a practical application).
All claims above reviewed also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When considered separately and as an ordered combination, the claims do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to perform a judicial exception by applying generic computer components and thereby automating the process cannot provide an inventive concept.
This Application's lack of providing significantly more than the judicial exception is also referred to as its claims lacking an “inventive concept. See MPEP 2106.05(f) where applying a computer as a tool to the abstract idea is not indicative of significantly more. The above detailed non-computer related elements do not change the outcome of the analysis, as they simply further limit ways which the abstract idea may be performed. (Step 2B: NO. The claims do not provide significantly more than the judicial exception).
In summary, the claim set reviewed as above does not include any additional elements that integrate its abstract idea into a practical application, or that are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Claims 1 – 20 are not patent-eligible pursuant to 35 USC 101.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 is incorrect, any correction of the statutory basis 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 USC 103 which forms the basis for all obviousness rejections set forth in this Office Action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating
obviousness or nonobviousness.
Claims 1 – 3, 6 – 10, 13 – 17, and 20 are rejected pursuant to 35 USC 103 as being unpatentable over Pilot (US20150248730A1) in view of Hayward (US20210256615A1).
Regarding claims 1, 8, and 15:
Pilot discloses:
communicating, by a data processing server system and to a particular adjuster device, job request data that comprises anonymized item data associated with one or more item features; (“Within the new claim scoping system, a claims adjuster carries a portable computing device (e.g., a tablet computer, a mobile “web pad,” or the like). A plurality of nodes organized as a workflow is presented as a flowchart on a display of the portable computing device. The nodes direct the actions of the claims adjuster before, during, and after the claims adjuster visits the site of the loss that is the subject of the insurance claim.”, [008]) and (“On a portable computing device, an insurance claim adjuster assigned to the insurance claim retrieves information prepared by the core system, including the claim scoping workflow. … The assigned claim adjuster visits the site of the loss and follows prompts presented by the workflow on the portable computing device to enter additional information related to the loss. Once the claim scoping process workflow is complete, the information is sent to another computing server device for estimating. Relevant information is also returned to the claims processing computing server.”, [025]) and (“The new claim scoping system comprises a computing-server-based core system and a portable-computing-device-based mobile application.”, [022]), examiner notes that the above noted “anonymizing” of the “item data” portion of this limitation is analyzed below in the Hayward reference;
receiving, by the data processing server system and from the particular adjuster device, first annotation data associated with a first set of item features associated with the anonymized item data; (“The new claim scoping system allows claim adjusters to scope different types of property damage from the site of the damage, and the new system automatically sends a loss information report to a computing-server-based estimating system.”, [022]) and (“Within the new claim scoping system, a claims adjuster carries a portable computing device (e.g., a tablet computer, a mobile “web pad,” or the like). … The nodes direct the actions of the claims adjuster before, during, and after the claims adjuster visits the site of the loss that is the subject of the insurance claim.”, [008]) and (“From the claims adjuster, the portable computing device accepts user input associated with the subject real property that is associated with the insurance claim. A second plurality of entries of the scope record is populated with information based on user input from the claims adjuster, and the scope record is communicated to a remote computing server.”, [ABSTRACT], published 09/03/2015);
[determining] one or more performance metrics associated with the particular adjuster device; and updating, by the data processing server system, an adjuster score associated with the particular adjuster device based on the one or more performance metrics. (“New information in a workflow or loss information report may be suitably identified as “new” or “updated.” The new or updated information may be color coded, presented in a different font or with different attributes, or distinguished in other ways.”, [0156]) and (“In some embodiments, the claim scoping application 119 is configured to update the external claim processing system 102. The update may be manual or automatic. The claim scoping application 119 may provide the update directly to the claim processing system,”, [0159]) and (“Quality Assurance (QA) reports produced by the analytics module 144 present details useful for validating the efficiency of the core system 106 and for identifying areas of potential improvement. QA reports can present scores rating individual claims adjusters, loss information reports generated with or without errors, “best” and “worst” scores, and other criteria. The QA report can identify specific areas of improvement in claim scoping workflows, loss information reports, sketches, videos, manually typed entries, measurements, and others. The QA reports may be prepared to provide report details per claims adjuster, event average score, score per “loss type,” score based on complexity level, and other criteria.”, [060]) and (“ communicating the insurance claim workflow to a remote computing device; receiving an updated insurance claim workflow from the remote computing device,”, [012]), as above, performance metrics may be both determined and updated, including to the adjuster’s device.
Pilot does not expressly disclose, but Hayward teaches:
generating, by a machine learning model, second annotation data associated with a second set of features associated with the anonymized item data; Interpreted broadly in the light of the Specification, this limitation includes the ML generation of data to be associated with adjuster based item data, … (“As discussed herein, data may be collected from various sourced to generate, update, and/or modify a dynamic data set that is used to train and apply a machine-learning analytics model.”, [0208]) and (“This may include, for example, generating a level of risk associated with … (or other suitable risk-based variables) output by the trained machine-learning analytics model,”, [0134]) and (“Method 700 may include one or more processors identifying (block 712) one or more loss-mitigating variables (or loss-prevention variables) that reduce the initial determined (block 710) level of risk. … in accordance with a re-trained or alternate machine-learning analytics model … to determine based upon a correlation to other similar actions ”, [0184]);
[as to the above noted “anonymized item data”] … (“These procedures may include, for example, secure login and authentication procedures and/or the encryption of data stored in database 120.2 (and/or one or more other back-end components 120).”, [066])
determining, based on a comparison of the first set of item features received from the particular adjuster device and the second set of item features generated by the machine learning model, (“To provide another example, as shown in FIG. 3, demographic information may include age (or age bracket), gender, location data such as the user's current address or residential region, blood type, etc., for various users. In various aspects, this demographic information may provide various insights when used as training data, such that correlations may be made amongst similar users and compared to future users as part of a machine-learning analytics model,”, [0100]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Pilot to incorporate the teachings of Hayward because Pilot would be more efficient and versatile should it employ machine learning models to process an insurance adjuster’s data as to a site loss so that the actual assessed sites will better correspond item-wise to generated, expected item losses as done in Hayward ("In doing so, the machine-learning analytics engine 200 helps to ensure that the user continues to perform the suggested actions, thereby minimizing the insurer's loss for … insurance claims.”, [0161]).
Regarding claims 2, 9, and 16:
The combination of Pilot and Hayward contain the limitations of claims 1, 8, and 15, respectively:
Pilot further teaches:
communicating, by the data processing server system and to the particular adjuster device, the updated adjuster score. (“In some embodiments, the claim scoping application 119 is configured to update the external claim processing system 102. The update may be manual or automatic. The claim scoping application 119 may provide the update directly to the claim processing system,”, [0159]).
Regarding claims 3, 10, and 17:
The combination of Pilot and Hayward contain the limitations of claims 1, 9, and 15, respectively:
Pilot further teaches:
routing, by the data processing server system, second job request data to the particular adjuster device based at least on the updated adjuster score. (“Once identified, the user can set a claim scoping workflow complexity level and a “score” for the claim scoping workflow such as a numerical scale that permits comparison of one workflow to another. In some cases, the user can also add notes to a record associated with the score. Certain data, such as the score, may be accessed by the core system 106 and used as criteria to assign the claim to a claims adjuster 114 having an appropriate skill level or training background to perform well when completing the claim scoping workflow.”, [061]), repeated / subsequent job requests may be sent to adjuster devices based on score.
Regarding claims 6, 13, and 20:
The combination of Pilot and Hayward contain the limitations of claims 1, 8, and 15, respectively:
Pilot further teaches:
generating, by the data processing server system, feedback data based on the one or more performance metrics; and communicating, by the data processing server system, the feedback data to the particular adjuster device. (“And, by continuing to monitor location data consensually shared by User A, machine-learning analytics engine 200 may further track User A's activity to determine whether User A is performing this new intervening activity. In this way, aspects include machine-learning analytics engine 200 continuously receiving feedback regarding the calculated loss-mitigating variables.”, [0149]).
Regarding claims 7 and 14:
The combination of Pilot and Hayward contain the limitations of claims 1 and 8, respectively:
Pilot further teaches:
determining a completeness of the first annotation data based on comparing a number of features identified in the first annotation data to a number of features identified in the second annotation data; and determining an accuracy of the first annotation data based on comparing the set of features described in the first annotation data to features described in the second annotation data. (“Insurance adjusters perform claim scoping procedures and produce loss information reports. The loss information reports are used to estimate the financial cost to repair or replace damaged property. In the present disclosure, a new claim scoping system is described, which increases the efficiency of performing a claim scoping process. In addition, the new claim scoping system also leads to increased accuracy of a loss information report.”, [007]).
Claims 4, 5, 11, 12, 18 and 19 re rejected pursuant to 35 USC 103 as being unpatentable over Pilot (US20150248730A1) in view of Hayward (US20210256615A1) and in further view of Jones (US20110131406A1).
Regarding claims 4, 11, and 18
The combination of Pilot and Hayward contain the limitations of claims 1, 8, and 15, respectively:
That combination does not expressly disclose, but Jones teaches:
obtaining, by the data processing server system and from a third-party server system, certification data that specifies one or more certifications associated with respective adjuster devices of a plurality of adjuster devices; and determining, by the data processing server system and based on the certification data, the particular adjuster device to correspond to an adjuster device of the plurality of adjuster devices associated with one or more particular certifications. Examiner interprets this claim to include the meaning that a particular mobile device used by an adjuster may be certified (“Preferably, the Certificate Authority is a standalone “air gap” computer that serves as a “trusted third party”. As will be described in greater detail, when a mobile device, a Distributed C3i computer or a message broker server is initially provisioned, part of the provisioning process involves signing the device's public key with an already-trusted key. The device then “inherits” trust.”, [010]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Pilot to incorporate the teachings of Jones because Pilot would be more efficient, versatile, hand secure should it take advantage of certification protocols respecting the adjuster devices, as done in Jones (“In general and as will be described in greater detail, any information sent is sent directly to the recipient, encrypted so that only the recipient may read the information, and signed so that the recipient knows that the message came from the sender and was not tampered with while in transit.”, see [011] of Jones).
Regarding claims 5, 12, and 19:
The combination of Pilot, Hayward and Jones contain the limitations of claims 4, 11, and 18, respectively:
Pilot further teaches:
determining the one or more particular certifications based on an item described in the anonymized item data. Examiner interprets this claim to include the meaning that the certification process may be done more than once, and to more than one device that obtained data. (“A secure communication system for mobile devices preferably includes the following components: the mobile devices;”, [007]).
CONCLUSION
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Choi (US20170352103A1) - A back-end application computer server may access a location data store containing information about a set of locations to be visited, including location identifiers and location coordinates. The computer server may then prioritize the locations to be visited. A mobile unit data store may contain information about a set of mobile units, including mobile unit identifiers, mobile unit location coordinates, and mobile unit communication addresses. The computer server may then automatically assign each location to a mobile unit based on the location coordinates, the mobile unit location coordinates, at least one geo-fence, and said prioritization. Indications of assigned locations may be transmitted to each mobile unit via the associated mobile unit communication address, and electronic messages may be exchanged to support an interactive user interface display associated with assignments of locations to mobile units. According to some embodiments, the back-end computer server facilitates collection of location information from mobile devices.
Sarkissian (US20200410001A1) - A system includes an event-source device; a state database holding first state data; a controllable computing device; and a monitoring device. The monitoring device receives an event record from the event-source device; determines, based at least in part on the first state data and the event record, a command; and transmits the command to the controllable computing device or otherwise causes the controllable computing device to carry out (e.g., perform an action associated with) the command. Some examples include determining a computational model based at least in part on first state data associated with a first data source. The computational model is operated based at least in part on an event record associated with a second data source to provide a command. A representation of the command is presented, via a user interface. A computing device can be caused to carry out the command.
Hargroder (US20100318383A1) - The interactive credential system and method has a database containing employee-employer-applicant surveyed information, industry specific criteria, such as insurance loss history and account performance, an authorization code for authorizing access to the database and a control device, operatively associated with the database, for presenting weighted scores. The system further includes a surveyed party processor operatively associated with the control device, and wherein the surveyed party processor is capable of transmitting the authorization code to view the surveyed information. The system also has a participant processor that is capable of requesting authorization to download the employee-employer-applicant information, including weighted scores computed from the system's algorithms processed from industry specific parameters.
Brandmaier (US20220051338A1) - Systems and methods provide for an 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.
Li (US11144889B2) - A system and method are provided for automatically estimating a repair cost for a vehicle. A method includes: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing image processing operations on each of the one or more images to detect external damage to a first set of parts of the vehicle; inferring internal damage to a second set of parts of the vehicle based on the detected external damage; and, calculating an estimated repair cost for the vehicle based on the detected external damage and inferred internal damage based on accessing a parts database that includes repair and labor costs for each part in the first and second sets of parts.
Collins (US10573012B1) - Systems and methods provide for an automated system for generating one or more three dimensional (3D) images of a vehicle and/or a baseline image for that vehicle. The system may receive 3D images of a plurality of vehicles of a same type (e.g., same make, model, year, etc.) and generate a 3D image of a baseline vehicle for vehicles of that same type based on 3D images of the plurality of vehicles of the particular type. The system may use a 3D image of the baseline vehicle to determine a characteristic of another vehicle, such as a modification made to the vehicle, damage to the vehicle, cost to repair the vehicle or replace parts of the vehicle, a value of the vehicle, an insurance quote for the vehicle, etc. In some aspects, the 3D images may optionally comprise 3D point clouds, and 3D laser scanners may be used to capture 3D images of vehicles.
Taliwal (US10692050B2) - A system and method are provided for automatically estimating a repair cost for a vehicle. A method includes: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing image processing operations on each of the one or more images to detect external damage to a first set of parts of the vehicle; inferring internal damage to a second set of parts of the vehicle based on the detected external damage; and, calculating an estimated repair cost for the vehicle based on the detected external damage and inferred internal damage based on accessing a parts database that includes repair and labor costs for each part in the first and second sets of parts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
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 call examiner Cobb as above, or to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661