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 .
DETAILED ACTION
The following is a Non-Final Office Action in response to communications received April 29, 2025. Claims 1, 14, and 17 have been amended. Claims 1-20 remain pending and examined.
Response to Amendments and Arguments
As to the rejection of Claims 1-20 under 35 U.S.C. § 101, Applicant’s arguments and amendments have been fully considered but are not persuasive. Applicant argues that the present claims are similar to Example 39 stating that “[s]imilarly, the present claims (a) do not recite mathematical concepts, (b) do not recite a mental process as the features are not practically performed in the human mind in real-time, and (c) do not recite any method of organizing human activity and rather are directed to an intelligent system fundamentally tied into computing technology, namely an intelligent system for crowdsourced online environments and platforms”. Examiner disagrees. Unlike Examples 39 and 48, the present claims are not addressing a problem within the technology of intelligent systems for crowdsourced online environments and platforms. The claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claims in the instant application include an abstract idea, and when considered as a whole, the claims (independent and dependent) do not integrate the exception into a practical application, and merely add the words “apply it” to the “the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The additional elements do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. And unlike Examples 40 and 42, simply relying on a computer to perform routine tasks or calculations more quickly or more accurately is insufficient to render a claim patent eligible as seen in the present claims. See Alice, 134 S. Ct. at 2359 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept); Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (a computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”); cf. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258–59 (Fed. Cir. 2014) (finding a computer-implemented method patent eligible where the claims recite a specific manipulation of a general-purpose computer such that the claims do not rely on a “computer network operating in its normal, expected manner”). As elaborated in the rejection below, using one or more processors is simply “apply it” using generic computer components rather than integrating the judicial exception into a Beard practical application. The rejection is thereby maintained.
As to the rejection of claims 1- 20 under 35 U.S.C. § 103, Applicant's arguments and amendments have been fully considered but are not persuasive. Applicant argues that Shapiro and/or Beard do not disclose or reasonably suggest amended claims 1, 14, and 17 which have been amended to include the limitations “allowing one or more of the plurality of adjusters each associated with a respective real-time adjuster priority score of the plurality of real-time adjuster priority scores access to the insurance claim” and “determine a top-ranked adjuster from the plurality of adjusters based on the plurality of real-time adjuster priority scores during the period of time prior to allowing the one or more of the plurality of adjusters access to the insurance claim and based on an association with one or more least recent assignments such that adjusters remaining active on the crowdsourcing platform for longer are prioritized “ and “automatically assign via an assignment the insurance claim to the top-ranked adjuster during the period of time prior to allowing the one or more of the plurality of adjusters access to the insurance claim, wherein the assignment comprises access to the insurance claim; and allow the top-ranked adjuster access to the insurance claim after the assignment such that the one or more of the plurality of adjusters not assigned to the insurance claim are not allowed access to the insurance claim”. Examiner asserts that using broadest reasonable interpretation of the amended claims, the limitations are still taught by Shapiro in view of Beard in view of Kulkarni. Beard teaches offering qualified adjusters access to the insurance claim. And “automatically” assigning the insurance claim can be interpreted as assigning the insurance claim using a computer, which would be automated. The rejection is thereby maintained.
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 non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an intelligent adjuster assignment system comprising: a crowdsourcing platform; one or more processors; one or more memory components communicatively coupled to the one or more processors and the crowdsourcing platform; and machine readable instructions stored in the one or more memory components that cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors: receive an insurance claim during a period of time; determine a plurality of real-time adjuster priority scores based on one or more weighted parameters for a plurality of adjusters of an adjuster pool on the crowdsourcing platform during the period of time prior to allowing one or more of the plurality of adjusters each associated with a respective real-time adjuster priority score of the plurality of real-time adjuster priority scores access to the insurance claim; determine a top-ranked adjuster from the plurality of adjusters based on the plurality of real-time adjuster priority scores during the period of time prior to allowing the one or more of the plurality of adjusters access to the insurance claim and based on an association with one or more least recent assignments such that adjusters remaining active on the crowdsourcing platform for longer are prioritized; automatically assign via an assignment the insurance claim to the top-ranked adjuster during the period of time prior to allowing one or more of the plurality of adjusters access to the insurance claim to allow the top-ranked adjuster access to the insurance claim, wherein the assignment comprises access to the insurance claim; and allow the top-ranked adjuster access to the insurance claim after the assignment such that the one or more of the plurality of adjusters not assigned to the insurance claim are not allowed access to the insurance claim. The portion in bold recites an abstract idea and is akin to the subject matter groupings of “certain methods of organizing human activity”.
(Step 2A prong 2) The additional elements are considered as follows:
“An intelligent adjuster assignment system comprising: a crowdsourcing platform; one or more processors; one or more memory components communicatively coupled to the one or more processors and the crowdsourcing platform; and machine readable instructions stored in the one or more memory components that cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:” This is merely “apply it” and amounts to the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks”, (see MPEP 2106.05(f)).
“receive an insurance claim during a period of time;” This is an extra solution activity, akin to data gathering.
(Step 2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of tracking users through an area, over a generic computer network with generic computing elements, and generic hardware.
Analysis of dependent claims 2-13, 15-16, and 18-20, recited additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
The dependent claims have also been examined and do not correct the deficiencies of the independent claims. Therefore, claims 2-13, 15-16, and 18-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over the publication by Glenn Shapiro (Publication No.: US 2020/0364665 A1) in view of the patent by Debra Beard (Patent No.: US 11,423,487) in view of the publication Kulkarni et al. (Publication No.: US 2013/0317871 A1).
As to Claim 1, Shapiro teaches an intelligent adjuster assignment system comprising:
a crowdsourcing platform (see Abstract);
one or more processors (see Figure 5, and ¶[0061]);
one or more memory components communicatively coupled to the one or more processors and the crowdsourcing platform (see Figure 5, and ¶[0061]); and
machine readable instructions stored in the one or more memory components that cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:
receive an insurance claim during a period of time (see ¶[0017]);
determine a plurality of real-time adjuster priority scores based on one or more weighted parameters for a plurality of adjusters of an adjuster pool on the crowdsourcing platform during the period of time (see Abstract, ¶[0017], and ¶[0021]), and ;
determine a top-ranked adjuster from the plurality of adjusters based on the plurality of real-time adjuster priority scores (see ¶[0019] – “The adjuster devices can then be scored based on their performance in performing the assigned tasks based on a variety of metrics including but not limited to, the time needed to complete the task, a comparison of the annotations provided by the adjuster device to annotations generated by a machine classifier and/or other adjuster devices (such as when the same annotation task is provided to multiple adjuster devices), and/or a comparison of the provided annotations to the corrective action actually taken in repairing and/or replacing the damaged items. For example, if an adjuster device indicates that a bumper requires replacement and the repair shop repairs and repaints the bumper, the adjuster device may receive a lower score for the annotation task vs a second adjuster device (and/or machine classifier) that provided an annotation that the bumper should be repainted.”);
automatically assign via an assignment the insurance claim to the top-ranked adjuster (see ¶[0019] – “The scoring data can be used to determine which adjuster devices are assigned to future annotation tasks. For example, adjuster devices with scores indicating that the adjuster device does not provide annotations consistent with other adjuster devices can be excluded from being offered future annotation tasks for a particular class of task.).
Although Shapiro substantially teaches the invention of claim 1, it does not explicitly teach prior to allowing one or more of the plurality of adjusters each associated with a respective real-time adjuster priority score of the plurality of real-time adjuster priority scores access to the insurance claim; determine a top-ranked adjuster from the plurality of adjusters based on the plurality of real-time adjuster priority scores during the period of time prior to allowing the one or more of the plurality of adjusters access to the insurance claim and based on an association with one or more least recent assignments such that adjusters remaining active on the crowdsourcing platform for longer are prioritized; automatically assign via an assignment the insurance claim to the top-ranked adjuster during the period of time prior to allowing the one or more of the plurality of adjusters access to the insurance claim, wherein the assignment comprises access to the insurance claim; and allow the top-ranked adjuster access to the insurance claim after the assignment such that the one or more of the plurality of adjusters not assigned to the insurance claim are not allowed access to the insurance claim. Beard does teach prior to allowing one or more of the plurality of adjusters each associated with a respective real-time adjuster priority score of the plurality of real-time adjuster priority scores access to the insurance claim; allow the top-ranked adjuster access to the insurance claim (see Figure 4, Col. 4, lines 51-59 – Beard teaches that the techniques described are done in real-time; and Col. 10, lines 24-65 – Beard teaches a number of scores, as claimed, see col. 10 lines 32-40, and an additional score is assigned to the first adjuster to reply, see col. 20 lines 11-27; when that adjuster is assigned access the insurance claim, see col. 2 lines 8-12). Kulkarni does teach determine a top-ranked [worker] from the plurality of [workers] based on the plurality of real-time [worker] priority scores during the period of time prior to allowing the one or more of the plurality of [agents] access to the [task] and based on an association with one or more least recent assignments such that [agents] remaining active on the crowdsourcing platform for longer are prioritized; automatically assign via an assignment the [task] to the top-ranked [worker] during the period of time prior to allowing the one or more of the plurality of [workers] access to the [task] , wherein the assignment comprises access to the [task]; and allow the top-ranked [worker] access to the [task] after the assignment such that the one or more of the plurality of [workers] not assigned to the [task] are not allowed access to the [task] (see ¶[0012] – “The task assignment system is structured and arranged to insert each of the tasks to be performed into a priority queue multiple times and, further, to push a next task in the priority queue to a selected worker. The data storage device stores information about workers, which can include a worker identification, a worker skill level, an accuracy rating, a list of historical tasks performed by the worker, and a list of historical tasks that the worker has not performed. The data storage device also stores a workflow presentation algorithm(s), e.g., a parallel workflow model, a majority vote workflow model, an iterative workflow model, a peer voting workflow model, a peer review workflow model, and any combination thereof, for identifying how to present the next task in the priority queue to selected worker.”, and ¶[0014] – “Other embodiments of the present invention include methods of online source-crowding via a network. The network includes a requester(s) and a plurality of workers. Each requester is equipped with a requester interface and a processing device and each worker is equipped with a worker interface and a processing device. The methods include receiving a task request from a corresponding requester; creating a priority queue of task requests from each corresponding requester; assigning a next task on the priority queue to the worker interface of selected workers; receiving one of an opt-out or a response from the worker interface of each of the selected workers; using the response from each of the selected workers to determine a final response; and transmitting the final response to the requester interface of the corresponding requester. In one aspect of the embodiment, assigning a next task includes: selecting a plurality of workers capable of performing the next task from a crowd of workers and transmitting the next task to the selected workers in accordance with a workflow presentation algorithm(s), e.g., a parallel workflow model, a majority vote workflow model, an iterative workflow model, a peer voting workflow model, a peer review workflow model, and any combination thereof. The workflow presentation algorithm(s) are further used to provide a final response. The step of creating a priority list includes inserting each task to be performed into a priority queue multiple times and assigning a next task on the priority queue to the worker interface of selected workers. The assigning step further includes identifying each available worker; ascertaining a skill level of each available workers; ascertaining an historical accuracy rating of each available worker; ascertaining historical tasks previously performed by each available worker; ascertaining historical tasks that each available worker has not performed; and evaluating an available worker's suitability for the next task using one or more of the above. In one aspect of the present invention, an available worker is not deemed suitability for the next task if the worker has previously performed the task.”). It would have been obvious to one of ordinary skill in the art at the time of filing to incorporate into the task assignment process of Shapiro and Beard with the task assignment order of Kulkarni as this change in order does not break the reference as according to MPEP 2144.04.
As to Claim 2, Shapiro teaches the intelligent adjuster assignment system of claim 1, wherein the crowdsourcing platform is configured to:
provide an adjuster portal configured to allow the plurality of adjusters to login to the crowdsourcing platform; and
automatically identify via the adjuster portal each of the plurality of adjusters as actively accepting claims or not actively accepting claims (see at least ¶[0017]-¶[0018] – “A claims processing server can manage analysis of damage associated with the item, generate an adjustment request, and provide the adjustment request to a variety of adjuster devices. In a variety of embodiments, adjuster devices are determined based on a third-party database of certifications associated with particular adjuster devices. An adjuster device can accept or decline the adjustment request. On accepting a request, the adjuster device can be provided with a set of data describing damage to an item and a variety of annotations can be applied to the data. In a variety of embodiments, multiple adjuster devices can review the same claim and a final claim outcome can be determined based on the multiple reviews. In many embodiments, machine classifiers can process the set of data to identify particular features within the data.”).
As to Claim 3, Shapiro teaches the intelligent adjuster assignment system of claim 2, further comprising machine readable instructions stored in the one or more memory components that cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:
select adjusters that are identified as actively accepting claims for the plurality of adjusters from the adjuster pool (see at least ¶[0017]).
As to Claim 4, Beard teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises at least three of:
an adjuster rating based on whether the adjuster is a customer of an insurance platform provider;
an adjuster location rating based on a distance each of the plurality of adjusters is to a location defined by the insurance claim (see Col. 10, lines 24-65 – “the purpose of referring assignments to the available adjuster in closest proximity to the insurance customer or the location of loss to help the insurance customers as soon as possible”);
an average insurance claims settlement cycle time value for each of the plurality of adjusters;
a platform average winning bid value;
a history of success settlement parameter;
a number of claims adjusted parameter (see Col. 10, lines 24-65 – “the users can be ranked based on one or more criteria such as, the number of assignments completed, customer satisfaction ratings, number of errors/findings, stars, percentage of availability, percentage of assignments accepted, response speed or time of accepting assignments, years (or other length of time) of experience, and other metrics.);
an adjuster history parameter including legal history;
a user rating for each of the plurality of adjusters (see Col. 10, lines 24-65 – “the users can be ranked based on one or more criteria such as, the number of assignments completed, customer satisfaction ratings, number of errors/findings, stars, percentage of availability, percentage of assignments accepted, response speed or time of accepting assignments, years (or other length of time) of experience, and other metrics.);
a reverse bidding algorithm to indicate adjuster preference; or
a combination thereof.
As to Claim 5, Beard teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises an adjuster rating based on whether the adjuster is a customer of an insurance platform provider, and the adjuster location rating is weighted to prioritize shorter distances such that each of the plurality of adjusters that is a shorter distance to the location defined by the insurance claim receive priority over farther distances (see Col. 10, lines 24-65 – “the purpose of referring assignments to the available adjuster in closest proximity to the insurance customer or the location of loss to help the insurance customers as soon as possible”).
As to Claim 6, Shapiro teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises an average insurance claims settlement cycle time value for each of the plurality of adjusters, and the average insurance claims settlement cycle time value for each of the plurality of adjusters is weighted such that priority is given to each adjuster of the plurality of adjusters that has the average insurance claims settlement cycle time value under a threshold percentage of a global average insurance claims settlement cycle time (see ¶[0019], and ¶[0040] – “Processed item data can be transmitted (224). The processed item data can be transmitted to one or more adjuster devices. The one or more adjuster devices can be determined based on the acceptance data, job request data, and/or a score associated with the adjuster devices. For example, the adjuster devices can be determined by providing a compensation metric below the compensation metric indicated in the job request data, by providing an anticipated completion time faster than the time frame indicated in the job request data, and the like. In many embodiments, differences between the job request data and acceptance data can be scored and determining adjuster devices can be based on a weighted score calculated based on the scored differences and a score associated with the adjuster device. In several embodiments, processed item data is transmitted to adjuster devices having a score exceeding a threshold value, which can be pre-determined and/or determined dynamically. For example, processed item data can be transmitted to adjuster devices providing an acceptance response for an annotation task and having a score in the top ninety percent of scores for adjuster devices providing acceptance data for the task. In this way, low performing adjuster devices can be excluded from being assigned annotation tasks. The adjuster devices can be determined based on a total compensation metric for an adjuster device. The total compensation metric can be determined based on compensation awarded to an adjuster device across a variety of annotation tasks performed by the adjuster device over a particular period. In several embodiments, adjuster devices with a total compensation metric over a threshold value can be de-prioritized from being assigned particular annotation tasks. The threshold value can be pre-determined and/or determined automatically based on the total compensation metrics for a variety of adjuster devices, such as all adjuster devices that provide an acceptance for a particular annotation task and/or all adjuster devices present in a data annotation system.”).
As to Claim 7, Shapiro teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises a platform average winning bid value, and the platform average winning bid value is weighted such that priority is given to each adjuster of the plurality of adjusters having a bid that is lower than the platform average winning bid value (see ¶[0019] – “ the annotation of the item data can be subdivided into a variety of tasks and each task can be performed by an adjuster device having a high skill in annotating the particular features indicated in the assigned task. The adjuster devices can then be scored based on their performance in performing the assigned tasks based on a variety of metrics including but not limited to, the time needed to complete the task, a comparison of the annotations provided by the adjuster device to annotations generated by a machine classifier and/or other adjuster devices (such as when the same annotation task is provided to multiple adjuster devices), and/or a comparison of the provided annotations to the corrective action actually taken in repairing and/or replacing the damaged items. For example, if an adjuster device indicates that a bumper requires replacement and the repair shop repairs and repaints the bumper, the adjuster device may receive a lower score for the annotation task vs a second adjuster device (and/or machine classifier) that provided an annotation that the bumper should be repainted. The scoring data can be used to determine which adjuster devices are assigned to future annotation tasks. For example, adjuster devices with scores indicating that the adjuster device does not provide annotations consistent with other adjuster devices can be excluded from being offered future annotation tasks for a particular class of task.”.
As to Claim 8, Shapiro teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises a history of success settlement parameter, and the history of success settlement parameter comprises a percentage score indicative of an amount of times a quote is unchallenged, and wherein the history of success settlement parameter is weighted such that priority is given to each adjuster of the plurality of adjusters having the percentage score indicative of the amount of times the quote is unchallenged that is greater than an unchallenged threshold value (see ¶[0053] – “Performance data can be determined (416). The performance data can be determined for the adjuster device providing the annotation data. The performance data can be based on, but is not limited to, a compensation metric associated with obtaining the annotation data, the time associated with generating the obtained annotation data, the accuracy of the annotation data, and/or the completeness of the annotation data. The completeness of the annotation data can be determined based on the number of features identified in the annotation data as compared to the number of features identified in the machine annotation data and/or secondary annotation data. The accuracy of the annotations can be determined based on the annotations describing the features (e.g. severity, cost to repair/replace, etc.) as compared to the specific annotations provided in the machine annotation data and/or secondary annotation data. In many embodiments, if the performance data indicates that the obtained annotation data is below a particular quality threshold, a secondary review can be initiated. The secondary review can include a manual review of the item data and/or initiating a second request for annotations as described herein with respect to FIG. 2. The performance data can be determined based on the accuracy recommendation made in the annotation data and an action performed with respect to the annotated item. For example, an adjuster device can recommend that the flooring in a home be replaced after experiencing flood damage, while the contractor fixing the home can determine that the flooring is experiencing minor damage and can just be cleaned. In this example, the provided recommendation is not accurate as a different action was taken to correct the item. Alternatively, multiple adjuster devices may have suggested that the flooring should have been replaced as opposed to being replaced, in which case the provided recommendation is not accurate as a different action was sufficient to correct the item.”).
As to Claim 9, Beard teaches the intelligent adjuster assignment system of claim 4, wherein the number of claims adjusted parameter is weighted such that priority is given to each adjuster of the plurality of adjusters having a greater number of claims compared to other adjusters (see Col. 4, lines Col. 10, lines 24-65 – “the users can be ranked based on one or more criteria such as, the number of assignments completed, customer satisfaction ratings, number of errors/findings, stars, percentage of availability, percentage of assignments accepted, response speed or time of accepting assignments, years (or other length of time) of experience, and other metrics.).
As to Claim 10, Shapiro teaches the intelligent adjuster assignment system of claim 4, wherein the user rating for each of the plurality of adjusters is weighted such that priority is given to each adjuster of the plurality of adjusters having the user rating that is above a threshold user rating (see ¶[0026])
As to Claim 11, Shapiro teaches the intelligent adjuster assignment system of claim 1, wherein the one or more weighted parameters comprises a reverse bidding algorithm to indicate adjuster preference, and the machine readable instructions stored in the one or more memory components includes the reverse bidding algorithm that further cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:
receive a bid from at least one adjuster of the plurality of adjusters; and assign a bid score to the at least one adjuster based on a comparison of the bid to at least one of a platform bid average or one or more competitive bids from other adjusters of the plurality of adjusters (see ¶[0024] – “the data processing server system can transmit job request data to the determined adjuster devices. The job request data can indicate that item data is available for annotation. The job request data can also indicate the class of item to be indicated, the number of items of data available, a compensation metric for providing annotation data, a period for providing annotations, and/or any other data as appropriate.” – the compensation parameter is interpreted as a “bid from at least one adjuster, and ¶[0040] – “The adjuster devices can be determined based on a total compensation metric for an adjuster device. The total compensation metric can be determined based on compensation awarded to an adjuster device across a variety of annotation tasks performed by the adjuster device over a particular period. In several embodiments, adjuster devices with a total compensation metric over a threshold value can be de-prioritized from being assigned particular annotation tasks. The threshold value can be pre-determined and/or determined automatically based on the total compensation metrics for a variety of adjuster devices, such as all adjuster devices that provide an acceptance for a particular annotation task and/or all adjuster devices present in a data annotation system.”).
As to Claim 12, Shapiro teaches the intelligent adjuster assignment system of claim 11, wherein the reverse bidding algorithm further cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:
assign priority to the at least one adjuster having the bid score that is within a predetermined percentage of a highest bid score (see ¶[0040] – “processed item data can be transmitted to adjuster devices providing an acceptance response for an annotation task and having a score in the top ninety percent of scores for adjuster devices providing acceptance data for the task. In this way, low performing adjuster devices can be excluded from being assigned annotation tasks.”).
As to Claim 13, Shapiro teaches the intelligent adjuster assignment system of claim 1, further comprising machine readable instructions that cause the intelligent adjuster assignment system to perform at least the following when executed by the one or more processors:
train the intelligent adjuster assignment system to adjust the one or more weighted parameters based on a history of claim assignments for each adjuster (see ¶[0056]).
Claim 14 is rejected under the same reasoning as Claims 1 and is rejected under the same reasoning as Claim 1.
Claim 15 is rejected under the same reasoning as Claims 1 and is rejected under the same reasoning as Claim 1.
Claim 16 is rejected under the same reasoning as Claims 2 and is rejected under the same reasoning as Claim 2.
Claim 17 is the method for using the system of Claim 1 and is rejected under the same reasoning as Claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE S KANG whose telephone number is (571)270-3611. The examiner can normally be reached on Monday through Friday between M-F 10am-2pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Gart may be reached at (571)-273-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IRENE KANG/
Examiner, Art Unit 3695
8/23/2025.
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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