DETAILED ACTION
This Office Action is in response to Applicant's response to application filed on 30 September 2024. Currently, claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (methods). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1 and 18 recite the abstract idea of prescribing a task session by a user to be initiated by a third party and receiving the user prescribed task session and sending the user prescribed task session to a designated third party and receiving with the designated third party the prescribed task session to be initiated and displaying at least a portion of the prescribed task session to be initiated and providing means for evidencing the prescribed task session has been initiated by the third party and sending a notification evidencing the prescribed task session has been initiated and receiving evidencing initiation of the prescribed task session by the designated third party responsive to the notification received. The claims are directed to a type of prescribing tasks to users and determining if the tasks are initiated. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Applicant’s claims show managing personal behavior or relationships or interactions between people because prescribing tasks to users which is a type to managing personal behavior or relationships. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as computer-implemented method for electronically prescribing a user defined task session to a third party via a communication network, via user interaction with a user’s computer device, a remote computer server, via the communication network, wherein the computer device associated with the third party is configured to: display, a notification signal to the remote computer server, receiving the user’s computer device a signal from the remote computer server and wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine a third party to be designated for receiving the prescribed task session from the computer server and to determine if a task session has been successfully completed by a third party are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as computer-implemented method for electronically prescribing a user defined task session to a third party via a communication network, via user interaction with a user’s computer device, a remote computer server, via the communication network, wherein the computer device associated with the third party is configured to: display, a notification signal to the remote computer server, receiving the user’s computer device a signal from the remote computer server and wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine a third party to be designated for receiving the prescribed task session from the computer server and to determine if a task session has been successfully completed by a third party (as evidenced by para [0017]-[0034] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-5, 7, 10-11, 14-17, 19-20 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further wherein a user prescribes a plurality of tasks bundled in a task session and wherein the bundled plurality of tasks are sent to a plurality of third parties whereby a third party from the plurality of third parties performs one or more of the bundled tasks for initiation thereof and wherein the user prescribes a third party to be designated for receiving the prescribed task session and determine a third party to be designated for receiving the prescribed task session and determine if a task session has been successfully completed by a third party and wherein the task session prescribed by the user includes a written description of the task session to be initiated and means provided by third party for evidencing the prescribed task session has been initiated by the third party includes enabling the third party to input a written description evidencing the task has been initiated and wherein the user is enabled to provide follow-up requests relating to prior prescribed task session and/or including additional tasks to be initiated for a certain task session, subsequent to the certain task session being prescribed for initiation by a third party and wherein the user has a prescribed time period, which duration may be variable based on specifics of task(s) requested for a task session, for completing a task session and store accessible by a third party task session initiator certain details relating to a completed initiated task by a third party, which includes: 1) date, start time, end time, duration each task session; 2) location areas associated with each task session; 3) task actions requested; 4) number of photos/videos uploaded for each task session; and 5) confirmation of completion from the third part task initiator and associated user requested the task session and initiate payment from a user to a third party task initiator and automatically initiate payment to a third party task initiator upon completion of a task session by the third party task initiator and wherein the user is enabled to provide follow-up requests relating to prior prescribed task session and/or including additional tasks to be initiated for a certain task session, subsequent to the certain task session being prescribed for initiation by a third party. Dependent claims 2-13, 15-17, 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as remote computer server and wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine a third party to be designated for receiving the prescribed task session from the computer server and wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine if a task session has been successfully completed by a third party and wherein the task session prescribed by the user includes one or more pictures and/or videos to be taken and means provided by the computer device of the third party and wherein the third party computer device is configured to be prevented from storing on local memory of the user computer device any pictures and/or videos recorded relating to initiating a prescribed task and wherein the third party computer device is configured to remove from memory of the user computer device any pictures and/or videos recorded relating to initiation of a prescribed task upon completion of a task session and wherein the computer server is further configured to store in a database accessible (as evidenced by para [0017]-[0034] of applicant’s own specification) are well understood, routine and conventional in the field.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-9, 11-12, 14-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nath et al. (US 20150317582 A1) (hereinafter Nath).
Claims 1 and 18:
Nath, as shown, discloses the following limitations of claims 1 and 18:
A computer-implemented method for electronically prescribing a user defined task session to a third party via a communication network, comprising the steps: prescribing a task session by a user, via user interaction with a user’s computer device, to be initiated by a third party (Fig. 1, showing task publishers using computers (129) for task input module to be done by workers);
receiving in a remote computer server, via the communication network, the user prescribed task session (Fig 1, showing task recommendation module where it is obvious to one of ordinary skill in the art that recommending tasks can be considered prescribing a task given broadest reasonable interpretation);
sending from the remote computer server, via the communication network, the user prescribed task session to a designated third party (see para [0063]-[0066], "As noted above, the Context-Aware Crowdsourced Task Optimizer creates and recommends bundles of tasks to particular workers such that the recommendations are likely to increase the number of successfully completed tasks, while optionally minimizing payments. Towards this purpose, the Context-Aware Crowdsourced Task Optimizer recommends tasks or task bundles to a particular worker when learned worker model corresponding to that worker indicates that there is a sufficiently high probability that the worker will complete that the recommended tasks or task bundles. When a task is recommended to a worker, the worker can respond in one of three ways: (1) The worker can accept and complete the task bundle within the task deadline; (2) The worker can reject the task bundle; or (3) The worker can accept the task bundle but fail to complete some or all of the tasks in the bundle within the task deadline.");
receiving in a computer device associated with the designated third party, via the communication network, the prescribed task session to be initiated wherein the computer device associated with the third party is configured to: display at least a portion of the prescribed task session to be initiated (see para [0057], "Context-Aware Crowdsourced Task Optimizer receives inputs including historical, real-time and future context information of multiple workers, and task properties (such as location, payment, deadline, etc.) from one or more task publishers. Given these inputs, the Context-Aware Crowdsourced Task Optimizer outputs recommended assignments of bundles of tasks to active workers. In making these recommendations, the Context-Aware Crowdsourced Task Optimizer uses learned worker models to make recommendations in a way that meets various criteria, including maximizing task completion rates where task payments are fixed, jointly maximizing task completion rates while minimizing task payments using adaptive pricing, etc." where it would be obvious to one of ordinary skill in the art that outputting to a worker would be a type of displaying);
provide means for evidencing the prescribed task session has been initiated by the third party (see para [0031], "A task verification and reporting module 190 is used to verify task 105 completions. Note that this verification may also include receiving task data (e.g., picture of a restaurant menu) or information (e.g., traffic observations at a particular intersection) from workers regarding completed tasks and task bundles. The task verification and reporting module 190 then provides a completion report and any optional or associated data or information to the corresponding task publisher (110, 115, or 120).");
send a notification signal to the remote computer server, via the communication network, evidencing the prescribed task session has been initiated (see para [0031], "A task verification and reporting module 190 is used to verify task 105 completions. Note that this verification may also include receiving task data (e.g., picture of a restaurant menu) or information (e.g., traffic observations at a particular intersection) from workers regarding completed tasks and task bundles. The task verification and reporting module 190 then provides a completion report and any optional or associated data or information to the corresponding task publisher (110, 115, or 120)."); and
receiving in the user’s computer device a signal from the remote computer server, via the communication network, evidencing initiation of the prescribed task session by the designated third party responsive to the notification signal being received in the remote computer server (see para [0032], showing verification of the task completion before payment shows receiving the evidence of task initiation)
wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine a third party to be designated for receiving the prescribed task session from the computer server and to determine if a task session has been successfully completed by a third party (see para [0067], "Advantageously the optimization process performed by the Context-Aware Crowdsourced Task Optimizer inherently minimizes cases (2) and (3) by considering machine-learned worker-models as part of the optimization and recommendation process. These machine-learned worker models use any of a variety of machine learning processes to automatically model workers' tendencies, preferences towards different types of tasks, completion histories, etc., to construct a machine-learned worker model for each worker. Note that these learned worker models are discussed in further detail below in Section 2.3." where the machine learning considering completion histories can be considered to show the AI determining if tasks have been successfully completed given broadest reasonable interpretation)
Claim 2:
Further, Nath discloses the following limitations:
wherein a user prescribes a plurality of tasks bundled in a task session sent to the remote computer server (see para [0005], "The Context-Aware Crowdsourced Task Optimizer's optimization algorithms differ from existing crowdsourced task assignment processes with respect to the optimization intent, bundling of multiple tasks with respect to particular workers, and the ability to consider worker context in general, rather than being specific to particular locations.")
Claim 3:
Further, Nath discloses the following limitations:
wherein the bundled plurality of tasks are sent to a plurality of third parties by the remote computer server whereby a third party from the plurality of third parties performs one or more of the bundled tasks for initiation thereof (Fig 1, showing bundled tasks sent to the worker pool where it is obvious to one of ordinary skill in the art that the worker can be considered a third party and see para [0099], "Note that in a variant of this example, the pizza maker can be considered the task publisher (where he has already been paid for pizzas by customers) and the drone (or a free-lance pizza delivery man with his own car) could be a third party worker that accepts pizza delivery tasks to deliver pizzas from the pizza maker to the customers for some price for delivery of a bundle of one or more pizza delivery tasks to one or more customers.")
Claims 4-6:
Further, Nath discloses the following limitations:
wherein the user prescribes a third party to be designated for receiving the prescribed task session from the computer server (see para [0027]-[0030], showing considering and determining worker context when assigning/recommending tasks and task bundles where the context can be based on inputs from the user such as price, location, etc. )
wherein the computer server is configured to determine a third party to be designated for receiving the prescribed task session from the computer server (see para [0027]-[0030], showing the use of machined learned models for performing the recommendations)
wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine a third party to be designated for receiving the prescribed task session from the computer server (see para [0029]-[0030], showing machine learning models for determining worker to recommend the task to)
Claims 7-8:
Further, Nath discloses the following limitations:
wherein the computer server is configured to determine if a task session has been successfully completed by a third party (see para [0060], "A task is considered to be successfully completed if it is performed by at least one worker within the deadline of the task. For each successfully completed task, the worker is paid by the task publisher. Note also that the Context-Aware Crowdsourced Task Optimizer can act as an intermediary for task payments where task publishers deposit funds with the Context-Aware Crowdsourced Task Optimizer (or otherwise allow the Context-Aware Crowdsourced Task Optimizer to access funds) to be paid to workers by the Context-Aware Crowdsourced Task Optimizer upon completion of particular tasks.")
wherein the computer server utilizes one or more Artificial Intelligence (AI) techniques to determine if a task session has been successfully completed by a third party (see para [0067], "Advantageously the optimization process performed by the Context-Aware Crowdsourced Task Optimizer inherently minimizes cases (2) and (3) by considering machine-learned worker-models as part of the optimization and recommendation process. These machine-learned worker models use any of a variety of machine learning processes to automatically model workers' tendencies, preferences towards different types of tasks, completion histories, etc., to construct a machine-learned worker model for each worker. Note that these learned worker models are discussed in further detail below in Section 2.3." where the machine learning considering completion histories can be considered to show the AI determining if tasks have been successfully completed given broadest reasonable interpretation)
Claim 9:
Further, Nath discloses the following limitations:
wherein the task session prescribed by the user includes one or more pictures and/or videos to be taken (see para [0055], "Another example of actively notifying workers is that in various embodiments, the Context-Aware Crowdsourced Task Optimizer pushes recommendations of tasks to workers that are near tasks that can be accepted, or to remind the worker to complete tasks depending upon time or current location of worker. In other words, when the worker is near (or heading towards) a task location, the system can remind the worker to perform a previously assigned task. For example, the Context-Aware Crowdsourced Task Optimizer can alert the worker or send a message to a computing device (e.g., a cell phone) of the worker, such as, for example, “Based on your typical daily commute (or current travel route), you will be passing near Restaurant X. If you stop in and take a picture of the menu we will pay you a $5 for completion of that task.” In other words, the Context-Aware Crowdsourced Task Optimizer tries to learn known present locations, travel routes, anticipated future locations, etc., for workers, and then to use this and other information to recommend bundles of one or more tasks to the workers based on the location of those tasks relative to the location of the worker.")
Claims 11 and 20:
Further, Nath discloses the following limitations:
wherein the user is enabled to provide follow-up requests relating to prior prescribed task session and/or including additional tasks to be initiated for a certain task session, subsequent to the certain task session being prescribed for initiation by a third party (see para [0091]-[0094], "Periodic or continuous updates of the predictive worker models allows the crowdsourcing platform of the Context-Aware Crowdsourced Task Optimizer to keep track of the changes in workers' behavior over time. Another advantage of performing model updates as additional worker information becomes available is that instead of processing a large number of samples at once, which can incur undesirable computational complexity or overhead, the Context-Aware Crowdsourced Task Optimizer can update individual worker models in real-time whenever a new sample or data point is received for a particular worker. As noted above, the Context-Aware Crowdsourced Task Optimizer's task bundling processes are framed as an optimization problem that acts to maximize task completion rates, with optional joint minimization of task costs, by bundling and recommending tasks to workers in view of the predictive models associated with each worker. The input to the task recommendation process includes workers real-time and historical context information, predictive worker models, and task properties (such as location, budget, deadline, etc.). The output of the task recommendation process includes bundles of tasks that are recommended to particular workers. As noted above, the recommendation process considers various objective functions, including maximizing task completion rates where task payments are fixed, and jointly optimizing both task completion rates (i.e., maximizing task completion rates) and task payments (i.e., minimizing task payments) by using adaptive pricing. Note that task bundle size is automatically adjusted by the Context-Aware Crowdsourced Task Optimizer during the optimization process.").
Claim 12:
Further, Nath discloses the following limitations:
wherein the third party computer device is configured to be prevented from storing on local memory of the user computer device any pictures and/or videos recorded relating to initiating a prescribed task (see para [0157]-[0158], "Further, software, programs, and/or computer program products embodying the some or all of the various embodiments of the Context-Aware Crowdsourced Task Optimizer described herein, or portions thereof, may be stored, received, transmitted, or read from any desired combination of computer or machine readable media or storage devices and communication media in the form of computer executable instructions or other data structures. Finally, the Context-Aware Crowdsourced Task Optimizer described herein may be further described in the general context of computer-executable instructions, such as program modules, being executed by a computing device. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The embodiments described herein may also be practiced in distributed computing environments where tasks are performed by one or more remote processing devices, or within a cloud of one or more devices, that are linked through one or more communications networks. In a distributed computing environment, program modules may be located in both local and remote computer storage media including media storage devices. Still further, the aforementioned instructions may be implemented, in part or in whole, as hardware logic circuits, which may or may not include a processor." where storing on a remote device as a desired combination would be obvious to one of ordinary skill in the art for security purposes)
Claims 14-15:
Further, Nath discloses the following limitations:
wherein the user has a prescribed time period, which duration may be variable based on specifics of task(s) requested for a task session, for completing a task session (see para [0026]-[0027], " In general, as illustrated by FIG. 1, the processes enabled by the Context-Aware Crowdsourced Task Optimizer begin operation by using a task input module 100 to receive one or more tasks 105 from human or virtual task publishers (110, 115, 120). In addition, the task input module 100 also receives one or more optional task contexts, e.g., prices, location, deadlines, number of instances, etc. In various embodiments, a task feedback module 125 compute completion rates for various contexts such as prices, deadlines, etc., and uses these completion rates to provide guidance to task publishers (110, 115, 120) for specifying various task contexts prior to publishing those tasks 105. Note that the concept of providing task context feedback to assist the task publishers in specifying or otherwise setting those contexts is discussed in further detail in Section 2.7 of this document.")
wherein the computer server is further configured to store in a database accessible by a third party task session initiator certain details relating to a completed initiated task by a third party, which includes: 1) date, start time, end time, duration each task session; 2) location areas associated with each task session; 3) task actions requested; 4) number of photos/videos uploaded for each task session; and 5) confirmation of completion from the third part task initiator and associated user requested the task session (see para [0026]-[0027], " In general, as illustrated by FIG. 1, the processes enabled by the Context-Aware Crowdsourced Task Optimizer begin operation by using a task input module 100 to receive one or more tasks 105 from human or virtual task publishers (110, 115, 120). In addition, the task input module 100 also receives one or more optional task contexts, e.g., prices, location, deadlines, number of instances, etc. In various embodiments, a task feedback module 125 compute completion rates for various contexts such as prices, deadlines, etc., and uses these completion rates to provide guidance to task publishers (110, 115, 120) for specifying various task contexts prior to publishing those tasks 105. Note that the concept of providing task context feedback to assist the task publishers in specifying or otherwise setting those contexts is discussed in further detail in Section 2.7 of this document." where prices, location, deadlines, number of instances show such task details would be obvious to one of ordinary skill in the art)
Claims 16-17:
Further, Nath discloses the following limitations:
wherein the computer server is further configured to initiate payment from a user to a third party task initiator (see para [0060], "A task is considered to be successfully completed if it is performed by at least one worker within the deadline of the task. For each successfully completed task, the worker is paid by the task publisher. Note also that the Context-Aware Crowdsourced Task Optimizer can act as an intermediary for task payments where task publishers deposit funds with the Context-Aware Crowdsourced Task Optimizer (or otherwise allow the Context-Aware Crowdsourced Task Optimizer to access funds) to be paid to workers by the Context-Aware Crowdsourced Task Optimizer upon completion of particular tasks.")
wherein the computer server is further configured to automatically initiate payment to a third party task initiator upon completion of a task session by the third party task initiator (see para [0060], "A task is considered to be successfully completed if it is performed by at least one worker within the deadline of the task. For each successfully completed task, the worker is paid by the task publisher. Note also that the Context-Aware Crowdsourced Task Optimizer can act as an intermediary for task payments where task publishers deposit funds with the Context-Aware Crowdsourced Task Optimizer (or otherwise allow the Context-Aware Crowdsourced Task Optimizer to access funds) to be paid to workers by the Context-Aware Crowdsourced Task Optimizer upon completion of particular tasks.")
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nath, as applied above, and further in view of Davis et al. (US 2016/0350691 A1) (hereinafter Davis).
Claims 10 and 19:
Nath does not specifically disclose wherein the task session prescribed by the user includes a written description of the task session to be initiated and/or the means provided by the computer device of the third party for evidencing the prescribed task session has been initiated by the third party includes enabling the third party to input a written description evidencing the task has been initiated. In analogous art, Davis discloses the following limitations:
wherein the task session prescribed by the user includes a written description of the task session to be initiated and/or the means provided by the computer device of the third party for evidencing the prescribed task session has been initiated by the third party includes enabling the third party to input a written description evidencing the task has been initiated (see para [0014], "Once task performance has begun, task completion data 108 may be collected and transmitted to the computing device 106. Task completion data 108 may include details related to task performance. For instance, task completion data 108 may include, but is not limited to: a beginning timestamp, an ending timestamp, a travel time to the task location, GPS (Global Positioning Satellite) data, photographs, a service provider review, a customer review, a third-party review, a materials list/invoice, etc. The task completion data 108 may be submitted by a service provider, a customer and/or a third-party. For example, a service provider, a customer or a third-party may submit task completion data 108 before, during and after a task is performed.").
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to combine the teachings of Nath with Davis because including written descriptions enables more effective ways of reviewing the services provided (see Davis, para [0001]-[0002]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the method for task quality assurance as taught by Davis in the system for optimizing task recommendation in context-aware mobile crowdsourcing as taught by Nath since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Nath, as applied above, and further in view of Davis et al. (US 2016/0350691 A1) (hereinafter Davis).
Claim 13:
Nath does not specifically disclose wherein the third party computer device is configured to remove from memory of the user computer device any pictures and/or videos recorded relating to initiation of a prescribed task upon completion of a task session. In analogous art, Gryka discloses the following limitations:
wherein the third party computer device is configured to remove from memory of the user computer device any pictures and/or videos recorded relating to initiation of a prescribed task upon completion of a task session (see para [0080], "In some embodiments, all of the 28 images and/or the 28 videos in the example may be provided, or made available, to the user who is associated with the first party 12 (the provider of the product). In other embodiments, only one image and/or one video may be provided, or made available, to the user who is associated with the first party 12. For example, if a certain task in the product testing results in a “failed” result for the product testing, then the system 10 may provide only the image and/or only the video associated with the failed task. This may be advantageous because the user may not be concerned with tasks that have “pass” status, but may be more interested in viewing results for the task that results in a “failed” status. Furthermore, in some embodiments, the system 10 may store only the image and/or only the video that is associated with a failed task, and may delete other images and videos for other tasks that have “pass” status after the product testing project is completed or after the product testing project is completed for a certain pre-determined duration.").
It would have been obvious to a person of ordinary skill in the art at the time the invention was made to combine the teachings of Nath with Gryka because removing data from memory improves the efficiency of the system (see Gryka, para [0144]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the electronic product testing system as taught by Gryka in the system for optimizing task recommendation in context-aware mobile crowdsourcing as taught by Nath since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lillie et al. (US 20210065292 A1), a system for the intelligent on-boarding, support, education of non-conventional resource account users that includes tools for aggregation and intelligent data sharing between multiple users and associated resource accounts in order to generate a comparison between resource accounts and identify and incentivize potential earning opportunities and the competitive comparison may be continuously updated as earning opportunities are completed by the various users, and educational modules are provided via a resource account interface related to various topics of resource management responsibility for guided and reinforced learning
Lu (CN 112270512 A), a method for data encryption and retrieval by generating a configuration file having details of the user input in a database via a server; and uploading, by the user, an image containing details of the task with the user's parent signature thereon, for associating the detected parent signature image with the configuration file to form an identification code representing details of the configuration file; wherein the identification code is a scannable detail for retrieving the configuration file.
Yasmin et al. "Improving Crowdsourcing-Based Image Classification Through Expanded Input Elicitation and Machine Learning", a paper that investigates how different forms of input elicitation obtained from crowdsourcing can be utilized to improve the quality of inferred labels for image classification tasks, where an image must be labeled as either positive or negative depending on the presence/absence of a specified object
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624