Systems, apparatuses, methods, and computer program products for generating responsive action programs

Abstract

A method provided herein includes accessing, using a monitoring engine, a computing file provided to a second computing device via a computing file exchange platform. In some embodiments, the method includes identifying, using the computing file and a computing file content model, a content deficiency associated with the computing file. In some embodiments, the method includes generating, using the content deficiency and the computing file content model, a responsive action procedure. In some embodiments, the method includes generating a responsive action procedure link interface element. In some embodiments, the responsive action procedure link interface element includes a responsive action procedure link representation corresponding to the responsive action procedure. In some embodiments, the method includes causing the responsive action procedure link interface element to be rendered to an interface associated with the computing file exchange platform.

Description

Cross-references to related applications

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 743,977, filed January 10, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The embodiments disclosed herein relate in general to systems, apparatus, methods, and computer program products for generating responsive action procedures. Background Technology

[0003] The applicant has recognized the numerous technical challenges and difficulties associated with systems, apparatuses, methods, and computer program products for responding to content defects associated with computational documents. Through the application of effort, ingenuity, and innovation, the applicant has solved the problems associated with systems, apparatuses, methods, and computer program products for responding to content defects associated with computational documents by developing solutions embodied in this disclosure, which are described in detail below. Summary of the Invention

[0004] The various implementation schemes described herein relate to systems, apparatuses, methods, and computer program products for generating responsive action programs.

[0005] According to one aspect of this disclosure, a method is provided. In some embodiments, the method includes: using a monitoring engine to access a computation file provided to a second computing device via a computation file exchange platform. In some embodiments, the method includes: using the computation file and a computation file content model to identify content defects associated with the computation file. In some embodiments, the method includes: using the content defects and the computation file content model to generate a responsive action. In some embodiments, the method includes: generating a responsive action link interface element. In some embodiments, the responsive action link interface element includes a responsive action link representation corresponding to the responsive action. In some embodiments, the method includes: rendering the responsive action link interface element to an interface associated with the computation file exchange platform.

[0006] In some implementations, the method includes: establishing a monitoring interface with a computing file exchange platform.

[0007] In some implementations, the method includes using one or more domain resources to train a computational document content model.

[0008] In some implementations, the method includes generating responsive action program interface elements.

[0009] In some implementations, the responsive action interface element includes a responsive action representation corresponding to the responsive action.

[0010] In some implementations, the responsive action procedure includes one or more auxiliary responsive action procedures.

[0011] In some implementations, the method includes rendering a responsive action interface element onto a second interface.

[0012] In some implementations, the method includes detecting the termination of the responsive action procedure.

[0013] In some implementations, the method includes generating a responsive action result based on the termination of the responsive action.

[0014] In some implementations, the method includes storing responsive action procedure results in a responsive action procedure database based on a computation file identifier associated with the computation file.

[0015] In some implementations, the method includes generating a responsive action procedure indicator using the responsive action procedure result and the implementation model.

[0016] In some implementations, the method includes storing a responsive action indicator in a responsive action database based on a computation file identifier associated with the computation file.

[0017] In some implementations, the monitoring engine, the computational file content model, and the implementation model are part of a composite model framework.

[0018] In some implementations, the method includes using a monitoring engine to access a second computing file provided to a second computing device via a computing file exchange platform.

[0019] In some implementations, the method includes determining a processing consumption value associated with a second computation file.

[0020] In some implementations, the method includes sending a second computation file to a remote computing device in response to a processing consumption value meeting or exceeding a processing consumption threshold.

[0021] In some implementations, the method includes receiving a second responsive action procedure corresponding to a second computational file from a remote computing device.

[0022] In some implementations, the computation file includes text or audio.

[0023] In some implementations, the computation file includes one or more images.

[0024] In some implementations, the interface associated with the computation file exchange platform includes a computation file representation corresponding to the computation file.

[0025] In some implementations, identifying content defects includes identifying one or more content features associated with the computation file.

[0026] According to another aspect of this disclosure, an apparatus is provided. In some embodiments, the apparatus includes a memory and one or more processors communicatively coupled to the memory. In some embodiments, the one or more processors are configured to perform operations including: accessing a computation file provided to a second computing device via a computation file exchange platform using a monitoring engine. In some embodiments, the one or more processors are configured to perform operations including: identifying content defects associated with the computation file using the computation file and a computation file content model. In some embodiments, the one or more processors are configured to perform operations including: generating responsive actions using content defects and the computation file content model. In some embodiments, the one or more processors are configured to perform operations including: generating responsive action link interface elements. In some embodiments, the responsive action link interface element includes a responsive action link representation corresponding to the responsive action. In some embodiments, the one or more processors are configured to perform operations including: rendering the responsive action link interface element to an interface associated with the computation file exchange platform.

[0027] In some implementations, the operation also includes: establishing a monitoring interface with a computing file exchange platform.

[0028] In some implementations, the operation also includes using one or more domain resources to train a computational document content model.

[0029] In some implementations, the operation also includes generating responsive action program interface elements.

[0030] In some implementations, the responsive action interface element includes a responsive action representation corresponding to the responsive action.

[0031] In some implementations, the responsive action procedure includes one or more auxiliary responsive action procedures.

[0032] In some implementations, the operation also includes rendering the responsive action interface element to a second interface.

[0033] In some implementations, the operation also includes detecting the termination of the responsive action procedure.

[0034] In some implementations, the operation further includes generating a responsive action result based on the termination of the responsive action procedure.

[0035] In some implementations, the operation further includes storing the results of the responsive action procedure in a responsive action procedure database based on a calculation file identifier associated with the calculation file.

[0036] In some implementations, the operation further includes generating a responsive action indicator using the responsive action result and the implementation model.

[0037] In some implementations, the operation further includes storing a responsive action procedure indicator in a responsive action procedure database based on a calculation file identifier associated with the calculation file.

[0038] In some implementations, the monitoring engine, the computational file content model, and the implementation model are part of a composite model framework.

[0039] According to another aspect of this disclosure, a computer program product is provided. In some embodiments, the computer program product includes at least one non-transitory computer-readable storage medium having computer program code stored thereon. In some embodiments, the computer program code, when executed using at least one processor, configures the computer program product to: access a computational file provided to a second computing device via a computational file exchange platform using a monitoring engine. In some embodiments, the computer program code, when executed using at least one processor, configures the computer program product to: identify content defects associated with the computational file using the computational file and a computational file content model. In some embodiments, the computer program code, when executed using at least one processor, configures the computer program product to: generate a responsive action program using the content defects and the computational file content model. In some embodiments, the computer program code, when executed using at least one processor, configures the computer program product to: generate a responsive action program link interface element. In some embodiments, the responsive action program link interface element includes a responsive action program link representation corresponding to the responsive action program. In some implementations, when the computer program code is executed using at least one processor, the computer program product is configured to: render responsive action program link interface elements to an interface associated with a computing file exchange platform.

[0040] The above description of the invention is provided merely to outline some exemplary embodiments in order to provide a basic understanding of some aspects of this disclosure. Therefore, it should be understood that the above embodiments are merely illustrative and should not be construed as limiting the scope or substance of this disclosure in any way. It should be understood that, in addition to those outlined herein, the scope of this disclosure covers many possible embodiments, some of which will be further described below. Attached Figure Description

[0041] Reference will now be made to the accompanying drawings. In some embodiments described herein, the components illustrated in the drawings may or may not be present. Some embodiments may include fewer (or more) components than those shown in the drawings of the exemplary embodiments according to this disclosure.

[0042] Figure 1 Example block diagrams illustrating the environment in which embodiments of this disclosure may operate;

[0043] Figure 2 An example block diagram illustrating an example device that can be specially configured according to an example embodiment of the present disclosure is shown;

[0044] Figure 3 An example composite model framework is illustrated according to an example implementation of the present disclosure;

[0045] Figure 4 Example interfaces are illustrated according to one or more embodiments of this disclosure;

[0046] Figure 5 Example interfaces are illustrated according to one or more embodiments of this disclosure;

[0047] Figure 6 A flowchart illustrating an example method according to one or more embodiments of this disclosure is provided;

[0048] Figure 7 Flowcharts illustrating example methods according to one or more embodiments of this disclosure are shown; and

[0049] Figure 8 A flowchart illustrating an example method according to one or more embodiments of this disclosure is provided. Detailed Implementation

[0050] Some embodiments of this disclosure will be described more fully herein with reference to the accompanying drawings, which illustrate some, but not all, embodiments of this disclosure. In fact, various embodiments of this disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will meet applicable legal requirements. Throughout this document, similar reference numerals refer to similar elements.

[0051] As used herein, the term “comprising” means including but not limited to, and should be interpreted in the manner in which it is typically used in the patent context. The use of broader terms such as “comprising,” “including,” and “having” should be understood to provide support for narrower terms such as “consisting of,” “substantially composed of,” and “substantially constituted by.”

[0052] The phrases “in one embodiment,” “according to one embodiment,” “in some embodiments,” etc., generally mean that the specific feature, structure, or characteristic following the phrase may be included in at least one embodiment of this disclosure, and may be included in more than one embodiment of this disclosure (importantly, such phrases do not necessarily refer to the same embodiment).

[0053] As used herein, the terms “example” or “exemplary” mean “serving as an example, instance, or illustration.” Any specific implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other specific implementations.

[0054] If this specification states that a component or feature is "may", "can", "may", "should", "will", "preferably", "possibly", "usually", "optionally", "for example", "often", or "maybe" (or other such language) included or has a characteristic, then the specific component or feature does not need to be included or have that characteristic. Such a component or feature may be optionally included in some embodiments, or it may be excluded.

[0055] The use of the term "circuit" as used herein with respect to components of a system or apparatus should be understood to include specific hardware configured to perform functions associated with a particular circuit as described herein. The term "circuit" should be broadly understood to include hardware, and in some embodiments, to include software for configuring the hardware. For example, in some embodiments, "circuit" may include processing circuitry, communication circuitry, input / output circuitry, etc. In some embodiments, other elements may provide or supplement the functionality of a particular circuit. Alternatively or additionally, in some embodiments, other elements of the system and / or apparatus described herein may provide or supplement the functionality of another particular group of circuits. For example, a processor may provide processing functionality to any group of circuits, a memory may provide storage functionality to any group of circuits, and / or communication circuitry may provide network interface functionality to any group of circuits, etc.

[0056] Overview

[0057] The example embodiments disclosed herein address technical problems related to responding to content defects associated with computational files. As those skilled in the art to which this disclosure pertains will understand, there are multiple example scenarios in which responding to content defects associated with computational files is desirable. In this regard, in many applications, it may be desirable to use systems, apparatuses, methods, and computer program products for generating responsive actions to respond to content defects associated with computational files. For example, it may be desirable to use systems, apparatuses, methods, and computer program products for generating responsive actions to respond to content defects in computational files exchanged in real time via a computational file exchange platform.

[0058] Example solutions for responding to content defects associated with computational files include using computing devices and databases to respond to such defects. However, such example solutions are simplistic, inefficient, reactive, and technically flawed. For example, such example solutions are simplistic because they cannot automatically detect content defects associated with computational files exchanged in real time via a computational file exchange platform. As another example, such example solutions are inefficient because they cannot determine the processing consumption value associated with the computational file and dynamically allocate the computational file to appropriately equipped computing devices for processing based on that value. Consequently, the computing devices and databases used in such example solutions to respond to content defects associated with computational files suffer from high latency, excessive processing power consumption, and excessive memory consumption. As another example, such example solutions are reactive because they cannot automatically generate responsive actions in response to identified content defects. As yet another example, such example solutions are technically flawed because they do not implement a model framework for a specialized configuration for generating responsive actions to respond to content defects associated with computational files. Conversely, such example solutions rely on independent components of computing devices that respond to content defects in a disjointed and fragmented manner. Therefore, there is a need for systems, apparatuses, methods, and computer program products capable of generating responsive actions in a sophisticated, efficient, proactive, and technically complete manner.

[0059] Therefore, to address these and / or other issues associated with such example solutions, this document discloses example systems, apparatuses, methods, and computer program products for generating responsive actions. For example, embodiments of this disclosure, described in more detail below, include a method comprising: using a monitoring engine to access a computational file provided to a second computing device via a computational file exchange platform. In some embodiments, the method includes: using the computational file and a computational file content model to identify content defects associated with the computational file. In some embodiments, the method includes: using the content defects and the computational file content model to generate a responsive action. In some embodiments, the method includes: generating a responsive action link interface element. In some embodiments, the responsive action link interface element includes a responsive action link representation corresponding to the responsive action. In some embodiments, the method includes: rendering the responsive action link interface element to an interface associated with the computational file exchange platform. Therefore, the systems, apparatuses, methods, and computer program products provided herein enable responses to content defects associated with computational files in a sophisticated, efficient, proactive, and technically sound manner.

[0060] Example systems and devices

[0061] The embodiments of this disclosure include systems, apparatus, methods, and computer program products configured to generate responsive action procedures. It should be readily understood that, in addition to those expressly described herein, embodiments of the apparatus, systems, methods, and computer program products described herein can be configured in various additional and alternative ways.

[0062] Figure 1 An exemplary block diagram of an environment 100 in which embodiments of the present disclosure may operate is illustrated. In some embodiments, environment 100 includes a first computing device 140. In some embodiments, the first computing device 140 is electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action database 150, and / or a user device 160. The first computing device 140 may be located remotely from the remote computing device 170, the computing file exchange platform 190, the second computing device 180, the responsive action database 150, and / or the user device 160. In some embodiments, the first computing device 140 may be located in a remote cloud server and is electronically and / or communicatively coupled to the remote computing device 170, the computing file exchange platform 190, the second computing device 180, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the first computing device 140 is an edge device and is electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action database 150, and / or a user device 160. Additionally or alternatively, the first computing device 140 may be located in the same location as the remote computing device 170, the computing file exchange platform 190, the second computing device 180, the responsive action database 150, and / or the user device 160, and is electronically and / or communicatively coupled to these devices via one or more direct connections (e.g., via wires). In some embodiments, the first computing device 140 is configured via hardware, software, firmware, and / or combinations thereof to perform data ingestion of one or more types of data.

[0063] Additionally or alternatively, in some embodiments, the first computing device 140 is configured via hardware, software, firmware, and / or combinations thereof to generate and / or send commands that control, adjust, or otherwise affect the operation of one or more of the following: electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action program database 150, and / or a user device 160. For example, the first computing device 140 may be configured to generate responsive actions. Additionally or alternatively, in some embodiments, the first computing device 140 is configured via hardware, software, firmware, and / or combinations thereof to perform data reporting, provide data, and / or other data output processes associated with monitoring or otherwise analyzing the operation of one or more of the following: electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action program database 150, and / or a user device 160. For example, in various embodiments, the first computing device 140 may be configured to perform and / or implement one or more operations and / or functions described herein.

[0064] In some embodiments, environment 100 includes a second computing device 180. In some embodiments, the second computing device 180 is electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a first computing device 140, a responsive action database 150, and / or a user device 160. The second computing device 180 may be located remotely from the remote computing device 170, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. In some embodiments, the second computing device 180 may reside in a remote cloud server and be electronically and / or communicatively coupled to the remote computing device 170, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the second computing device 180 is an edge device and is electronically and / or communicatively coupled to the remote computing device 170, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the second computing device 180 may be located in the same location as the remote computing device 170, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160, and is electronically and / or communicatively coupled to these devices via one or more direct connections (e.g., via wires). In some embodiments, the second computing device 180 is configured via hardware, software, firmware, and / or combinations thereof to perform data ingestion of one or more types of data.

[0065] Additionally or alternatively, in some embodiments, the second computing device 180 is configured via hardware, software, firmware, and / or combinations thereof to generate and / or send commands that control, adjust, or otherwise affect the operation of one or more of the following: electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a first computing device 140, a responsive action program database 150, and / or a user device 160. For example, the second computing device 180 may be configured to generate responsive actions. Additionally or alternatively, in some embodiments, the second computing device 180 is configured via hardware, software, firmware, and / or combinations thereof to perform data reporting, provide data, and / or other data output processes associated with monitoring or otherwise analyzing the operation of one or more of the following: electronically and / or communicatively coupled to a remote computing device 170, a computing file exchange platform 190, a first computing device 140, a responsive action program database 150, and / or a user device 160. For example, in various embodiments, the second computing device 180 may be configured to perform and / or implement one or more operations and / or functions described herein.

[0066] In some embodiments, environment 100 includes a remote computing device 170. In some embodiments, the remote computing device 170 is electronically and / or communicatively coupled to a second computing device 180, a computing file exchange platform 190, a first computing device 140, a responsive action database 150, and / or a user device 160. The remote computing device 170 may be located remotely from the second computing device 180, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. In some embodiments, the remote computing device 170 may reside in a remote cloud server and be electronically and / or communicatively coupled to the second computing device 180, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the remote computing device 170 is an edge device and is electronically and / or communicatively coupled to the second computing device 180, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the remote computing device 170 may be located in the same location as the second computing device 180, the computing file exchange platform 190, the first computing device 140, the responsive action database 150, and / or the user device 160, and is electronically and / or communicatively coupled to these devices via one or more direct connections (e.g., via wires). In some embodiments, the remote computing device 170 is configured via hardware, software, firmware, and / or combinations thereof to perform data ingestion of one or more types of data.

[0067] Additionally or alternatively, in some embodiments, the remote computing device 170 is configured via hardware, software, firmware, and / or combinations thereof to generate and / or send commands that control, adjust, or otherwise influence the operation of one or more of the following: electronically and / or communicatively coupled to a second computing device 180, a computing file exchange platform 190, a first computing device 140, a responsive action program database 150, and / or a user device 160. For example, the remote computing device 170 may be configured to generate a second responsive action program. Additionally or alternatively, in some embodiments, the remote computing device 170 is configured via hardware, software, firmware, and / or combinations thereof to perform data reporting, provide data, and / or other data output processes associated with monitoring or otherwise analyzing the operation of one or more of the following: electronically and / or communicatively coupled to a second computing device 180, a computing file exchange platform 190, a first computing device 140, a responsive action program database 150, and / or a user device 160. For example, in various implementations, the remote computing device 170 may be configured to perform and / or implement one or more operations and / or functions described herein.

[0068] In some embodiments, environment 100 includes a computing file exchange platform 190. In some embodiments, the computing file exchange platform 190 is electronically and / or communicatively coupled to a second computing device 180, a remote computing device 170, a first computing device 140, a responsive action database 150, and / or a user device 160. The computing file exchange platform 190 may be located remotely from the second computing device 180, the remote computing device 170, the first computing device 140, the responsive action database 150, and / or the user device 160. In some embodiments, the computing file exchange platform 190 may reside in a remote cloud server and be electronically and / or communicatively coupled to the second computing device 180, the remote computing device 170, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, the computational file exchange platform 190 may be located in the same location as the second computing device 180, the remote computing device 170, the first computing device 140, the responsive action database 150, and / or the user equipment 160, and be electronically and / or communicatively coupled to these devices via one or more direct connections (e.g., via wires). In some embodiments, the computational file exchange platform 190 is configured via hardware, software, firmware, and / or combinations thereof to perform data ingestion of one or more types of data.

[0069] Additionally or alternatively, in some embodiments, the computing file exchange platform 190 is configured via hardware, software, firmware, and / or combinations thereof to generate and / or send commands that control, adjust, or otherwise affect the operation of one or more of the following: electronically and / or communicatively coupled to a second computing device 180, a remote computing device 170, a first computing device 140, a responsive action database 150, and / or a user device 160. For example, the computing file exchange platform 190 may be configured to facilitate the exchange of one or more computing files between the second computing device 180, the remote computing device 170, the first computing device 140, the responsive action database 150, and / or the user device 160. Additionally or alternatively, in some embodiments, the computing file exchange platform 190 is configured via hardware, software, firmware, and / or combinations thereof to perform data reporting, provide data, and / or other data output processes associated with monitoring or otherwise analyzing one or more of the following operations: electronically and / or communicatively coupled to a second computing device 180, a remote computing device 170, a first computing device 140, a responsive action procedure database 150, and / or a user device 160. For example, in various embodiments, the computing file exchange platform 190 may be configured to perform and / or implement one or more of the operations and / or functions described herein.

[0070] In some implementations, environment 100 includes user equipment 160. User equipment 160 may be associated with a user of a second computing device 180, a remote computing device 170, a first computing device 140, a responsive action database 150, and / or a computing file exchange platform 190. In various implementations, the second computing device 180, remote computing device 170, first computing device 140, responsive action database 150, and / or computing file exchange platform 190 may generate and / or send messages, alarms, or instructions to the user via user equipment 160. Additionally or alternatively, user equipment 160 may be used by the user to remotely access the second computing device 180, remote computing device 170, first computing device 140, responsive action database 150, and / or computing file exchange platform 190. This can be achieved, for example, through an application operating on user equipment 160.

[0071] In some embodiments, environment 100 includes a responsive action database 150. The responsive action database 150 can be configured to receive, store, and / or send data. In various embodiments, the responsive action database 150 may be associated with data associated with a second computing device 180, a remote computing device 170, a first computing device 140, a user device 160, and / or a computing file exchange platform 190. Additionally or alternatively, in some embodiments, the responsive action database 150 stores user-input data. The responsive action program database 150 can be located away from the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160 and / or the computing file exchange platform 190, or near the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160 and / or the computing file exchange platform 190, and / or within the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160 and / or the computing file exchange platform 190.

[0072] Network 130 can be embodied in any of a multitude of network configurations. In some embodiments, network 130 can be a public network (e.g., the Internet). In some embodiments, network 130 can be a private network (e.g., an internal localized or closed network between specific devices). In some other embodiments, network 130 can be a hybrid network (e.g., a network capable of enabling internal communication between devices with specific connections and external communication with other devices). In various embodiments, network 130 may include one or more base stations, repeaters, routers, switches, cell towers, communication cables, routing stations, etc. In various embodiments, components of environment 100 can be communicatively coupled to send data to and / or receive data from each other via network 130. Such configurations include, but are not limited to, wired or wireless personal area networks (PANs), local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), etc.

[0073] Additionally, although Figure 1 Some components are exemplified as separate, independent entities communicating over network 130, but various embodiments are not limited to this configuration. In other embodiments, one or more components may be directly connected and / or shared hardware, etc. For example, in some embodiments, the first computing device 140 may include a responsive action program database 150.

[0074] Figure 2 An exemplary block diagram illustrating an example apparatus that can be specially configured according to an example embodiment of the present disclosure is shown. Specifically, Figure 2An example computing device 200 (“device 200”) with a specific configuration according to at least some example embodiments of the present disclosure is depicted. Examples of device 200 may include, but are not limited to, a second computing device 180, a remote computing device 170, a first computing device 140, a user device 160, a responsive action program database 150, and / or a computing file exchange platform 190. Device 200 includes a processor 202, a memory 204, input / output circuitry 206, communication circuitry 208, and / or optional artificial intelligence (“AI”) and machine learning circuitry 210. In some embodiments, device 200 is configured to perform and implement the operations described herein.

[0075] While the components are described with respect to functional limitations, it should be understood that a particular implementation must include the use of specific computing hardware. It should also be understood that in some implementations, certain components described herein include similar or common hardware. For example, in some implementations, two circuit groups utilize the same processor, memory, circuitry, etc., to perform their associated functions, so that each circuit group does not require duplicate hardware.

[0076] In various embodiments, computing device 200, such as a second computing device 180, a remote computing device 170, a first computing device 140, a user device 160, a responsive action database 150, and / or a computing file exchange platform 190, may refer to, for example, one or more computers, computing entities, desktop computers, mobile phones, tablet computers, phablets, laptops, laptops, distributed systems, servers, etc., and / or any combination of devices or entities adapted to perform the functions, operations, and / or processes described herein. Such functions, operations, and / or processes may include, for example, transmitting, receiving, operating, processing, displaying, storing, determining, creating / generating, monitoring, evaluating, comparing, and / or similar terms as used herein. In one embodiment, these functions, operations, and / or processes may be implemented with respect to data, content, information, and / or similar terms as used herein. In this respect, as described herein, device 200 embodies a specific computing entity specifically configured to enable the specific operations described herein and provide the associated specific advantages.

[0077] Processor 202 or processor circuitry 202 can be embodied in a variety of different ways. In various embodiments, the term "processor" should be understood to include a single-core processor, a multi-core processor, multiple processors within device 200, and / or one or more remote or "cloud" processors external to device 200. In some example embodiments, processor 202 may include one or more processing devices configured to perform independently. Alternatively or additionally, processor 202 may include one or more processors configured in series via a bus to enable independent execution of operations, instructions, pipelines, and / or multithreading.

[0078] In example embodiments, processor 202 may be configured to execute instructions stored in memory 204 or otherwise accessible by the processor. Alternatively or additionally, processor 202 may be configured to perform hard-coded functionality. Thus, whether configured by hardware or software methods, or by a combination thereof, processor 202 may represent an entity capable of operating and being configured accordingly according to embodiments of this disclosure (e.g., physically embodied in circuit form). Alternatively or additionally, processor 202 may be embodied as an executor of software instructions that may specifically configure processor 202 to implement various algorithms embodied in one or more operations described herein when executing such instructions. In some embodiments, processor 202 includes hardware, software, firmware, and / or combinations thereof for implementing one or more operations described herein.

[0079] In some implementations, processor 202 (and / or coprocessor, or assistant processor or any other processing circuitry otherwise associated with the processor) communicates with memory 204 via a bus for transferring information between components of device 200.

[0080] Memory 204 or memory circuitry 204 may be non-transitory and may include, for example, one or more volatile and / or non-volatile memories. In some embodiments, memory 204 includes or embodies an electronic storage device (e.g., a computer-readable storage medium). In some embodiments, memory 204 is configured to store information, data, content, applications, instructions, etc., for enabling device 200 to perform various operations and / or functions according to exemplary embodiments of this disclosure.

[0081] Input / output circuitry 206 may be included in device 200. In some embodiments, input / output circuitry 206 may provide output to a user and / or receive input from a user. Input / output circuitry 206 may communicate with processor 202 to provide such functionality. Input / output circuitry 206 may include one or more user interfaces. In some embodiments, the user interface may include a display that includes an interface presented as a web user interface, application user interface, user device, back-end system, etc. In some embodiments, input / output circuitry 206 may also include a keyboard, mouse, joystick, touchscreen, touch area, softkeys, microphone, speaker, or other input / output mechanism. Processor 202 and / or input / output circuitry 206 including the processor may be configured to control one or more operations and / or functions of one or more user interface elements via computer program instructions (e.g., software and / or firmware) stored in processor-accessible memory (e.g., memory 204, etc.). In some embodiments, input / output circuitry 206 includes or utilizes user-oriented applications to provide input / output functionality to a computing device and / or other display associated with a user.

[0082] Communication circuitry 208 may be included in device 200. Communication circuitry 208 may include any component, such as a device or circuit embodied in hardware or a combination of hardware and software, configured to receive and / or transmit data from / to a network and / or any other device, circuitry, or module communicating with device 200. In some embodiments, communication circuitry 208 includes, for example, a network interface for enabling communication with wired or wireless communication networks. Additionally or alternatively, communication circuitry 208 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware, firmware, and / or software, or any other device suitable for enabling communication via one or more communication networks. In some embodiments, communication circuitry 208 may include circuitry for interacting with antennas and / or other hardware or software to induce reception of signals transmitted via the antenna and / or processing of signals received via the antenna. In some embodiments, communication circuitry 208 enables the transmission and / or reception of data to and / or from user equipment and / or other external computing devices communicating with device 200.

[0083] Data acquisition circuitry 212 may be included in device 200. Data acquisition circuitry 212 may include hardware, software, firmware, and / or combinations thereof, which are designed and / or configured to capture, receive, request, and / or otherwise collect data associated with the operation of the second computing device 180, remote computing device 170, first computing device 140, user device 160, responsive action database 150, and / or computing file exchange platform 190. In some implementations, the data ingestion circuitry 212 includes hardware, software, firmware, and / or combinations thereof that communicate with one or more components of the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160, the responsive action database 150, and / or the computing file exchange platform 190 to receive specific data associated with such operations of the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160, the responsive action database 150, and / or the computing file exchange platform 190. The data ingestion circuitry 212 may support such operations of the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160, the responsive action database 150, and / or the computing file exchange platform 190. Additionally or alternatively, in some embodiments, the data ingestion circuit 212 includes hardware, software, firmware, and / or combinations thereof that retrieve specific data associated with the second computing device 180, the remote computing device 170, the first computing device 140, the user device 160, the responsive action program database 150, and / or the computing file exchange platform 190.

[0084] AI and machine learning circuitry 210 may be included in device 200. AI and machine learning circuitry 210 may include hardware, software, firmware, and / or combinations thereof designed and / or configured to request, receive, process, generate, and transmit data, data structures, control signals, and electronic information for training and executing a trained AI and machine learning model configured to facilitate the operation and / or functionality described herein. For example, in some embodiments, AI and machine learning circuitry 210 includes hardware, software, firmware, and / or combinations thereof that identify training data and / or utilize such training data to train a particular machine learning model, AI, and / or other model to generate specific output data at least in part based on learning from the training data. Additionally or alternatively, in some embodiments, AI and machine learning circuitry 210 includes hardware, software, firmware, and / or combinations thereof that embody or retrieve a trained machine learning model, AI, and / or other specially configured model for processing input data. Additionally or alternatively, in some embodiments, the AI ​​and machine learning circuitry 210 includes hardware, software, firmware, and / or combinations thereof that process the received data using one or more algorithms, functions, and / or subroutines in one or more preprocessing operations and / or subsequent operations that do not require the use of a machine learning or AI model.

[0085] Data output circuitry 214 may be included in device 200. Data output circuitry 214 may include hardware, software, firmware, and / or combinations thereof that are configured and / or generate output based at least in part on data processed by device 200. In some embodiments, data output circuitry 214 includes hardware, software, firmware, and / or combinations thereof that generate a specific report based at least in part on the processed data, for example, wherein the report is generated at least in part based on a specific reporting protocol. Additionally or alternatively, in some embodiments, data output circuitry 214 includes hardware, software, firmware, and / or combinations thereof that configure specific output data objects, output data files, and / or user interfaces for storage, transmission, and / or display. For example, in some embodiments, data output circuitry 214 generates and / or specifically configures specific data output for transmission to another system subsystem for further processing. Additionally or alternatively, in some embodiments, data output circuitry 214 includes hardware, software, firmware, and / or combinations thereof that cause a specially configured user interface to be presented based at least in part on data received and / or processed by device 200.

[0086] In some embodiments, two or more circuits in the group of circuits 202-214 are composable. Alternatively or additionally, one or more circuits in the group of circuits 202-214 implement some or all of the operations and / or functionalities described herein as associated with another circuit. In some embodiments, two or more circuits in the group of circuits 202-214 are combined into a single module embodied in hardware, software, firmware, and / or combinations thereof. For example, in some embodiments, one or more circuits in the group of circuits (e.g., AI and machine learning circuit 210) may be combined with processor 202 such that processor 202 performs one or more of the operations described herein with respect to AI and machine learning circuit 210.

[0087] refer to Figures 1 to 5 In some embodiments, the first computing device 140 is configured to access computing files. In some embodiments, the computing files are provided to the second computing device 180. For example, the first computing device 140 may be configured to provide computing files to the second computing device 180. In some embodiments, the computing files may be provided by the first computing device 140 to the second computing device 180 via a computing file exchange platform 190. In this regard, for example, the first computing device 140 and / or the second computing device 180 may be configured to exchange computing files via the computing file exchange platform 190.

[0088] In some embodiments, a computation file includes one or more data items exchanged between computing devices (such as from a first computing device 140 to a second computing device 180 (e.g., via a computation file exchange platform 190)). In this regard, in some embodiments, the computation file includes text. For example, the computation file may include text messages provided from the first computing device 140 to the second computing device 180. Additionally or alternatively, the computation file includes audio. For example, the computation file may include audio messages provided from the first computing device 140 to the second computing device 180. Additionally or alternatively, the computation file includes one or more images. For example, the computation file may include image-based messages and / or video messages provided from the first computing device 140 to the second computing device 180.

[0089] In some embodiments, the first computing device 140 is configured to access computing files using a monitoring engine 304. In some embodiments, the monitoring engine 304 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model configured to access the computing files (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training). In this regard, in some embodiments, the monitoring engine 304 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques. For example, the monitoring engine 304 may be configured to use one of the techniques described above to monitor the exchange of computing files between the first computing device 140 and / or the second computing device 180 via a computing file exchange platform 190. In some implementations, the monitoring engine 304 is part of a composite model framework 300 for generating responsive actions.

[0090] In some implementations, the first computing device 140 is configured to establish a monitoring interface with the computing file exchange platform 190. In some implementations, the monitoring interface is a gateway, communication channel, application programming interface (API), etc., that enables the first computing device 140 to use the monitoring engine 304 to monitor the exchange of computing files between the first computing device 140 and / or the second computing device 180 via the computing file exchange platform 190.

[0091] In some embodiments, the first computing device 140 is configured to identify content defects associated with a computing file (e.g., an accessed computing file). In some embodiments, the first computing device 140 is configured to identify content defects using a computing file content model 306. In some embodiments, content defects represent and / or indicate defects, missing components, errors, information requests, etc., associated with the content of the computing file. In this regard, in some embodiments, content defects may be associated with text, audio, and / or one or more images in the computing file that are related to a question. For example, a content defect may be associated with text, audio, and / or one or more images in the computing file that are related to a question such as “How are labels loaded into the warehouse label printer?”. Additionally or alternatively, content defects may be associated with text, audio, and / or one or more images in the computing file that are related to incorrect information. For example, a content defect may be associated with text, audio, and / or one or more images in the computing file that are related to incorrect information such as “Labels are loaded into tray four of the warehouse label printer” when labels are actually loaded into tray three of the warehouse label printer. Additionally or alternatively, content defects may be associated with text, audio, and / or one or more images in the calculation file that are linked to incomplete information. For example, a content defect may be associated with text, audio, and / or one or more images in the calculation file that are linked to incomplete information such as “Labels are loaded into tray four of the warehouse label printer” when the labels are actually required to be loaded into trays three and four of the warehouse label printer.

[0092] In some embodiments, identifying content defects includes configuring the first computing device 140 to identify one or more content features associated with the computation file. In some embodiments, the content features include keywords. In this regard, for example, the first computing device 140 may be configured to identify keywords associated with the computation file by performing keyword detection. Additionally or alternatively, the content features include sentiment (e.g., the sentiment of audio in the computation file). In this regard, for example, the first computing device 140 may be configured to identify the sentiment associated with the computation file by performing sentiment analysis. Additionally or alternatively, the content features include emotional tone (e.g., the emotional tone of text in the computation file). In this regard, for example, the first computing device 140 may be configured to identify the emotional tone associated with the computation file by performing emotional tone analysis.

[0093] In some embodiments, the first computing device 140 is configured to generate responsive actions. In some embodiments, the first computing device 140 is configured to generate responsive actions using content defects (e.g., content defects associated with a computed document identified by the first computing device 140). Additionally or alternatively, the first computing device 140 is configured to generate responsive actions using a computed document content model 306. In some embodiments, the responsive actions include one or more auxiliary responsive actions. In some embodiments, auxiliary responsive actions are tasks, actions, information items, etc., that can be implemented or reviewed to at least partially address, resolve, remedy, repair, etc., content defects. In this respect, in some embodiments, the responsive actions include one or more auxiliary responsive actions that, when implemented or reviewed, can address, resolve, remedy, repair, etc., content defects.

[0094] In some implementations, for example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are related to a problem, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the answer to the question (e.g., "Labels are loaded into tray three of the warehouse label printer"). As another example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are associated with incorrect information, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the incorrect information (e.g., "Labels are loaded into tray three of the warehouse label printer instead of tray two"). As another example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are associated with incomplete information, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the incomplete information (e.g., "Labels are loaded into tray four and tray three of the warehouse label printer").

[0095] In some implementations, the computational document content model 306 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training) configured to identify content defects associated with a computational document and / or generate responsive action procedures. In this regard, in some implementations, the computational document content model 306 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network techniques), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques. For example, the computational document content model 306 may include a large language model configured to identify content defects and / or generate responsive action procedures.

[0096] In some embodiments, the computational document content model 306 is part of a composite model framework 300 for generating responsive actions. In some embodiments, the computational document content model 306 is configured to be trained using one or more domain resources. In some embodiments, domain resources are one or more documents, information resources, historical responsive actions, etc., associated with a domain. In some embodiments, the domain represents and / or indicates the domain in which the first computing device 140 operates. For example, a domain could be a warehouse for printing labels using a printer. In this regard, in some embodiments, one or more domain resources may include one or more documents associated with a printer (e.g., instruction manuals), information resources associated with a printer (e.g., printer troubleshooting guides), and / or historical responsive actions associated with a printer.

[0097] In some embodiments, the first computing device 140 is configured to generate a responsive action link interface element 408. In some embodiments, the responsive action link interface element 408 includes a responsive action link representation. In some embodiments, a responsive action link corresponds to a responsive action. In some embodiments, the responsive action link representation includes one or more of the following text, image, or visual elements that indicate the association of the computed document with a content defect and / or that the responsive action is available for the content defect. For example, when a content defect is associated with text, audio, and / or one or more images in a computed document related to a question such as “How are labels loaded into a warehouse label printer?”, the responsive action link representation could be one or more of the following text, image, or visual elements that indicate “Hello, it seems there is a question about how labels are loaded into a warehouse label printer. Please visit the responsive action to answer the question.” In some embodiments, the responsive action link interface element 408 is optional. In this respect, in some embodiments, the responsive action link interface element 408 is optional to access the responsive action.

[0098] In some embodiments, the first computing device 140 is configured to render a responsive action link interface element 408 onto the interface 400. In some embodiments, the interface 400 includes a computation file representation 406 corresponding to a computation file. In some embodiments, the computation file representation 406 includes one or more text, image, or visual elements representing and / or indicating the computation file (e.g., the data of the computation file may be rendered on the interface 400). In some embodiments, the interface 400 includes one or more other computation file representations 404 corresponding to one or more other computation files. In some embodiments, one or more other computation file representations 404 include one or more text, image, or visual elements representing and / or indicating one or more other computation files that have been provided between the first computing device 140 and / or the second computing device 180 via the computation file exchange platform 190. In some embodiments, the interface 400 is associated with the computation file exchange platform 190. In this regard, in some embodiments, the responsive action link interface element 408 is configured to be rendered on interface 400 close to the computed file representation 406, making the responsive action readily accessible for addressing content defects associated with the computed file. Additionally or alternatively, interface 400 may be provided via a first computing device 140, a second computing device 180, a remote computing device 170, and / or a user device 160. In some embodiments, the first computing device 140 is configured to render the responsive action link interface element 408 to interface 400 in real time (e.g., in response to the identification of a content defect and / or the generation of a responsive action). Additionally or alternatively, the first computing device 140 is configured to render the responsive action link interface element 408 to interface 400 on a periodic basis (e.g., according to a schedule).

[0099] In some embodiments, the first computing device 140 is configured to generate a responsive action interface element 502. In some embodiments, the responsive action interface element 502 includes a responsive action representation corresponding to a responsive action. In some embodiments, the responsive action representation includes one or more of text, image, or visual elements that represent and / or indicate the responsive action. In this regard, in some embodiments, the responsive action interface element 502 includes one or more auxiliary responsive action representations 504. In some embodiments, one or more auxiliary responsive action representations 504 include one or more auxiliary responsive action representations 504. In some embodiments, the responsive action representation includes one or more of text, image, or visual elements that represent and / or indicate the auxiliary responsive action.

[0100] In some embodiments, the first computing device 140 is configured to render the responsive action interface element 502 to the second interface 500. In some embodiments, the responsive action interface element 502 is rendered to the second interface 500 in response to the selection of the responsive action link interface element 408. In some embodiments, the second interface 500 may be provided via a computing file exchange platform 190, the first computing device 140, the second computing device 180, a remote computing device 170, and / or a user device 160.

[0101] In some embodiments, the first computing device 140 is configured to detect the termination of a responsive action. In some embodiments, the first computing device 140 is configured to detect the termination of a responsive action when it is removed from the second interface 500 (e.g., by a user reviewing the responsive action). Additionally or alternatively, the first computing device 140 is configured to detect the termination of a responsive action upon its completion. In this respect, in some embodiments, a responsive action may be completed when each task, action item, and / or information item in the responsive action has been implemented or reviewed.

[0102] In some embodiments, the first computing device 140 is configured to generate a responsive action procedure result. In some embodiments, the first computing device 140 is configured to generate a responsive action procedure result based on the termination of the responsive action procedure. In some embodiments, the responsive action procedure result includes one or more data items indicating and / or indicating the completion of the responsive action procedure. In this regard, for example, the responsive action procedure result may include one or more data items indicating that each task, action item, and / or information item in the responsive action procedure has been implemented or reviewed. Additionally or alternatively, the responsive action procedure result includes one or more data items indicating and / or indicating partial completion of the responsive action procedure. In this regard, for example, the responsive action procedure result may include one or more data items indicating that some of the tasks, action items, and / or information items in the responsive action procedure have been implemented or reviewed.

[0103] In some embodiments, the first computing device 140 is configured to store responsive action procedure results in a responsive action procedure database 150. In some embodiments, the responsive action procedure results are stored in the responsive action procedure database 150 based on a computation file identifier associated with the computation file. In some embodiments, the computation file identifier is a data object representing a user corresponding to the computation file (e.g., the user who generated the computation file). In this respect, in some embodiments, the responsive action procedure results are stored in the responsive action procedure database 150 such that the responsive action procedure results are associated with the user corresponding to the computation file. For example, the responsive action procedure results are stored in the responsive action procedure database 150 such that the responsive action procedure results are associated with the profile of the user corresponding to the computation file.

[0104] In some embodiments, the first computing device 140 is configured to generate responsive action procedure indicators. In some embodiments, the responsive action procedure indicators are generated using responsive action procedure results and / or implementation model 308. In some embodiments, the responsive action procedure indicators are data objects representing numeric icons, symbols, badges, etc. In this respect, in some embodiments, the responsive action procedure indicators are configured to symbolically represent responsive action procedure results.

[0105] In some implementations, implementing model 308 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model configured to generate responsive action procedure indicators (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training). In this regard, in some implementations, implementing model 308 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network techniques), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques.

[0106] In some embodiments, implementation model 308 is part of a composite model framework 300 for generating responsive actions. In some embodiments, the composite model framework 300 is hosted, implemented, operated, etc., by a first computing device 140, a second computing device 180, and / or a remote computing device 170. In some embodiments, monitoring engine 304, computed file content model 306, and / or implementation model 308 are configured to communicate via bus 302 of the composite model framework 300. In this respect, in some embodiments, monitoring engine 304, computed file content model 306, and / or implementation model 308 are each specifically configured to perform various aspects of the process for generating responsive actions and are configured to work collaboratively to generate responsive actions. In this way, in some embodiments, by using the composite model framework 300, the first computing device 140, the second computing device 180, and / or the remote computing device 170 are configured to generate responsive actions in a way that would be impossible using individual models.

[0107] In some embodiments, the first computing device 140 is configured to store responsive action procedure indicators in a responsive action procedure database 150. In some embodiments, the responsive action procedure indicators are stored in the responsive action procedure database 150 based on a computing file identifier associated with a computing file. In this respect, in some embodiments, the responsive action procedure indicators are stored in the responsive action procedure database 150 such that the responsive action procedure indicators are associated with a user corresponding to the computing file. For example, the responsive action procedure indicators are stored in the responsive action procedure database 150 such that the responsive action procedure indicators are associated with a profile of the user corresponding to the computing file.

[0108] In some embodiments, the first computing device 140 is configured to access a second computing file. In some embodiments, the second computing file is provided to the second computing device 180. For example, the first computing device 140 may be configured to provide the second computing file to the second computing device 180. In some embodiments, the second computing file may be provided by the first computing device 140 to the second computing device 180 via a computing file exchange platform 190. In some embodiments, similar to the computing file described above, the second computing file includes one or more data items exchanged between computing devices (such as exchanged from the first computing device 140 to the second computing device 180 (e.g., via the computing file exchange platform 190)). In some embodiments, the first computing device 140 is configured to access the second computing file using a monitoring engine 304.

[0109] In some embodiments, the first computing device 140 is configured to determine a processing consumption value associated with the second computing file. In some embodiments, the processing consumption value is a data object representing and / or indicating the amount of processing consumption, memory consumption, etc., required to identify a second content defect associated with the second computing file and / or to generate a second responsive action procedure associated with the second computing file.

[0110] In some embodiments, the first computing device 140 is configured to determine whether a processing consumption value meets or exceeds a processing consumption threshold. In some embodiments, the processing consumption threshold is a specific amount of processing consumption, memory consumption, etc. In this respect, in some embodiments, if the processing consumption value meets or exceeds the processing consumption threshold, identifying a second content defect associated with the second computed file and / or generating a second responsive action procedure associated with the second computed file requires more processing consumption, memory consumption, etc., compared to a case where the processing consumption value is below the processing consumption threshold. For example, the processing consumption value is greater when the second computed file includes one or more images than when the second computed file does not include one or more images (e.g., when the second computed file only includes text).

[0111] In some embodiments, the first computing device 140 is configured to send a second computation file to a remote computing device 170. In some embodiments, the first computing device 140 is configured to send the second computation file to the remote computing device 170 in response to a processing consumption value meeting or exceeding a processing consumption threshold. In this regard, in some embodiments, the remote computing device 170 is configured to enable it to process computation files, such as the second computation file, associated with a processing consumption value that meets or exceeds a processing consumption threshold. For example, the remote computing device 170 may have larger processing resources and / or memory resources.

[0112] In some embodiments, the first computing device 140 is configured to receive a second responsive action procedure corresponding to a second computed file. In some embodiments, the first computing device 140 is configured to receive the second responsive action procedure from a remote computing device 170. In this regard, in some embodiments, the remote computing device 170 is configured to generate the second responsive action procedure in a manner similar to that described above with respect to the first computing device 140 and the responsive action procedure.

[0113] Example Method

[0114] See now Figure 6 The flowchart showing example method 600 is illustrated. In this respect, Figure 6Examples are illustrated of operations that can be performed by a first computing device 140, a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action program database 150, a user device 160, etc. In some embodiments, method 600 includes operations for generating responsive actions. In some embodiments, example method 600 defines a process, as described herein, that can be performed by any of a device and / or system embodied in hardware, software, firmware, and / or combinations thereof. In some embodiments, computer program code including one or more computer-decoded instructions is stored in at least one non-transitory computer-readable storage medium, such that execution of the computer program code initiates the implementation of method 600.

[0115] As shown in box 602, method 600 includes: using a monitoring engine to access computing files provided to a second computing device via a computing file exchange platform. As described above, in some embodiments, computing files are provided to the second computing device 180. For example, a first computing device 140 may be configured to provide computing files to the second computing device 180. In some embodiments, computing files may be provided by the first computing device 140 to the second computing device 180 via a computing file exchange platform 190. In this regard, for example, the first computing device 140 and / or the second computing device 180 may be configured to exchange computing files via the computing file exchange platform 190.

[0116] In some embodiments, a computation file includes one or more data items exchanged between computing devices (such as from a first computing device 140 to a second computing device 180 (e.g., via a computation file exchange platform 190)). In this regard, in some embodiments, the computation file includes text. For example, the computation file may include text messages provided from the first computing device 140 to the second computing device 180. Additionally or alternatively, the computation file includes audio. For example, the computation file may include audio messages provided from the first computing device 140 to the second computing device 180. Additionally or alternatively, the computation file includes one or more images. For example, the computation file may include image-based messages and / or video messages provided from the first computing device 140 to the second computing device 180.

[0117] In some embodiments, the first computing device 140 is configured to access computing files using a monitoring engine 304. In some embodiments, the monitoring engine 304 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model configured to access the computing files (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training). In this regard, in some embodiments, the monitoring engine 304 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques. For example, the monitoring engine 304 may be configured to use one of the techniques described above to monitor the exchange of computing files between the first computing device 140 and / or the second computing device 180 via a computing file exchange platform 190. In some implementations, the monitoring engine 304 is part of a composite model framework 300 for generating responsive actions.

[0118] As shown in box 604, method 600 includes: using a computed file and a computed file content model to identify content defects associated with the computed file. As described above, in some embodiments, content defects represent and / or indicate defects, missing components, errors, information requests, etc., associated with the content of the computed file. In this regard, in some embodiments, content defects may be associated with text, audio, and / or one or more images in the computed file that are associated with a question. For example, a content defect may be associated with text, audio, and / or one or more images in the computed file that are associated with a question such as “How are labels loaded into the warehouse label printer?”. Additionally or alternatively, content defects may be associated with text, audio, and / or one or more images in the computed file that are associated with incorrect information. For example, a content defect may be associated with text, audio, and / or one or more images in the computed file that are associated with incorrect information such as “Labels are loaded into tray four of the warehouse label printer” when labels are actually loaded into tray three of the warehouse label printer. Additionally or alternatively, content defects may be associated with text, audio, and / or one or more images in the computed file that are associated with incomplete information. For example, content defects may be associated with text, audio, and / or one or more images in the calculation file that are associated with incomplete information such as “the label is loaded into tray four of the warehouse label printer” when the label actually needs to be loaded into tray three and tray four of the warehouse label printer.

[0119] In some embodiments, identifying content defects includes configuring a first computing device to identify one or more content features associated with a computational file. In some embodiments, content features include keywords. In this regard, for example, the first computing device 140 may be configured to identify keywords associated with the computational file by performing keyword detection. Additionally or alternatively, content features include sentiment (e.g., the sentiment of audio in a computational file). In this regard, for example, the first computing device 140 may be configured to identify the sentiment associated with the computational file by performing sentiment analysis. Additionally or alternatively, content features include emotional tone (e.g., the emotional tone of text in a computational file). In this regard, for example, the first computing device 140 may be configured to identify the emotional tone associated with the computational file by performing emotional tone analysis.

[0120] As shown in box 606, method 600 includes: generating responsive actions using content defects and a computed document content model. As described above, in some embodiments, the responsive actions include one or more auxiliary responsive actions. In some embodiments, auxiliary responsive actions are tasks, actions, information items, etc., that can be implemented or reviewed to at least partially address, resolve, remedy, repair, etc., content defects. In this respect, in some embodiments, the responsive actions include one or more auxiliary responsive actions that, when implemented or reviewed, can address, resolve, remedy, repair, etc., content defects.

[0121] In some implementations, for example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are related to a problem, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the answer to the question (e.g., "Labels are loaded into tray three of the warehouse label printer"). As another example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are associated with incorrect information, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the incorrect information (e.g., "Labels are loaded into tray three of the warehouse label printer instead of tray two"). As another example, when a content defect is associated with text, audio, and / or one or more images in a calculation file that are associated with incomplete information, the responsive action procedure may include one or more auxiliary responsive action procedures that, upon implementation or review, can address, resolve, remedy, or repair the incomplete information (e.g., "Labels are loaded into tray four and tray three of the warehouse label printer").

[0122] In some implementations, the computational document content model 306 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training) configured to identify content defects associated with a computational document and / or generate responsive action procedures. In this regard, in some implementations, the computational document content model 306 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network techniques), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques. For example, the computational document content model 306 may include a large language model configured to identify content defects and / or generate responsive action procedures.

[0123] As shown in box 608, method 600 includes generating a responsive action link interface element. As described above, in some embodiments, the responsive action link interface element 408 includes a responsive action link representation. In some embodiments, the responsive action link corresponds to a responsive action. In some embodiments, the responsive action link representation includes one or more of the text, image, or visual elements that indicate the association of a computed file with a content defect and / or that the responsive action is available for the content defect. For example, when a content defect is associated with text, audio, and / or one or more images in a computed file related to a question such as “How are labels loaded into a warehouse label printer?”, the responsive action link representation could be one or more of the text, image, or visual elements indicating “Hello, it seems there is a question about how labels are loaded into a warehouse label printer. Please visit the responsive action to answer the question.” In some embodiments, the responsive action link interface element 408 is optional. In this respect, in some embodiments, the responsive action link interface element 408 is optional to access the responsive action.

[0124] As shown in box 610, method 600 includes: rendering a responsive action procedure link interface element onto an interface associated with a computation file exchange platform. As described above, in some embodiments, interface 400 includes a computation file representation 406 corresponding to a computation file. In some embodiments, computation file representation 406 includes one or more of text, image, or visual elements representing and / or indicating the computation file (e.g., data of the computation file may be rendered on interface 400). In some embodiments, interface 400 includes one or more other computation file representations 404 corresponding to one or more other computation files. In some embodiments, one or more other computation file representations 404 include one or more of text, image, or visual elements representing and / or indicating one or more other computation files that have been provided between a first computing device 140 and / or a second computing device 180 via computation file exchange platform 190. In some embodiments, interface 400 is associated with computation file exchange platform 190. In this regard, in some embodiments, the responsive action link interface element 408 is configured to be rendered on interface 400 close to the computed file representation 406, making the responsive action readily accessible for addressing content defects associated with the computed file. Additionally or alternatively, interface 400 may be provided via a first computing device 140, a second computing device 180, a remote computing device 170, and / or a user device 160. In some embodiments, the first computing device 140 is configured to render the responsive action link interface element 408 to interface 400 in real time (e.g., in response to the identification of a content defect and / or the generation of a responsive action). Additionally or alternatively, the first computing device 140 is configured to render the responsive action link interface element 408 to interface 400 on a periodic basis (e.g., according to a schedule).

[0125] As shown in box 612, method 600 includes: establishing a monitoring interface with a computing file exchange platform. As described above, in some embodiments, the monitoring interface is a gateway, communication channel, application programming interface (API), etc., that enables the first computing device 140 to use the monitoring engine 304 to monitor the exchange of computing files between the first computing device 140 and / or the second computing device 180 via the computing file exchange platform 190.

[0126] As shown in box 614, method 600 includes: training a computational document content model using one or more domain resources. As described above, in some embodiments, domain resources are one or more documents, information resources, historical responsive actions, etc., associated with a domain. In some embodiments, a domain represents and / or indicates the environment in which the first computing device 140 operates. For example, a domain could be a warehouse for printing labels using a printer. In this regard, in some embodiments, one or more domain resources may include one or more documents (e.g., instruction manuals) associated with a printer, information resources (e.g., a printer troubleshooting guide) associated with a printer, and / or historical responsive actions associated with a printer.

[0127] See now Figure 7 The flowchart illustrating example method 700 is shown. In this respect, Figure 7 Examples are illustrated of operations that can be performed by a first computing device 140, a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action program database 150, a user device 160, etc. In some embodiments, method 700 includes operations for generating responsive action program results. In some embodiments, example method 700 defines a process, as described herein, that can be performed by any of a device and / or system embodied in hardware, software, firmware, and / or combinations thereof. In some embodiments, computer program code including one or more computer-decoded instructions is stored in at least one non-transitory computer-readable storage medium, such that execution of the computer program code initiates execution of method 700.

[0128] As shown in box 702, method 700 includes: generating a responsive action interface element. As described above, in some embodiments, the responsive action interface element 502 includes a responsive action representation corresponding to a responsive action. In some embodiments, the responsive action representation includes one or more of text, image, or visual elements that represent and / or indicate the responsive action. In this regard, in some embodiments, the responsive action interface element 502 includes one or more auxiliary responsive action representations 504. In some embodiments, one or more auxiliary responsive action representations 504 include one or more auxiliary responsive action representations. In some embodiments, the responsive action representation includes one or more of text, image, or visual elements that represent and / or indicate the auxiliary responsive action.

[0129] As shown in box 704, method 700 includes rendering a responsive action interface element to a second interface. As described above, in some embodiments, a responsive action interface element 502 is rendered to the second interface 500 in response to the selection of a responsive action link interface element 408. In some embodiments, the second interface 500 may be provided via a computing file exchange platform 190, a first computing device 140, a second computing device 180, a remote computing device 170, and / or a user device 160.

[0130] As shown in box 706, method 700 includes detecting the termination of a responsive action. As described above, in some embodiments, the first computing device 140 is configured to detect the termination of the responsive action when it is removed from the second interface 500 (e.g., by a user reviewing the responsive action). Additionally or alternatively, the first computing device 140 is configured to detect the termination of the responsive action upon completion. In this respect, in some embodiments, the responsive action may be completed when each task, action item, and / or information item in the responsive action has been implemented or reviewed.

[0131] As shown in box 708, method 700 includes generating a responsive action procedure result based on the termination of the responsive action procedure. As described above, in some embodiments, the first computing device 140 is configured to generate a responsive action procedure result based on the termination of the responsive action procedure. In some embodiments, the responsive action procedure result includes one or more data items indicating and / or indicating the completion of the responsive action procedure. In this regard, for example, the responsive action procedure result may include one or more data items indicating that each task, action item, and / or information item in the responsive action procedure has been implemented or reviewed. Additionally or alternatively, the responsive action procedure result includes one or more data items indicating and / or indicating partial completion of the responsive action procedure. In this regard, for example, the responsive action procedure result may include one or more data items indicating that some of the tasks, action items, and / or information items in the responsive action procedure have been implemented or reviewed.

[0132] As shown in box 710, method 700 includes storing responsive action results in a responsive action database based on a computation file identifier associated with the computation file. As described above, in some embodiments, the computation file identifier is a data object representing a user corresponding to the computation file (e.g., the user who generated the computation file). In this regard, in some embodiments, the responsive action results are stored in the responsive action database 150 such that the responsive action results are associated with the user corresponding to the computation file. For example, the responsive action results are stored in the responsive action database 150 such that the responsive action results are associated with the profile of the user corresponding to the computation file.

[0133] As shown in box 712, method 700 includes generating a responsive action procedure indicator using the responsive action procedure result and an implementation model. As described above, in some embodiments, the responsive action procedure indicator is a data object representing a numeric icon, symbol, badge, etc. In this respect, in some embodiments, the responsive action procedure indicator is configured to symbolically represent a responsive action procedure result.

[0134] In some implementations, implementing model 308 may be a data entity describing the parameters, hyperparameters, and / or defined operations of a rule-based machine learning model and / or generative artificial intelligence model configured to generate responsive action procedure indicators (e.g., a model including at least one of one or more rule-based layers, parameters, coefficients, etc., depending on training). In this regard, in some implementations, implementing model 308 may be configured to utilize one or more of any type of machine learning technique, rule-based technique, and / or artificial intelligence technique, including clustering techniques (e.g., k-means, expectation-maximization, centroid neural network techniques), computer vision techniques, supervised learning (e.g., using user feedback), unsupervised learning, semi-supervised learning, reinforcement learning, sequence modeling techniques, language processing techniques, neural network techniques, and / or generative artificial intelligence techniques.

[0135] In some embodiments, implementation model 308 is part of a composite model framework 300 for generating responsive actions. In some embodiments, the composite model framework 300 is hosted, implemented, operated, etc., by a first computing device 140, a second computing device 180, and / or a remote computing device 170. In some embodiments, monitoring engine 304, computed file content model 306, and / or implementation model 308 are configured to communicate via bus 302 of the composite model framework 300. In this respect, in some embodiments, monitoring engine 304, computed file content model 306, and / or implementation model 308 are each specifically configured to perform various aspects of the process for generating responsive actions and are configured to work collaboratively to generate responsive actions. In this way, in some embodiments, by using the composite model framework 300, the first computing device 140, the second computing device 180, and / or the remote computing device 170 are configured to generate responsive actions in a way that would be impossible using individual models.

[0136] As shown in box 714, method 700 includes storing a responsive action indicator in a responsive action database based on a computation file identifier associated with a computation file. As described above, in some embodiments, the responsive action indicator is stored in the responsive action database 150 based on a computation file identifier associated with a computation file. In this regard, in some embodiments, the responsive action indicator is stored in the responsive action database 150 such that the responsive action indicator is associated with a user corresponding to the computation file. For example, the responsive action indicator is stored in the responsive action database 150 such that the responsive action indicator is associated with a profile of the user corresponding to the computation file.

[0137] See now Figure 8 The flowchart illustrating example method 800 is shown. In this respect, Figure 8 Examples are illustrated of operations that can be performed by a first computing device 140, a remote computing device 170, a computing file exchange platform 190, a second computing device 180, a responsive action program database 150, a user device 160, etc. In some embodiments, method 800 includes operations for receiving responsive actions. In some embodiments, example method 800 defines a process, as described herein, that can be performed by any of a device and / or system embodied in hardware, software, firmware, and / or combinations thereof. In some embodiments, computer program code including one or more computer-decoded instructions is stored in at least one non-transitory computer-readable storage medium, such that execution of the computer program code initiates execution of method 800.

[0138] As shown in box 802, method 800 includes: using a monitoring engine to access a second computing file provided to a second computing device via a computing file exchange platform. As described above, in some embodiments, a first computing device 140 may be configured to provide the second computing file to a second computing device 180. In some embodiments, the second computing file may be provided by the first computing device 140 to the second computing device 180 via a computing file exchange platform 190. In some embodiments, similar to the computing file described above, the second computing file includes one or more data items exchanged between computing devices (such as exchanged from the first computing device 140 to the second computing device 180 (e.g., via the computing file exchange platform 190)). In some embodiments, the first computing device 140 is configured to use a monitoring engine 304 to access the second computing file.

[0139] As shown in box 804, method 800 includes: determining a processing consumption value associated with a second computation file. As described above, in some embodiments, the processing consumption value is a data object representing and / or indicating the amount of processing consumption, memory consumption, etc., required to identify a second content defect associated with the second computation file and / or to generate a second responsive action procedure associated with the second computation file.

[0140] As shown in box 806, method 800 includes sending a second computed file to a remote computing device. As described above, in some embodiments, the processing consumption threshold is a specific amount of processing consumption, memory consumption, etc. In this respect, in some embodiments, if the processing consumption value meets or exceeds the processing consumption threshold, identifying a second content defect associated with the second computed file and / or generating a second responsive action procedure associated with the second computed file requires more processing consumption, memory consumption, etc., compared to a case where the processing consumption value is below the processing consumption threshold. For example, the processing consumption value is greater when the second computed file includes one or more images than when the second computed file does not include one or more images (e.g., when the second computed file only includes text).

[0141] In some embodiments, the first computing device 140 is configured to send a second computation file to a remote computing device 170. In some embodiments, the first computing device 140 is configured to send the second computation file to the remote computing device 170 in response to a processing consumption value meeting or exceeding a processing consumption threshold. In this regard, in some embodiments, the remote computing device 170 is configured to enable it to process computation files, such as the second computation file, associated with a processing consumption value that meets or exceeds a processing consumption threshold. For example, the remote computing device 170 may have larger processing resources and / or memory resources.

[0142] As shown in box 808, method 800 includes receiving a second responsive action procedure corresponding to a second computed file from a remote computing device. As described above, in some embodiments, remote computing device 170 is configured to generate the second responsive action procedure in a manner similar to that described above with respect to the first computing device 140 and the responsive action procedure.

[0143] The operations and / or functions of this disclosure have been described herein, such as in flowcharts. It should be understood that computer program instructions may be loaded onto a computer or other programmable device (e.g., hardware) to produce a machine, such that the resulting computer or other programmable device performs the operations and / or functions described in the flowchart frames herein. These computer program instructions may also be stored in a computer-readable storage medium that instructs a computer, processor, or other programmable device to operate and / or function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of writing, the execution of which implements the operations and / or functions described in the flowchart frames. Computer program instructions may also be loaded onto a computer, processor, or other programmable device to cause a series of operations to be performed on the computer, processor, or other programmable device, thereby producing a computer-implemented process, such that the instructions, which execute on the computer, processor, or other programmable device, provide operations for implementing the functions and / or operations specified in the flowchart frames. Flowchart frames support combinations of components for performing the specified operations and / or functions, as well as combinations of operations and / or functions for performing the specified operations and / or functions. It should be understood that one or more boxes in a flowchart, as well as combinations of boxes in a flowchart, can be implemented by a hardware-based dedicated computer system or a combination of dedicated hardware and computer instructions that performs the specified operations and / or functions.

[0144] Although this specification contains many specific embodiments and detailed implementations, these details should not be construed as limiting the scope of any disclosure or claimable content, but rather as descriptions of features specific to a particular disclosure and a particular embodiment. Certain features described herein in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. Furthermore, although features may be described above as functioning in certain combinations and even initially claimed as such, in some cases, one or more features from the claimed combination may be removed from the combination, and the claimed combination may be for sub-combinations or variations thereof.

[0145] Although operations and / or functions are shown in a specific order in the accompanying drawings, this should not be construed as requiring such operations and / or functions to be performed in the specific order shown or in sequential order, or to perform all shown operations to achieve the desired result. In some cases, alternating sequences of operations and / or functions may be advantageous. In some cases, the actions described in the claims may be performed in a different order and still achieve the desired result. Therefore, while specific embodiments of the subject matter have been described, other embodiments are also within the scope of the appended claims.

[0146] Similarly, although the operations are shown in a specific order in the accompanying drawings, this should not be construed as requiring that such operations be performed in the specific order shown or in sequential order, or that all the shown operations be performed to achieve the desired result. In some cases, alternating sequences of operations may be advantageous. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result.

Claims

1. A method, the method comprising: Use a monitoring engine to access computing files provided to a second computing device via a computing file exchange platform; The computational file and its content model are used to identify content defects associated with the computational file. The content defects and the computational file content model are used to generate responsive action programs; Generate a responsive action link interface element, wherein the responsive action link interface element includes a responsive action link representation corresponding to the responsive action; as well as The responsive action program link interface element is rendered to the interface associated with the computing file exchange platform.

2. The method according to claim 1, further comprising: Establish a monitoring interface with the computing file exchange platform.

3. The method according to claim 1, further comprising: The computational file content model is trained using one or more domain resources.

4. The method according to claim 1, further comprising: Generate a responsive action procedure interface element, wherein the responsive action procedure interface element includes a responsive action procedure representation corresponding to the responsive action procedure, wherein the responsive action procedure includes one or more auxiliary responsive action procedures; The responsive action program interface element is rendered onto the second interface; Detect the termination of the responsive action procedure; as well as The responsive action procedure result is generated based on the termination of the responsive action procedure.

5. The method according to claim 4, further comprising: The results of the responsive action procedure are stored in the responsive action procedure database based on the calculation file identifier associated with the calculation file.

6. The method according to claim 4, further comprising: The responsive action procedure results and implementation model are used to generate responsive action procedure indicators; as well as The responsive action indicator is stored in the responsive action database based on the computation file identifier associated with the computation file.

7. The method according to claim 6, wherein the monitoring engine, the computational file content model, and the implementation model are part of a composite model framework.

8. The method according to claim 1, further comprising: The monitoring engine is used to access the second computing file provided to the second computing device via the computing file exchange platform; Determine the processing consumption value associated with the second calculation file; In response to the processing consumption value meeting or exceeding the processing consumption threshold, the second calculation file is sent to a remote computing device; as well as Receive a second responsive action program corresponding to the second calculation file from the remote computing device.

9. An apparatus comprising a memory and one or more processors communicatively coupled to the memory, the one or more processors being configured to perform operations including: Use a monitoring engine to access computing files provided to a second computing device via a computing file exchange platform; The computational file and its content model are used to identify content defects associated with the computational file. The content defects and the computational file content model are used to generate responsive action programs; Generate a responsive action link interface element, wherein the responsive action link interface element includes a responsive action link representation corresponding to the responsive action; as well as The responsive action program link interface element is rendered to the interface associated with the computing file exchange platform.

10. A computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon, the computer program code configuring the computer program product for: Use a monitoring engine to access computing files provided to a second computing device via a computing file exchange platform; The computational file and its content model are used to identify content defects associated with the computational file. The content defects and the computational file content model are used to generate responsive action programs; Generate a responsive action link interface element, wherein the responsive action link interface element includes a responsive action link representation corresponding to the responsive action; as well as The responsive action program link interface element is rendered to the interface associated with the computing file exchange platform.

Images