Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Application Status Present office action is in response to the application filed 03/20/2023 . C laims 1- 20 are currently pending in the application. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. In regard to independent claim 1, analyzed as representative claim : Step 1: Statutory Category? Independent Claim 1 recites “ A computer implemented method comprising : ”. Independent Claim 1 falls within the “process” category of 35 U.S.C. § 101. Step 2A – Prong 1: Judicial Exception Recited? The Independent Claim 1/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea and in bold the additional (non-abstract) claim limitations that are generic computer components. Independent Claim 1 Revised 2019 Guidance A computer implemented method comprising: A process (method) is a statutory subject matter class. See 35 U.S.C. § 101 (“Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.”). [L 1 ] accessing instructional content that describes a task for completion by a user; A ccessing instructional content … is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31. [L 2 ] applying a named entity recognition natural language processing model to derive actions described in the instructional content; The “ named entity recognition natural language processing model ” is an additional non-abstract limitation, namely, a generic computer component. Abstract: “ to derive actions described in the instructional content ” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could collect data on the at least one operator visually and/or by hearing the information. [L 3 ] accessing telemetry data containing logged actions taken by users; A ccessing telemetry data … is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31. [L 4 ] processing the telemetry data to identify actions taken in the telemetry data associated with the task; Abstract: “ to identify actions taken in the telemetry data associated with the task ” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could collect data on the at least one operator visually and/or by hearing the information. [L 5 ] identifying features from the instructional content, telemetry data, derived actions and actions taken; Abstract: “ identifying features from the instructional content, telemetry data, derived actions and actions taken could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” to the extent that the person/educator could collect data on the at least one operator visually and/or by hearing the information. [L6a] inputting the features to a machine learning model trained on training data that includes labeled instances of instructional content, telemetry data and features identified therefrom to select a task completion path endpoint label for the instructional content; The “ machine learning model ” is an additional non-abstract limitation, namely, a generic computer component. Inputting data … is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31. [L6b] to select a task completion path endpoint label for the instructional content; Abstract: “ to select a task completion path endpoint label for the instructional content” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[ ] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)”. See January 2019 Memorandum , 84 Fed. Reg. at 52. [L 7 ] monitoring user-initiated actions in response to access of the instructional content; Abstract: “ monitoring user-initiated actions in response to access of the instructional content ” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[ ] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)”. See January 2019 Memorandum , 84 Fed. Reg. at 52. [L 8 ] generating an effectiveness measure for the instructional content as a function of the task completion path endpoint label and the monitored user-initiated actions to identify a portion of the instructional content for editing; and Abstract: “ generating an effectiveness measure for the instructional content as a function of the task completion path endpoint label and the monitored user-initiated actions to identify a portion of the instructional content for editing ” could be performed as a mental process, i.e., concept performed in the human mind or using pencil and paper (including an observation, evaluation, judgment, opinion) and a “[c]ertain method[] of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” and mathematical concept . See January 2019 Memorandum , 84 Fed. Reg. at 52. [L 9 ] providing the effectiveness measure to an editor for editing the instructional content. Provid ing the effectiveness measure to an editor for editing the instructional content is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data presentation . See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31. The published Specification discloses “ [ T ] utorial or help articles may contain instructions describing how to complete tasks … ” (¶ 1 ) , that “[ D ] etermining whether tutorials are accurate, effective, or helpful can involve asking users if a tutorial was helpful and then generating satisfaction scores …” . (¶ 2 ) and that “ Manual labeling of tutorials with [ toolbar command identifiers ] TCIDs can be inaccurate as well as extremely time consuming … ” (¶ 3 ) . Based at least on the above, i t is apparent that other than reciting the additional non-abstract limitations of the “ named entity recognition natural language processing model ” and “ machine learning model ” noted in the Independent Claim 1/Revised 2019 Guidance Table above, nothing in the claim precludes the steps from practically being performed by a human, in the mind, and/or using pen and paper. The mere nominal recitation of the “ named entity recognition natural language processing model ” and “ machine learning model ” does not take the claim out of the method of organizing human activity , mental processes and mathematical concept groupings. Accordingly, the claim recites a judicial exception (Step 2A, Prong One: YES). Step 2A – Prong 2: Integrated into a Practical Application? The body of the claim, as noted in the Independent Claim 18/Revised 2019 Guidance Table above, recites the additional limitation of the “ named entity recognition natural language processing model ” and “ machine learning model ” . The published Specification provides supporting exemplary descriptions of generic computer components: at least ¶ 2 1 : … the actions may be user actions to interact with software generated user interfaces to accomplish the task … ; ¶ 34 : … more general purpose … ; ¶ 88 : computing device in the form of a computer 700 may include a processing unit 702, memory 703, removable storage 710, and non-removable storage 712. Although the example computing device is illustrated and described as computer 700, the computing device may be in different forms in different embodiments. For example, the computing device may instead be a smartphone, a tablet, smartwatch, smart storage device (SSD), or other computing device including the same or similar elements as illustrated and described with regard to FIG. 7. Devices, such as smartphones, tablets, and smartwatches, are generally collectively referred to as mobile devices or user equipment ; ¶ 91 : C omputer 700 may include or have access to a computing environment that includes input interface 706, output interface 704, and a communication interface 716. Output interface 704 may include a display device, such as a touchscreen, that also may serve as an input device. The input interface 706 may include one or more of a touchscreen, touchpad, mouse, keyboard, camera, one or more device-specific buttons, one or more sensors integrated within or coupled via wired or wireless data connections to the computer 700, and other input devices. The computer may operate in a networked environment using a communication connection to connect to one or more remote computers, such as database servers. The remote computer may include a personal computer (PC), server, router, network PC, a peer device or other common data flow network switch, or the like. The communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN), cellular, Wi-Fi, Bluetooth, or other networks …; ¶ 118 : … invention also proposes a device 200, shown in FIG. 3, for assessing technical and non-technical skills of at least one operator in a training situation on a real or simulated platform 200 …; ¶ 12 5 : The functions or algorithms described herein may be implemented in software in one embodiment. The software may consist of computer executable instructions stored on computer readable media or computer readable storage device such as one or more non-transitory memories or other type of hardware-based storage devices, either local or networked. Further, such functions correspond to modules, which may be software, hardware, firmware or any combination thereof. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples. The software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system, turning such computer system into a specifically programmed machine ; ¶ 12 6 : … software, hardware, firmware, or the like. For example, the phrase “configured to” can refer to a logic circuit structure of a hardware element that is to implement the associated functionality. The phrase “configured to” can also refer to a logic circuit structure of a hardware element that is to implement the coding design of associated functionality of firmware or software. The term “module” refers to a structural element that can be implemented using any suitable hardware (e.g., a processor, among others), software (e.g., an application, among others), firmware, or any combination of hardware, software, and firmware. The term, “logic” encompasses any functionality for performing a task. For instance, each operation illustrated in the flowcharts corresponds to logic for performing that operation. An operation can be performed using, software, hardware, firmware, or the like. The terms, “component,” “system,” and the like may refer to computer-related entities, hardware, and software in execution, firmware, or combination thereof. A component may be a process running on a processor, an object, an executable, a program, a function, a subroutine, a computer, or a combination of software and hardware. The term, “processor,” may refer to a hardware component, such as a processing unit of a computer system ; ¶ 12 7 : … the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computing device to implement the disclosed subject matter. The term, “article of manufacture,” as used herein is intended to encompass a computer program accessible from any computer-readable storage device or media. Computer-readable storage media can include, but are not limited to, magnetic storage devices, e.g., hard disk, floppy disk, magnetic strips, optical disk, compact disk (CD), digital versatile disk (DVD), smart cards, flash memory devices, among others. In contrast, computer-readable media, i.e., not storage media, may additionally include communication media such as transmission media for wireless signals and the like . The lack of details about the recited additional elements indicates that these additional elements are generic, or part of generic computer elements performing generic computer-implemented steps. The claimed limitations of “ accessing instructional content ”, “ applying a named entity recognition natural language processing model to derive actions described in the instructional content ”, “ accessing telemetry data ”, “ processing the telemetry data to identify actions ”, “ identifying features ”, “ inputting the features to a machine learning model ”, “ select a task completion path endpoint label ”, “ monitoring user-initiated actions ”, “ generating an effectiveness measure for the instructional content ”, and “ providing the effectiveness measure to an editor for editing the instructional content ” as recited do not purport to improve the functioning of the “ named entity recognition natural language processing model ” and “ machine learning model ” , do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic computer components. Further, the claim as a whole fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely uses the claimed computer elements as a tool to perform the abstract idea. Considering the elements of the claim both individually and as “an ordered combination” the functions performed by the computer system at each step of the process are purely conventional. Each step performed in the claim does no more than require a generic computer to perform a generic computer function. Thus, the claimed elements have not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO). Step 2B: Claim provides an Inventive Concept? As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above ( ¶¶ 21, 34, 88, 91, 118, 125, 126, 127) describes the “ named entity recognition natural language processing model ” and “ machine learning model ” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional elements “in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co. , 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of “ named entity recognition natural language processing model ” and “ machine learning model ” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering (i.e., [L 1 ] “ a ccessing instructional content ” ; [L 3 ] “ accessing telemetry data ” ; [L 6 a ] “ inputting the features ” ) and data presentation (i.e., [L 9 ] “ providing the effectiveness measure to an editor ” ) are nothing more than well-understood, routine, and conventional activity because it is not distinguished from the generic, conventional data gathering and data presentation with a computer. See Elec. Power Grp. , 830 F.3d at 1356 (claims to gathering, analyzing, and displaying data in real time using conventional, generic technology do not have an inventive concept). Hence, the additional elements are generic, well-known, and conventional computing elements. The use of the additional elements either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept , and thus the claims are patent ineligible . (Step 2B: NO). In regard to independent Claim 1 6 : Independent claim 1 6 is a machine-readable storage device , which falls within the “machine” category of 35 U.S.C. § 101. The machine-readable storage device is claimed as having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method, the operations comprising steps similar to the steps of Claim 1 . As a result, independent claim 1 6 is rejected similarly to representative independent Claim 1. In regard to independent Claim 20: Independent claim 16 is a device , which falls within the “machine” category of 35 U.S.C. § 101. The device is claimed as comprising: a processor; and a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising steps similar to the steps of Claim 1. As a result, independent claim 20 is rejected similarly to representative independent Claim 1. In regard to the dependent claims: Dependent claims 2- 15 and 1 7 -1 9 include all the limitations of independent claim 1 from which they depend and as such recite the same abstract idea(s) noted above for claim 1 . None of the additional claim activities is used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-15 and 17-19 integrates the judicial exception into a practical application. W hile dependent claims 2-15 and 17-19 may have a narrower scope than the representative claim, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-15 and 17-19 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more. Conclusion The prior art made of record and not relied upon is listed in the attached PTO Form 892 and is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Enter examiner's name" \* MERGEFORMAT EDDY SAINT-VIL whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-9845 . The examiner can normally be reached FILLIN "Work schedule?" \* MERGEFORMAT Mon-Fri 6:30 AM -6:00 PM . 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