DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on June 9, 2026, for the application with serial number 19/010,268.
Claims 1, 9, and 17 are amended.
Claims 3, 11, and 19 are canceled.
Claims 1, 2, and 4-10, 12-18, and 20 are pending.
Interview
The Examiner acknowledges the interview conducted on June 9, 2026, in which proposed amendments were discussed.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the newly recited language that recites execution of a skill gap reduction plan renders the claims eligible. See Remarks p. 8. The Examiner respectfully disagrees. Executing the plan is part of the abstract idea of generating executing a skill gap reduction plan. No apparent improvement to a technology or technical field is recited in the claims. The rejection, below, does not conclude that the claims are directed to a mental process. Therefore, the Applicant’s arguments with respect to the mental processes category of abstract idea are moot. No particular machine is recited in the claims. Instead, the claims merely recite the use of a generic computer implementing a digital twin to implement the abstract idea. In contrast to the claims from Example 47, the present claims do not recite any apparent improvement to a technology or technical field. Lack of conventionality does not imply subject matter eligibility.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Lymperopoulos reference, which is cited in the rejection of the independent claims, below. The Applicant’s arguments with respect to the previous prior art rejection are moot in light of the newly cited reference.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1, 2, and 4-10, 12-18, and 20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1, 2, and 4-10, 12-18, and 20 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: generating and executing a skill gap reduction plan (as evidenced by exemplary independent claim 1; “generating a skill gap reduction plan that mitigates adverse impacts associated with skill gaps;” and “executing the skill gap reduction plan”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: [1] “receiving a digital twin model;” [2] “integrating real-time data from wearable sensors . . . to update the digital twin model;” [3] “identify an activity requiring human involvement and required skills for performing the activity;” [4] “analyzing a performance of the activity of [a] worker;” [5] “identifying skill gaps of the worker;” [6] executing digital twin simulations . . . including the activity;” [7] “creating virtual reality [ ] visualizations . . . of the identified skill gaps;” and [8] “generating a skill gap reduction plan that mitigates adverse impacts associated with skill gaps;” and [9] “executing the skill gap reduction plan.” Steps [3],[4] [8], and [9] are steps for managing personal behavior related to the abstract idea of generating a skill gap reduction plan that, when considered alone and in combination, are part of the abstract idea of generating a skill gap reduction plan. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating a skill gap reduction plan. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes creating educational training based on determined needed skills of workers.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a computer-implemented method in independent claim 1; a system with a memory and processing device in independent claim 9; and a computer program product on computer-readable storage media in independent claim 17). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of a digital twin machine learning model and virtual reality environment with input from sensors (see elements [1], [2], ,[6], and [7], identified above), but the abstract idea of generating a skill gap reduction plan is generally linked to a virtual reality environment with a digital twin for implementation. Therefore, the recitation of virtual reality and a digital twin does not provide a practical application or significantly more than the recited abstract idea. See MPEP §2106.05(h). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 9, 11-13, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240257276 A1 to Srivastava et al. (hereinafter ‘SRIVASTAVA’) in view of US 20200342988 A1 to Lymperopoulos et al. (hereinafter ‘LYMPEROPOULOUS’), US 20220044792 A1 to Shrubsole et al. (hereinafter ‘SHRUBSOLE’) and US 20050151743 A1 to Sitrick (hereinafter ‘SITRICK’).
Claim 1 (Currently Amended)
SRIVASTAVA discloses a computer-implemented method (see abstract; a computer-implemented method for generating manufacturing deviation event data) for visualizing consequences of skill gaps by a worker on an industrial floor (see ¶[0079] and [0207]; identify skill and knowledge gaps. Simulate outcomes. See also ¶[0103]; a display), the method comprising:
receiving a digital twin model of the industrial floor (see ¶[0090]-[0091] and [0224] & Fig. 1; a digital twin simulation models associated with the battery manufacturing facility. Conveyors and manufacturing devices).
SRIVASTAVA does not specifically disclose, but LYMPEROPOULOS discloses, integrating real-time data from wearable sensors worn by the worker on the industrial floor to update the digital twin model, wherein the wearable sensors are configured to track worker biometrics including at least one of heart rate, body temperature, and fatigue level (see abstract and ¶[0035], [0043], and [0057]; the inputs comprise sensor data including different biometrics measured such as hear [sic] beat rate, body temperature, and environmental conditions. A virtual environment may be seen as a digital twin of the process. Increase productivity and efficiency by engaging a worker based on alertness level);
SRIVASTAVA further discloses analyzing the digital twin model to identify an activity requiring human involvement and required skills for preforming the activity (see ¶[0143] and [0150]; the battery manufacturing operation devices 501 may be in data communications with a skill/knowledge/rule data repository 519. Learn actions that overcome deviations for future reference);
analyzing a performance of the activity of the worker on the industrial floor to determine a skill level possessed by the worker (see ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
identifying skill gaps of the worker based on the required skills and the skill level of the worker (see again ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
executing digital twin simulations of manufacturing processes including the activity (see ¶[0224]-[0225]; the computing device may generate a digital twin and/or enterprise simulation models associated with the battery manufacturing facility at step/operation 919) to model consequences resulting from the identified skill gaps (see ¶[0080], [0113], and [0204] & Fig. 3; the example method shown in FIG. 3 improves the safety level in the battery manufacturing facility while reduces the likelihood of critical errors or failures by generating one or more battery manufacturing adjustment data objects based at least in on one or more machine learning models. The analysis of these data and the access of the real-time process data can provide guidelines to the multi-agent autonomous system to respond to the process 1alarms in an efficient and safe manner.).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, creating virtual reality (VR) visualizations (see ¶[0007], [0022], and [0032]; extract inefficiencies or errors from records and deliver virtual reality content based on the inefficiencies or errors. Inefficiencies and errors are determined from a usage pattern. Education correlation is based on combinations of error types).
The combination of SRIVASTAVA and SHRUBSOLE does not explicitly disclose, but SITRICK discloses, that illustrate the consequences of the identified skill gaps (see ¶[0134]; show the user doing something the wrong way and simulate an injury).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, generating a skill gap reduction plan that mitigates adverse impacts associated with skill gaps (see abstract and ¶[0006]-[0007] & [0032]-[0033]; delivery content based on inefficiencies and errors. Determine whether the error continues after the content is presented).
SRIVASTAVA further discloses executing the skill gap plan including altering an operation of a machine on the industrial floor to reduce a likelihood of one or more modeled mistakes (see ¶[0083]-[0084], [0113], and [0234]; the confidence value associated with a predicted battery manufacturing outcome data object indicates a likelihood that one or more existing abnormal battery manufacturing processes or existing abnormal battery manufacturing operations associated with the battery manufacturing in the battery manufacturing facility will be resolved after implementing the one or more adjustments described in a candidate battery manufacturing adjustment data object. Reduce the likelihood of critical errors).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). LYMPEROPOULOS discloses a support system for an operator that builds a digital twin of an industrial process using biometric data from an operator. It would have been obvious to include the digital twin as taught by LYMPEROPOULOS with the motivation to determine alertness in operating an industrial process and increase productivity.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. It would have been obvious for one of ordinary skill in the art at the time of invention to include the virtual reality training content as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation to address and prevent errors resulting from skill gaps.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. SITRICK discloses image tracking in audio-visual presentations to create simulations that demonstrate an injury as a consequence of doing something the wrong way. It would have been obvious for one of ordinary skill in the art at the time of invention to include the injury simulation as taught by SITRICK in the system executing the method of SRIVASTAVA and SHRUBSOLE with the motivation to simulate injury resulting from errors caused by skill gaps without any real harm.
Claim 4 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, wherein generating the skill gap reduction plan includes scheduling one or more of additional training for the worker and additional oversight of the worker by a supervisor (see ¶[0032]; an education correlation module that selects training modules for the user).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices, where the content includes training modules for the user. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training modules as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation address errors in use of devices caused by skill gaps.
Claim 5 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, further comprising providing the VR visualizations to the worker via an VR display device (see ¶[0007], [0022], and [0032]; extract inefficiencies or errors from records and deliver virtual reality content based on the inefficiencies or errors. See also ¶[0019] a display device for education content).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. It would have been obvious for one of ordinary skill in the art at the time of invention to include the virtual reality training content as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation to address and prevent errors resulting from skill gaps.
Claim 9 (Currently Amended)
SRIVASTAVA discloses a system comprising: a memory comprising computer readable instructions (see abstract and ¶[0005]; computer program code on a non-transitory memory); and a processing device for executing the computer readable instructions (see abstract; a computer-implemented method for generating manufacturing deviation event data), the computer readable instructions controlling the processing device to perform operations comprising:
receiving a digital twin model of an industrial floor (see ¶[0090]-[0091] and [0224] & Fig. 1; a digital twin simulation models associated with the battery manufacturing facility. Conveyors and manufacturing devices).
SRIVASTAVA does not specifically disclose, but LYMPEROPOULOS discloses, integrating real-time data from wearable sensors worn by a worker on the industrial floor to update the digital twin model, wherein the wearable sensors are configured to track worker biometrics including at least one of heart rate, body temperature, and fatigue level (see abstract and ¶[0035], [0043], and [0057]; the inputs comprise sensor data including different biometrics measured such as hear [sic] beat rate, body temperature, and environmental conditions. A virtual environment may be seen as a digital twin of the process. Increase productivity and efficiency by engaging a worker based on alertness level);
SRIVASTAVA further discloses, analyzing the digital twin model to identify an activity requiring human involvement and required skills for preforming the activity (see ¶[0143] and [0150]; the battery manufacturing operation devices 501 may be in data communications with a skill/knowledge/rule data repository 519. Learn actions that overcome deviations for future reference);
analyzing a performance of the activity of a worker on the industrial floor to determine a skill level possessed by the worker (see ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
identifying skill gaps of the worker based on the required skills and the skill level of the worker (see again ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
executing digital twin simulations of manufacturing processes including the activity (see ¶[0224]-[0225]; the computing device may generate a digital twin and/or enterprise simulation models associated with the battery manufacturing facility at step/operation 919) to model consequences resulting from the identified skill gaps (see ¶[0080], [0113], and [0204] & Fig. 3; the example method shown in FIG. 3 improves the safety level in the battery manufacturing facility while reduces the likelihood of critical errors or failures by generating one or more battery manufacturing adjustment data objects based at least in on one or more machine learning models. The analysis of these data and the access of the real-time process data can provide guidelines to the multi-agent autonomous system to respond to the process 1alarms in an efficient and safe manner.).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, creating virtual reality (VR) visualizations (see ¶[0007], [0022], and [0032]; extract inefficiencies or errors from records and deliver virtual reality content based on the inefficiencies or errors. Inefficiencies and errors are determined from a usage pattern. Education correlation is based on combinations of error types).
The combination of SRIVASTAVA and SHRUBSOLE does not explicitly disclose, but SITRICK discloses, that illustrate the consequences of the identified skill gaps (see ¶[0134]; show the user doing something the wrong way and simulate an injury).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, generating a skill gap reduction plan that mitigates adverse impacts associated with skill gaps (see abstract and ¶[0006]-[0007] & [0032]-[0033]; delivery content based on inefficiencies and errors. Determine whether the error continues after the content is presented).
SRIVASTAVA further discloses executing the skill gap plan including altering an operation of a machine on the industrial floor to reduce a likelihood of one or more modeled mistakes (see ¶[0083]-[0084], [0113], and [0234]; the confidence value associated with a predicted battery manufacturing outcome data object indicates a likelihood that one or more existing abnormal battery manufacturing processes or existing abnormal battery manufacturing operations associated with the battery manufacturing in the battery manufacturing facility will be resolved after implementing the one or more adjustments described in a candidate battery manufacturing adjustment data object. Reduce the likelihood of critical errors).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). LYMPEROPOULOS discloses a support system for an operator that builds a digital twin of an industrial process using biometric data from an operator. It would have been obvious to include the digital twin as taught by LYMPEROPOULOS with the motivation to determine alertness in operating an industrial process and increase productivity.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. It would have been obvious for one of ordinary skill in the art at the time of invention to include the virtual reality training content as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation to address and prevent errors resulting from skill gaps.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. SITRICK discloses image tracking in audio-visual presentations to create simulations that demonstrate an injury as a consequence of doing something the wrong way. It would have been obvious for one of ordinary skill in the art at the time of invention to include the injury simulation as taught by SITRICK in the system executing the method of SRIVASTAVA and SHRUBSOLE with the motivation to simulate injury resulting from errors caused by skill gaps without any real harm.
Claim 12 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, wherein generating the skill gap reduction plan includes scheduling one or more of additional training for the worker and additional oversight of the worker by a supervisor (see ¶[0032]; an education correlation module that selects training modules for the user).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices, where the content includes training modules for the user. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training modules as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation address errors in use of devices caused by skill gaps.
Claim 13 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, wherein the operations further comprise providing the VR visualizations to the worker via a VR display device (see ¶[0007], [0022], and [0032]; extract inefficiencies or errors from records and deliver virtual reality content based on the inefficiencies or errors. See also ¶[0019] a display device for education content).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. It would have been obvious for one of ordinary skill in the art at the time of invention to include the virtual reality training content as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation to address and prevent errors resulting from skill gaps.
Claim 17 (Currently Amended)
SRIVASTAVA discloses a computer program product (see abstract and ¶[0005]; computer program code on a non-transitory memory) for visualizing consequences of skill gaps by a worker on an industrial floor see ¶[0079] and [0207]; identify skill and knowledge gaps. Simulate outcomes. See also ¶[0103]; a display) the computer program product comprising: a set of one or more computer-readable storage media; program instructions, collectively stored in the set of one or more storage media (see again abstract; a computer-implemented method for generating manufacturing deviation event data), for causing a processor set to perform the following computer operations:
receiving a digital twin model of an industrial floor (see ¶[0090]-[0091] and [0224] & Fig. 1; a digital twin simulation models associated with the battery manufacturing facility. Conveyors and manufacturing devices).
SRIVASTAVA does not specifically disclose, but LYMPEROPOULOS discloses, integrating real-time data from wearable sensors worn by a worker on the industrial floor to update the digital twin model, wherein the wearable sensors are configured to track worker biometrics including at least one of heart rate, body temperature, and fatigue level (see abstract and ¶[0035], [0043], and [0057]; the inputs comprise sensor data including different biometrics measured such as hear [sic] beat rate, body temperature, and environmental conditions. A virtual environment may be seen as a digital twin of the process. Increase productivity and efficiency by engaging a worker based on alertness level);
SRIVASTAVA further discloses analyzing the digital twin model to identify an activity requiring human involvement and required skills for preforming the activity (see ¶[0143] and [0150]; the battery manufacturing operation devices 501 may be in data communications with a skill/knowledge/rule data repository 519. Learn actions that overcome deviations for future reference);
analyzing a performance of the activity of a worker on the industrial floor to determine a skill level possessed by the worker (see ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
identifying skill gaps of the worker based on the required skills and the skill level of the worker (see again ¶[0207]; implement machine learning models to identify the skill and knowledge gaps when operators, maintenance engineers, system engineers and field engineers perform their tasks by analyzing the data recorded and accumulated in DCS, asset managers, change management systems and the records of configuration);
executing digital twin simulations of manufacturing processes including the activity (see ¶[0224]-[0225]; the computing device may generate a digital twin and/or enterprise simulation models associated with the battery manufacturing facility at step/operation 919) to model consequences resulting from the identified skill gaps (see ¶[0080], [0113], and [0204] & Fig. 3; the example method shown in FIG. 3 improves the safety level in the battery manufacturing facility while reduces the likelihood of critical errors or failures by generating one or more battery manufacturing adjustment data objects based at least in on one or more machine learning models. The analysis of these data and the access of the real-time process data can provide guidelines to the multi-agent autonomous system to respond to the process 1alarms in an efficient and safe manner.).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, creating virtual reality (VR) visualizations (see ¶[0007], [0022], and [0032]; extract inefficiencies or errors from records and deliver virtual reality content based on the inefficiencies or errors. Inefficiencies and errors are determined from a usage pattern. Education correlation is based on combinations of error types).
The combination of SRIVASTAVA and SHRUBSOLE does not explicitly disclose, but SITRICK discloses, that illustrate the consequences of the identified skill gaps (see ¶[0134]; show the user doing something the wrong way and simulate an injury).
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, and generating a skill gap reduction plan that mitigates adverse impacts associated with skill gaps (see abstract and ¶[0006]-[0007] & [0032]-[0033]; delivery content based on inefficiencies and errors. Determine whether the error continues after the content is presented).
SRIVASTAVA further discloses executing the skill gap plan including altering an operation of a machine on the industrial floor to reduce a likelihood of one or more modeled mistakes (see ¶[0083]-[0084], [0113], and [0234]; the confidence value associated with a predicted battery manufacturing outcome data object indicates a likelihood that one or more existing abnormal battery manufacturing processes or existing abnormal battery manufacturing operations associated with the battery manufacturing in the battery manufacturing facility will be resolved after implementing the one or more adjustments described in a candidate battery manufacturing adjustment data object. Reduce the likelihood of critical errors).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). LYMPEROPOULOS discloses a support system for an operator that builds a digital twin of an industrial process using biometric data from an operator. It would have been obvious to include the digital twin as taught by LYMPEROPOULOS with the motivation to determine alertness in operating an industrial process and increase productivity.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. It would have been obvious for one of ordinary skill in the art at the time of invention to include the virtual reality training content as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation to address and prevent errors resulting from skill gaps.
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices. SITRICK discloses image tracking in audio-visual presentations to create simulations that demonstrate an injury as a consequence of doing something the wrong way. It would have been obvious for one of ordinary skill in the art at the time of invention to include the injury simulation as taught by SITRICK in the system executing the method of SRIVASTAVA and SHRUBSOLE with the motivation to simulate injury resulting from errors caused by skill gaps without any real harm.
Claim 20 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer program product as set forth in claim 17.
SRIVASTAVA does not specifically disclose, but SHRUBSOLE discloses, wherein generating the skill gap reduction plan includes scheduling one or more of additional training for the worker and additional oversight of the worker by a supervisor (see ¶[0032]; an education correlation module that selects training modules for the user).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SHRUBSOLE discloses injury support in a healthcare setting that uses virtual reality content to address errors in use of devices, where the content includes training modules for the user. It would have been obvious for one of ordinary skill in the art at the time of invention to include the training modules as taught by SHRUBSOLE in the system executing the method of SRIVASTAVA with the motivation address errors in use of devices caused by skill gaps.
Claim(s) 2, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240257276 A1 to SRIVASTAVA et al. in view of US 20200342988 A to LYMPEROPOULOUS et al., US 20220044792 A1 to SHRUBSOLE et al. and US 20050151743 A1 to SITRICK as applied to claim 1 above, and further in view of US 20240144151 A1 to Maikhuri et al. (hereinafter ‘MAIKHURI’).
Claim 2 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but MAIKHURI discloses, wherein the consequences of the identified skill gaps include accidents, poor quality work, and loss of productivity (see ¶[0012]; utilizing under-skill labor can lead to bad product quality, reduced productivity, and on-the-job accidents).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). MAIKURI discloses artificial intelligence powered worker productivity and safety, where use of under-skilled labor leads to negative outcomes. It would have been obvious to include the negative outcomes as taught by MAIKHURI in the system executing the method of SRIVASTAVA with the motivation to identify outcomes of simulated results of skill gaps.
Claim 10 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but MAIKHURI discloses, wherein the consequences of the identified skill gaps include accidents, poor quality work, and loss of productivity (see ¶[0012]; utilizing under-skill labor can lead to bad product quality, reduced productivity, and on-the-job accidents).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). MAIKURI discloses artificial intelligence powered worker productivity and safety, where use of under-skilled labor leads to negative outcomes. It would have been obvious to include the negative outcomes as taught by MAIKHURI in the system executing the method of SRIVASTAVA with the motivation to identify outcomes of simulated results of skill gaps.
Claim 18 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer program product as set forth in claim 17.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but MAIKHURI discloses, wherein the consequences of the identified skill gaps include accidents, poor quality work, and loss of productivity (see ¶[0012]; utilizing under-skill labor can lead to bad product quality, reduced productivity, and on-the-job accidents).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). MAIKURI discloses artificial intelligence powered worker productivity and safety, where use of under-skilled labor leads to negative outcomes. It would have been obvious to include the negative outcomes as taught by MAIKHURI in the system executing the method of SRIVASTAVA with the motivation to identify outcomes of simulated results of skill gaps.
Claim(s) 6 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240257276 A1 to SRIVASTAVA et al. in view of US 20200342988 A to LYMPEROPOULOUS et al., US 20220044792 A1 to SHRUBSOLE et al. and US 20050151743 A1 to SITRICK as applied to claim 1 above, and further in view of US 20250298858 A1 to Smith et al. (hereinafter ‘SMITH’).
Claim 6 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but SMITH discloses, further comprising ranking the skill gaps based on the consequences resulting from the identified skill gaps and wherein the skill gap reduction plan is generated based at least in part on the rankings (see ¶[0056]; rank deficiencies, such as gaps in skill development to classify errors in specific order, such as ranking common misunderstandings to prioritize issues for instructors).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SMITH discloses ranking gaps in skill development to prioritize issues for instructors. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of issues as taught by SMITH in the system executing the method of SRIVASTAVA with the motivation to identify skill gaps in operation.
Claim 14 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but SMITH discloses, wherein the operations further comprise ranking the skill gaps based on the consequences resulting from the identified skill gaps and wherein the skill gap reduction plan is generated based at least in part on the rankings (see ¶[0056]; rank deficiencies, such as gaps in skill development to classify errors in specific order, such as ranking common misunderstandings to prioritize issues for instructors).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). SMITH discloses ranking gaps in skill development to prioritize issues for instructors. It would have been obvious for one of ordinary skill in the art at the time of invention to include the prioritization of issues as taught by SMITH in the system executing the method of SRIVASTAVA with the motivation to identify skill gaps in operation.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240257276 A1 to SRIVASTAVA et al. in view of US 20200342988 A to LYMPEROPOULOUS et al., US 20220044792 A1 to SHRUBSOLE et al. and US 20050151743 A1 to SITRICK as applied to claim 1 above, and further in view of US 20210110331 A1 to Chavan (hereinafter ‘CHAVAN’).
Claim 7 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not explicitly disclose, but CHAVAN discloses, further comprising using historical data from the industrial floor to identify common skill gaps among workers and using machine learning algorithms to predict potential skill gaps based on worker performance data (see abstract and ¶[0019]; predict a skill gap of each individual in an organization. Include historical data).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). CHAVAN discloses quantifying workforce transformation of an organization that includes predicting skill gaps of individuals based on historical data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the skill gap prediction as taught by CHAVAN in the system executing the method of SRIVASTAVA with the motivation to identify skill gaps in an organization.
Claim 15 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not explicitly disclose, but CHAVAN discloses, wherein the operations further comprise using historical data from the industrial floor to identify common skill gaps among workers and using machine learning algorithms to predict potential skill gaps based on worker performance data (see abstract and ¶[0019]; predict a skill gap of each individual in an organization. Include historical data).
SRIVASTAVA discloses battery manufacturing that is simulated using digital twins and machine learning models to identify skill gaps in operation (see ¶[0113] and [0207]). CHAVAN discloses quantifying workforce transformation of an organization that includes predicting skill gaps of individuals based on historical data. It would have been obvious for one of ordinary skill in the art at the time of invention to include the skill gap prediction as taught by CHAVAN in the system executing the method of SRIVASTAVA with the motivation to identify skill gaps in an organization.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240257276 A1 to SRIVASTAVA et al. in view of US 20200342988 A to LYMPEROPOULOUS et al., US 20220044792 A1 to SHRUBSOLE et al. and US 20050151743 A1 to SITRICK as applied to claim 1 above, and further in view of US 20250307480 A1 to Venugopal et al. (hereinafter ‘VENUGOAPAL’).
Claim 8 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the computer-implemented method as set forth in claim 1.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but VENUGOPAL discloses, further comprising integrating real-time data from IoT sensors on the industrial floor to update the digital twin model (see ¶[0031]; the system 200 outlines an integration of an industrial metaverse VR application with a cloud infrastructure, a generative AI, and Internet of Things (IoT) components).
Claim 16 (Original)
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK discloses the system as set forth in claim 9.
The combination of SRIVASTAVA, LYMPEROPOULOUS, SHRUBSOLE and SITRICK does not specifically disclose, but VENUGOPAL discloses, wherein the operations further comprise integrating real-time data from IoT sensors on the industrial floor to update the digital twin model (see ¶[0031]; the system 200 outlines an integration of an industrial metaverse VR application with a cloud infrastructure, a generative AI, and Internet of Things (IoT) components).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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