DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/16/2026 has been entered.
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, 8 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception(s) without significantly more.
[STEP 1] The claim recites at least one step or structure. Thus, the claim is to a process or product, which is one of the statutory categories of invention (Step 1: YES).
[STEP2A PRONG I] The claim(s) 1 recite(s):
A system comprising:
a digital twin of an educational institution in a jurisdiction comprising a three-dimensional virtual representation of a physical environment;
an encrypted database configured to store the digital twin and user-specific training data along with other digital twins of other educational institutions in the jurisdiction;
an access control module configured to authenticate an authorized individual affiliated with at least one of a law enforcement and fire department in the jurisdiction based on their organization type and access credentials, and to restrict access to the encrypted database based on role-specific permission for law enformance and fire department personnel;
an artificial intelligence model comprising a machine learning model trained using historical security incident data and reinforcement learning techniques, the model being configured to dynamically generated training scenarios and evaluate user performance metrics;
a training module to configured automatically generated scenario-training simulations in virtual realities and augmented reality wherein the simuations incorporate security threats based on the digital twin and are customized by the artificial intelligence model based on user performance and institution layout;
wherein the authorized individual is configured to access the scenario based training simulations through a headset,
wherein the scenario based training simulation is at least one of a virtual reality based training and an augmented reality based training,
wherein the scenario based training simulations enable the authorized individual to practice decision-making, tactics, and de-escalation techniques with simulated potential threats,
wherein each training scenario of the scenario-based training simulations is automatically customized by the artificial intelligence model based on a unique layout of the educational institution, and
wherein the system is configured to permit a trainer to customize a training scenario to specify at least one of a risk and a hazard at the educational institution:
wherein the decision point generator is configured to utilize the artificial intelligence model to determine points of time in the training scenario when the authorized individual is requested to make a conscious choice that is analyzed by the assessment module to determine at least one of an area for improvement of the authorized individual using the artificial intelligence model and the score of the authorized individual when compared to other authorized individuals;
an assessment module comprising a decision point generator that determines key user actions during scenario-based training simulations and configured to evaluate user’s response by comparing decision timing, accuracy, and outcomes to historical benchmarks and generating a performance scores;
and
a management module configured to retrieve performance statistics from the assessment module and present a dashboard interface for viewing comparative user metrics and trainings effectiveness, wherein access is restricted to supervisory personnel of the law enforcement agency and fire department.
a recommendation module to automatically generate a set of recommendations on how the educational institution can modify a physical space at the educational institution to improve at least one of security and safety of students at the educational institution using the artificial intelligence model: and
a discussion point generator to utilize the artificial intelligence model to automatically generate pauses in the training scenario of the scenario-based training simulations in which the authorized individual is taught about unique and notable safety and security aspects of the educational institution.
Claim 8 recites:
A method comprising:
encrypting a digital twin of an educational institution, the digital twin comprising a three-dimensional spatial representation of the educational institution’s physical environment and storing it along with other digital twins in a secure database;
authenticating and providing secure access to the digital twin to an authorized individual affiliated with at least one of a law enforcement agency and a fire department operating in the jurisdiction;
training an artificial intelligence model, implemented as a machine learning system, using historical safety and security incident data from educational institution across multiple jurisdictions, wherein the model is refined using reinforcement learning to optimize threat detection accuracy; and
determining by analyzing the digital twin using the trained artificial intelligence model, at least one of a security threat and a safety threat specific to the educational institution.
automatically generating a scenario based training using the artificial intelligence model to be used by the authorized individual;
determining at least one of an area for improvement of the authorized individual using the artificial intelligence model and a score of the authorized individual when compared to other authorized individuals:
permitting a responsible individual in at least one of the law enforcement agency and the fire department to access performance statistics of each authorized individual;
automatically generating a set of recommendations on how the educational institution can modify a physical space at the educational institution to improve at least one of security and safety of students at the educational institution using the artificial intelligence model;
providing access to the scenario based training through a headset to the authorized individual, wherein the scenario based training is at least one of a virtual reality based training and an augmented reality based training;
enabling the authorized individual to practice decision-making, tactics, and de-escalation techniques with simulated potential threats;
customizing a training scenario of the scenario based training to specify at least one of a risk and a hazard at the educational institution;
automatically customizing the scenario based on a unique layout of the educational institution using the artificial intelligence based model;
utilizing a decision point generator of the artificial intelligence model to determine points of time in the training scenario when the authorized individual is requested to make a conscious choice
utilizing a discussion point generator of the artificial intelligence model to automatically generate pauses in the training scenario, in which the authorized individual is taught about unique and notable safety and security aspects of the educational institution.
Claim 16 recites:
A system comprising:
an artificial intelligence model implemented as a learning machine system trained on historical safety and security incident data and refined using reinforcement learning, the artificial intelligence model is configured to identify at least one of a security threat and a safety threat specific to an educational institution;
a training module configured to automatically generate scenario-based training simulations incorporating the identified threat, wherein the scenario-based training simulations are delivered to an authorized individuals through an interactive digital interface; and
an assessment module configured to determine, using the artificial intelligence model, at least one of an area for improvement of the authorized individual and a performance score of the authorized individual when compared to other authorized individuals;
a management module configured to permit a responsible individual affiliated with at least one of a law enforcement agency and a fire department to access performance statistics of each authorized individual based on the assessment module;
a headset through which the authorized individual is configured to access the scenario-based training simulations, wherein the scenario-based training simulations comprise at least one of a virtual-reality-based training and an augmented-reality-based training: and
a recommendation module configured to analyze outcome of the scenario-based training simulations and generate a set of actionable recommendations for modifying a physical space at the educational institution to improve at least one of a student security and safety, based on spatial data and performance metrics derived from the scenario-based training simulations,
wherein the scenario-based training simulations enable the authorized individual to practice decision-making, tactics, and de-escalation techniques with simulated potential threats,
and wherein the system is configured to permit a trainer to customize a training scenario to specify at least one of a risk and a hazard at the educational institution.
.
The non-highlighted aforementioned limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation between people but for the recitation of generic computer components. That is, other than reciting “encrypted database” [claim 1], “an artificial intelligence model” [claim 1, 8 and 16] , nothing in the claim element precludes the step from practically being performed between people. For example, but for the recited language, the step in the context of this claim encompasses a user performing security analysis, creating a recommendation and assessment of the threat faced by an educational institution.
If a claim limitation, under its broadest reasonable interpretation, covers managing interactions between people, then it falls within the “Organization of Human Activity” grouping of abstract ideas.
Accordingly, the claim recites a judicial exception, and the analysis must therefore proceed to Step 2A Prong Two.
[STEP2A PRONG II] This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional element(s) – “encrypted database” and “an artificial intelligence module”.
The “computer-implemented,” “encrypted database” and “an artificial intelligence module” in the aforementioned steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
[STEP2B] The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the aforementioned steps amounts to no more than mere instructions to apply the exception using a generic computer component, which cannot provide an inventive concept (for example, see paragraph 47-48) or are directed to generally linking the use of a judicial exception to a particular technological environment or field of use (machine learning environment).
As noted previously, the claim as a whole merely describes how to generally “apply” the aforementioned concept in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
The claim is not patent eligible. (Step 2B: NO).
Response to Arguments
Applicant's arguments filed 0 have been fully considered but they are not persuasive.
The applicant argued that the limitation of claim 1, 8 and 16 are not directed to a mental process. Since a human cannot:
generate immersive three-dimensional VR/AR simulations spatially positioned within a digital twin,
apply reinforcement learning to historical security data to dynamically customize simulations,
automatically determine and insert decision points within a running simulation, or
benchmark user responses across stored historical datasets to generate comparative performance metrics.
However, the MPEP states that the claim limitations that are directed to a generic computing component can still be interpreted to be directed to an abstract idea. A review of the specification shows the artificial intelligence described in high level of generality and is merely used as a tool to perform the mental process (see specification paragraph 47 and 48).
The applicant’s argument also provide argument that the claim limitation is directed to an improvement to the functioning of the compute or improvement to other technology or technical field. However, in order to show improvement to the functioning of the compute or improvement to other technology or technical field; the specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see MPEP 2106.04(d)(1)). In this particular case, the specification only provides high level description of the artificial intelligence (see paragraph 56-58) and does not show any evidence of an improvement to the functioning of the compute or improvement to other technology or technical field.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J UTAMA whose telephone number is (571)272-1676. The examiner can normally be reached 9:00 - 17:30 Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at (571)270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT J UTAMA/Primary Examiner, Art Unit 3715
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