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 preliminary amendment filed 06/30/2026. Claims 3, 11 and 15 are amended. Claims 1-36 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 1-36 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 of the claimed subject matter:
Step 1: Statutory Category?
Independent Claim 1 recites “computer-implemented method of generating a simulated environment, 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, in bold the additional (non-abstract) claim limitations that are generic computer components and underline limitations representing extra or post-solution activity.
Independent Claim 1
Revised 2019 Guidance
A computer-implemented method of generating a simulated environment, 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.”).
[L1] obtaining prior performance metrics indicating behavior of a student in performing a prior learning task
“Obtaining prior performance metrics indicating behavior of a student in performing a prior learning task …” 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.
Alternatively, “obtaining prior performance metrics indicating behavior of a student in performing a prior learning 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 a human, such as a teacher could visually or aurally obtain information about another person such as a student.
[L2] updating a student profile based on the prior performance metrics;
“Updating a student profile based on the prior performance metrics” 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 teacher mentally, and/or in writing update the student profile based on the prior performance metrics.
[L3] obtaining lesson parameters indicating content to be included in an interactive lesson;
“Obtaining lesson parameters …” 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 teacher could obtain the lesson parameters visually or aurally.
[L4] generating rules for the interactive lesson based on the student profile and the lesson parameters
“Generating rules for the interactive lesson …” could be performed alternatively 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 teacher could generate rules for the interactive lesson verbally and/or in writing.
[L5] applying the rules to a classifier trained via a reference data set representing prior performance of a student population
“Applying the rules to a classifier trained via a reference data set …” 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.
Alternatively, “applying the rules … via a reference data set …” could be performed alternatively 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 teacher could choose/filter high-impact messages during an activity based on evaluation, judgment, opinion.
The classifier is an additional element – generic component.
[L6] generating, via the classifier, instructions for generating a simulated environment encompassing the interactive lesson; and;
“Generating instructions for generating a simulated environment encompassing the interactive lesson” 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.
Alternatively, “generating … instructions for generating a simulated environment encompassing the interactive lesson” 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 a human, such as a teacher could visually or aurally obtain information about another person such as a student.
The classifier and simulated environment are additional elements – generic components.
[L7] generating a representation of the simulated environment based on the instructions.
“Generating a representation of the simulated environment …” 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.
As shown above in the Claim 1/Revised 2019 Guidance Table, under a broadest reasonable interpretation, the claim steps encompass a method of teaching, providing rules or instructions by the teacher to the student during interaction between the teacher and the student and thereby fall under certain methods of organizing human activity category and mental processes of abstract ideas. It is apparent that, other than reciting the additional non-abstract limitations of the “classifier” and “simulated environment” noted 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 “classifier” and “simulated environment” does not take the claim out of the method of organizing human activity and mental processes 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 bold in the Independent Claim 1/Revised
2019 Guidance Table above, recites the additional limitations of the “classifier” and “simulated environment” at a high level of generality. The instant Specification, as published, provides supporting exemplary descriptions of generic computer components: at least ¶ 6: The classifier may be an artificial neural network (ANN) operating a large language model (LLM). A student device may be configured to operate the simulated environment, the student device being at least one of a virtual reality (VR) headset, and augmented reality (AR) headset, and a smartphone; ¶ 14: The structured textual data may be transmitted to a remote server for processing by the ANN, wherein the ANN is hosted on the remote server. The 3D virtual learning environment may be accessed via a device selected from one or more of a desktop computer, a mobile device, a virtual reality (VR) headset, and an augmented reality (AR) device; ¶ 46: Cross-platform compatibility allows the system to function seamlessly across different devices such as desktops, mobile devices, Virtual Reality (VR), and Augmented Reality (AR), ensuring a consistent and engaging user experience 206; ¶ 138: The architecture of example embodiments supports a wide range of devices, including desktop and laptop computers, tablets and smartphones, Virtual Reality (VR) headsets, and Augmented Reality (AR) devices, ensuring a consistent and coherent learning experience across different hardware; ¶ 149: … The system 100 can be fully compatible across multiple platforms, including desktops, mobile devices, VR, and AR device…; ¶ 164: embodiments may be configured to function consistently across various devices and platforms, including desktops 901, mobile devices 903, VR headsets 902, and AR devices 904. The user interface furthermore adapts to different screen sizes and input methods, ensuring a seamless experience; ¶ 203: The adaptive learning system's cross-platform compatibility allows access through various devices, including smartphones, tablets, VR/AR headsets, and computers; ¶ 268: The system is engineered to operate seamlessly across a wide range of platforms, including desktops, mobile devices, virtual reality (VR) headsets, and augmented reality (AR) devices; ¶ 469: The system's ability to function consistently across various devices and platforms, including desktop computers, mobile devices, VR headsets, and AR devices. The lack of details about the “classifier” and “simulated environment” indicates that the above-mentioned additional elements are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The claim does not change the way in which each of the recited “classifier” and “simulated environment” performs its tasks, the claim simply uses each component for its ordinary purpose to carry out the abstract idea of generating a simulated environment. See 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 claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP § 2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP § 2106.05(c)); or (iv) any other meaningful limitation (see MPEP § 2106.05(e)). See 84 Fed. Reg. at 55. The claimed invention merely implements the abstract idea using instructions executed on generic computer components, as shown in bold above, and as supported in the above noted pertinent portions of the instant Specification, as published. The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The additional limitations noted above, [L1] “obtaining prior performance metrics …” (i.e., data gathering), [L3] “obtaining lesson parameters …” (i.e., data gathering), [L5] “applying the rules to a classifier trained via a reference data set …” (i.e., data gathering), and [L7] “generating a representation of the simulated environment …” (i.e., data presentation) reflect the type of extra-solution activity (i.e., activities in addition to the judicial exception) the courts have determined insufficient to transform judicially excepted subject matter into a patent-eligible application when they are claimed in a merely generic manner. See MPEP § 2106.05(g); see, e.g., CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1370 (Fed. Cir. 2011) (“We have held that mere ‘[data-gathering] step[s] cannot make an otherwise nonstatutory claim statutory.”’ (alterations in original) (quoting In re Grams, 888 F.2d 835, 840 (Fed. Cir. 1989))); see also Elec. Power, 830 F.3d at 1354 (“[W]e have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.”). The instant claim as a whole merely uses computer instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. The claim limitations amount to merely indicating a field of use or technological environment (a computer) in which to apply a judicial exception and, as such, cannot integrate the judicial exception into a practical application. See MPEP § 2106.05(h). Hence, as per MPEP §§ 2106.05(a)–(c), (e)–(h), the additional elements in claim 7, namely the “classifier” and “simulated environment” do not, either individually or in combination, integrate the abstract idea into a practical application. Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong 2: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts 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. The fact that the instant Specification, as published does not further describe the “classifier” and “simulated environment”, indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional element to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Hence, the additional elements are generic, well-understood, routine, and conventional computing elements. Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. Using a computer for obtaining, updating, obtaining, generating, applying, generating, and generating data amounts to electronic data retrieval, modification, analysis, generation and display —some of the most basic functions of a computer. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). See also In re Katz Interactive Call Processing Patent Litig., 639 F.3d 1303, 1316 (Fed. Cir. 2011) (“Absent a possible narrower construction of the terms ‘processing,’ ‘receiving,’ and ‘storing,’ . . . those functions can be achieved by any general purpose computer without special programming.”). None of these activities is used in some unconventional manner nor does any produce some unexpected result. Applicant does not contend it invented any of these activities. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, “even if a process of collecting and analyzing information is ‘limited to particular content’ or a particular ‘source,’ that limitation does not make the collection and analysis other than abstract.” SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (citation omitted).
Considered as an ordered combination, the computer components of representative claim 1 add nothing that is not already present when the steps are considered separately. The sequence of obtaining, updating, obtaining, generating, applying, generating, and generating data is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring), Miller Mendel, Inc. v. City of Anna, Texas, 107 F.4th 1345, 1351 (Fed. Cir. 2024) (sequence of receiving, storing, transmitting, determining, selecting, and generating information). The ordering of the steps is therefore ordinary and conventional.
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 17:
Step 1: Statutory Category?
Independent Claim 17 recites “computer-implemented method adapting a three-dimensional (3D) virtual learning environment, comprising:”. Independent Claim 17 falls within the “process” category of 35 U.S.C. § 101.
Step 2A – Prong 1: Judicial Exception Recited?
The Independent Claim 17/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea, in bold the additional (non-abstract) claim limitations that are generic computer components and underline limitations representing extra or post-solution activity.
Independent Claim 17
Revised 2019 Guidance
A computer-implemented method adapting a three-dimensional (3D) virtual learning environment, 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.”).
The three-dimensional (3D) virtual is an additional element – generic component.
[L1] capturing a current state of the 3D virtual learning environment, the state including data representing objects within the environment and the learner's interactions with the objects;
“Capturing a current state of the 3D virtual learning environment …” 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.
Alternatively, “capturing a current state of the 3D virtual learning environment …” 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 a human, such as a teacher could visually or aurally obtain information about an educational environment and another person such as a student.
The three-dimensional (3D) virtual is an additional element – generic component.
[L2] generating, from the captured state, structured textual data representing the objects and the interactions within the 3D virtual environment
“Generating, from the captured state, structured textual data representing the objects and the interactions …” could be performed alternatively 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 teacher could generate structured textual data verbally and/or in writing.
The three-dimensional (3D) virtual is an additional element – generic component.
[L3] processing, by an artificial neural network (ANN), the structured textual data to generate adaptation instructions, wherein the adaptation instructions include at least one of function calls and commands for modifying the 3D virtual environment;
“Applying the rules to a classifier trained via a reference data set …” 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.
Alternatively, “applying the rules … via a reference data set …” could be performed alternatively 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 teacher could choose/filter high-impact messages during an activity based on evaluation, judgment, opinion.
The artificial neural network (ANN) and three-dimensional (3D) virtual are additional elements – generic components.
[L4] modifying the 3D virtual environment based on the adaptation instructions
“Modifying the … environment based on the adaptation instructions” 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.
Alternatively, “modifying the … environment based on the adaptation instructions” 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 a human, such as a teacher could manually and/or using pen and paper modify environment based on the adaptation instructions.
The three-dimensional (3D) virtual is an additional element – generic component.
Similarly to representative Claim 1 above, the artificial neural network (ANN) and three-dimensional (3D) virtual are recited at a high level of generality (See instant Specification, as published, at least ¶ 6: The classifier may be an artificial neural network (ANN) operating a large language model (LLM); ¶ 135: The modular and scalable architecture facilitates easy integration of new technologies, components, and features, ensuring longevity and adaptability in the rapidly evolving field of educational technology and Artificial Neural Networks; ¶ 138: The architecture of example embodiments supports a wide range of devices, including desktop and laptop computers, tablets and smartphones, Virtual Reality (VR) headsets, and Augmented Reality (AR) devices, ensuring a consistent and coherent learning experience across different hardware; ¶ 455: Artificial Neural Network (ANN): A computational model inspired by the human brain's neural structure. ANNs comprise interconnected nodes organized in layers, designed to recognize patterns and solve complex problems by learning from examples. In our adaptive learning system, ANNs are crucial for processing learner data and enabling personalized content adaptation; ¶ 467: Structured Textual Data: Data that is organized in a specific format (e.g., JSON, XML) that allows for easy parsing and processing by software programs, in this case, enabling an ANN to interpret 3D environment states without visual data). Accordingly, independent claim 17 is rejected for reasons similar to those previously explained when addressing representative claim 1.
In regard to the dependent claims:
Dependent claims 2-16 and 18-34 include all the limitations of corresponding independent claims 1 and 17 from which they depend and, as such, recite the same abstract idea(s) noted above for claims 1 and 17. Claims 2-16 and 18-34 only provide more detailed limitations of the abstract idea, which do not make the abstract idea(s) any less abstract. Any additional claim element is recited as a generic component being used according to its conventional purpose in a conventional manner. See Spec., ¶¶ 6, 14, 46, 135, 138, 149, 164, 203, 268, 455, 467 and 469. No claim activity 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 2-17 integrates the judicial exception into a practical application. While dependent claims 2-16 and 18-34 may have a narrower scope than the representative claims, 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 2-16 and 18-34 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-4, 6-7 and 9-15 are rejected under 35 U.S.C. 103 as obvious over Alailima (US 20200380882 A1) (Alailima) in view of LAI et al. (US 20230316594 A1) (LAI).
Re claims 1-4, 6-7 and 9-15:
[Claim 1] Alailima discloses a computer-implemented method of generating a simulated environment (at least ¶ 74: a virtual reality (VR) system (a simulated environment including as an immersive, interactive 3-D experience for a user), comprising: obtaining prior performance metrics indicating behavior of a student in performing a prior learning task (at least ¶ 292: The difficulty levels (including the difficulty of the task and/or interference, and of the evocative element) of a subsequent session can be set based on the performance metric computed for the individual's performance from a previous session, and can be optimized to modify an individual's performance metric); updating a student profile based on the prior performance metrics; obtaining lesson parameters indicating content to be included in an interactive lesson (at least ¶¶ 25-28, 30: … identify a change in the individual's cognitive response capabilities, (iv) recommend a treatment regimen, or (v) recommend or determine a degree of effectiveness of at least one of a behavioral therapy, counseling, or physical exercise … analyze data indicative of the first response and the secondary response at a second difficulty level to generate at least one second performance metric representative of a performance of the individual …; ¶ 62: the computerized task is rendered using programmed computerized components, and the individual is instructed (e.g., using a computing device) as to the intended goal or objective from the individual for performing the computerized task …; ¶ 71: measuring data indicative of a user's performance at one or more tasks, to provide a user performance metric. The performance metric can be used to derive an assessment of a user's cognitive abilities under emotional load and/or to measure a user's response to a cognitive treatment, and/or to provide data or other quantitative indicia of a user's mood or cognitive or affective bias …; ¶ 78: analyze the differences in the individual's performance based on determining the differences between the user's responses, and/or adjust the difficulty level of the computerized stimuli or interaction (CSI) or other interactive elements based on the individual's performance determined in the analysis, and/or provide an output or other feedback from the platform product indicative of the individual's performance, and/or cognitive assessment, and/or response to cognitive treatment. In some examples, the results of the analysis may be used to modify the difficulty level or other property of the computerized stimuli or interaction (CSI) or other interactive elements).
Alailima appears to be silent on but LAI teaches or at least suggests generating rules for the interactive lesson based on the student profile and the lesson parameters; applying the rules to a classifier trained via a reference data set representing prior performance of a student population; generating, via the classifier, instructions for generating a simulated environment encompassing the interactive lesson; and generating a representation of the simulated environment based on the instructions (at least ¶ 2: … virtual assistants in an extended reality environment … integrating customized digital information into the artificial reality environment via a virtual assistant to recommend and lead the user into suggested actions; ¶ 7: generating a graph of objects, attributes, and relationships between objects extracted from the input data, determining one or more interactions to be presented, initiated, or executed based on the graph and a profile associated with the user, determining virtual content data to be used for rendering virtual content based on the one or more interactions, and rendering the virtual content in the extended reality environment displayed to the user based on the virtual content data, wherein the virtual content is used to present, initiate, or execute the one or more interactions for the user; ¶ 8: … the interactions are defined using sets of rules, decisions trees, or vectors, the rules, decisions trees, or vectors connect context to the action spaces and enable a virtual assistant to determine the one or more interactions should be presented, initiated, or executed …; ¶ 9: the determining the one or more interactions to be presented, initiated, or executed, comprises: (i) inputting values of the graph into the rules or decisions trees to determine the one or more interactions …; ¶ 11: determining learned behavior of the user associated with the one or more interactions using rule-based artificial intelligence, machine learning based artificial intelligence, or both, wherein the virtual content data is determined based on the one or more interactions and the learned behavior; ¶ 12: the determining the learned behavior of the user comprises: collecting historical input data from the user, wherein the historical input data comprises: (i) historical data regarding activity of the user in the extended reality environment …, retraining or fine-tuning rule based systems, algorithms, models, or a combination thereof for implementing the rule-based artificial intelligence, the machine learning based artificial intelligence, or both, and determining the learned behavior of the user associated with the one or more interactions using the retrained or fine-tuned rule based systems, algorithms, models, or a combination thereof; ¶¶ 48, 97, 98, 107, 109, 117,149: The rules, decisions trees, or vectors 530 connect context 532 to the action spaces 527 and enable the virtual assistant engine to determine whether a given interaction should be presented to a user and/or initiated based on the context 532 … The encoding of the action space 527 (A) may further comprise assigning rules, decisions trees, or vectors 530 (A) to the interaction 515 (A) … The artificial intelligence platform 560 comprises rule based systems 562, algorithms 565, and models 567 for implementing rule-based artificial intelligence and machine learning based artificial intelligence … the rule based systems 562, algorithms 565, and/or models 567 of the artificial intelligence platform 560 may be configured for any known scene graph generation (SGG) techniques such as … convolutional neural network (CNN)-based SGG, recurrent neural network/long short-term memory (RNN/LSTM)-based SGG, and graph-based SGG). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the virtual assistant features of LAI and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 2] Alailima in view of LAI teaches or at least suggests wherein the prior performance metrics include at least one of success rate, time taken to complete the prior learning task, and number of attempts at completing the prior learning task (at least Alailima: ¶ 65: … degree of success to which an individual's actions in interacting with the platform; ¶ 80: indicate to the individual their degree of success in performing the physical actions; ¶ 98: reaction time of a user's response relative to the time of presentation of the tasks; ¶ 130: … reaction time … percent accuracy … number of attempts at a task needed to complete a task).
[Claim 3] Alailima in view of LAI teaches or at least suggests wherein updating the student profile includes determining, based on the prior performance metrics, the student's aptitude for at least one of a plurality of distinct learning abilities (at least Alailima: ¶ 3: … methods can be implemented for enhancing certain cognitive abilities; ¶ 101: … measurable improvement of the abilities of a user, such as but not limited to improvements related to cognition, a user's mood or level of cognitive or affective bias. The degree or level of improvement can be quantified based on user performance measures).
[Claim 4] Alailima in view of LAI teaches or at least suggests wherein the lesson parameters include representations of at least one of 1) required subject matter, 2) restricted subject matter, 3) proportion of passive lesson time versus interactive lesson time, and 4) proportion of collaborative time versus non-collaborative time (at least Alailima: ¶ 6: … requiring a first response from the individual to the first instance of the primary task in the presence of the interference and a secondary response from the individual to the interference).
[Claim 6] Alailima in view of LAI teaches or at least suggests wherein the rules represent at least a subset of the lesson parameters and the student profile (at least Alailima: ¶ 34: … varying characteristics of an aspect of the primary task or the interference may include adjusting a temporal length of the rendering of the task or interference at the user interface between two or more sessions of interactions of the individual …; ¶ 158: … analyze at least some portion of the data to compute at least one response profile representative of the performance of the individual, and determine a decision boundary metric (such as but not limited to the response criterion) from the response profile; ¶ 222: measure change in response profile in individuals or groups after use of an intervention).
[Claim 7] Alailima in view of LAI teaches or at least suggests wherein the instructions include a table storing a set of parameters for generating the simulated environment encompassing the interactive lesson (at least Alailima: ¶ 34: … varying characteristics of an aspect of the primary task or the interference may include adjusting a temporal length of the rendering of the task or interference at the user interface between two or more sessions of interactions of the individual …; ¶ 158: … analyze at least some portion of the data to compute at least one response profile representative of the performance of the individual, and determine a decision boundary metric (such as but not limited to the response criterion) from the response profile; ¶ 222: measure change in response profile in individuals or groups after use of an intervention; ¶ 181: the predictive model can be used to generate a standards table, which can be applied to the data collected from the individual's response to task and/or interference to classify the individual's cognitive response capabilities; ¶ 299: requiring the individual to perform physical actions to indicate the target (interruptor) Table 1 describes possible combinations of target/interruptor and non-target/distractor presented on the differing sides (left vs right) of the user interface.
TABLE-US-00001 TABLE 1 Left Right Target/interruptor Nontarget/distractor Nontarget/distractor Target/interruptor Nontarget/distractor Nontarget/distractor Target/interruptor Target/interruptor).
[Claim 9] Alailima in view of LAI teaches or at least suggests configuring a student device to operate the simulated environment, the student device being at least one of a virtual reality (VR) headset, and augmented reality (AR) headset, and a smartphone (at least Alailima: ¶ 74: … the example system employs an App program running on a mobile communication device or other hand-held devices. Non-limiting examples of such mobile communication devices or hand-held device include a smartphone … a head-mounted device, such as smart eyeglasses with built-in displays, a smart goggle with built-in displays, or a smart helmet with built-in displays, and the user can hold a controller or an input device having one or more sensors in which the controller or the input device communicates wirelessly with the head-mounted device).
[Claims 10-11] Alailima in view of LAI teaches or at least suggests obtaining performance metrics indicating behavior of the student associated with the simulated environment; and generating subsequent rules for a subsequent interactive lesson based on the performance metrics, subsequent instructions for generating a subsequent simulated environment encompassing the subsequent interactive lesson; and generating a representation of the subsequent simulated environment based on the subsequent instructions (at least LAI: ¶¶ 8, 9 11, 12, 48, 97, 98, 107, 109, 117,149: … the determining the learned behavior of the user comprises: collecting historical input data from the user, wherein the historical input data comprises: (i) historical data regarding activity of the user in the extended reality environment …, retraining or fine-tuning rule based systems, algorithms, models, or a combination thereof for implementing the rule-based artificial intelligence, the machine learning based artificial intelligence, or both, and determining the learned behavior of the user associated with the one or more interactions using the retrained or fine-tuned rule based systems, algorithms, models, or a combination thereof …). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have further utilized the virtual assistant features of LAI and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 12] Alailima in view of LAI teaches or at least suggests wherein the simulated environment is a simulated 3D environment encompassing interactive simulated objects within the 3D environment (at least Alailima: ¶ 74: … system can be formed as a virtual reality (VR) system (a simulated environment including as an immersive, interactive 3-D experience for a user), an augmented reality (AR) system (including a live direct or indirect view of a physical, real-world environment whose elements are augmented by computer-generated sensory input such as but not limited to sound, video, graphics and/or GPS data), or a mixed reality (MR) system (also referred to as a hybrid reality which merges the real and virtual worlds to produce new environments and visualizations where physical and digital objects co-exist and interact substantially in real time)).
[Claim 13] Alailima in view of LAI teaches or at least suggests wherein the reference data set is a first reference data set, and wherein the classifier is trained via a second reference data set including parameters of reference simulated environments encompassing reference interactive lessons (at least LAI: ¶ 12: … the determining the learned behavior of the user comprises: collecting historical input data from the user, wherein the historical input data comprises: (i) historical data regarding activity of the user in the extended reality environment …, retraining or fine-tuning rule based systems, algorithms, models, or a combination thereof for implementing the rule-based artificial intelligence, the machine learning based artificial intelligence, or both, and determining the learned behavior of the user associated with the one or more interactions using the retrained or fine-tuned rule based systems, algorithms, models, or a combination thereof …; ¶ 39: … determining one or more interactions to be presented, initiated, or executed based on the graph and a profile associated with the user; determining virtual content data to be used for rendering virtual content based on the one or more interactions; and rendering the virtual content in the extended reality environment displayed to the user based on the virtual content data. The virtual content is used to present, initiate, or execute the one or more interactions for the user…; ¶ 57: … generation and rendering of virtual content based on a current field of view of user 220, as may be determined by real-time gaze 255 tracking of the user, or other conditions; ¶ 96: … one or more interactions 515 based on the input data that could be executed to request information or services, and/or complete a goal 520 or task 522 of the user …; ¶ 100: a user can make adjustments or modifying the goals 525, action spaces 527 (including the rules, decisions trees, or vectors), and user information 537 within the user profile 515 using the virtual assistant application 505 or a separate application accessed via the client system; ¶ 122: … the workflows associated with an interaction can be modified by the user (e.g., add or remove workflow or add or remove task within individual workflows), or the workflows and tasks can be updated …; ¶ 150: …The historical data may be used to retrain or fine tune rule based systems, algorithms, and models for implementing the rule-based artificial intelligence and the machine learning based artificial intelligence. The retrained or fine-tuned rule based systems, algorithms, and models may be implemented prior to initiation or execution of the one or more interactions to determine learned behavior for the one or more interactions. In some instances, the learned behavior for the one or more interactions can be linked or associated with the active spaces, workflows, or tasks for the one or more interactions). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have further utilized the virtual assistant features of LAI and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 14] Alailima in view of LAI teaches or at least suggests wherein the rules for the interactive lesson include a pedagogy mode defining at least one of 1) a sequence of content presentation and 2) a mode of content presentation (at least Alailima: ¶ 153: … compute a response criterion based on the detection or classification task(s) described herein that are composed of signal and non-signal response targets (as stimuli), in which a user indicates a response that indicates a feature, or multiple features, are present in a series of sequential presentations of stimuli or simultaneous presentation of stimuli).
[Claim 15] Alailima in view of LAI teaches or at least suggests determining an emotional state of the student during performance of the prior learning task based on the prior performance metrics; and generating the rules for the interactive lesson further based on the emotional state (at least Alailima: ¶ 3: quantifying aspects of cognition (including cognitive abilities) under emotional load. In certain configurations; ¶ 4: … the data includes at least one measure of emotional processing capabilities of the individual under emotional load; and to analyze the data indicative of the first response and the secondary response to generate at least one performance metric including at least one quantified indicator of cognitive abilities of the individual under emotional load; ¶¶ 6, 8-11: generating an indication of cognitive skills in an individual using evocative elements presented according to one or more integration rules … The one or more integration rules may include a first integration rule that requires each evocative element to be presented with a non-evocative feature that is not correlated with a facial expression, the interruptor including an evocative element having a specified facial expression; ¶ 20: At least one evocative element may include an image of a face that represents or correlates with an expression of a specific emotion or a combination of emotion; ¶ 24: The generated performance metric may include a quantitative indicator of at least one of a mood, a cognitive bias, and/or an affective bias of the individual; ¶ 26: The data may be indicative of the first response and the secondary response at a first difficulty level, and the one or more processors may be further configured to analyze data indicative of the first response and the secondary response at a second difficulty level to generate at least one second performance metric representative of a performance of the individual of interference processing under emotional load; ¶ 28: adjust a difficulty of at least one of the task and/or the interference based on the at least one generated first performance metric such that the primary task with the interference are rendered at a second difficulty level; and generate a second performance metric representative of cognitive abilities of the individual under emotional load based at least in part on the data indicative of the first response and the response of the individual to the at least one evocative element; ¶ 33: enhancing cognitive skills in an individual using evocative elements presented according to one or more integration rules; ¶ 71: The performance metric can be used to derive an assessment of a user's cognitive abilities under emotional load and/or to measure a user's response to a cognitive treatment, and/or to provide data or other quantitative indicia of a user's mood or cognitive or affective bias. As used herein, indicia of cognitive or affective bias include data indicating a user's preference for a negative emotion, perspective, or outcome as compared to a positive emotion, perspective, or outcome; ¶ 100: “cognition” refers to the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses. This includes, but is not limited to, psychological concepts/domains such as, executive function, memory, perception, attention, emotion, motor control, and interference processing; ¶ 105: the term “emotional load” refers to cognitive load that is specifically associated with processing emotional information or regulating emotions or with affective bias in an individual's preference for a negative emotion, perspective, or outcome as compared to a positive emotion, perspective, or outcome; ¶ 112: a cognitive platform configured to render at least one integrating evocative element (EAE) in a MTG or a STG, where the user interface is configured to not explicitly call attention to the evocative element (EAE). The user interface of the platform product may be configured to render evocative element (EAE) for the purpose of assessing or adjusting emotional biases in attention, interpretation, or memory, and to collected data indicative of the user interaction with the platform product; ¶ 119: evaluate the user's ability to discern emotional cues, or to choose appropriate emotional response; ¶ 134: modify the type or a difficulty level of one or more of the task, the interference, and the evocative element based on the measurement data indicating, e.g., the individual's attention, mood, or emotional state … the biofeedback can be based on physiological measurements of the individual as they interact with the apparatus, to modify the type or a difficulty level of one or more of the task, the interference, and the evocative element based on the measurement data indicating, e.g., the individual's attention, mood, or emotional state; ¶ 190: the foregoing time-varying characteristic can be applied to an object that includes the evocative element to modify an emotional load of the individual's interaction with the apparatus (e.g., computing device or cognitive platform); ¶ 265: a form of multi-tasking to provide measures of the individual's capabilities in deciding whether to perform one action instead of another and to activate the rules of the current task in the presence of an interference such that the interference diverts the individual's attention from the task, as a measure of an individual's cognitive abilities in executive function control; ¶ 292: The difficulty levels (including the difficulty of the task and/or interference, and of the evocative element) of a subsequent session can be set based on the performance metric computed for the individual's performance from a previous session, and can be optimized to modify an individual's performance metric (e.g., to lower or optimize the interference cost under emotional load); ¶ 306: the dynamics of tasks and interferences can be presented according to different modes based on integration rules; ¶ 314: the adapting of difficulty levels to the performance of the individual can be effected by changing from one integration rule and/or target mode to another between two or more different trials or sessions. In another example, the adapting of difficulty levels to the performance of the individual can be effected by changing from one integration rule and/or target mode to another within a given trial or a given session).
Claim 5 is rejected under 35 U.S.C. 103 as obvious over Alailima in view of LAI, as applied to claim 1 and further in view of Leonardo et al. (US 20130157242 A1 (Leonardo).
Re claim 5:
[Claim 5] Alailima in view of LAI appears to be silent on but Leonardo teaches or at least suggests wherein the lesson parameters are based on a selection by an educator (at least ¶ 4: … allows a teacher user to specify activity parameters that define an activity for one or more students to complete on a computer or a mobile device, uses the activity parameters to determine appropriate subject matter from a content asset database, generates an activity incorporating the determined appropriate subject matter). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the educational activity creation features of Leonardo and to have modified Alailima in view of LAI as claimed, because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claim 8 is rejected under 35 U.S.C. 103 as obvious over Alailima in view of LAI, as applied to claim 1 and further in view of SIKKA (US 20240370703 A1) (SIKKA).
Re claim 8:
[Claim 8] Alailima in view of LAI teaches or at least suggests wherein the classifier is artificial neural network (ANN) (at least Alailima: ¶19: the predictive model may include at least a linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural network; ¶ 156: … the example classifier can be built using a machine learning tool, such as but not limited to linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural networks; ¶ 158: … analyze at least some portion of the data to compute at least one response profile representative of the performance of the individual, and determine a decision boundary metric (such as but not limited to the response criterion) from the response profile). A large language model (LLM) is a computerized language model, embodied by an artificial neural network.
Alailima in view of LAI appears to be silent on but SIKKA teaches or at least suggests the artificial neural network (ANN) operating a large language model (LLM) (at least (¶ 50: the trained language model 150 can be an artificial neural network, such as a large language model (LLM) …). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the large language model of SIKKA and to have modified Alailima in view of LAI as claimed, because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claim 16 is rejected under 35 U.S.C. 103 as obvious over Alailima in view of LAI, as applied to claim 1 and further in view of DESGARENNES et al. (US 20240289686 A1) (DESGARENNES).
Re claim 16:
[Claim 16] Alailima in view of LAI appears to be silent on but DESGARENNES teaches or at least suggests wherein generating the rules for the interactive lesson includes generating a text-based representation of a simulated 3D environment, the rules for the interactive lesson including the text-based representation (at least ¶ 21; ¶¶ 23, 24: machine learning and/or artificial intelligence; ¶ 33: The user and/or developer may provide input (e.g., to the user device 102 and developer device 108, respectively) as verbal input, textual input, programmatic code, and/or as any of a variety of other input; ¶ 48: The virtual assistant engine 110 may use artificial intelligence systems 140 (e.g., rule based systems or machine-learning based systems such as natural-language understanding models) to analyze the input based on a user's profile and other relevant information; ¶¶ 115 ). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized DESGARENNES’ integration of interactive elements between users, generative machine learning model, and interactive environment and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 17-29 and 32-36 are rejected under 35 U.S.C. 103 as obvious over Alailima in view of DESGARENNES.
Re claims 17-29 and 32-36:
[Claim 17] Alailima discloses a computer-implemented method adapting a three-dimensional (3D) virtual learning environment (at least ¶ 74: a virtual reality (VR) system (a simulated environment including as an immersive, interactive 3-D experience for a user), comprising: capturing a current state of the 3D virtual learning environment, the state including data representing objects within the environment and the learner's interactions with the objects (at least ¶ 43: FIGS. 7A-7D show examples of the time-varying features of example objects (targets or non-targets) that can be presented to an example user interface; ¶ 56: a cognitive platform configured for using evocative elements rendered in modes (i.e., emotional or affective elements) in computerized tasks (including computerized tasks that appear to a user as platform interactions) that employ one or more interactive user elements to provide cognitive assessment or deliver a cognitive treatment; ¶ 89: an “evocative element” is a computerized element that is configured to evoke from the individual an emotional response (i.e., a response based on the individual's cognitive and/or neurologic processing of emotion/affect/mood or parasympathetic arousal) and/or an affective response (i.e., a response based on the individual's preference for a negative emotion, perspective, or outcome as compared to a positive emotion, perspective, or outcome); ¶ 92: the cognitive platform can be configured to render multi-task interactive elements. In some examples, the multi-task interactive elements are referred to as multi-task gameplay (MTG). The multi-task interactive elements include interactive mechanics configured to engage the user in multiple temporally-overlapping tasks, i.e., tasks that may require multiple, substantially simultaneous responses from a user; ¶ 95: For a navigation task, the cognitive platform may require a position-specific and/or a motion-specific response from the user. For a facial expression recognition or object recognition task, the cognitive platform may require temporally-specific and/or position-specific responses from the user. In non-limiting examples, the user response to tasks, such as but not limited to targeting and/or navigation and/or facial expression recognition or object recognition task(s), can be recorded using an input device of the cognitive platform … user response recorded using the cognitive platform for tasks, such as but not limited to targeting and/or navigation and/or facial expression recognition or object recognition task(s), can include user actions that cause changes in a position, orientation, or movement of a computing device including the cognitive platform; ¶ 169: time-varying characteristics is one or more of a speed of an object, a rate of change of a facial expression, a direction of trajectory of an object, a change of orientation of an object, at least one color of an object, a type of an object, or a size of an object; ¶ 290: render to the user interfaces (including graphical user interfaces) an avatar or other processor-rendered guide 504 that an individual is required to control (such as but not limited to navigate a path or other environment in a visuo-motor task, and/or to select an object in a target discrimination task) …).
Alailima further discloses “the predictive model may include at least a linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural network” and that “the example classifier can be built using a machine learning tool, such as but not limited to linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural networks”. See ¶¶ 19, 156. However, Alailima appears to be silent on but DESGARENNES teaches or at least suggests generating, from the captured state, structured textual data representing the objects and the interactions within the 3D virtual environment; processing, by an artificial neural network (ANN), the structured textual data to generate adaptation instructions, wherein the adaptation instructions include at least one of function calls and commands for modifying the 3D virtual environment; and modifying the 3D virtual environment based on the adaptation instructions (at least ¶ 21: a multi-modal output for an interactive element within the interactive environment 140, such as NL output associated with an NPC and/or other artificial intelligence (AI) agent … ; ¶¶ 23, 24: machine learning and/or artificial intelligence; ¶ 33: The user and/or developer may provide input (e.g., to the user device 102 and developer device 108, respectively) as verbal input, textual input, programmatic code, and/or as any of a variety of other input; ¶ 37: The environment guidelines may also include one or more NL rules for use in processing natural language input as well as for use in providing NL output ; ¶ 40: collects and analyzes specific context that may be used for prompt generation … one or more interactive elements within the interactive environment 140, among other aspects of specific context … perform function and/or action tracking of the user/developer and interactive elements within the input scenario of the interactive environment 140; ¶ 48: receive one or more of the input, systemic context, specific context, environment guidelines, and/or the intent objectives and utilize them to generate one or more prompts for the ML model. The prompt generator 128 may generate one or more prompts that, when processed by an ML model, cause the ML model to generate output responsive to the one or more intent objectives associated with the input, with which the interactive environment may be adapted … an output may be one or more of a text file … a NL output … programmatic language (e.g., code), and/or any other type of output which may cause an interactive element of the interactive environment 140 to act in a way that is responsive to the user/developer input within the context provided by the DS 120; ¶ 115: receive an input, by a director service, to modify an interactive element of an interactive environment; analyze, by the director service, the interactive environment for a specific context based on the input; receive one or more environment guidelines; associate, by the director service, the input with one or more environment guidelines that provide systemic context about the interactive environment; determine, by the director service, a intent objective based on one or more of the input, specific context and one or more environment guidelines; generate, by the director service, a prompt for a generative machine learning model based on the intent objective; execute the generative machine learning model with the prompt to produce a model output; evaluate, by the director service, the model output for responsiveness to the input and the environment guidelines; and when the model output is responsive, modify the interactive element of the interactive environment based on the model output). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized DESGARENNES’ integration of interactive elements between users, generative machine learning model, and interactive environment and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 18] Alailima in view of DESGARENNES teaches or at least suggests wherein capturing the current state includes detecting positions, properties, and relationships of interactive objects within the 3D virtual environment (at least Alailima: ¶ 65: a trial can be a period of time during a navigation task (including a visuo-motor navigation task) in which the individual's performance is assessed, such as but not limited to, assessing whether or the degree of success to which an individual's actions in interacting with the platform result in a guide (including a computerized avatar) navigating along at least a portion of a certain path or in an environment for a time interval (such as but not limited to, fractions of a second, a second, several seconds, or more) and/or causes the guide (including computerized avatar) to cross (or avoid crossing) performance milestones along the path or in the environment; ¶ 67: instructions can be provided to the individual to specify how the individual is expected to perform the task and/or interference (either or both with evocative element) in a trial and/or a session. In non-limiting examples, the instructions can inform the individual of the expected performance of a navigation task (e.g., stay on this path, go to these parts of the environment, cross or avoid certain milestone objects in the path or environment), a targeting task (e.g., describe or show the type of object that is the target object versus the non-target object, or describe or show the type of object that is the target object versus the non-target object, or two different types of target object that the individual is expected to choose between (e.g., happy face versus happier face)), and/or describe how the individual's performance is to be scored; ¶ 78: the results of the analysis may be used to modify the difficulty level or other property of the computerized stimuli or interaction (CSI) or other interactive elements; ¶ 95: the cognitive platform may require a temporally-specific and/or a position-specific response from an individual … For a navigation task, the cognitive platform may require a position-specific and/or a motion-specific response from the user. For a facial expression recognition or object recognition task, the cognitive platform may require temporally-specific and/or position-specific responses from the user … the user response to tasks, such as but not limited to targeting and/or navigation and/or facial expression recognition or object recognition task(s), can be recorded using an input device of the cognitive platform … targeting and/or navigation and/or facial expression recognition or object recognition task(s), can include user actions that cause changes in a position, orientation, or movement of a computing device including the cognitive platform).
[Claim 19] Alailima in view of DESGARENNES teaches or at least suggests wherein the structured textual data includes descriptions of the learner's interactions, including movements, object manipulations, and inputs (at least Alailima: ¶ 56: a cognitive platform configured for using evocative elements rendered in modes (i.e., emotional or affective elements) in computerized tasks (including computerized tasks that appear to a user as platform interactions) that employ one or more interactive user elements to provide cognitive assessment or deliver a cognitive treatment; ¶ 65: assessing whether or the degree of success to which an individual's actions in interacting with the platform result in identification/selection of a target versus a non-target (e.g., red object versus yellow object), or discriminates between two different types of targets (a happy face versus a happier face); ¶ 95: the user response recorded using the cognitive platform for tasks, such as but not limited to targeting and/or navigation and/or facial expression recognition or object recognition task(s), can include user actions that cause changes in a position, orientation, or movement of a computing device including the cognitive platform. Such changes in a position, orientation, or movement of a computing device can be recorded using an input device disposed in or otherwise coupled to the computing device; ¶ 118: The user response measurement may employ use of inputs such as touchscreens, keyboards, or accelerometers, or passive external sensors such as video cameras, microphones, eye-tracking software/devices, bio-sensors, and/or neural recording (e.g., electroencephalogram), and may include responses that are not directly related to interactions with the platform product, as well as responses based on user interactions with the platform product; Alailima: ¶¶ 21, 23, 33, 37, 40, 48, 115).
[Claim 20] Alailima in view of DESGARENNES teaches or at least suggests processing, via the ANN, the structured textual data in conjunction with a learner profile that includes the learner's preferences, performance metrics, and learning objectives (at least Alailima: ¶ 6: analyze the data indicative of the first response and the secondary response to generate at least one performance metric including at least one quantified indicator of cognitive abilities of the individual under emotional load; ¶ 19: the predictive model may include at least a linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural network; ¶156: the example classifier can be built using a machine learning tool, such as but not limited to linear/logistic regression, principal component analysis, generalized linear mixed models, random decision forests, support vector machines, and/or artificial neural networks; ¶ 32: analyze the data indicative of the first response and the secondary response to generate a first performance metric including at least one quantified indicator of cognitive abilities of the individual under emotional load, adjust a difficulty of at least one of the primary task and/or the interference based on the generated at least one first performance metric such that the primary task with the interference are rendered at a second difficulty level in a second iteration, and generate a second performance metric representative of cognitive abilities of the individual under emotional load based at least in part on the data indicative of the first response and the second response from the second iteration to provide an indication of a difference in cognitive abilities of the individual; ¶ 62: the individual is instructed (e.g., using a computing device) as to the intended goal or objective from the individual for performing the computerized task; ¶ 71: measuring data indicative of a user's performance at one or more tasks, to provide a user performance metric. The performance metric can be used to derive an assessment of a user's cognitive abilities under emotional load and/or to measure a user's response to a cognitive treatment, and/or to provide data or other quantitative indicia of a user's mood or cognitive or affective bias … indicia of cognitive or affective bias include data indicating a user's preference for a negative emotion, perspective, or outcome as compared to a positive emotion, perspective, or outcome; ¶ 73: analyze the data indicative of the first response and the response of the individual to the at least one evocative element to compute at least one performance metric comprising at least one quantified indicator of cognitive abilities; ¶ 80: provide the individual with performance progress milestones to maintain or modify the path of a computerized avatar in the computer environment, and/or to indicate to the individual their degree of success in performing the physical actions measured; ¶ 81: provide the individual with the performance progress milestones to maintain or modify the path of a computerized avatar in the computer environment, and/or to indicate to the individual their degree of success in performing the physical actions measured by the sensors of the computing device to cause the computerized avatar to maintain the expected course or path; ¶ 100: collect data indicative of user interaction with a platform product, and to compute metrics that quantify user performance; ¶ 158: receive data indicative of a first response of the individual to the task and a second response of the individual to the interference, analyze at least some portion of the data to compute at least one response profile representative of the performance of the individual, and determine a decision boundary metric (such as but not limited to the response criterion) from the response profile).
[Claim 21] Alailima in view of DESGARENNES teaches or at least suggests updating the learner profile based on the learner's interactions and performance within the 3D virtual environment (at least Alailima: ¶ 158: receive data indicative of a first response of the individual to the task and a second response of the individual to the interference, analyze at least some portion of the data to compute at least one response profile representative of the performance of the individual, and determine a decision boundary metric (such as but not limited to the response criterion) from the response profile; ¶ 164: applying at least one adaptive procedure to modify the task and/or the interference, such that analysis of the data indicative of the first response and/or the second response indicates a modification of the first response profile; ¶ 222: measure change in response profile in individuals or groups after use of an intervention).
[Claim 22] Alailima in view of DESGARENNES teaches or at least suggests executing, via an adaptive agent, the adaptation instructions generated by the ANN to modify the 3D virtual environment (at least DESGARENNES: ¶ 10: conversational agent evaluation portal; ¶ 61: a conversational agent which may utilize one or more aspects of an ML model to engage in the feedback session). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the conversational agent of DESGARENNES and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 23] Alailima in view of DESGARENNES teaches or at least suggests providing, via the adaptive agent, at least one of real-time feedback, guidance, and instructional content to the learner within the 3D virtual environment (at least Alailima: ¶ 297: the difficulty level of a task and/or interference of a subsequent level can also be changed in real-time as feedback, e.g., the difficulty of a subsequent level can be increased or decreased in relation to the data indicative of the performance of the task; DESGARENNES: ¶ 5: allows an ML model to be integrated in context with substantially real-time interactive elements to perform a plurality of tasks).
[Claim 24] Alailima in view of LAI appears to be silent on but DESGARENNES teaches or at least suggests wherein converting the captured state into structured textual data reduces data transmission requirements compared to transmitting visual data, thereby optimizing bandwidth usage (at least ¶ 21; ¶¶ 23, 24: machine learning and/or artificial intelligence; ¶ 33: The user and/or developer may provide input (e.g., to the user device 102 and developer device 108, respectively) as verbal input, textual input, programmatic code, and/or as any of a variety of other input; ¶ 48: The virtual assistant engine 110 may use artificial intelligence systems 140 (e.g., rule based systems or machine-learning based systems such as natural-language understanding models) to analyze the input based on a user's profile and other relevant information; ¶¶ 115 ). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized DESGARENNES’ integration of interactive elements between users, generative machine learning model, and interactive environment and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 25] Alailima in view of DESGARENNES teaches or at least suggests wherein the real-time modification of the 3D virtual environment includes dynamically creating, altering, or removing virtual objects or scenarios based on the adaptation instructions (at least Alailima: ¶ 34: Adjusting the difficulty level may include modifying a time-varying aspect of the first instance of the primary task and/or the interference Modifying the time-varying characteristics of an aspect of the primary task or the interference may include adjusting a temporal length of the rendering of the task or interference at the user interface between two or more sessions of interactions of the individual … The time-varying characteristics may be at least one of a speed of an evocative element, a rate of change of a facial expression, a direction of trajectory of an evocative element, a change of orientation of an evocative element, at least one color of an evocative element, a type of an evocative element, or a size of an evocative element. The change in type of evocative element may be effected using morphing from a first type of evocative element to a second type of evocative element or rendering a blendshape as a proportionate combination of the first type of evocative element and the second type of evocative element; ¶ 297: the difficulty level of a task and/or interference of a subsequent level can also be changed in real-time as feedback, e.g., the difficulty of a subsequent level can be increased or decreased in relation to the data indicative of the performance of the task; ¶ 304: the target evocative element (e.g., facial expression) can be modulated either dynamically (i.e., in real-time on the user interface) or in differing static renditions to vary the degrees of a facial expression; ¶ 317: the results of the analysis may be used to modify the difficulty level or other property of the computerized stimuli or interaction (CSI) or other interactive elements).
[Claim 26] Alailima in view of DESGARENNES teaches or at least suggests generating, via the ANN, adaptation instructions that adjust at least one of the difficulty level, presentation style, and pacing of the learning content based on the learner's interactions (at least Alailima: ¶ 22: Adjusting the difficulty level may include applying an adaptive algorithm to progressively adjust a level of valence of the at least one evocative element; ¶ 34: Adjusting the difficulty level may include modifying a time-varying aspect of the first instance of the primary task and/or the interference Modifying the time-varying characteristics of an aspect of the primary task or the interference may include adjusting a temporal length of the rendering of the task or interference at the user interface between two or more sessions of interactions of the individual; ¶ 78: the results of the analysis may be used to modify the difficulty level or other property of the computerized stimuli or interaction (CSI) or other interactive elements; ¶ 98: provide smaller or larger reaction time window for a user to provide a response to the tasks as an example way of adjusting the difficulty level; ¶ 109: analyze the differences in the individual's performance based on determining the differences between the measures of the user's first type and second type of responses, and/or adjust the difficulty level of the first task and/or the first interference based on the individual's performance determined in the analysis; ¶ 134: adjusting a type or a difficulty level of one or more of the task, the interference, and the evocative element, to achieve the desired performance level of the individual).
[Claim 27] Alailima in view of DESGARENNES teaches or at least suggests transmitting the structured textual data to a remote server for processing by the ANN, wherein the ANN is hosted on the remote server (at least Alailima: ¶ 325: The computing device 1810 can include a network interface 1822 configured to interface via one or more network devices 1832 with one or more networks, for example, Local Area Network (LAN), Wide Area Network (WAN) or the Internet through a variety of connections … the computing device 1810 can be any computational device, such as a workstation, desktop computer, server, laptop, handheld computer, tablet computer, or other form of computing or telecommunications device that is capable of communication; ¶ 334: a system, method or operation herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components; ¶ 335: computing system 400 can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network).
[Claim 28] Alailima in view of DESGARENNES teaches or at least suggests wherein the 3D virtual learning environment is accessed via at least one of a desktop computer, a mobile device, a virtual reality (VR) headset, and an augmented reality (AR) device (at least Alailima: ¶ 74: … the example system employs an App program running on a mobile communication device or other hand-held devices. Non-limiting examples of such mobile communication devices or hand-held device include a smartphone … a head-mounted device, such as smart eyeglasses with built-in displays, a smart goggle with built-in displays, or a smart helmet with built-in displays, and the user can hold a controller or an input device having one or more sensors in which the controller or the input device communicates wirelessly with the head-mounted device).
[Claim 29] Alailima in view of DESGARENNES teaches or at least suggests wherein the structured textual data includes dynamic attributes of objects, including state changes, temperature, or other properties relevant to the learning experience (at least Alailima: ¶ 82: … an evocative element may be controlled using a processing unit to actuate an actuating component to present differing types of tactile stimuli (e.g., sensation of touch, textured surfaces or temperatures) for interaction with an individual).
[Claim 32] Alailima in view of DESGARENNES teaches or at least suggests wherein the adaptive learning experience includes personalized narratives or storylines generated by the ANN to enhance learner engagement (at least Alailima: ¶ 85: the computerized element includes at least one element to indicate positive feedback to a user. Each element can include an auditory signal and/or a visual signal emitted to the user that indicates success at a task or other platform interaction element, i.e., that the user responses at the platform product has exceeded a threshold success measure on a task or platform interaction (gameplay) element; ¶ 86: the computerized element includes at least one element to indicate negative feedback to a user. Each element can include an auditory signal and/or a visual signal emitted to the user that indicates failure at a task or platform interaction (gameplay) element, i.e., that the user responses at the platform product has not met a threshold success measure on a task or platform interaction element; ¶ 317: provide an output or other feedback from the platform product indicative of the individual's performance; ¶ 333: feedback (i.e., output) provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input).
[Claim 33] Alailima in view of DESGARENNES teaches or at least suggests detecting, via the ANN, an emotional state of the learner based on at least one of the structured textual data and emotion-tracking data (at least Alailima: ¶¶ 3, 4, 6, 8-11, 20, 24, 26, 28, 33, 71, 100, 105, 112, 119, 134, 190, 265, 292, 306, 314).
[Claim 34] Alailima in view of DESGARENNES teaches or at least suggests wherein the adaptation instructions indicate modifications to the learning content or environment to maintain or enhance the learner's engagement based on the detected emotional state (at least Alailima: ¶¶ 3, 4, 6, 8-11, 20, 24, 26, 28, 33, 71, 100, 105, 112, 119, 134, 190, 265, 292, 306, 314).
[Claim 35] Alailima in view of DESGARENNES teaches or at least suggests wherein the structured textual data is formatted in at least one of a plaintext, JSON, or XML format (at least DESGARENNES: ¶ 48: an output may be one or more of a text file, an audio file, an image, a video, a NL output (e.g., verbal and/or non-verbal), programmatic language (e.g., code), and/or any other type of output which may cause an interactive element of the interactive environment 140 to act in a way that is responsive to the user/developer input within the context provided by the DS 120). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the type of output feature of DESGARENNES and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 36] Alailima in view of DESGARENNES teaches or at least suggests wherein the adaptation instructions generated by the ANN include instructions for generating or selecting pre-designed pedagogical frameworks or learning activity templates (at least DESGARENNES: ¶ 30: the user specific data may be one or more of the input, intent objective, prompt, prompt templates, and/or model output; ¶ 50: a prompt may be comprised of a plurality of prompt templates. A prompt template may include any of a variety of data, including, but not limited to, natural language, image data, audio data, video data, and/or binary data, among other examples; ¶ 51: a prompt template includes known entities, previously stored objects, and/or previously known prompt templates that were previously created or input to the system 100, thereby enabling a user to reference previously created model output and/or any of a variety of other content for further use). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the prompt templates of DESGARENNES and to have modified Alailima as claimed, because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 30-31 are rejected under 35 U.S.C. 103 as obvious over Alailima in view of DESGARENNES as applied to claim 17 and further in view of Leonardo.
Re claims 30-31:
[Claims 30-31] Alailima in view of DESGARENNES appears to be silent on but Leonardo teaches or at least suggests generating the adaptation instructions based on the structured textual data and lesson parameters provided by an educator, wherein the lesson parameters include educational objectives, content restrictions, or preferred pedagogical strategies (at least ¶ 4: … allows a teacher user to specify activity parameters that define an activity for one or more students to complete on a computer or a mobile device, uses the activity parameters to determine appropriate subject matter from a content asset database, generates an activity incorporating the determined appropriate subject matter … The teacher user selects the appropriate subject, grade level, and activity template data that the system will use in creating an activity. Using the teacher user selected data, the activity editor retrieves applicable topic data in the knowledge database for use in creating the activity and displays the topic information to the teacher user. The teacher user specifies the appropriate topic data for use in the activity… the teacher user may customize each specific value by determining whether to include or to omit particular items in the activity for the one or more students). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have utilized the educational activity creation features of Leonardo and to have modified Alailima in view of LAI as claimed, because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
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.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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