Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Application Status
Present office action is in response to the preliminary amendment filed 02/13/2025. Claims 1-15 are currently pending in the application.
Information Disclosure Statement
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder (unit) that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “analysis engine” in claim 1, “content generation engine” in claims 1, 3, 6, 7 and 12, “mathematics education module” in claim 10, and “visual storytelling module” in claim 11. This interpretation is based off of the language “engine” and “module” as consistent with MPEP §2181(I)(A).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure/algorithm described in the specification as performing the claimed function, and equivalents thereof. In particular, the originally filed specification discloses: ¶ 8: An Al processing engine coupled to a network of computers and an information storage system; ¶ 14: The invention is a web and mobile application…; ¶ 16: The invention is a web and mobile platform, and the underlying background processes that can be hosted in physical servers or with cloud providers, that uses different Al models… ; ¶ 18: A content generator using natural language processing (NLP)… ; ¶ 26: Figure 3a describes the flow diagram of the background process when a user (ie: Teacher) logins to the mathematics module; ¶ 36: Figure 8a describes a process that leverages the smart content generation to combine generated stories from LLM based on saved configuration parameters and generates images to illustrate the story. It is basically a visual storyteller that can be used to create content... ; ¶ 66: The smart analysis engine serves as the core foundation of our platform… the engine is equipped to interpret Individualized Education Programs (IEPs), ensuring that the platform can cater to students with special needs. The “engine” and “module” limitations are generally interpreted as software programs/sets of instructions for execution on the system.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim 1 is narrative in form and replete with indefinite language. For example, the claim recites: “a teacher interface configured to enable educator interaction with the system.”, “A smart generative engine is able to generate different types of media based on teachers personalized request, this includes but not limited to images, videos, html, JSON, canvas 2D and 3D interactive content, custom transformation on text.”, “All generated content can be saved or shared.”, and “All generated content is subject to vulnerability scans and is run on a secure sandbox before being released.”. The present claim contains four (4) periods. Each claim must begin with a capital letter and end with a period. Periods may not be used elsewhere in the claims except for abbreviations (see MPEP 608.01(m)). Note the format of the claims in the patent(s) cited. The USPTO provides a number or resources to assist pro se applicants in prosecuting their own applications. A compilation of those resources can be found at: https://www.uspto.gov/patents/basics/using-legal-services/pro-se-assistance-program. The following two recitations: “All generated content can be saved or shared.”, and “All generated content is subject to vulnerability scans and is run on a secure sandbox before being released.” are inconsistent with the definition of a system because a system claim is defined by its structural limitations, and not by the operations it performs. Hewlett Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1469 (Fed. Cir. 1990) (“[A]pparatus claims cover what a device is, not what a device does.”). As a result, it is unclear whether these two recitations were intended to be part of the claim or whether they were intended to be expressed differently. It is further unclear what the actual format of the claim was intended to be in relation to the recitations associated with the four periods in the claim. In view of the foregoing, the claim does not clearly and precisely define the metes and bounds of the claimed invention.
Claim 3 recites “wherein the content generation engine leverages pre-trained language models fine-tuned on educational data such as assignments, quizzes, word problems, mathematical reasoning” (emphasis added). The phrase "such as" renders the claim indefinite because it is unclear whether each of the “assignments, quizzes, word problems, mathematical reasoning” features introduced by such language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. See MPEP § 2173.05(d).
In view of the above rejections under 35 U.S.C. 112(b), the claims are rejected as best understood.
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 1-15 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:
Step 1: Statutory Category?
Independent Claim 1 recites “A system comprising:”. Independent Claim 1 falls within the “machine” category of 35 U.S.C. § 101.
Step 2A – Prong 1: Judicial Exception Recited?
The Independent Claim 1/Revised 2019 Guidance Table below identifies in italics the specific claim limitations found to recite an abstract idea and in bold the additional (non-abstract) claim limitations that are generic computer components.
Independent Claim 1
Revised 2019 Guidance
A system comprising:
A system (machine) 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] an analysis engine configured to process student data including IEPs and curriculum guidelines to generate student profiles;
The “analysis engine” is an additional non-abstract limitation.
Abstract:, “Process[ing] student data including IEPs and curriculum guidelines to generate student profiles” 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 humans (person/educator) have long processed student data to generate student profiles, mentally and/or using pen and paper.
[L2] a content generation engine configured to utilize natural language AI models to dynamically generate personalized lessons and assignments based on the student profiles;
The “content generation engine” and “natural language AI models” are additional non-abstract limitations.
Abstract: “[D]ynamically generating personalized lessons and assignments based on student profiles” 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 humans (person/educator) have long generated personalized lessons and assignments based on student profiles mentally and/or using pen and paper.
[L3] a recommendation system configured to suggest personalized learning interventions based on the generated content;
The “recommendation system” is an additional non-abstract limitation.
Abstract: “Suggest[ing] personalized learning interventions based on the generated content” 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 humans (person/educator) have long suggested learning content, mentally and/or using pen and paper.
[L4a] a teacher interface configured to enable educator interaction with the system.
Providing an interactive “teacher interface” is an additional claim element that is a generic computer component.
[L4b] A smart generative engine is able to generate different types of media based on teachers personalized request, this includes but not limited to images, videos, html, JSON, canvas 2D and 3D interactive content, custom transformation on text.
The “smart generative engine” is an additional non-abstract limitation.
Generating information in the form of different types of media is merely insignificant extra-solution. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31; also see MPEP § 2106.05(g).
[L4c] All generated content can be saved or shared.
Saving and sharing data is an additional element that adds insignificant extra-solution activity to the judicial exception, e.g., mere data gathering and data transmission. See January 2019 Memorandum, 84 Fed. Reg. 55, n. 31; also see MPEP § 2106.05(g).
Abstract: Saving and sharing data 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) to the extent that humans (person/educator) have long saved and shared information mentally and/or in writing.
[L4d] All generated content is subject to vulnerability scans and is run on a secure sandbox before being released.
The “secure sandbox” is an additional non-abstract limitation.
Abstract: Scanning data 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) to the extent that a person/educator could visually scan information.
It is apparent that, other than reciting the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox” additional non-abstract limitations noted in the Independent Claim 1/Revised 2019 Guidance Table above, nothing in the claim precludes the steps from practically being performed by a human as a certain method of organizing human activity. . . managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), in the mind, and/or using pen and paper. The mere nominal recitation of the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox” and automation of a manual process does not take the claim out of the certain method of organizing human activity and mental processes groupings. Accordingly, the claim recites an abstract idea under Step 2A: Prong 1.
Step 2A – Prong 2: Integrated into a Practical Application?
The body of the claim, as noted in the Independent Claim 1/Revised 2019 Guidance Table above, recites the additional limitations of the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox”. The originally filed Specification provides supporting exemplary descriptions of generic computer components: at least ¶ 8: An Al processing engine coupled to a network of computers and an information storage system; ¶ 14: The invention is a web and mobile application…; ¶ 16: The invention is a web and mobile platform, and the underlying background processes that can be hosted in physical servers or with cloud providers, that uses different Al models… ; ¶ 18: A content generator using natural language processing (NLP)… ; ¶ 26: Figure 3a describes the flow diagram of the background process when a user (ie: Teacher) logins to the mathematics module; ¶ 36: Figure 8a describes a process that leverages the smart content generation to combine generated stories from LLM based on saved configuration parameters and generates images to illustrate the story. It is basically a visual storyteller that can be used to create content... ; ¶ 66: The smart analysis engine serves as the core foundation of our platform… the engine is equipped to interpret Individualized Education Programs (IEPs), ensuring that the platform can cater to students with special needs. The lack of details about the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox” indicates that the above-mentioned additional element is a generic computer component, performing generic functions. 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 Specification. The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The data gathering and data transmission steps ([L4c]) 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 Cellspin Soft, Inc. v. Fitbit, Inc., 927 F.3d 1306, 1315 (Fed. Cir. 2019) (“[A]sserted claims are drawn to the idea of capturing and transmitting data from one device to another.”); iLife Techs., Inc. v. Nintendo of Am., Inc., 839 F. App’x 534, 536–37 (Fed. Cir. 2021) (system that evaluates and communicates body movements using sensors without details for performing those functions merely recites a system for sensing information, processing collected information, and transmitting processed information, and merely gathering and processing data is an abstract idea). 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 element in claim 1, namely the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox” 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 elements 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 Specification does not further describe the “analysis engine”, “content generation engine”, “natural language AI models”, “recommendation system”, “teacher interface”, and “secure sandbox”, indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). 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. 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 claim is patent ineligible. (Step 2B: NO).
In regard to the dependent claims:
Dependent claims 2-15 include all the limitations of independent claim 1 from which they depend and as such recite the same abstract idea(s) noted above for claim 1. Claims 2-15 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., ¶¶ 8, 14, 16, 18, 26, 36, 66. The Examiner fails to see any claim activity used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-15 integrates the judicial exception into a practical application. While dependent claims 2-15 may have a narrower scope than the representative claim, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-15 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Claim 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 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 patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 10-15 are rejected under 35 U.S.C. 103 as being obvious over Ahn et al. (US 20180330628 A1) (Ahn).
Re claim 1:
[Claim 1] Ahn teaches or at least suggests a system comprising: an analysis engine configured to process student data including IEPs and curriculum guidelines to generate student profiles; a content generation engine configured to utilize natural language AI models to dynamically generate personalized lessons and assignments based on the student profiles; a recommendation system configured to suggest personalized learning interventions based on the generated content (at least ¶ 1: interactive and intelligent learning systems that are adaptive to a student; ¶ 12: Education content is provided to a student in a way that is sensitive to the profile of the student. A topic to be taught to the student is received. A user model of the student is identified. A content model for the topic is determined. One or more presentation templates are identified based on the content model; ¶ 18: a profile database 110 that has information that helps create a user model for a student. Such information may include a student's past performance for various subject matters. Example indicia of performance include grades, test scores, teacher input, class standing, whether there is an individual education program (IEP) … All the indicia stored in the profile database 110 collectively provides a user model 111 of the student, which is used as one of the factors to select an appropriate presentation template that better accommodates teaching a topic to a student …; ¶ 19: … identify a presentation template that is most likely to be effective to teach the topic to the student (e.g., 101(1)) via their respective user device (e.g., (102); ¶ 53: natural language processing (NLP) is used to interpret the raw content to create a content model therefrom …; ¶ 71: there is a natural language processing module 556 that is operative to interpret various indicia provided of the user model…); a teacher interface configured to enable educator interaction with the system (at least ¶ 18: … the indicia stored in the profile database 110 may be provided by the student, a parent, an authorized educator (e.g., a human), or an administrator involved with the education of a student),
Ahn further teaches or at least suggests A smart generative engine is able to generate different types of media based on teachers personalized request, this includes but not limited to images, videos, html, JSON, canvas 2D and 3D interactive content, custom transformation on text (at least ¶ 3: A user model of the student is identified. A content model for the topic is determined. One or more presentation templates are identified based on the content model. A concept map is determined based on the one or more presentation templates. A presentation template is selected from the one or more presentation templates … ; ¶ 4: upon determining that a presentation of the segment of the educational content of the selected presentation template can be augmented, augmenting an interchangeable element of the segment of the educational content based on the user model of the student; ¶ 18: the indicia stored in the profile database 110 may be provided by the student, a parent, an authorized educator (e.g., a human), or an administrator involved with the education of a student; ¶ 21: A topic may be taught in the form of a lecture, which may be provided as an audio-visual presentation (e.g., a video) ; ¶ 24: virtual reality (VR) or augmented reality (AR) may be used for effectively teaching a topic … 3D sound effects within the VR or AR environment is provided to more deeply immerse the student in the topic being taught; ¶ 27: computing platforms may be implemented by virtual computing devices in the form of virtual machines or software containers that are hosted in a cloud, thereby providing an elastic architecture for processing and storage; ¶ 30: the user interface 206 may include a data output device (e.g., visual display, audio speakers, haptic device, etc.) that may be used to display notifications from the template selector engine 103 of the APS 120. More advanced user devices may include a VR or AR capability as part of the hardware 210 of the user device 200; ¶ 36: a speech recognition module 242 that enables the recognition (and possible translation) of spoken language into text… ; ¶ 37: provide electronic conversion of images that a student may provide in response to an inquiry, into recognizable printed text and/or images; ¶ 72: operating the system as a Web server… the HDD 506 can store an executing application that includes one or more library software modules, such as those for the Java™ Runtime Environment program for realizing a JVM (Java™ virtual machine)). Any difference in the content type generated would have been an obvious matter of choice.
Ahn further teaches or at least suggests All generated content can be saved or shared (at least ¶ 40: components that enable the user device 200 to receive and transmit data via various interfaces … as well as process data using the processor(s) 208 to generate output. The operating system 250 may include a presentation component that presents the output …, store the data in memory 216, transmit the data to another electronic device).
As shown above, Ahn discloses “computing platforms may be implemented by virtual computing devices in the form of virtual machines or software containers that are hosted in a cloud, thereby providing an elastic architecture for processing and storage” (¶ 27). However, Ahn appears to be silent on all generated content is subject to vulnerability scans and is run on a secure sandbox before being released. Nonetheless, it is common knowledge that “in computer security, a sandbox is a security mechanism for separating running programs, usually in an effort to mitigate system failures and/or software vulnerabilities from spreading… In the sense of providing a highly controlled environment, sandboxes may be seen as a specific example of virtualization. Sandboxing is frequently used to test unverified programs that may contain a virus or other malicious code without allowing the software to harm the host device … A sandbox is implemented by executing the software in a restricted operating system environment, thus controlling the resources (e.g. file descriptors, memory, file system space, etc.) that a process may use…”. See Wikipedia (https://en.wikipedia.org/wiki/Sandbox_(computer_security)). Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claims 2 and 14:
[Claims 2 and 14] Ahn teaches or at least suggests a social platform for educator collaboration ([Claim 14]) and sharing of content generation prompts (at least ¶ 13: Architecture 100 includes a network 106 that allows various user devices 102(1) to 102(n) to communicate with each other, as well as any other components that are connected to the network 106, such as an Adaptive Presentation Server (APS) 120, a student profile database 110, a presentation template database 112, a content database 114, and a concept map source 116; ¶ 18: the indicia stored in the profile database 110 may be provided by the student, a parent, an authorized educator (e.g., a human), or an administrator involved with the education of a student; ¶ 40: The operating system 250 may include a presentation component that presents the output (e.g., display the data on an electronic display and/or a VR/AR interface of the user device 200, store the data in memory 216, transmit the data to another electronic device; ¶ 49: the adaptive presentation server 120, by an authorized educator, or from another computerized curriculum source; ¶ 62: the learning application 240 of the user device may provide a questionnaire on a user interface of the user device to solicit feedback from the student; ¶ 67: there may be an interaction module 542 that is operative to provide questions to the student and receive responses therefrom).
In the event the above interpretation is viewed as not being reasonable, the Examiner takes official notice that social networks have long focused on supporting relationships between teachers and their students and have been used for learning, educator professional development, and content sharing. For example, Ning for teachers, TermWiki, Learn Central, TeachStreet and other sites are being built to foster relationships that include educational blogs, eportfolios, formal and ad hoc communities, as well as communication such as chats, discussion threads, and synchronous forums. Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claim 3:
[Claim 3] Ahn teaches or at least suggests wherein the content generation engine leverages pre-trained language models fine-tuned on educational data such as assignments, quizzes, word problems, mathematical reasoning (at least ¶ 53: natural language processing (NLP) is used to interpret the raw content to create a content model therefrom …; ¶ 71: there is a natural language processing module 556 that is operative to interpret various indicia provided of the user model…).
Re claim 4:
[Claim 4] Ahn teaches or at least suggests an IEP analysis system configured to extract information from IEP documents and generate IEP recommendations using natural language processing (at least ¶ 18: there is a profile database 110 that has information that helps create a user model for a student… whether there is an individual education program (IEP) … ; ¶ 53: natural language processing (NLP) is used to interpret the raw content to create a content model therefrom …; ¶ 71: there is a natural language processing module 556 that is operative to interpret various indicia provided of the user model…).
Re claims 5 and 13:
[Claim 5] Ahn teaches or at least suggests administrator controls to select AI models, set access permissions, and enable customization (at least ¶ 6: an interactive learning system that provides an individualized teaching approach based on presentation templates; ¶ 13: an interactive learning system that provides individualized teaching based on presentation templates. Architecture 100 includes a network 106 that allows various user devices 102(1) to 102(n) to communicate with each other, as well as any other components that are connected to the network 106; ¶ 14: The network 106 may be, without limitation, a local area network (“LAN”), a virtual private network (“VPN”), a cellular network, the Internet, or a combination thereof. For example, the network 106 may include a mobile network that is communicatively coupled to a private network that provides various ancillary services, such as communication with various application stores, libraries, and the Internet … a mobile network as may be operated by a carrier or service provider to provide a wide range of mobile communication services and supplemental services or features to its subscriber customers and associated mobile device users; ¶ 18: there is a profile database 110 that has information that helps create a user model for a student… indicia of performance include grades, test scores, teacher input, class standing, whether there is an individual education program (IEP)) … All the indicia stored in the profile database 110 collectively provides a user model 111 of the student, which is used as one of the factors to select an appropriate presentation template that better accommodates teaching a topic to a student … upon identifying the personal situation of a student from the user model 111, a concept can be explained with reference to a student's personal experience; ¶ 23: A topic may be taught based on a personalized analogy; ¶ 36: the learning application 240 of the user device 200 may include a speech recognition module 242 that enables the recognition (and possible translation) of spoken language into text, such that it can be further processed by the learning application 240 and/or the template selector engine 103; ¶ 37: the learning application 240 includes a text parsing module 244 operative to provide electronic conversion of images that a student may provide in response to an inquiry, into recognizable printed text and/or images …; ¶ 49: the topic may be provided in the form of a data packet by the student being taught, the adaptive presentation server 120, by an authorized educator; ¶ 53: natural language processing (NLP) is used to interpret the raw content to create a content model therefrom. NLP is a field of artificial intelligence, computer science, and computational linguistics that deals with the processing and interpretation of language generated by a human (i.e. the natural element) by a computer …; ¶ 71: there is a natural language processing module 556 that is operative to interpret various indicia provided of the user model…). Providing an individualized teaching approach, at least suggests some of that information is selected by an educator/administrator. Additionally, it is common knowledge that access to a network generally requires permission based on roles/credentials, for example, student, parent, teacher/educator, administrator, etc. which, in turn, requires a network administrator to setup desired accounts for the student, parent, teacher/educator, administrator. Furthermore, the number of possible natural language algorithm/models (see earlier Wikipedia reference to NLP) at least suggests an educator/administrator having the option to select desired natural language algorithm/models. In view of the foregoing, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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 13] Ahn teaches or at least suggests a model management interface to select AI models to power the various system modules (at least ¶ 1: interactive and intelligent learning systems that are adaptive to a student; ¶ 20: An intelligent teaching system (ITS) of the APS provides questions and receives responses from a student. Such interaction guides the student to achieve a desired knowledge level of the topic being taught; ¶ 53: natural language processing (NLP) is used to interpret the raw content to create a content model therefrom. NLP is a field of artificial intelligence, computer science, and computational linguistics that deals with the processing and interpretation of language generated by a human (i.e. the natural element) by a computer …; ¶ 71: there is a natural language processing module 556 that is operative to interpret various indicia provided of the user model…). The number of possible natural language algorithm/models (see earlier Wikipedia reference to NLP) at least suggests an educator/administrator having the option to select desired natural language algorithm/models. In view of the foregoing, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claim 10:
[Claim 10] Ahn teaches or at least suggests a subject matter topic education module configured to generate visual subject matter topic aids comprising subject matter topic concept illustrations tailored to individual students (at least ¶ 2: intelligent tutoring systems (ITS) can interact with a student to teach various subject matters; ¶ 12: A topic to be taught to the student is received. A user model of the student is identified. A content model for the topic is determined. One or more presentation templates are identified based on the content model. A concept map is determined based on the one or more presentation templates; ¶ 17: determine the appropriate template for teaching a topic to the student; ¶ 25: the content model may include definitions of key terms of the topic, a history of the topic, various examples of the topic, publications on the topic; ¶ 54: presentation template 113 includes one or more approaches in teaching the topic to the student. Such approaches may include, without limitation, lecture, demonstration, fable, personalized analogy, compare-and-contrast, fill-in-the blank, true or false, quiz, game). However, Ahn appears to be silent on the subject matter being mathematics, as claimed. Nonetheless, it is common knowledge that, mathematical concept illustrations, number lines, and geometric drawings are commonly used in mathematics instructions, Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claim 11:
[Claim 11] Ahn appears to be silent on a visual storytelling module configured to generate visual stories with illustrations and associated text. The Examiner takes official notice that it is old and well-known to provide visual stories with illustrations and associated text. Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claim 12:
[Claim 12] As shown above, Ahn, which relates to interactive and intelligent learning systems that are adaptive to a student (¶ 1), discloses using machine learning (¶ 53). However, Ahn appears to be silent on wherein the content generation engine leverages generative adversarial networks for image generation. Nonetheless, it is common knowledge that “a generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.…”. See Wikipedia (https://en.wikipedia.org/wiki/Generative_adversarial_network). Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claim 15:
[Claim 15] Ahn appears to be silent on wherein the system is compatible with legal requirements related to student data privacy and security. The Examiner takes official notice that it is old and well-known educational environments have long been required to comply with security and privacy as well as with governance of policies and procedures of school districts, and laws and regulations of state and federal governments relative to the area where service is provided. Hence, 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 modified Ahn as claimed because this would amount to no more than applying known techniques to a known system 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 6 and 8-9 are rejected under 35 U.S.C. 103 as being obvious over Ahn, as applied to claim 1, in view of Kundu (US 20230236660 A1).
Re claim 6:
[Claim 6] Ahn teaches or at least suggests wherein the content generation engine is configured to generate visual aids including videos generated from story texts (at least ¶ 21: A topic may be taught in the form of a lecture, which may be provided as an audio-visual presentation (e.g., a video) … ; ¶ 24: virtual reality (VR) or augmented reality (AR) may be used for effectively teaching a topic; 52: content from textbooks, databases, videos, websites, images, etc., that are used by the content database 114 to create a content model 115 therefrom).
Ahn appears to be silent on but Kundu teaches or at least suggests interactive 3D concepts (at least ¶ 44: enabling a user to be represented within a three-dimensional scene and to control the movement and interaction of their representation within the three-dimensional scene by use of an imaging unit … adding the representation or image of user … to a three-dimensional scene… and then enabling that representation to interact with virtual objects in the three-dimensional scene (including representations of other users) in response to changes in location, poses, and/or gestures of the actual user; ¶ 59: FIGS. 13A and 13B, the system could receive a three-dimensional input data 1302 (also referred to as “three-dimensional scene”)… ; ¶ 68: in FIG. 16, the system behaves as a user control mechanism enabling the physical user to control their virtual self (i.e., the virtual user or user representation produced on the display) within a virtual three-dimensional world, as well as interact with objects in that virtual world, by displaying to the physical user a realtime representation of the virtual user as seen within the virtual world; ¶ 141: interaction feature can be used by the actor to trigger an animation in a presentation slide from software such as Powerpoint or Google Slides …a student (i.e., the actor) in a virtual classroom (i.e., the scene) can virtually touch a virtual flashcard (i.e., the interactive object) to cause it to flip over (i.e., the triggered or caused action) … the action is a change in some other state, such as the increasing of a score in a video game … a calculating function is triggered, and graphical presentation of the calculated result is the action. A change in the visual state of the interactive object is observed from 1108 to 1110 in the scene. As shown in FIG. 11, the system and method of the present disclosure can be applied in an educational setting to enhance the student's learning ability by adding an interactive element to the learning process). 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 used Kundu’s virtual representation of a user in three-dimensional scene features to modify Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.”).
Re claims 8 and 9:
[Claim 8] Ahn appears to be silent on but Kundu teaches or at least suggests a flashcard generation system configured to generate custom flashcards with text and associated images, videos, interactive 3D content (at least ¶ 141: interaction feature can be used by the actor to trigger an animation in a presentation slide from software such as Powerpoint or Google Slides …a student (i.e., the actor) in a virtual classroom (i.e., the scene) can virtually touch a virtual flashcard (i.e., the interactive object) to cause it to flip over (i.e., the triggered or caused action) … the action is a change in some other state, such as the increasing of a score in a video game … a calculating function is triggered, and graphical presentation of the calculated result is the action. A change in the visual state of the interactive object is observed from 1108 to 1110 in the scene. As shown in FIG. 11, the system and method of the present disclosure can be applied in an educational setting to enhance the student's learning ability by adding an interactive element to the learning process). 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 used Kundu’s virtual representation of a user in three-dimensional scene features to modify Ahn as claimed because this would amount to no more than applying known techniques to a known system 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 9] Ahn in view of Kundu teaches or at least suggests wherein the flashcard generation system includes features for sharing flashcard sets, exporting content, and generating quizzes (at least Ahn: ¶ 40: The operating system 250 may include a presentation component that presents the output (e.g., display the data on an electronic display and/or a VR/AR interface of the user device 200, store the data in memory 216, transmit the data to another electronic device …. – exporting content; Kundu: ¶ 5: enable the user(s) to interact in real-time with other objects or items in the scene or even with each other in the case of multiple users; ¶ 118: … annotating a training set of data, such as images, videos, slides, screen shares; ¶ 129: … more than one actor is displayed in the same scene with a shared space …; ¶ 141: … a student (i.e., the actor) in a virtual classroom (i.e., the scene) can virtually touch a virtual flashcard (i.e., the interactive object) to cause it to flip over (i.e., the triggered or caused action) … the action is a change in some other state, such as the increasing of a score in a video game … a calculating function is triggered, and graphical presentation of the calculated result is the action. A change in the visual state of the interactive object is observed from 1108 to 1110 in the scene. As shown in FIG. 11, the system and method of the present disclosure can be applied in an educational setting to enhance the student's learning ability by adding an interactive element to the learning process; ¶ 148: … FIG. 12B … the interactive object 1208 is placed in a three-dimensional scene 1314 and the multiple actors/user representations 1204 1206 are interacting with the interactive object 1208. The multiple users 1204 1206 are caused to interact with the interactive object 1208 by the actual users captured by the corresponding imaging units of the system). 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 modified Ahn in view of Kundu as claimed because this would amount to no more than applying known techniques to a known system 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 7 is rejected under 35 U.S.C. 103 as being obvious over Ahn, as applied to claim 1, in view of Haynes et al. (US 20020156632 A1) (Haynes).
Re claims 7:
[Claim 7] Ahn appears to be silent on but Haynes teaches or at least suggests wherein the content generation engine is configured to provide text comprehension tools comprising summarization, translation, text leveling, and comprehension aid features (at least ¶ 8: a student's ability to produce a good summary of lesson text is superior to other forms of assessment in evaluating the student's reading comprehension …; ¶ 14: analysis of student-produced summaries of lesson text in reading tutoring systems and methods as a measure of reading comprehension; ¶ 20: the summaries used to assess a student's reading comprehension … a student is assisted in developing comprehension of the lesson text as well as strategies to improve comprehension skills in general; ¶ 35: a plurality of passages or lessons 32 arranged hierarchically according to their levels of reading difficulty …; ¶ 36: … a plurality of passages arranged hierarchically according to their levels of reading difficulty …; ¶ 37: … evaluating student summaries by reference to the text of the original passage …). 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 used the literacy tutoring in conjunction with user-specific content learning features of Haynes
to Ahn as claimed because this would amount to no more than applying known techniques to a known system 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDDY SAINT-VIL whose telephone number is (571)272-9845. The examiner can normally be reached Mon-Fri 6:30 AM -6:00 PM.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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