DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
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 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 limitation(s) is/are claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Step 1:
According to the first part of the analysis, in the instant case, claims 1, and 11 are system claims, claim 16 is a system claim with storage and claim 6 is a method claim. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Claims 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 2A Prong 1:
“ flagging a plurality key elements from the plurality of elements which should remain constant
unless the user input indicates otherwise” is a mental step of data selection and marking process. The term “unless user input indicates otherwise” condition simply means the selection is dynamic, but the underlying act remains mental.
Additional Elements
Step 2A Prong 2:
“A computing system for an artificial intelligence-powered large-scale content generator, the computing system comprising: one or more hardware processors configured for:” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ receiving a user input from a user interface” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generating a cohesive experience from the plurality of generative AI subsystems where the experience is based on the user input” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ displaying the experience to a user device” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ A computing system for an artificial intelligence-powered large-scale content generator, the computing system comprising: one or more hardware processors configured for:” recited in the preamble does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“ receiving a user input from a user interface” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generating a cohesive experience from the plurality of generative AI subsystems where the experience is based on the user input” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ displaying the experience to a user device” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 2:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not amount to significantly more than the judicial exception in the claim. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 3:
Step 2A Prong 2:
“wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 4:
Step 2A Prong 2:
“further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not integrate the judicial exception into a practical application. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 5:
Step 2A Prong 2:
“wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, song segments” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
““ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, song segments
does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 6:
Step 2A Prong 1:
“ flagging a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise” is a mental step of data selection and marking process. The term “unless user input indicates otherwise” condition simply means the selection is dynamic, but the underlying act remains mental.
Additional Elements
Step 2A Prong 2:
“A computer-implemented method executed on an artificial intelligence-powered large-scale content generator, the computer-implemented method comprising:” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“receiving a user input from a user interface” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generating a cohesive experience from the plurality of generative AI subsystems where the experience is based on the user input” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ displaying the experience to a user device” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“A computer-implemented method executed on an artificial intelligence-powered large-scale content generator, the computer-implemented method “recited in the preamble does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“receiving a user input from a user interface” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“generating a cohesive experience from the plurality of generative AI subsystems where the experience is based on the user input” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“displaying the experience to a user device” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not amount to significantly more than the judicial exception in the claim. The additional element of “a computer system” is merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 7:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not amount to significantly more than the judicial exception in the claim. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 8:
Step 2A Prong 2:
“ wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 9:
Step 2A Prong 2:
“further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 10:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
““ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song”
does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 11:
Step 2A Prong 1:
“flag a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise” is a mental step of data selection and marking process. The term “unless user input indicates otherwise” condition simply means the selection is dynamic, but the underlying act remains mental.
Additional Elements
Step 2A Prong 2:
“A system for an artificial intelligence-powered large-scale content generator, comprising one or more computers with executable instruction that, when executed, cause the system to:
” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ receive a user input from a user interface” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segment the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generate an experience from the plurality of generative AI subsystems where the experience is based on the user input” preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“display the experience to a user device” preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receive user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“A system for an artificial intelligence-powered large-scale content generator, comprising one or more computers with executable instruction that, when executed, cause the system to: “recited in the preamble does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“receive a user input from a user interface” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“segment the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea.
See MPEP 2106.05(h).
“ process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“generate an experience from the plurality of generative AI subsystems where the experience is based on the user input” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ displaying the experience to a user device” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 12:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not amount to significantly more than the judicial exception in the claim. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 13:
Step 2A Prong 2:
“ wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 14:
Step 2A Prong 2:
“ further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 15:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
““ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song”
does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 16:
Step 2A Prong 1:
“ flag a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise” is a mental step of data selection and marking process. The term “unless user input indicates otherwise” condition simply means the selection is dynamic, but the underlying act remains mental.
Additional Elements
Step 2A Prong 2:
“Non-transitory, computer-readable storage media having computer executable instruction embodied thereon that, when executed by one or more processors of a computing system employing an artificial intelligence-powered large-scale content generator, cause the computing system to:” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ receive a user input from a user interface” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segment the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generate an experience from the plurality of generative AI subsystems where the experience is based on the user input” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ display the experience to a user device” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receive user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ Non-transitory, computer-readable storage media having computer executable instruction embodied thereon that, when executed by one or more processors of a computing system employing an artificial intelligence-powered large-scale content generator, cause the computing system to:” recited in the preamble does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“ receive a user input from a user interface” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ segment the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ generate an experience from the plurality of generative AI subsystems where the experience is based on the user input” does not amount to significantly more than the judicial exception in the claim. The additional merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“ display the experience to a user device” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
“and receive user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience” does not amount to significantly more than the judicial exception in the claim. The additional element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 17:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments” does not amount to significantly more than the judicial exception in the claim. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 18:
Step 2A Prong 2:
“wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 19:
Step 2A Prong 2:
“ further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
“ further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs” does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Regarding claim 20:
Step 2A Prong 2:
“ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song” does not integrate the judicial exception into a practical application. This element is merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(h).
Step 2B:
““ wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song”
does not amount to significantly more than the judicial exception in the claim. This element merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Claim Rejections - 35 USC § 102
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated over
Katelijn Vleugels et al. (hereinafter Vleu) US 2024/0274025 A1.
[Note: EFD: 5/20/24, Prior Art Filing: 2/14/24, Prior Art Published: 8/15/24]
Regarding claim 1:
Vleu discloses:
- A computing system for an artificial intelligence-powered large-scale content generator, the computing system comprising: one or more hardware processors configured for:
[0059]; [0099],
- receiving a user input from a user interface; segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters;
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments leveraging vectorized representations (embeddings) to deeply understand both user (eg educator, content creator) input criteria and educational content
[0047]:
format characteristics of the content element may include language, font size, maximum number of words per slide, voice tone, voice emotion, pace, accentuation of specific words or phrases, difficulty level of vocabulary. Examples of format of the returned content response may include language, syntax of the response, use of specific tags in the response, capitalization or punctuation, maximum length, and test format such as open-ended, fill in the blanks, multiple choice etc,
[0049]:
The method also includes creating a first content element, by sending at least one create request (step 102) for at least a portion of the first content element over the interface wherein the create request includes at least one prompt from the set of prompts and one or more attribute values from the first set of attribute values, receiving a content response (step 103) with the at least a portion of the first content element over the interface, processing the content response (step 104) to extract or derive the at least a portion of the first content element,
[0053]:
The educational content database may be personalized for a user (eg student) or a group of users (eg group of students). The personalization may include making one or more sets of attributes specific to the users or group of users, requesting and receiving one or more user-specific content elements, and storing the user-specific content elements in a user-specific educational content database or as a user-specific entry in a central educational content database,
- flagging a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise;
[0044]:
A content element may also include an answer key or a sample response. A content element may also consist of a combination of pieces of texts, practice problems, assessment questions, writing prompts, etc.,
[0098]:
The output of embeddings generator (1021) is a set of high-dimensional vector representations, or embeddings (1032). Each vector is a point in an n-dimensional space where ‘n’ is determined by the model architecture. These embeddings capture the essential semantic features of the input content, enabling effective matching and retrieval based on semantic similarity.
[0094]:
Assessment Delivery & Storage Subsystem (1002): Assessment completion and storage subsystem (1002) is configured to receive assessments from the assessment creation subsystem (1001), conduct any necessary further processing, and facilitate the delivery of these assessments to one or more assessment takers, such as students. Upon completion of the assessments by the takers, this subsystem is designed to collect and store a variety of data. This data encompasses the assessment information, which may include the questions or assessment itself, the embeddings utilized to create the assessment, and one or more assessment criteria provided by the user/assessment creator via user input 1009,
[BRI: an AI-enhanced assessment systems, assessment criteria — the specific standards or descriptors that define what students must demonstrate and are generally designed to remain constant unless explicitly changed by the user or assessment creator. This stability is a core principle in fair and reliable assessment, whether conducted manually or with AI support]
[0080]:
The preprocessed content is passed through an embedding generator, which converts the content into vector representations. These vector representations facilitate semantic analysis. These vector representations (embeddings) are stored in association with their source content within the content database or in a separate database that is linked to the content database. Embeddings may optionally be periodically updated to reflect changes in educational content or improvements in embedding generation techniques,
[BRI: Flagging in this context means marking or setting a value in that key to indicate a change or update. In the context of embeddings tied to source, flagging a plurality of key elements should remain constant unless user input indicates otherwise is a deliberate design choice to preserve semantic integrity and avoid unnecessary re-embedding]
- processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element;
[0091]:
FIG. 1D illustrates a high-level functional diagram of an adaptive system for the automated generation of customized educational assessments using embeddings in accordance with an example embodiment. The system comprises four interconnected subsystems: a content embeddings creation subsystem (1000), an assessment creation subsystem (1001), an assessment completion and storage subsystem (1002), and a embeddings optimizer subsystem (1003),
[0092]:
Content without embeddings (1004) may be stored in a database or other storage unit. This database or storage unit is functionally connected to a content embeddings creation subsystem (1000) which processes the educational materials to generate embeddings. A trained language model (1008) is employed by the content embeddings creation subsystem to produce high-dimensional vector representations of content, also referred to as embeddings,
[0018]:
special-purpose AI platforms that have emerged in recent years, such as D-DI, Fliki.ai, steve.ai, murf.ai, Descript, Synthesia, and Google Cloud Text-to-Speech. These platforms are designed for specific tasks such as creating videos, audio, animations, and speech from written text or images.
[BRI: An AI system design, an AI subsystem is a dedicated module or component within a larger system that is responsible for a specific AI-related function that may include processing a specific type of element (e.g., text, images, sensor data, or structured datasets]
- generating a cohesive experience from the plurality of generative AI subsystems where the experience is based on the user input;
[0009]:
In recent years, new solutions have emerged that aim to address some of the limitations of existing online educational platforms. One of these solutions is the use of machine learning and artificial intelligence to build adaptive learning systems. These systems leverage AI algorithms to analyze student data and adjust the learning experience in real-time to meet the needs of individual students,
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments,
[0023]:
The system dynamically adjusts content embeddings based on aggregated performance data and real-time feedback, enhancing personalized learning experiences by tailoring difficulty levels, content relevance, and format relevance to improve learning outcomes.
[A cohesive user experience in the context of generative AI means creating a unified, adaptive, and personalized interaction that seamlessly integrates multiple AI subsystems such as language models, and databases into a single, responsive interface].
- displaying the experience to a user device;
[0169]:
The grading system includes a user interface for retrieving a user's response to one or more mastery-assessment prompts such as for example a comprehension, critical thinking, or writing question or prompt. Other kinds of mastery-assessment prompts are also possible. The grading system may present the user with a single mastery-assessment prompt or with a series of mastery-assessment prompts and process the one or the series of user's response(s) to the prompt(s). The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts.
[BRI: modern AI systems can dynamically adjust content embeddings based on aggregated performance data and real-time feedback, creating a personalized learning experience that adapts to each user’s mastery level]
- and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience.
[0214]:
The disclosed system and method provide a dynamic and personalized educational experience, as it presents a scalable solution to reformatting educational content to meet an individual's or group of users' specific needs and preferences through the use of a natural language interface to one or more generative Artificial Intelligence models,
[0181]:
Processing may include but is not limited to one or more of the following: applying a first weighting factor to the original grade, applying a second weighting factor to a test correction, averaging unweighted or weighted grades, and applying a penalty to account for the fact that the user had multiple attempts. This test correction feature provides users with the ability to learn from their mistakes, correct any mistakes made in the initial assessment and have their grade more accurately reflect their comprehension, critical thinking, or writing skills. The updated grade, generated through the grading methods described herein, may help improve the accuracy of the user's evaluation and provide an updated understanding of their performance.
[BRI: personalized and engaging educational experiences that leverage AI to provide tailored, interactive feedback are indeed representing an updated and transformative approach to learning. AI-enabled personalized learning shifts toward learner-centric, adaptive instruction. This approach recognizes that students have different abilities, learning styles, paces, and interests, and tailors’ content, pacing, and feedback accordingly]
[0139]:
The system provides a more personalized and engaging educational experience, leveraging the capabilities of AI to provide tailored and interactive feedback to students.
[0169]:
The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts,
[0172]:
The grading system may define or select at least one prompt to request, from a computational or generative AI system, a grade for one or a set of user responses, and sends one or more grading requests over the interface, which may include the mastery-assessment prompt(s), the user's response(s), the prompt and/or one or more attributes,
[0173]:
the grading response(s) may include one or more of the following: a grade per user response, an overall grade, clarification on why a certain grade was assigned, additional feedback or explanations on the correctness of the user's response, and feedback on the quality of the user's response
Regarding claim 2:
Vleu discloses:
- wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments.
[0210]:
The method includes retrieving information about the desirable content format and retrieving content in a first format. At least one prompt is defined to request one or more formatting tasks, and a set of attributes is created that may include one or more of the following: (1) attributes specifying the requested format type of a second format (e.g. video, speech, doodle, text);
[0214]:
Additionally, the customization features of the system make it easily adaptable to different cultures, special needs groups, or other groups with diverse requirements, further enhancing the personalized learning experience for all users.
Regarding claim 3:
Vleu discloses:
- wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem.
[0082]:
In one embodiment, a system includes a performance feedback loop. The system is configured to collect, aggregate, and analyze student performance data related to specific pieces of the educational content database. The performance data may include, but is not limited to, assessment scores, time spent on individual questions, rates of correct versus incorrect answers, and a student's or teacher's difficulty level or quality level rating. The system utilizes this data to identify patterns indicating for example the relative difficulty or case of the educational content for specific student groups,
[0114]:
The content generator (1065) may be a separate AI-based model capable of creating content through generative techniques,
[0098]:
Processed content (1031) is sent to or can be accessed by embeddings generator (1021). This component applies machine learning algorithms to the preprocessed content to create high-dimensional vector representations, known as embeddings. These embeddings capture the semantic significance of the educational content, facilitating the alignment of assessment questions with educational standards and objectives
[BRI: in a multi-generative AI subsystem environment, these metrics are checked for consistency across systems and over time to maintain data integrity and ensure that AI-driven decisions are reliable. When embedding models are carefully designed and aligned with educational standards, generative AI can capture the semantic significance of content and produce assessments that are conceptually consistent over time and across subsystems. This approach supports semantic alignment between generated materials and learning objectives, a key step toward trustworthy, standards-compliant AI in education]
Regarding claim 4:
Vleu discloses:
- further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs.
[0012]:
Additionally, solutions that use machine learning and natural language processing to score assessments or assignments have also been developed. For instance, Intelligent Essay Assessor uses Latent Semantic Analysis, a technique in natural language processing, to score written essays. However, solutions like Intelligent Essay Assessor are designed for a specific purpose and are trained on a specialized dataset, making them useful for one specific task only,
[0084]:
The dynamic adjustment of embeddings can be achieved through various machine learning techniques, including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning. In case of transfer learning, the system may implement a process for retraining the embedding model on a subset of data labeled with difficulty levels derived from performance analysis. In case of reinforcement learning, the system may implement a reinforcement learning model that adjusts embeddings to maximize educational outcomes, using performance feedback as the reward signal. These techniques allow for fine-tuning of the model based on new data inputs, such as student performance metrics, to optimize educational outcomes. The system employs sophisticated algorithms to reposition content embeddings within the vector space, ensuring that the difficulty and focus of the educational material are dynamically aligned with the evolving needs and abilities of the student population.
Regarding claim 5:
Vleu discloses:
- wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, song segments.
[0161]:
The interactive tutoring apparatus 230 may also include a Q&A module 246 that communicates with user over user interface 235 and allows the user, after receiving an explanation, to ask one or more follow-on questions.
[0122]:
retrieve labeled dataset, or a portion thereof, from performance data database (1088) and uses the retrieved dataset to generate a retrained embeddings model (1083). The retrained embeddings model (1083) therefor takes into account difficulty levels inferred from student performance data,
[0125]:
Examples of assessment modality may include but are not limited to type of questions, sequence of questions, length of test, test delivery method (paper, online, audio, oral, interactive video, gamified, etc). An embeddings optimizer may be used to optimize the instructional content and/or assessment content embeddings for a specific student group. The content database with optimized embeddings may be used to generate one or more assessments customized for a specific student group,
[0126]:
The system and methods included herein may further be directed towards a method and apparatus for an interactive tutoring system that provides real-time personalized feedback to a user (eg student) on their answer to skills-assessment prompt. The skills-assessment prompt may for example be in the format of a practice problem, a test question, or a writing prompt. Other formats are also possible. The skills-assessment prompt may for example be a multiple-choice question, an open-ended question, a free-form question, or a fill-in-the-blanks question, but other formats are also possible.
[BRI: an interactive student tutoring system within the context of an education system can represent a portion of a larger experience, such as a chapter of a novel, a scene in a movie, or a segment of a son For example, the assessment can be thought of as structural elements that shape the learner’s experience, much like chapters, scenes, or segments in a story, film, or song. An analogy is a practice-problem could be a scene in a movie, an open-ended question could be a chapter in a novel , a gamified assessment could be a video game and an interactive video could be a song segment]
Regarding claim 6:
Vleu discloses:
- A computer-implemented method executed on an artificial intelligence-powered large-scale content generator, the computer-implemented method comprising:
[0059]; [0099],
- receiving a user input from a user interface; segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters;
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments leveraging vectorized representations (embeddings) to deeply understand both user (eg educator, content creator) input criteria and educational content
[0047]:
format characteristics of the content element may include language, font size, maximum number of words per slide, voice tone, voice emotion, pace, accentuation of specific words or phrases, difficulty level of vocabulary. Examples of format of the returned content response may include language, syntax of the response, use of specific tags in the response, capitalization or punctuation, maximum length, and test format such as open-ended, fill in the blanks, multiple choice etc,
[0049]:
The method also includes creating a first content element, by sending at least one create request (step 102) for at least a portion of the first content element over the interface wherein the create request includes at least one prompt from the set of prompts and one or more attribute values from the first set of attribute values, receiving a content response (step 103) with the at least a portion of the first content element over the interface, processing the content response (step 104) to extract or derive the at least a portion of the first content element,
[0053]:
The educational content database may be personalized for a user (eg student) or a group of users (eg group of students). The personalization may include making one or more sets of attributes specific to the users or group of users, requesting and receiving one or more user-specific content elements, and storing the user-specific content elements in a user-specific educational content database or as a user-specific entry in a central educational content database,
- flagging a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise;
[0044]:
A content element may also include an answer key or a sample response. A content element may also consist of a combination of pieces of texts, practice problems, assessment questions, writing prompts, etc.,
[0098]:
The output of embeddings generator (1021) is a set of high-dimensional vector representations, or embeddings (1032). Each vector is a point in an n-dimensional space where ‘n’ is determined by the model architecture. These embeddings capture the essential semantic features of the input content, enabling effective matching and retrieval based on semantic similarity.
[0094]:
Assessment Delivery & Storage Subsystem (1002): Assessment completion and storage subsystem (1002) is configured to receive assessments from the assessment creation subsystem (1001), conduct any necessary further processing, and facilitate the delivery of these assessments to one or more assessment takers, such as students. Upon completion of the assessments by the takers, this subsystem is designed to collect and store a variety of data. This data encompasses the assessment information, which may include the questions or assessment itself, the embeddings utilized to create the assessment, and one or more assessment criteria provided by the user/assessment creator via user input 1009,
[0080]:
The preprocessed content is passed through an embedding generator, which converts the content into vector representations. These vector representations facilitate semantic analysis. These vector representations (embeddings) are stored in association with their source content within the content database or in a separate database that is linked to the content database. Embeddings may optionally be periodically updated to reflect changes in educational content or improvements in embedding generation techniques,
- processing the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element;
[0091]:
FIG. 1D illustrates a high-level functional diagram of an adaptive system for the automated generation of customized educational assessments using embeddings in accordance with an example embodiment. The system comprises four interconnected subsystems: a content embeddings creation subsystem (1000), an assessment creation subsystem (1001), an assessment completion and storage subsystem (1002), and a embeddings optimizer subsystem (1003),
[0092]:
Content without embeddings (1004) may be stored in a database or other storage unit. This database or storage unit is functionally connected to a content embeddings creation subsystem (1000) which processes the educational materials to generate embeddings. A trained language model (1008) is employed by the content embeddings creation subsystem to produce high-dimensional vector representations of content, also referred to as embeddings,
[0018]:
special-purpose AI platforms that have emerged in recent years, such as D-DI, Fliki.ai, steve.ai, murf.ai, Descript, Synthesia, and Google Cloud Text-to-Speech. These platforms are designed for specific tasks such as creating videos, audio, animations, and speech from written text or images.
- generating an experience from the plurality of generative AI subsystems where the experience is based on the user input;
[0009]:
In recent years, new solutions have emerged that aim to address some of the limitations of existing online educational platforms. One of these solutions is the use of machine learning and artificial intelligence to build adaptive learning systems. These systems leverage AI algorithms to analyze student data and adjust the learning experience in real-time to meet the needs of individual students,
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments,
[0023]:
The system dynamically adjusts content embeddings based on aggregated performance data and real-time feedback, enhancing personalized learning experiences by tailoring difficulty levels, content relevance, and format relevance to improve learning outcomes.
- displaying the experience to a user device;
[0169]:
The grading system includes a user interface for retrieving a user's response to one or more mastery-assessment prompts such as for example a comprehension, critical thinking, or writing question or prompt. Other kinds of mastery-assessment prompts are also possible. The grading system may present the user with a single mastery-assessment prompt or with a series of mastery-assessment prompts and process the one or the series of user's response(s) to the prompt(s). The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts.
- and receiving user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience.
[0214]:
The disclosed system and method provide a dynamic and personalized educational experience, as it presents a scalable solution to reformatting educational content to meet an individual's or group of users' specific needs and preferences through the use of a natural language interface to one or more generative Artificial Intelligence models,
[0181]:
Processing may include but is not limited to one or more of the following: applying a first weighting factor to the original grade, applying a second weighting factor to a test correction, averaging unweighted or weighted grades, and applying a penalty to account for the fact that the user had multiple attempts. This test correction feature provides users with the ability to learn from their mistakes, correct any mistakes made in the initial assessment and have their grade more accurately reflect their comprehension, critical thinking, or writing skills. The updated grade, generated through the grading methods described herein, may help improve the accuracy of the user's evaluation and provide an updated understanding of their performance.
[0139]:
The system provides a more personalized and engaging educational experience, leveraging the capabilities of AI to provide tailored and interactive feedback to students.
[0169]:
The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts,
[0172]:
The grading system may define or select at least one prompt to request, from a computational or generative AI system, a grade for one or a set of user responses, and sends one or more grading requests over the interface, which may include the mastery-assessment prompt(s), the user's response(s), the prompt and/or one or more attributes,
[0173]:
the grading response(s) may include one or more of the following: a grade per user response, an overall grade, clarification on why a certain grade was assigned, additional feedback or explanations on the correctness of the user's response, and feedback on the quality of the user's response
Regarding claim 7:
Vleu discloses:
- wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments.
[0210]:
The method includes retrieving information about the desirable content format and retrieving content in a first format. At least one prompt is defined to request one or more formatting tasks, and a set of attributes is created that may include one or more of the following: (1) attributes specifying the requested format type of a second format (e.g. video, speech, doodle, text);
[0214]:
Additionally, the customization features of the system make it easily adaptable to different cultures, special needs groups, or other groups with diverse requirements, further enhancing the personalized learning experience for all users.
Regarding claim 8:
Vleu discloses:
- wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem.
[0082]:
In one embodiment, a system includes a performance feedback loop. The system is configured to collect, aggregate, and analyze student performance data related to specific pieces of the educational content database. The performance data may include, but is not limited to, assessment scores, time spent on individual questions, rates of correct versus incorrect answers, and a student's or teacher's difficulty level or quality level rating. The system utilizes this data to identify patterns indicating for example the relative difficulty or case of the educational content for specific student groups.
[BRI: in a multi-generative AI subsystem environment, these metrics are checked for consistency across systems and over time to maintain data integrity and ensure that AI-driven decisions are reliable]
[0114]:
The content generator (1065) may be a separate AI-based model capable of creating content through generative techniques,
[0098]:
Processed content (1031) is sent to or can be accessed by embeddings generator (1021). This component applies machine learning algorithms to the preprocessed content to create high-dimensional vector representations, known as embeddings. These embeddings capture the semantic significance of the educational content, facilitating the alignment of assessment questions with educational standards and objectives
[BRI: in a multi-generative AI subsystem environment, these metrics are checked for consistency across systems and over time to maintain data integrity and ensure that AI-driven decisions are reliable. When embedding models are carefully designed and aligned with educational standards, generative AI can capture the semantic significance of content and produce assessments that are conceptually consistent over time and across subsystems. This approach supports semantic alignment between generated materials and learning objectives, a key step toward trustworthy, standards-compliant AI in education]
Regarding claim 9:
Vleu discloses:
- further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs.
[0012]:
Additionally, solutions that use machine learning and natural language processing to score assessments or assignments have also been developed. For instance, Intelligent Essay Assessor uses Latent Semantic Analysis, a technique in natural language processing, to score written essays. However, solutions like Intelligent Essay Assessor are designed for a specific purpose and are trained on a specialized dataset, making them useful for one specific task only,
[0084]:
The dynamic adjustment of embeddings can be achieved through various machine learning techniques, including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning. In case of transfer learning, the system may implement a process for retraining the embedding model on a subset of data labeled with difficulty levels derived from performance analysis. In case of reinforcement learning, the system may implement a reinforcement learning model that adjusts embeddings to maximize educational outcomes, using performance feedback as the reward signal. These techniques allow for fine-tuning of the model based on new data inputs, such as student performance metrics, to optimize educational outcomes. The system employs sophisticated algorithms to reposition content embeddings within the vector space, ensuring that the difficulty and focus of the educational material are dynamically aligned with the evolving needs and abilities of the student population.
Regarding claim 10:
Vleu discloses:
- wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song
[0161]:
The interactive tutoring apparatus 230 may also include a Q&A module 246 that communicates with user over user interface 235 and allows the user, after receiving an explanation, to ask one or more follow-on questions.
[0122]:
retrieve labeled dataset, or a portion thereof, from performance data database (1088) and uses the retrieved dataset to generate a retrained embeddings model (1083). The retrained embeddings model (1083) therefor takes into account difficulty levels inferred from student performance data,
[0125]:
Examples of assessment modality may include but are not limited to type of questions, sequence of questions, length of test, test delivery method (paper, online, audio, oral, interactive video, gamified, etc). An embeddings optimizer may be used to optimize the instructional content and/or assessment content embeddings for a specific student group. The content database with optimized embeddings may be used to generate one or more assessments customized for a specific student group,
[0126]:
The system and methods included herein may further be directed towards a method and apparatus for an interactive tutoring system that provides real-time personalized feedback to a user (eg student) on their answer to skills-assessment prompt. The skills-assessment prompt may for example be in the format of a practice problem, a test question, or a writing prompt. Other formats are also possible. The skills-assessment prompt may for example be a multiple-choice question, an open-ended question, a free-form question, or a fill-in-the-blanks question, but other formats are also possible.
[BRI: an interactive student tutoring system within the context of an education system can represent a portion of a larger experience, such as a chapter of a novel, a scene in a movie, or a segment of a son For example, the assessment can be thought of as structural elements that shape the learner’s experience, much like chapters, scenes, or segments in a story, film, or song. An analogy is a practice-problem could be a scene in a movie, an open-ended question could be a chapter in a novel , a gamified assessment could be a video game and an interactive video could be a song segment]
Regarding claim 11:
- A system for an artificial intelligence-powered large-scale content generator, comprising one or more computers with executable instruction that, when executed, cause the system to:
[0059]; [0099],
- receive a user input from a user interface; segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters;
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments leveraging vectorized representations (embeddings) to deeply understand both user (eg educator, content creator) input criteria and educational content
[0047]:
format characteristics of the content element may include language, font size, maximum number of words per slide, voice tone, voice emotion, pace, accentuation of specific words or phrases, difficulty level of vocabulary. Examples of format of the returned content response may include language, syntax of the response, use of specific tags in the response, capitalization or punctuation, maximum length, and test format such as open-ended, fill in the blanks, multiple choice etc,
[0049]:
The method also includes creating a first content element, by sending at least one create request (step 102) for at least a portion of the first content element over the interface wherein the create request includes at least one prompt from the set of prompts and one or more attribute values from the first set of attribute values, receiving a content response (step 103) with the at least a portion of the first content element over the interface, processing the content response (step 104) to extract or derive the at least a portion of the first content element,
[0053]:
The educational content database may be personalized for a user (eg student) or a group of users (eg group of students). The personalization may include making one or more sets of attributes specific to the users or group of users, requesting and receiving one or more user-specific content elements, and storing the user-specific content elements in a user-specific educational content database or as a user-specific entry in a central educational content database,
- flag a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise;
[0044]:
A content element may also include an answer key or a sample response. A content element may also consist of a combination of pieces of texts, practice problems, assessment questions, writing prompts, etc.,
[0098]:
The output of embeddings generator (1021) is a set of high-dimensional vector representations, or embeddings (1032). Each vector is a point in an n-dimensional space where ‘n’ is determined by the model architecture. These embeddings capture the essential semantic features of the input content, enabling effective matching and retrieval based on semantic similarity.
[0094]:
Assessment Delivery & Storage Subsystem (1002): Assessment completion and storage subsystem (1002) is configured to receive assessments from the assessment creation subsystem (1001), conduct any necessary further processing, and facilitate the delivery of these assessments to one or more assessment takers, such as students. Upon completion of the assessments by the takers, this subsystem is designed to collect and store a variety of data. This data encompasses the assessment information, which may include the questions or assessment itself, the embeddings utilized to create the assessment, and one or more assessment criteria provided by the user/assessment creator via user input 1009,
[0080]:
The preprocessed content is passed through an embedding generator, which converts the content into vector representations. These vector representations facilitate semantic analysis. These vector representations (embeddings) are stored in association with their source content within the content database or in a separate database that is linked to the content database. Embeddings may optionally be periodically updated to reflect changes in educational content or improvements in embedding generation techniques,
- process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element.
[0091]:
FIG. 1D illustrates a high-level functional diagram of an adaptive system for the automated generation of customized educational assessments using embeddings in accordance with an example embodiment. The system comprises four interconnected subsystems: a content embeddings creation subsystem (1000), an assessment creation subsystem (1001), an assessment completion and storage subsystem (1002), and a embeddings optimizer subsystem (1003),
[0092]:
Content without embeddings (1004) may be stored in a database or other storage unit. This database or storage unit is functionally connected to a content embeddings creation subsystem (1000) which processes the educational materials to generate embeddings. A trained language model (1008) is employed by the content embeddings creation subsystem to produce high-dimensional vector representations of content, also referred to as embeddings,
[0018]:
special-purpose AI platforms that have emerged in recent years, such as D-DI, Fliki.ai, steve.ai, murf.ai, Descript, Synthesia, and Google Cloud Text-to-Speech. These platforms are designed for specific tasks such as creating videos, audio, animations, and speech from written text or images.
- generate an experience from the plurality of generative AI subsystems where the experience is based on the user input;
[0009]:
In recent years, new solutions have emerged that aim to address some of the limitations of existing online educational platforms. One of these solutions is the use of machine learning and artificial intelligence to build adaptive learning systems. These systems leverage AI algorithms to analyze student data and adjust the learning experience in real-time to meet the needs of individual students,
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments,
[0023]:
The system dynamically adjusts content embeddings based on aggregated performance data and real-time feedback, enhancing personalized learning experiences by tailoring difficulty levels, content relevance, and format relevance to improve learning outcomes.
- display the experience to a user device;
[0169]:
The grading system includes a user interface for retrieving a user's response to one or more mastery-assessment prompts such as for example a comprehension, critical thinking, or writing question or prompt. Other kinds of mastery-assessment prompts are also possible. The grading system may present the user with a single mastery-assessment prompt or with a series of mastery-assessment prompts and process the one or the series of user's response(s) to the prompt(s). The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts.
[BRI: modern AI systems can dynamically adjust content embeddings based on aggregated performance data and real-time feedback, creating a personalized learning experience that adapts to each user’s mastery level]
- and receive user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience.
[0214]:
The disclosed system and method provide a dynamic and personalized educational experience, as it presents a scalable solution to reformatting educational content to meet an individual's or group of users' specific needs and preferences through the use of a natural language interface to one or more generative Artificial Intelligence models,
[0181]:
Processing may include but is not limited to one or more of the following: applying a first weighting factor to the original grade, applying a second weighting factor to a test correction, averaging unweighted or weighted grades, and applying a penalty to account for the fact that the user had multiple attempts. This test correction feature provides users with the ability to learn from their mistakes, correct any mistakes made in the initial assessment and have their grade more accurately reflect their comprehension, critical thinking, or writing skills. The updated grade, generated through the grading methods described herein, may help improve the accuracy of the user's evaluation and provide an updated understanding of their performance.
[0139]:
The system provides a more personalized and engaging educational experience, leveraging the capabilities of AI to provide tailored and interactive feedback to students.
[0169]:
The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts,
[0172]:
The grading system may define or select at least one prompt to request, from a computational or generative AI system, a grade for one or a set of user responses, and sends one or more grading requests over the interface, which may include the mastery-assessment prompt(s), the user's response(s), the prompt and/or one or more attributes,
[0173]:
the grading response(s) may include one or more of the following: a grade per user response, an overall grade, clarification on why a certain grade was assigned, additional feedback or explanations on the correctness of the user's response, and feedback on the quality of the user's response
Regarding claim 12:
Vleu discloses:
- wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments.
[0210]:
The method includes retrieving information about the desirable content format and retrieving content in a first format. At least one prompt is defined to request one or more formatting tasks, and a set of attributes is created that may include one or more of the following: (1) attributes specifying the requested format type of a second format (e.g. video, speech, doodle, text);
[0214]:
Additionally, the customization features of the system make it easily adaptable to different cultures, special needs groups, or other groups with diverse requirements, further enhancing the personalized learning experience for all users.
Regarding claim 13:
Vleu discloses:
- wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem.
[0082]:
In one embodiment, a system includes a performance feedback loop. The system is configured to collect, aggregate, and analyze student performance data related to specific pieces of the educational content database. The performance data may include, but is not limited to, assessment scores, time spent on individual questions, rates of correct versus incorrect answers, and a student's or teacher's difficulty level or quality level rating. The system utilizes this data to identify patterns indicating for example the relative difficulty or case of the educational content for specific student groups.
[BRI: in a multi-generative AI subsystem environment, these metrics are checked for consistency across systems and over time to maintain data integrity and ensure that AI-driven decisions are reliable]
[0114]:
The content generator (1065) may be a separate AI-based model capable of creating content through generative techniques,
[0098]:
Processed content (1031) is sent to or can be accessed by embeddings generator (1021). This component applies machine learning algorithms to the preprocessed content to create high-dimensional vector representations, known as embeddings. These embeddings capture the semantic significance of the educational content, facilitating the alignment of assessment questions with educational standards and objectives
[BRI: in a multi-generative AI subsystem environment, these metrics are checked for consistency across systems and over time to maintain data integrity and ensure that AI-driven decisions are reliable. When embedding models are carefully designed and aligned with educational standards, generative AI can capture the semantic significance of content and produce assessments that are conceptually consistent over time and across subsystems. This approach supports semantic alignment between generated materials and learning objectives, a key step toward trustworthy, standards-compliant AI in education]
Regarding claim 14:
Vleu discloses:
- further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs.
[0012]:
Additionally, solutions that use machine learning and natural language processing to score assessments or assignments have also been developed. For instance, Intelligent Essay Assessor uses Latent Semantic Analysis, a technique in natural language processing, to score written essays. However, solutions like Intelligent Essay Assessor are designed for a specific purpose and are trained on a specialized dataset, making them useful for one specific task only,
[0084]:
The dynamic adjustment of embeddings can be achieved through various machine learning techniques, including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning. In case of transfer learning, the system may implement a process for retraining the embedding model on a subset of data labeled with difficulty levels derived from performance analysis. In case of reinforcement learning, the system may implement a reinforcement learning model that adjusts embeddings to maximize educational outcomes, using performance feedback as the reward signal. These techniques allow for fine-tuning of the model based on new data inputs, such as student performance metrics, to optimize educational outcomes. The system employs sophisticated algorithms to reposition content embeddings within the vector space, ensuring that the difficulty and focus of the educational material are dynamically aligned with the evolving needs and abilities of the student population.
Regarding claim 15:
Vleu discloses:
- wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song
[0161]:
The interactive tutoring apparatus 230 may also include a Q&A module 246 that communicates with user over user interface 235 and allows the user, after receiving an explanation, to ask one or more follow-on questions.
[0122]:
retrieve labeled dataset, or a portion thereof, from performance data database (1088) and uses the retrieved dataset to generate a retrained embeddings model (1083). The retrained embeddings model (1083) therefor takes into account difficulty levels inferred from student performance data,
[0125]:
Examples of assessment modality may include but are not limited to type of questions, sequence of questions, length of test, test delivery method (paper, online, audio, oral, interactive video, gamified, etc). An embeddings optimizer may be used to optimize the instructional content and/or assessment content embeddings for a specific student group. The content database with optimized embeddings may be used to generate one or more assessments customized for a specific student group,
[0126]:
The system and methods included herein may further be directed towards a method and apparatus for an interactive tutoring system that provides real-time personalized feedback to a user (eg student) on their answer to skills-assessment prompt. The skills-assessment prompt may for example be in the format of a practice problem, a test question, or a writing prompt. Other formats are also possible. The skills-assessment prompt may for example be a multiple-choice question, an open-ended question, a free-form question, or a fill-in-the-blanks question, but other formats are also possible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Katelijn Vleugels et al. (hereinafter Vleu) US 2024/0274025 A1,
[Note: EFD: 5/20/24, Prior Art Filing: 2/14/24, Prior Art Published: 8/15/24]
In view of Christfried Focke et al. (hereinafter Focke) US 2025/0045528 A1.
Regarding claim 16:
Vleu discloses:
- receive a user input from a user interface; segmenting the user input into a plurality of elements, wherein the elements include plot, setting, descriptors, and characters;
[0058]:
the AI model may be implemented as a set of program instructions that are executed on a central server, utilizing one or more processors and one or storage units in the central server
[0059]; [0099];
[Note: this mapping also teaches Non-Transitory Storage Medium as a server storage unit that stores program instructions]
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments leveraging vectorized representations (embeddings) to deeply understand both user (eg educator, content creator) input criteria and educational content
[0047]:
format characteristics of the content element may include language, font size, maximum number of words per slide, voice tone, voice emotion, pace, accentuation of specific words or phrases, difficulty level of vocabulary. Examples of format of the returned content response may include language, syntax of the response, use of specific tags in the response, capitalization or punctuation, maximum length, and test format such as open-ended, fill in the blanks, multiple choice etc,
[0049]:
The method also includes creating a first content element, by sending at least one create request (step 102) for at least a portion of the first content element over the interface wherein the create request includes at least one prompt from the set of prompts and one or more attribute values from the first set of attribute values, receiving a content response (step 103) with the at least a portion of the first content element over the interface, processing the content response (step 104) to extract or derive the at least a portion of the first content element,
[0053]:
The educational content database may be personalized for a user (eg student) or a group of users (eg group of students). The personalization may include making one or more sets of attributes specific to the users or group of users, requesting and receiving one or more user-specific content elements, and storing the user-specific content elements in a user-specific educational content database or as a user-specific entry in a central educational content database,
- flag a plurality key elements from the plurality of elements which should remain constant unless the user input indicates otherwise.
[0044]:
A content element may also include an answer key or a sample response. A content element may also consist of a combination of pieces of texts, practice problems, assessment questions, writing prompts, etc.,
[0098]:
The output of embeddings generator (1021) is a set of high-dimensional vector representations, or embeddings (1032). Each vector is a point in an n-dimensional space where ‘n’ is determined by the model architecture. These embeddings capture the essential semantic features of the input content, enabling effective matching and retrieval based on semantic similarity.
[0094]:
Assessment Delivery & Storage Subsystem (1002): Assessment completion and storage subsystem (1002) is configured to receive assessments from the assessment creation subsystem (1001), conduct any necessary further processing, and facilitate the delivery of these assessments to one or more assessment takers, such as students. Upon completion of the assessments by the takers, this subsystem is designed to collect and store a variety of data. This data encompasses the assessment information, which may include the questions or assessment itself, the embeddings utilized to create the assessment, and one or more assessment criteria provided by the user/assessment creator via user input 1009,
[0080]:
The preprocessed content is passed through an embedding generator, which converts the content into vector representations. These vector representations facilitate semantic analysis. These vector representations (embeddings) are stored in association with their source content within the content database or in a separate database that is linked to the content database. Embeddings may optionally be periodically updated to reflect changes in educational content or improvements in embedding generation techniques,
- process the plurality of elements and the plurality of key elements through a plurality of generative AI subsystems where each generative AI subsystem is configured to process a certain type of element;
[0091]:
FIG. 1D illustrates a high-level functional diagram of an adaptive system for the automated generation of customized educational assessments using embeddings in accordance with an example embodiment. The system comprises four interconnected subsystems: a content embeddings creation subsystem (1000), an assessment creation subsystem (1001), an assessment completion and storage subsystem (1002), and a embeddings optimizer subsystem (1003),
[0092]:
Content without embeddings (1004) may be stored in a database or other storage unit. This database or storage unit is functionally connected to a content embeddings creation subsystem (1000) which processes the educational materials to generate embeddings. A trained language model (1008) is employed by the content embeddings creation subsystem to produce high-dimensional vector representations of content, also referred to as embeddings,
[0018]:
special-purpose AI platforms that have emerged in recent years, such as D-DI, Fliki.ai, steve.ai, murf.ai, Descript, Synthesia, and Google Cloud Text-to-Speech. These platforms are designed for specific tasks such as creating videos, audio, animations, and speech from written text or images.
- generate an experience from the plurality of generative AI subsystems where the experience is based on the user input;
[0009]:
In recent years, new solutions have emerged that aim to address some of the limitations of existing online educational platforms. One of these solutions is the use of machine learning and artificial intelligence to build adaptive learning systems. These systems leverage AI algorithms to analyze student data and adjust the learning experience in real-time to meet the needs of individual students,
[0023]:
Further aspects and embodiment of the invention are generally directed to systems and methods that provide automated and adaptive generation of customized educational assessments,
[0023]:
The system dynamically adjusts content embeddings based on aggregated performance data and real-time feedback, enhancing personalized learning experiences by tailoring difficulty levels, content relevance, and format relevance to improve learning outcomes.
- display the experience to a user device;
[0169]:
The grading system includes a user interface for retrieving a user's response to one or more mastery-assessment prompts such as for example a comprehension, critical thinking, or writing question or prompt. Other kinds of mastery-assessment prompts are also possible. The grading system may present the user with a single mastery-assessment prompt or with a series of mastery-assessment prompts and process the one or the series of user's response(s) to the prompt(s). The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts.
- and receive user feedback to which is processed by the plurality of generative AI subsystems to create an updated experience.
[0214]:
The disclosed system and method provide a dynamic and personalized educational experience, as it presents a scalable solution to reformatting educational content to meet an individual's or group of users' specific needs and preferences through the use of a natural language interface to one or more generative Artificial Intelligence models,
[0181]:
Processing may include but is not limited to one or more of the following: applying a first weighting factor to the original grade, applying a second weighting factor to a test correction, averaging unweighted or weighted grades, and applying a penalty to account for the fact that the user had multiple attempts. This test correction feature provides users with the ability to learn from their mistakes, correct any mistakes made in the initial assessment and have their grade more accurately reflect their comprehension, critical thinking, or writing skills. The updated grade, generated through the grading methods described herein, may help improve the accuracy of the user's evaluation and provide an updated understanding of their performance.
[0139]:
The system provides a more personalized and engaging educational experience, leveraging the capabilities of AI to provide tailored and interactive feedback to students.
[0169]:
The series of prompts may be presented to the user one at a time, a subset at a time, or all at once. The user's responses may be processed one at a time, a subset at a time, or all at once after the user has submitted all mastery-assessment prompts,
[0172]:
The grading system may define or select at least one prompt to request, from a computational or generative AI system, a grade for one or a set of user responses, and sends one or more grading requests over the interface, which may include the mastery-assessment prompt(s), the user's response(s), the prompt and/or one or more attributes,
[0173]:
the grading response(s) may include one or more of the following: a grade per user response, an overall grade, clarification on why a certain grade was assigned, additional feedback or explanations on the correctness of the user's response, and feedback on the quality of the user's response
Vleu does not explicitly disclose:
- Non-transitory, computer-readable storage media having computer executable instruction embodied thereon that, when executed by one or more processors of a computing system employing an artificial intelligence-powered large-scale content generator, cause the computing system to:
However, Focke discloses:
- Non-transitory, computer-readable storage media having computer executable instruction embodied thereon that, when executed by one or more processors of a computing system employing an artificial intelligence-powered large-scale content generator, cause the computing system to:
[0005]:
In some aspects, a method is provided. In some aspects, the method includes receiving, from a client device connected to a property management software system (PMSS), a first communication indicating a request pertaining to a PMSS, determining a category type associated with the request, routing, using a chat module, the first communication to a first support module configured to perform support operations associated with the category type of the request,
[0005];
a first machine communication from the first support module associated with an output of the first support operation, providing, using the chat module, the first communication and first machine communication as an input to a generative AI model, obtaining an output of the generative AI model, and providing, using the output of the generative AI model, a response to the indicated request through a user interface (UI) of the client device.
[0047]:
When a user transfers a user query through the chat functionalities to the PMSS with the intent of performing a content search, such information can ultimately be transferred to, and handled, by chat module 180
[0007]:
In some aspects, a non-transitory computer readable storage medium is provided. In some aspects, the non-transitory computer readable storage medium includes instructions that, when executed by a processing device, causes the processing device to perform operations including receiving, from a client device connected to a property management software system (PMSS), a device communication indicating a request pertaining to a PMSS,
[0197:
Data storage device 718 may include a computer-readable storage medium (or, more specifically, a non-transitory computer-readable storage medium) 728 on which is stored one or more sets of executable instructions 722. In accordance with one or more aspects of the present disclosure, executable instructions 722 may include executable instructions.
It would be obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Vleu and Focke.
Vleu teaches AI generative network for content generation.
Focke teaches non-transitory storage medium within the context of an AI generative network for content generation.
One of ordinary skills would be motivated to combine Vleu and Focke that can use generative AI models for automating and supporting tasks that can overcome complexities associated with PMSS (Focke [0020, 0022]).
Regarding claim 17:
Vleu discloses:
- wherein the plurality of generative AI subsystems are configured to process and generate text, images, videos, sounds, and environments.
[0210]:
The method includes retrieving information about the desirable content format and retrieving content in a first format. At least one prompt is defined to request one or more formatting tasks, and a set of attributes is created that may include one or more of the following: (1) attributes specifying the requested format type of a second format (e.g. video, speech, doodle, text);
[0214]:
Additionally, the customization features of the system make it easily adaptable to different cultures, special needs groups, or other groups with diverse requirements, further enhancing the personalized learning experience for all users.
Regarding claim 18:
Vleu discloses:
- wherein the outputs from the plurality of generative AI subsystems are checked to ensure that the plurality of key elements are consistent in both time and between each generative AI subsystem.
[0082]:
In one embodiment, a system includes a performance feedback loop. The system is configured to collect, aggregate, and analyze student performance data related to specific pieces of the educational content database. The performance data may include, but is not limited to, assessment scores, time spent on individual questions, rates of correct versus incorrect answers, and a student's or teacher's difficulty level or quality level rating. The system utilizes this data to identify patterns indicating for example the relative difficulty or case of the educational content for specific student groups.
[0114]:
The content generator (1065) may be a separate AI-based model capable of creating content through generative techniques,
[0098]:
Processed content (1031) is sent to or can be accessed by embeddings generator (1021). This component applies machine learning algorithms to the preprocessed content to create high-dimensional vector representations, known as embeddings. These embeddings capture the semantic significance of the educational content, facilitating the alignment of assessment questions with educational standards and objectives
Regarding claim 19:
Vleu discloses:
- further comprising a generative AI training system which trains each generative AI subsystem on user feedback and a plurality of user inputs.
[0012]:
Additionally, solutions that use machine learning and natural language processing to score assessments or assignments have also been developed. For instance, Intelligent Essay Assessor uses Latent Semantic Analysis, a technique in natural language processing, to score written essays. However, solutions like Intelligent Essay Assessor are designed for a specific purpose and are trained on a specialized dataset, making them useful for one specific task only,
[0084]:
The dynamic adjustment of embeddings can be achieved through various machine learning techniques, including but not limited to supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning. In case of transfer learning, the system may implement a process for retraining the embedding model on a subset of data labeled with difficulty levels derived from performance analysis. In case of reinforcement learning, the system may implement a reinforcement learning model that adjusts embeddings to maximize educational outcomes, using performance feedback as the reward signal. These techniques allow for fine-tuning of the model based on new data inputs, such as student performance metrics, to optimize educational outcomes. The system employs sophisticated algorithms to reposition content embeddings within the vector space, ensuring that the difficulty and focus of the educational material are dynamically aligned with the evolving needs and abilities of the student population.
Regarding claim 20:
Vleu discloses:
- wherein the plurality of generative AI subsystem may be configured to generate a portion of an experience, such as chapters of a novel, single scenes in a movie, of portions of a song
[0161]:
The interactive tutoring apparatus 230 may also include a Q&A module 246 that communicates with user over user interface 235 and allows the user, after receiving an explanation, to ask one or more follow-on questions.
[0122]:
retrieve labeled dataset, or a portion thereof, from performance data database (1088) and uses the retrieved dataset to generate a retrained embeddings model (1083). The retrained embeddings model (1083) therefor takes into account difficulty levels inferred from student performance data,
[0125]:
Examples of assessment modality may include but are not limited to type of questions, sequence of questions, length of test, test delivery method (paper, online, audio, oral, interactive video, gamified, etc). An embeddings optimizer may be used to optimize the instructional content and/or assessment content embeddings for a specific student group. The content database with optimized embeddings may be used to generate one or more assessments customized for a specific student group,
[0126]:
The system and methods included herein may further be directed towards a method and apparatus for an interactive tutoring system that provides real-time personalized feedback to a user (eg student) on their answer to skills-assessment prompt. The skills-assessment prompt may for example be in the format of a practice problem, a test question, or a writing prompt. Other formats are also possible. The skills-assessment prompt may for example be a multiple-choice question, an open-ended question, a free-form question, or a fill-in-the-blanks question, but other formats are also possible.
Conclusion
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to TIRUMALE KRISHNASWAMY RAMESH whose telephone number is (571)272-4605. The examiner can normally be reached by phone.
Examiner interviews are available via telephone, in-person, and video conferencing
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached on phone (571-272-3768). The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIRUMALE K RAMESH/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
