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 .
Response to Amendment
The amendment filed November 4, 2025 has been entered. Claims 1, 3-6, 8-9, 11, 13-16, 18-19, and 21-26 remain pending in the application. Claims 1, 11, 19, and 25 are noted as amended. Applicant’s amendments to the claims have overcome all previous objections set forth in the Non-Final Office Action mailed September 16, 2025 and all objections therein have been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-6, 8, 11, 13-16, 18, and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal, III et al. (US PGPub 20220384027), hereinafter referred to as Kaleal, in view of Borovikov et al. (US PGPub 20210327135), hereinafter referred to as Borovikov, further in view of Kasaba (US 11140360), further in view of Solomon et al. (US PGPub 20170206797), hereinafter referred to as Solomon, further in view of Aggarwal et al. (US PGPub 20180253985), hereinafter referred to as Aggarwal, further in view of Stewart et al. (US PGPub 20230237722), hereinafter referred to as Stewart ‘722, further in view of Stewart et al. (US PGPub 20230252418), hereinafter referred to as Stewart ‘418, and further in view of Gonzalez (US 5413355).
In regards to claims 1 and 11, Kaleal teaches an apparatus (Abstract; Paragraph 0043; “avatar guidance system”) for an education platform and ecosystem using extended reality [claim 1] (Paragraphs 0067, 0264 teach the system can apply to an AR, VR, MR, or XR environment using the corresponding devices), and a method (Abstract; Paragraph 0069; “method”) for educating an entity using extended reality [claim 11] (Paragraphs 0067, 0264; extended reality device for a mixed reality environment), comprising:
at least a processor [claim 1] (Abstract; Paragraphs 0066, 0221 “processor”); and
a memory communicatively connected to the processor (Paragraphs 0066, 0221 “processor that executes the computer executable components stored in the memory”), the memory containing instructions configuring the at least a processor to [claim 1] (Paragraphs 0066, 0221 “memory that stores computer executable components”):
receive entity data associated with an entity (Paragraphs 0076, 0164-0165 teaches the system can receive input of user profile data including, per paragraph 0106, age, gender, education level, and/or profession), wherein the entity data comprises at least an entity data item (Paragraphs 0076, 0106 teaches the user profile data includes age, gender, education level, and/or profession as well as physical characteristics; Paragraphs 0076, 0226 teach that image data of the user can also be collected);
generate an educational model (Paragraphs 0068, 0077-0078 teach the system is programed to monitor a user’s actions and receive the users actions and physiological data as inputs to generate a response based on the inputs), wherein the educational model is configured to receive at least an input from a user (Paragraph 0077; receiving various inputs about a user) and generate at least an educational response as a function of the at least an input (Paragraphs 0046, 0073 teach the system can generate a guidance response if the user input is determined to be a deviation, wherein the guidance is an “educational response” based on the user input);
extract the entity data item from the entity data (Paragraphs 0062, 0082 teach the avatar is based on the captured image data and profile data and is updated to replicate the user);
generating the virtual avatar as a function of the entity data (Paragraphs 0062, 0082 teach the avatar is based on the captured image data and profile data and is updated to replicate the user);
instantiate, in a user interface (Paragraph 0124; Fig. 2, Ref 206; “interface component configures a graphical user interface(s)”), the virtual avatar (Paragraphs 0064, 0124; “generate and present an avatar to the user”), wherein the virtual avatar comprises at least a base image (Paragraphs 0054, 0062 teach the avatar can have an appearance (base image) that is based on the user to mirror the user, and Paragraphs 0150-0151 teach the user can customize the avatar’s appearance (base image)) and a plurality of animations of the base image (Paragraphs 0112, 0125-0126 teach the avatar can be animated by the avatar control component to generate actions, responses, and reactions of the avatar), wherein the virtual avatar comprises at least a personalized characteristic of the user (Paragraph 0374 teaches the system can further develop the user identity avatar to reflect other characteristics of the user in addition to their appearance), wherein the at least a personalized characteristic comprises a unique gait of the user (Paragraph 0374 teaches the other characteristics can include the system modeling the user’s gait for the user identity avatar), wherein the virtual avatar is configured to:
receive the at least an educational response (Paragraphs 0069, 0073, 0126 teach that the system can determine if the user has deviated from the routine and generate and transmit control commands for the avatar to perform), wherein the at least an educational response is communicated to the virtual avatar using blockchain technology (Paragraphs 0205, 0251, 0256 teach the avatar system can record user health and fitness information and provide rewards and responses via a blockchain); and
display an animation of the plurality of animations as a function of the at least an educational response (Paragraphs 0069, 0073, 0126 the avatar performs the response (animation) that corresponds to the response including speaking a command, performing a motion, or changing appearance); and
generate a non-fungible token linked to the entity data and the virtual avatar (Paragraphs 0383-0384 teach the system can generate the avatar as an NFT that can be included in a digital wallet that includes the avatar data, user profile data, and historical data; per paragraph 0436, any of the particular features of the reference can be combined with any other features of other implementations), wherein the non-fungible token comprises a non-fungible token avatar and is purchased with a cryptocurrency in a crypto wallet (Paragraphs 0372, 0378, 0391 teach the system can mint the avatar as an NFT on the blockchain (NFT avatar) which can be purchased and sold for cryptocurrency or another digital asset).
Kaleal may not explicitly teach generate a virtual avatar using a virtual avatar model, wherein generating the virtual avatar comprises: training the virtual avatar model using virtual avatar training data, wherein the virtual avatar training data comprises a plurality of pre-existing virtual avatars from a virtual avatar database. However, Borovikov teaches a system and method for generating character models for a user using a neural network model (machine learning model) trained using images of other characters existing in the game, pre-existing assets, and images of the user or other people (Paragraphs 0003, 0051-0052, 0076, 0091).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal to incorporate the teachings of Borovikov by applying the technique of training a machine learning model to generate a user avatar by using pre-existing assets and user images as training data, as both references and the claimed invention are directed to systems for generating virtual avatars for users. While the fields of endeavor are different as Kaleal is directed to training/education and Borovikov is directed to gaming, the prior arts are similar in their use of machine learning with regards to virtual user models and one of ordinary skill would appreciate the similarities in the art to improve Kaleal in the same was as Borovikov. Further, Borovikov teaches the improvements provide a better way to maintain a design style and maintain a user’s privacy (Paragraphs 0052, 0091). One of ordinary skill in the art would modify Kaleal by coding the machine learning models of Kaleal to include generating the virtual avatar of the user based on the training data including the user profile information, user image data, and pre-existing avatars and assets. Upon such modification, the method and system of Kaleal would include generate a virtual avatar using a virtual avatar model, wherein generating the virtual avatar comprises: training the virtual avatar model using virtual avatar training data, wherein the virtual avatar training data comprises a plurality of pre-existing virtual avatars from a virtual avatar database. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Borovikov with Kaleal’s system and method in order to maintain/improve the stylization of the avatars and maintain user privacy while improving system performance.
Kaleal further teaches the system includes user profile data and information including entity data as discussed above (see also paragraphs 0106, 0384), but Kaleal in view of Borovikov may not explicitly teach wherein the at least an entity data item comprises a hair color of a user and an eye color of the user; generate a user education report as a function of the entity data; and generate a virtual avatar as a function of the user educational report. However, Kasaba a system and method for an interactive digitally rendered avatar for interacting with a user interacting with educational content wherein the avatar can physically mimic the subject person/user and includes creating a user file (user education report) that includes the student’s history, test results, grades, and more (see Paragraph 0011 of the instant application which states the user education report includes assessments of the user including education level and courses for the user), including personalization data including the user’s hair and/or eye color, such that the avatar can determine the skill level of the user and base its responses on the skill level and thereby the user file of the user (Abstract; Col. 5, lines 52-60; Col. 7, lines 15-26; Col. 8, lines 21-31; Col. 19, lines 35-58).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov to incorporate the teachings of Kasaba by coding the system to use the user profile/demographic data of Kaleal to generate user file data that can be used by the virtual avatar to determine appropriate actions/responses as taught by Kasaba, as both references and the claimed invention are directed to training platforms using virtual avatars. One of ordinary skill in the art would modify Kaleal in view of Borovikov by using the profile/demographic data of Kaleal to generate a user file and use that user file to generate the avatars responses as taught by Kasaba. Further, one of ordinary skill would include hair and/or eye color as taught by Kasaba as some of the physical characteristics captured by Kaleal. Upon such modification, the method and system of Kaleal in view of Borovikov would include wherein the at least an entity data item comprises a hair color of a user and an eye color of the user; generate a user education report as a function of the entity data; and generate a virtual avatar as a function of the user educational report. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Kasaba with Kaleal in view of Borovikov’s system and method in order to improve user engagement and effectively convey educational material by improving the avatars responses to ensure the responses meet the skill level of the user/student.
Kaleal in view of Borovikov and Kasaba may not explicitly teach wherein receiving the at least an input from the user comprises using a chatbot, wherein the chatbot is trained using a chatbot classifier, wherein the chatbot classifier correlates an answer datum to the at least an educational response, wherein the chatbot classifier is further configured to output at least a question output as a function of the answer datum. However, Solomon teaches a system and method for training a student using a virtual avatar to respond to user queries and asking users questions (abstract; paragraphs 0020, 0035) including wherein receiving the input from the user by the educational model comprises the use of a chatbot (Paragraphs 0032, 0042 teach the avatar can have a conversation with the user using chatbot methods), wherein the chatbot is trained using a chatbot classifier (Paragraphs 0020, 0033; “Learning manager function”), wherein the chatbot classifier correlates an answer datum to the at least an educational response (Paragraph 0035 teaches the avatar receives and recognizes student’s answers and can ask a clarification response/question such as ‘did you mean…A or B…?’), wherein the chatbot classifier is further configured to output at least a question output as a function of the answer datum (Paragraph 0035 teaches the avatar receives and recognizes student’s answers and can ask a clarification response/question such as ‘did you mean…A or B…?’).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov and Kasaba to incorporate the teachings of Solomon by coding the avatar of Kaleal to use chatbot conversational agents of Solomon to converse with the user, as both references and the claimed invention are directed to training platforms using virtual avatars. It would have been obvious to one of ordinary skill to use the techniques of conversational chatbots/AI/Machine learning to improve the avatars of Kaleal in the same way in order to better engage the users (Solomon Paragraph 0004) and make the avatar more robust (Solomon Paragraph 0042). Upon such modification, the method and system of Kaleal in view of Borovikov and Kasaba would include wherein receiving the at least an input from the user comprises using a chatbot, wherein the chatbot is trained using a chatbot classifier, wherein the chatbot classifier correlates an answer datum to the at least an educational response, wherein the chatbot classifier is further configured to output at least a question output as a function of the answer datum. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Solomon with Kaleal in view of Borovikov and Kasaba’s system and method in order to improve user engagement and effectively convey educational material and interactions to the user.
Kaleal in view of Borovikov, Kasaba, and Solomon may not explicitly teach generate at least a second educational response by creating a feedback loop wherein the feedback loop is configured to trigger the chatbot to generate the at least a second educational response as a function of an answer datum received as a function of the at least an educational response; and regenerate the at least a second education response as a function of the feedback loop until a terminal response is received. However, Aggarwal teaches a system and method for generating and managing a messaging stream including a chat bot for administering a test to a user and scoring the test (Abstract; paragraph 0033) including generating responses to user answers or statements such as progressing questions (see Fig 9A) and creating a loop (feedback loop) of questions, answers, and corresponding responses until either all the questions have been presented and completed, wherein a final answer/completion of the last question, would end the loop (terminal response) or the user enters a request to quit (terminal response) such as “Exit now” or “I want to quit the test” (Fig. 9A; Paragraphs 0089-0090, 0100).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, and Solomon to incorporate the teachings of Aggarwal by further coding a chat bot of Kaleal in view of Borovikov, Kasaba, and Solomon to include looping through a series of questions, answers, and corresponding other responses until the conversation/test is done or the user exits out, as the references and the claimed invention are directed to training platforms using conversational technology. It would have been obvious to one of ordinary skill to further modify the chat bot of Solomon to include looping through a series of questions/a test of questions as the loop is merely duplicating the question, answer, and response process and exiting the loop either by completing all the questions or the user inputting a request to exit the process. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, and Solomon would include generate at least a second educational response by creating a feedback loop wherein the feedback loop is configured to trigger the chatbot to generate the at least a second educational response as a function of an answer datum received as a function of the at least an educational response; and regenerate the at least a second education response as a function of the feedback loop until a terminal response is received. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Aggarwal with Kaleal in view of Borovikov, Kasaba, and Solomon’s system and method in order to improve user engagement and more effectively handle user responses by getting clarification as needed.
Kaleal in view of Borovikov, Kasaba, Solomon, and Aggarwal may not explicitly teach calculate a degree of similarity index value, wherein the degree of similarity index value is a distance measurement between a data entry cluster and the virtual avatar; and determine a degree of similarity between the virtual avatar and the data entry cluster. However, Stewart ‘722 teaches a system and method for generating a user’s video avatar including using unsupervised machine learning algorithms such as clustering models and k-means clustering to generate the video/user avatar based on user data and data entry clusters wherein the algorithm includes calculate a degree of similarity index value, wherein the degree of similarity index value is a distance measurement between a data entry cluster and the virtual avatar; and determine a degree of similarity between the virtual avatar and the data entry cluster (Abstract; Paragraphs 0019, 0038, 0040-0041).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon and Aggarwal to incorporate the teachings of Stewart ‘722 by further applying the use of clustering/k-means clustering ML models for generating user avatars/models including calculating a similarity index between the user data/data entry and the user avatar, as the references of Kaleal, Borovikov, and Stewart ‘722 and the claimed invention are directed to generating avatars/models for users based on user data. It would have been obvious to one of ordinary skill to further modify the machine learning model of Borovikov to include using a clustering or K-means clustering algorithm to calculate a degree of similarity index value and determine a degree of similarity between the user avatar and data entry clusters/user data and applying the machine learning model to the avatar generation of Kaleal. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, and Aggarwal would include calculate a degree of similarity index value, wherein the degree of similarity index value is a distance measurement between a data entry cluster and the virtual avatar; and determine a degree of similarity between the virtual avatar and the data entry cluster. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Stewart ‘722 with Kaleal in view of Borovikov, Kasaba, Solomon, and Aggarwal’s system and method in order to improve user avatar similarity and increase relatedness between user avatar and user data (Stewart ‘722 Paragraph 0040-0041).
Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722 may not explicitly teach a chatbot comprising a decision tree, wherein the chatbot classifier is configured to train one or more nodes of the decision tree using the answer datum, and wherein the decision tree is constructed by recursively mapping execution results of a first subtree to a root node of a second subtree as part of a feedback loop configured to generate the educational response, wherein the decision tree further incorporate one or more decision criteria using an application programmer interface (API); wherein the chatbot is further configured to classify a student using a computing device configured to: analyze an input to the chatbot, wherein the input is associated with the student, based on identifying at least one of: a pre-determined factor, a keyword, composition, and clarity; and classify the student based on at least one of: identifying the keyword wherein the keyword is associated with at least one of: a student's skill, ability, and aptitude to fulfill an education requirement, and executing a chatbot input classifier to analyze the input, wherein the chatbot input classifier is configured to be trained using at least one of: a historical chatbot input and a datum; wherein generating the at least a second educational response further comprises: building a decision tree by recursively performing mapping of execution results output by a first subtree to root nodes of a second subtree and connecting the first subtree to the second subtree using the mapping; and generating the at least a second educational response using the decision tree. However, Stewart ‘418 teaches a system and method for using a chatbot to communicate questions and answers with a user wherein the chatbot responses may be based on a decision tree wherein the decision tree is built/generated by following relational identification including recursively performing mapping of results of one tree and/or subtree to nodes of another tree as part of a feedback loop such that one tree may be connected to another tree and generated responses are based on these decision trees and the recursive mapping of results and wherein the decision trees may incorporate decision criteria using an API (Paragraphs 0025, 0027-0030). Stewart ‘418 further teaches classifying candidates based on their chatbot inputs by analyzing the candidate’s/student’s text entries/chatbot input based on factors, key words, composition, and/or clarity and classifying the student based on identifying keywords associated with a candidate’s skill, ability, and aptitude to full a responsibility wherein the chatbot is trained using past (historical) chatbot inputs and/or other datum (Paragraphs 0026-0027, 0043).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722 to incorporate the teachings of Stewart ‘418 by programming the chatbot of Solomon and Aggarwal using decision trees that are recursively improved in order to generate the responses and chatbot input analysis, as, while Stewart ‘418 may be considered a different field of endeavor, Stewart ‘418 is pertinent to the problem of programing and using a chatbot and the use of decision trees for chatbots is well-known in the art and obvious to one of ordinary skill. It would have been obvious to one of ordinary skill to further modify the virtual avatar of Kaleal and the chatbot of Solomon and Aggarwal to include using a decision tree built using recursion to program the chatbot including using decision criteria using an API and decide the subsequent responses of the chatbot to user educational responses and using chatbot input analysis and classification to classify a user/student/candidate based on the user’s chatbot inputs by analyzing the input for keywords based on user factors and training the chatbot on historical data and user datum. One of ordinary skill in the art would have found the modification obvious as using a decision tree to improve the chatbot of Solomon and Aggarwal is use of a known technique to improve similar methods in the same way as decision trees are a known method of programming chatbots (see Aggarwal Paragraph 0072). Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722 would include comprising a decision tree, wherein the chatbot classifier is configured to train one or more nodes of the decision tree using the answer datum, and wherein the decision tree is constructed by recursively mapping execution results of a first subtree to a root node of a second subtree as part of a feedback loop configured to generate the educational response, wherein the decision tree further incorporate one or more decision criteria using an application programmer interface (API); wherein the chatbot is further configured to classify a student using a computing device configured to: analyze an input to the chatbot, wherein the input is associated with the student, based on identifying at least one of: a pre-determined factor, a keyword, composition, and clarity; and classify the student based on at least one of: identifying the keyword wherein the keyword is associated with at least one of: a student's skill, ability, and aptitude to fulfill an education requirement, and executing a chatbot input classifier to analyze the input, wherein the chatbot input classifier is configured to be trained using at least one of: a historical chatbot input and a datum; wherein generating the at least a second educational response further comprises: building a decision tree by recursively performing mapping of execution results output by a first subtree to root nodes of a second subtree and connecting the first subtree to the second subtree using the mapping; and generating the at least a second educational response using the decision tree. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Stewart ‘418 with Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722’s system and method in order to improve chatbot performance and communication with the user by generating connections between various data, iteratively improving the model, and providing subsequent questions and responses based on chatbot analysis and student classification (Stewart ‘418 Paragraphs 0027, 0029).
Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart ‘418 may not explicitly teach wherein the at least an educational response is categorized as positive or negative, wherein a positive education response is configured to generate a positive response animation. However, Gonzalez teaches an educational device including animated indicia which are responsive to a user input wherein the responses are classified as positive or negative responses based on if the user got an answer correct or incorrect and the positive responses including positive animations such as clapping (Col 2, line 52-61; Col 5, line 58 – Col 6, line 7).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart ‘418 to incorporate the teachings of Gonzalez by including positive and negative responses/reactions for the avatar of Kaleal, as, while Gonzalez is a physical animation, Gonzalez is pertinent to the problem of providing user feedback based on performance and providing positive and negative reinforcement for user performance is well-known in the art and obvious to one of ordinary skill. It would have been obvious to one of ordinary skill to further modify the virtual avatar of Kaleal to provide positive and negative feedback/reactions including a clapping animation based on user inputs. One of ordinary skill in the art would have found the modification obvious as positive and negative reinforcement are well-known within education and user interaction and modifying Kaleal in such a way would further reinforce user performance and improvement. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722, and Stewart ‘418 would include wherein the at least an educational response is categorized as positive or negative, wherein a positive education response is configured to generate a positive response animation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Gonzalez with Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart 418’s system and method in order to provide further feedback and reinforcement of user behavior and performance.
In regards to claims 3 and 13, Kaleal, as modified, further teaches wherein generating the virtual avatar comprises creating a digital representative for simulating one or more interactions in the extended reality space (Paragraph 0381 teaches the system can include 2D and/or 3D avatars including role avatars and user avatars as digital representations within an extended reality environment; per paragraph 0436, any of the particular features of the reference can be combined with any other features of other implementations).
In regards to claims 4 and 14, Kaleal, as modified, further teaches wherein instantiating the virtual avatar further comprises generating a plurality of rules linking educational responses to animations (Paragraphs 0077-0078, 0116-0117 teach the avatar actions and responses can be based on rules-based classification schemes include response rules for deviations (educational responses) that can be modified/adapted and/or newly generated).
In regards to claims 5 and 15, Kaleal, as modified, further teaches wherein generating plurality of rules further comprises: receiving a plurality of training examples correlating educational response data to animations (Paragraphs 0077, 0117, 0388 teach the avatar is trained using training data and modified/adapted based on user interactions over time wherein the avatar responses (animations) are based on the training including responses (educational responses) to deviations such that the guidance responses are connected to an avatar response/animation); and training a classifier using the plurality of training examples (Paragraphs 0123, 0388 teach the system can include training a classifier/machine learning model based on historic and current user data), wherein the classifier is configured to input an educational response and output a rule linking the educational response to an animation (Paragraphs 0078, 0116-0117, 0388 teach that the model can be trained and used to modify the rules and thereby the reactions of the avatar wherein the rule determine a deviation response (educational response) and thereby the avatar response (animation)).
In regards to claims 6 and 16, Kaleal, as modified, further teaches wherein the educational model is hosted in a decentralized platform (Paragraphs 0247, 0273 “a decentralized database”).
In regards to claims 7 and 17, Kaleal in view of Borovikov and Kasaba may not explicitly teach wherein receiving the input from the user by the educational model comprises the use of a chatbot. However, Solomon teaches a system and method for training a student using a virtual avatar to respond to user queries and asking users questions (abstract; paragraphs 0020, 0035) including wherein receiving the input from the user by the educational model comprises the use of a chatbot (Paragraphs 0032, 0042 teach the avatar can have a conversation with the user using chatbot methods).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov and Kasaba to incorporate the teachings of Solomon by coding the avatar of Kaleal to use chatbot conversational agents of Solomon to converse with the user, as both references and the claimed invention are directed to training platforms using virtual avatars. It would have been obvious to one of ordinary skill to use the techniques of conversational chatbots/AI/Machine learning to improve the avatars of Kaleal in the same way in order to better engage the users (Solomon Paragraph 0004) and make the avatar more robust (Solomon Paragraph 0042). Upon such modification, the method and system of Kaleal in view of Borovikov and Kasaba would include wherein receiving the input from the user by the educational model comprises the use of a chatbot. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Solomon with Kaleal in view of Borovikov and Kasaba’s system and method in order to improve user engagement and effectively convey educational material and interactions to the user.
In regards to claims 8 and 18, Kaleal in view of Borovikov and Kasaba may not explicitly teach wherein the chatbot is further configured to generate, using a chatbot classifier, at least a chatbot response as a function of the answer datum, wherein the chatbot response includes at least an additional question. However, Solomon further teaches wherein the chatbot is further configured to generate, using a chatbot classifier (Paragraphs 0020, 0033; “Learning manager function”), at least a chatbot response as a function of the answer datum (Paragraph 0035 teaches the avatar receives and recognizes student’s answers and can ask a clarification response/question such as ‘did you mean…A or B…?’), wherein the chatbot response includes at least an additional question (Paragraph 0035 teaches the avatar receives and recognizes student’s answers and can ask a clarification response/question such as ‘did you mean…A or B…?’).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov and Kasaba to incorporate the teachings of Solomon by coding the avatar of Kaleal to use chatbot conversational agents of Solomon to converse with the user, as both references and the claimed invention are directed to training platforms using virtual avatars. It would have been obvious to one of ordinary skill to use the techniques of conversational chatbots/AI/Machine learning to improve the avatars of Kaleal in the same way in order to better engage the users (Solomon Paragraph 0004) and make the avatar more robust (Solomon Paragraph 0042). One of ordinary skill in the art would modify Kaleal in view of Borovikov and Kasaba to include following up on user responses including responding to user inputs/answers with subsequent questions. Upon such modification, the method and system of Kaleal in view of Borovikov and Kasaba would include wherein the chatbot is further configured to generate, using a chatbot classifier, at least a chatbot response as a function of the answer datum, wherein the chatbot response includes at least an additional question. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Solomon with Kaleal in view of Borovikov and Kasaba’s system and method in order to improve user engagement and effectively convey educational material and interactions to the user.
In regards to claims 21 and 22, Kaleal, as modified, further teaches wherein the at least an entity data item further comprises an educational history of the user (Paragraph 0106; “education level”; Paragraphs 0133, 0143 teach the user data can include historical information such as user performance and completed routines/activities).
In regards to claims 23 and 24, Kaleal in view of Borovikov and Kasaba may not explicitly teach wherein the at least an input comprises a verbal response, and wherein the chatbot is configured to convert the verbal response to text. However, Solomon further teaches wherein the at least an input comprises a verbal response (Paragraphs 0031, 0035; “spoken query”), and wherein the chatbot is configured to convert the verbal response to text (Paragraphs 0031, 0035 teach the spoken query of the user is converted to text using speech recognition and text-to-speech software).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov and Kasaba to incorporate the teachings of Solomon by coding the avatar of Kaleal to use chatbot conversational agents of Solomon to converse with the user and convert user responses to text, as both references and the claimed invention are directed to training platforms using virtual avatars. It would have been obvious to one of ordinary skill to use the techniques of conversational chatbots/AI/Machine learning to improve the avatars of Kaleal in the same way in order to better engage the users (Solomon Paragraph 0004) and make the avatar more robust (Solomon Paragraph 0042). One of ordinary skill in the art would modify Kaleal in view of Borovikov and Kasaba to include following up on user responses including responding to user inputs/answers with subsequent questions as Kaleal teaches a user can converse with the avatar and other avatars as part of the system (see Kaleal paragraph 0376). Upon such modification, the method and system of Kaleal in view of Borovikov and Kasaba would include wherein the at least an input comprises a verbal response, and wherein the chatbot is configured to convert the verbal response to text. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Solomon with Kaleal in view of Borovikov and Kasaba’s system and method in order to improve user engagement and effectively convey educational material and interactions to the user.
With regard to claims 25 and 26, Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart ‘418 may not explicitly teach wherein the positive response animation comprises at least an animation of clapping hands. However, Gonzalez teaches an educational device including animated indicia which are responsive to a user input wherein the responses are classified as positive or negative responses based on if the user got an answer correct or incorrect and the positive responses including positive animations such as clapping (Col 2, line 52-61; Col 5, line 58 – Col 6, line 7).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart ‘418 to incorporate the teachings of Gonzalez by including positive and negative responses/reactions for the avatar of Kaleal, as, while Gonzalez is a physical animation, Gonzalez is pertinent to the problem of providing user feedback based on performance and providing positive and negative reinforcement for user performance is well-known in the art and obvious to one of ordinary skill. It would have been obvious to one of ordinary skill to further modify the virtual avatar of Kaleal to provide positive and negative feedback/reactions including a clapping animation based on user inputs. One of ordinary skill in the art would have found the modification obvious as positive and negative reinforcement are well-known within education and user interaction and modifying Kaleal in such a way would further reinforce user performance and improvement. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, and Stewart ‘722, and Stewart ‘418 would include wherein the positive response animation comprises at least an animation of clapping hands. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Gonzalez with Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, and Stewart 418’s system and method in order to provide further feedback and reinforcement of user behavior and performance.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez as applied to claims 8 and 18 above, and further in view of Rajagopal et al. (US PGPub 20200227026), hereinafter referred to as Rajagopal.
In regards to claim 9, Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez may not explicitly teach wherein training a chatbot classifier comprises: training the chatbot classifier using training data correlating answer datum to question datum; and retraining the chatbot classifier using updated training data comprising the answer datum. However, Rajagopal teaches a method and system for using and training a question and answer engine including virtual agents or chatbots (Abstract; Paragraphs 0003, 0027) wherein training a chatbot classifier comprises: training the chatbot classifier using training data correlating answer datum to question datum (Paragraphs 0010, 0049, 0054 teach the system includes training the system to classify questions and corresponding answers); and retraining the chatbot classifier using updated training data comprising the answer datum (Abstract; Paragraphs 0008, 0027, 0035 teaches the system can continuously improve the system by analyzing new utterances, questions, and answers, and updating the model (retraining) to include the updated data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez to incorporate the teachings of Rajagopal by applying the technique of updating a QNA engine/chatbot based on subsequent user utterances of Rajagopal to the avatar/chatbot of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez. While the prior arts are not in the exact same field of endeavor, the arts are considered equivalent as the art is directed to methods of communicating with a user using a virtual avatar/agent including questions and answers. It would have been obvious to one of ordinary skill to apply the techniques of the technique of updating a QNA engine/chatbot based on subsequent user utterances of Rajagopal to the similar chatbot of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez in order to improve the system in the same way to continuously improve the system performance and improve user engagement and interaction. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez would include teach wherein training a chatbot classifier comprises: training the chatbot classifier using training data correlating answer datum to question datum; and retraining the chatbot classifier using updated training data comprising the answer datum. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Rajagopal with Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez’s system and method in order to improve user engagement and improve system performance (Rajagopal Paragraphs 0009, 0058).
In regards to claim 19, Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez may not explicitly teach wherein the user responses to the chatbot are input into the chatbot input classifier creating a feedback loop. However, Rajagopal further teaches wherein the user responses to the chatbot are input into the chatbot input classifier creating a feedback loop (Abstract; Paragraphs 0008, 0027, 0035 teaches the system can continuously improve the system by analyzing new utterances, questions, and answers, and updating the model (retraining) to include the updated data, which is interpreted as a feedback loop as the system would thereby continuously give new responses and receive new utterances to further update the model).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez to incorporate the teachings of Rajagopal by applying the technique of updating a QNA engine/chatbot based on subsequent user utterances of Rajagopal to the avatar/chatbot of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez. While the prior arts are not in the exact same field of endeavor, the arts are considered equivalent as the art is directed to methods of communicating with a user using a virtual avatar/agent including questions and answers. It would have been obvious to one of ordinary skill to apply the techniques of the technique of updating a QNA engine/chatbot based on subsequent user utterances of Rajagopal to the similar chatbot of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez in order to improve the system in the same way to continuously improve the system performance and improve user engagement and interaction. Upon such modification, the method and system of Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez would include teach wherein the user responses to the chatbot are input into the chatbot input classifier creating a feedback loop. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Rajagopal with Kaleal in view of Borovikov, Kasaba, Solomon, Aggarwal, Stewart ‘722, Stewart ‘418, and Gonzalez’s system and method in order to improve user engagement and improve system performance (Rajagopal Paragraphs 0009, 0058).
Response to Arguments
Applicant’s arguments, see Remarks, filed November 4, 2025, with respect to the rejection(s) of claim(s) 1, 3-6, 8-9, 11, 13-16, 18-19, and 21-26 under 35 U.S.C. 103 have been fully considered, but they are not persuasive. Applicant repeatedly references an agreement reached during the interview held on October 21, 2025. No such agreement was reached as Examiner stated in the interview and in this action that the claim amendments do not overcome the rejection as the amended limitations are taught by Stewart ‘418. Further, the current amendments differ from those presented during the interview. As stated during the interview and discussed above, the amended limitations are taught almost verbatim by Stewart ‘418 as further evidenced by paragraphs 13-15 of the instant application, which support the amended limitations, being almost verbatim with paragraphs 26-28 of Stewart ‘418 wherein the only notable difference is the user being a candidate in Stewart ‘418 rather than the student of the instant application. Such a difference, a user being a candidate versus a student, would have been obvious to one of ordinary skill in the art which is why the teachings of Stewart ‘418 are used to modify the combination of prior art as discussed above. Therefore, the claims stand rejected under 35 U.S.C. 103.
Conclusion
Accordingly, claims 1, 3-6, 8-9, 11, 13-16, 18-19, and 21-26 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715