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
In response to the amendment filed 8/7/2025; claims 5 – 12 are pending; claims 1 – 4 have been cancelled.
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 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 5 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Solomon et al. (US 2017/0206797 A1) in view of Rajagopal et al. (US 2020/0227026 A1) and Alcorn et al. (US 2004/0153508 A1).
Re claim 5:
5. A computer-implemented method for providing a virtual education platform and virtual voice coach using a web-server, the web server configured to communicatively interconnect to one or more student devices, and to one or more instructor devices over the internet, the web server comprises a memory having instructions stored thereon, and a processor configured to execute the instructions on the memory causing the web server to perform the method (Solomon, Abstract; [0031], “internet connected computer or mobile device ... database”; [0033], “reside on multiple servers ... the internet, an intranet”), the method comprising:
deploying by the web-server, an instructor version of a programmable virtual voice coach application on an instructor device (Solomon, [0023], “authoring manager”; [0022], “author the anticipated queries and responses”; [0058], “This interface can be utilized by teachers”; [0059], “A system interface will also be provided to teachers to input to the knowledge base learning goal scores for queries and to review their student’s progress”; [0032], “The course teacher/instructor can access the student profile database to obtain a formative assessment of the student’s progress and by the students, for feedback on their progress”);
receiving by the web-server one or more anticipated questions from an instructor device via the instructor version of the virtual voice coach application (Solomon, [0023], “authoring manager”; [0022], “author the anticipated queries and responses”; [0058], “This interface can be utilized by teachers”; [0059], “A system interface will also be provided to teachers to input to the knowledge base learning goal scores for queries and to review their student’s progress”; [0032], “The course teacher/instructor can access the student profile database to obtain a formative assessment of the student’s progress and by the students, for feedback on their progress”);
receiving by the web-server one or more answers corresponding to the one or more anticipated questions from the instructor device via the instructor version of the virtual voice coach application (Solomon, [0023], “authoring manager”; [0022], “author the anticipated queries and responses”; [0058], “This interface can be utilized by teachers”; [0059], “A system interface will also be provided to teachers to input to the knowledge base learning goal scores for queries and to review their student’s progress”; [0032], “The course teacher/instructor can access the student profile database to obtain a formative assessment of the student’s progress and by the students, for feedback on their progress”);
storing by the web-server the one or more anticipated questions and the one or more answers on an instructor database (Solomon, [0016], “a database of anticipated queries with pre-scripted responses”; [0030]);
deploying by the web-server a student version of a programmable virtual voice coach application on a student device (Solomon, [0033], “by providing answers to a student's questions”; [0035], “find the first or best match to the student's query”; [0044]; fig. 4, “STUDENT logged Into XA SYSTEM”; [0056], “responses that the student should receive from the Expert Avatar”; [0031], “on the display screen of a student's internet connected computer or mobile device”; [0018], “a software-based educational platform delivered on a student's computer or mobile device”);
receiving by the web-server one or more student questions from the student device via the instructor version of the programmable virtual voice coach application (Solomon, Abstract, “queries posed by the student”; [0014], “a student's questions on the subject matter”);
determining by the web-server a similarity between each of the one or more student questions to each of the one or more anticipated questions (Solomon, [0016], “The Expert Avatar provides responses by matching a student's question to a database of anticipated queries”; [0044], “a match to a query in the knowledge base is found with a high confidence value, the artificial intelligence system delivers the knowledge base's corresponding response … If no high confidence value match is found, a request to rephrase the question”; [0023], “the authoring manager will expand existing and new queries and responses to allow for multiple variations of the same or similar queries to generate the same or similar responses”; [0035], “find the first or best match to the student's query”; a level of similarity – confidence value; “first or best” match suggest that is a level of similarity in matching);
determining by the web-server a most similar anticipated question of the one or more anticipated questions based the similarity between each of the one or more student questions to each of the one or more anticipated questions (Solomon, [0031], “The system includes software to match student questions to those in the primary knowledge base”; [0033], “by providing answers to a student's questions”; [0035], “find the first or best match to the student's query”; [0044]);
retrieving by the web-server the one or more answers from the instructor database based on the most similar anticipated question (Solomon, [0033], “by providing answers to a student's questions”; [0035], “find the first or best match to the student's query”; [0044]);
returning by the web-server the one or more answers to the student device via the student version of the virtual voice coach application (Solomon, [0033], “by providing answers to a student's questions”; [0035], “find the first or best match to the student's query”; [0044]; fig. 4, “STUDENT logged Into XA SYSTEM”; [0018], “a software-based educational platform delivered on a student's computer or mobile device”); and
causing by the web-server the student device to play the one or more answers via the student version of the virtual voice coach application (Solomon, [0056], “responses that the student should receive from the Expert Avatar”; [0031], “on the display screen of a student's internet connected computer or mobile device”; [0044]; fig. 4, “STUDENT logged Into XA SYSTEM”; [0018], “a software-based educational platform delivered on a student's computer or mobile device”).
Solomon teaches determining similarity between the at least one student question to the at least one anticipated question; but does not explicitly disclose a level of similarity. Rajagopal teaches a system and method for using, training, building, and managing a question and answer engine to automatically generate responses.
Rajagopal further teaches determining a level of similarity between each of the one or more student
questions to each of the one or more anticipated questions; determining a most similar anticipated question of the one or more anticipated questions based on the level of similarity between each of the one or more student questions to each of the one or more anticipated questions (Rajagopal, Abstract, “New user utterances may be analyzed to identify questions, with a cluster predictor identifying the corresponding topic and subtopics for each question, and a similarity scorer may identify the closest known question for the user's question to a recommender as an answer”). Therefore, in view of Rajagopal, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method, system and computer program described in Solomon, by providing the similarity score as taught by Rajagopal, since a recommender may use the similarity scoring module to identify the closest match for the category and the subcategory of the user's question (Rajagopal, [0041]).
Solomon does not explicitly disclose wherein the student version of the programmable virtual voice coach application comprises different access permissions than the instructor version of the programmable virtual voice coach application.
Alcorn et al. (US 2004/0153508 A1) teaches systems and methods for the exchange of information between instructors and students in an educational context (Alcorn, Abstract). Alcorn teaches wherein the student version of the programmable virtual voice coach application comprises different access permissions than the instructor version of the programmable virtual voice coach application (Alcorn, [0027], “further object of the invention is to provide a system that allows multiple types of users to access the features of the system as a function of their predefined role within the framework of the system, such as, a student, a teacher, or an administrator”; [0038], “he user level associated with a student user, an instructor level is associated with an instructor user, and an administrator level is associated with an administrator user. However, multiple levels may be associated particular users. For example an instructor of one course may also be a student in another course”; [0039], “The instructor user is provided with an access level to enable the creation and editing of a plurality of course files associated with a course”; [0040], “The student user is provided with an access level to enable reading of course files associated with a course”). Therefore, in view of Alcorn, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Solomon, by providing multiple user types as taught by Alcorn, since Users who may have one or several roles, such as a student, instructor, teaching assistant (TA), or administrator, access and interact with system via web browser (Alcorn, [0142]) and some users have role who can view the same content as a user, but cannot modify or interact with content (Alcorn, [0236]) and teacher user can create and edit courses.
Re claims 2, 4, 6:
2. The method of claim 2, wherein the at least one answer transmitted to the student computing device and received by the student presented in a unique teaching style corresponding to the instructor. 4. The system of claim 3, wherein the instructor computing device is further configured to reproduce the at least one answer to in a unique teaching style corresponding to the instructor. 6. The computer-implemented method of claim 5, wherein the one or more answers comprises a unique teaching style of an instructor providing the one or more answers utilizing the instructor device (Solomon, [0023], “authoring manager”; [0022], “author the anticipated queries and responses”; 0051], “The Expert Avatars for a given subject can be chosen from among the discoverers, contributors to the subject, other subject experts, or fictional characters including animals or robots.”; [0052], “The Expert Avatar can be created from photos or portraits of the subject using software which maps the photo or portrait onto a 3D head model”; [0020], “the anticipated queries and responses for the Expert Avatar, the primary knowledge base will include a learning manager database which provides the learning goals for the courses being given by the teacher/ instructor”; teacher and instructor create queries and responses in his/her own style).
Re claims 7 – 8:
7. The computer-implemented method of claim 5, further comprising, upon a determination that the level of similarity does not meet a threshold similarity, retrieving an alternate different instructor answer stored on an interconnected instructor database. 8. The computer-implemented method of claim 5, further comprising receiving a request for an alternative explanation from the one or more student devices and returning an alternative explanation to the one or more student devices (Rajagopal, [0040], “a selected score threshold is met or exceeded at step 562, the answer is retrieved at step 564 from the master question and answer database 105 and provided to the customer as the answer. If instead the selected score threshold is not met at step 572, but the category and subcategory are identified, the most popular or frequently asked questions to the subcategory may be given in response at step 574”; [0041]; fig. 5B).
Re claim 9:
9. The computer-implemented method of claim 5, the method further comprising, prior to returning the answer to the student device, converting the one or answers to a preferred language of the one or more students (Solomon, [0034], “responses to queries spoken or otherwise asked by the student are delivered verbally, visually, or otherwise by the Expert Avatar … visual communication could be text, gestures, graphics, videos, or sign language”).
Re claims 10 – 11:
10. The computer-implemented method of claim 5, the method further comprising, upon a determination that the level of similarity does not meet a threshold similarity, storing the at least one student question into the instructor database as an unanticipated student question. 11. The computer-implemented method of claim 10, the method further comprising sending a notification to the instructor device that a new unanticipated student question has been entered into the instructor database (Solomon, [0044], “If no high confidence value match is found, a request to rephrase the question is made. If this fails to resolve the query, a response is given that the Expert Avatar does not have a response for that query”; [0031], “the primary knowledge base for a matching question. The corresponding response is given if a high confidence match is achieved. If no match is achieved with the backup artificial intelligence”; [0041], “or example, entering the words “why is Albert” into the Yahoo search engine resulted in seven usable questions related to Albert Einstein that are frequently asked. Other sources include standard lists such as when “were you born?” or “where do you live?” which would be asked for any Expert Avatar, crowd source questions solicited on social media and author inputs. As the Expert Avatar is used, and unanticipated questions are received from students, these unanticipated questions would be added to the list of questions in the knowledge base. The authoring manager would also identify responses to the unanticipated questions using search engines such as Google or Watson, for addition to the knowledge base. The questions would then be analyzed to associate a topic and key words. Then natural language processing software would be employed to analyze the initial list to create semantic equivalent questions, basic intents and questions incorporating synonyms and equivalent terms. These operations would create an updated list for automatic matching optimization and finally for the primary author to approve”).
Re claim 12:
12. The computer-implemented method of claim 11, the method further comprising sending a notification to the student device that the new unanticipated student question has been answered once a corresponding answer to the new unanticipated student question has been received from the instructor device and stored in the instructor database (Solomon, [0044], “If no high confidence value match is found, a request to rephrase the question is made. If this fails to resolve the query, a response is given that the Expert Avatar does not have a response for that query”; [0031], “the primary knowledge base for a matching question. The corresponding response is given if a high confidence match is achieved. If no match is achieved with the backup artificial intelligence”; [0041], “or example, entering the words “why is Albert” into the Yahoo search engine resulted in seven usable questions related to Albert Einstein that are frequently asked. Other sources include standard lists such as when “were you born?” or “where do you live?” which would be asked for any Expert Avatar, crowd source questions solicited on social media and author inputs. As the Expert Avatar is used, and unanticipated questions are received from students, these unanticipated questions would be added to the list of questions in the knowledge base. The authoring manager would also identify responses to the unanticipated questions using search engines such as Google or Watson, for addition to the knowledge base. The questions would then be analyzed to associate a topic and key words. Then natural language processing software would be employed to analyze the initial list to create semantic equivalent questions, basic intents and questions incorporating synonyms and equivalent terms. These operations would create an updated list for automatic matching optimization and finally for the primary author to approve.”).
Response to Arguments
Applicant’s arguments, see pages 2 - 10, filed 8/7/2025, with respect to claims 5 - 12 have been fully considered and are persuasive. The Claim Rejections under 35 USC § 101 of claims 25 - 12 have been withdrawn.
Applicant’s arguments with respect to claim(s) 5 - 12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues: Solomon discloses "an interactive avatar representing a subject matter expert in a particular field on a student's device, where the avatar can respond to queries posed by the student, and accompany the response with additional supporting information" (Solomon, Abstract). However, Solomon fails to disclose the novel element of a "student version" of an application, and an "instructor version" of the application, as taught by Applicant's disclosure and presently recited in the amended claims.
The Examiner disagrees. Solomon teaches student user / role and instructor user / role. A student user has his/her own login interface and features (Solomon, [0056], “responses that the student should receive from the Expert Avatar”; [0031], “on the display screen of a student's internet connected computer or mobile device”; [0044]; fig. 4, “STUDENT logged Into XA SYSTEM”; [0018], “a software-based educational platform delivered on a student's computer or mobile device) and a course teacher/instructor can access the student profile database to obtain a formative assessment of the student’s progress and by the students, for feedback on their progress (Solomon, [0023], “authoring manager”; [0022], “author the anticipated queries and responses”; [0058], “This interface can be utilized by teachers”; [0059], “A system interface will also be provided to teachers to input to the knowledge base learning goal scores for queries and to review their student’s progress”).
Conclusion
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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/JACK YIP/Primary Examiner, Art Unit 3715