DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement (IDS)
3. Regarding the IDS filed on 12/22/2025, the document, titled “Integrating LLMs into Database Systems Education”, is not considered since the publication date listed on the IDS form does not appear to be correct.
Specification
4. The original specification is objected to at least for the following reason: the phrase, “wherein the is based on an approved or rewritten answer of the pairs of artificial questions and related approved or rewritten answers” (see each of paragraphs [0030] and [0112] of the specification, emphasis added), appears to be incomplete since the term “the is based on” does not positively specify the element (if any) being considered.
Applicant is further advised to review each of the paragraphs in the specification and make appropriate corrections if additional discrepancies are discovered.
Claim objections
5. Claims 1-13 are objected to for the following informalities:
- Regarding claims 1-6, claim 1 recites, “generate a new question or answer in the dialogue to be answered or commented by the student based on at least the first data and the second data; and and a personalization database comprising personal attributes of the student” (emphasis added).
However, the term, “and and”, appears to be a typographical error for --and--. Accordingly, an appropriate correction is required.
- Regarding claims 7-13, claim 7 recites, “select a problem generated by the AI bot
dependent on the actual knowledge state the student” (emphasis added).
However, the term “the actual knowledge state the student” appears to be a typographical error for -- the actual knowledge state of the student -- (emphasis added). Accordingly, an appropriate correction is required.
- In addition, regarding claim 11, the term, “an final exam”, is considered to be a typographical error for --a final exam--; and thus, appropriate correction is required.
Applicant is further advised to evaluate each of the claims and make appropriate corrections if additional discrepancies are discovered.
Claim Rejections - 35 USC § 101
6. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
35 U.S.C.101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
● Claims 1-17 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 7 and 14 as the representative claim, the following claimed limitations recite an abstract idea:
I. Claim 1:
[collect] first data of a course material to be studied by [a] student for a specific course;
[collect] second data of exam relevant questions related to the course material to be studied by the student, a predetermined percentage of which is required to be answered by the student to pass an exam related to the course;
carry out an interactive dialogue with the student about the course material to be studied by the student and regarding the exam relevant questions, wherein the interactive dialogue includes a first answer given in response to a first question;
obtain first data; obtain second data; and use the first answer given by the student in the dialogue to generate a new question or answer in the dialogue to be answered or commented by the student based on at least the first data and the second data; c
conduct] the interactive dialogue based on third data, such that the interactive dialogue deals with topics which are relevant to [personal] attributes [of the student] and covers the course material to be studied by the student and takes the exam relevant questions into account.
II. Claim 7:
[collect] data of a course material to be studied by the student for a specific course;
use the data to set up multiple problems to be solved in an exam;
determine the actual knowledge state the student;
select a problem generated dependent on the actual knowledge state [of] the student;
give quantitative feedback to the student in relation to his input solution of the problem;
give qualitative feedback to the student in relation to his input solution of the problem;
obtain a solution input from the student and provide quantitative feedback and qualitative feedback in response.
III. Claim 14:
[collect] data of a course material to be studied by the student for the specific course;
obtain the data; use the data to generate questions which may be potentially based on content of the course material;
use the data to generate a respective expected responses to each question based upon the content of the course material; and
create a data set where each question is assigned to a respective expected response;
the answers to the questions in the data set are reviewed by a human person having knowledge in the field of the specific course and approved or rewritten, so that the dataset comprises pairs of questions and related approved or rewritten answers;
analyze a question input from the student whether it has a similarity equal to or above a predetermined amount of similarity to at least one of the artificial questions;
generate, if the similarity is equal to or above the predetermined amount of similarity to at least one of the artificial questions, a response to the question, wherein the is based on an approved or rewritten answer of the pairs of questions and related approved or rewritten answers, which corresponding question has the highest or at least a predetermined similarity
provide the answer to the student.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, and/or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)).
I. Considering claim 1:
Regarding the abstract idea group certain methods of organizing human activity, each of claims 1-6 correspond to managing personal behavior. For instance, while considering (i) information regarding course material that the student is studying for a specific course, and (ii) information regarding exam relevant questions related to the course material, an interactive dialog is conducted with the student; wherein the student provides an answer in response to a a question during interaction; and wherein, based on the answer that the student is providing, the student is presented with a new question or answer during the dialog, so that the student provides further answer or comment based on information regarding the course material and the exam relevant questions; and furthermore, such interaction proceeds based on the student’s personal attributes; so that the dialog is further conducted based on topics that are relevant to the student’s personal attributes, etc.
Similarly, given the limitations that recite the process of: carrying out an interactive dialog with a student about a course material and exam relevant questions; presenting—based on the student’s answer to a question—a new question or answer, which the student is required to provide answer or comment based on information regarding course material and exam relevant questions; presenting the interactive dialog based on the student’s personal attribute, etc., claims 1-6 also correspond to the group mental processes, which encompasses concepts that can be performed in the human mind and/or using a pen and paper (e.g., an evaluation, an observation, a judgment, etc.).
II. Considering claim 7:
Regarding the abstract idea group certain methods of organizing human activity, each of claims 7-13 correspond to managing personal behavior. For instance, based on the actual knowledge state of a student, a problem is selected and presented to the student; and furthermore, based on a solution that the student is providing to the problem, a qualitative and quantitative feedback is presented to the user, etc.
Similarly, given the limitations that recite the concept of: determining the actual knowledge of a student; selecting a problem deepening on the actual knowledge state of the student; giving quantitative feedback to the student based on the student’s solution to the problem; giving qualitative feedback to the student based on the student’s solution to the problem, etc., claims 7-13 also correspond the abstract idea group mental processes, which encompasses concepts that can be performed in the human mind and/or using a pen and paper (e.g., an evaluation, an observation, a judgment, etc.).
III. Considering claim 14:
Regarding the abstract idea group certain methods of organizing human activity, each of claims 14-17 correspond to managing personal behavior. For instance, questions that are potentially based on content for a course material, including respective expected responses to each question, are drafted as a set; and a subject matter expert (e.g., a teacher/instructor) reviews and approves or rewrites the answers, so that a certified set that comprises questions and related approved/rewritten answers is created; and accordingly, as a student is providing a question, based on the degree of similarity of the student’s question to at least one of the questions in the certified set above, an approved/rewritten answer, which corresponds to the question with the highest/predetermined degree of similarity, is selected and presented the student, etc.
Similarly, given the limitations that recite the process of: generating questions that may be based on content of a course material; generating respective expected responses/answers to each question based on content of the course material; reviewing the answers to the questions and approving or rewriting the answers; analyzing whether a student’s question is similar to at least one of the generated questions; generating a response to the student’s question if the similarity is equal to or above a predetermined amount of similarity to at least one of the generated questions, etc., claims 7-13 also correspond the abstract idea group mental processes, which encompasses concepts that can be performed in the human mind and/or using a pen and paper (e.g., an evaluation, an observation, a judgment, etc.).
(Step 2A) [Wingdings font/0xE0] Prong Two:
The current claims recite additional element(s), wherein a computer-based system that executes artificial intelligence model is utilized to facilitate the recited functions/steps regarding: storing data (e.g. “a course database comprising . . . data of a course material . . . a question database comprising second data of exam relevant questions . . . percentage of which is required to be answered by the student to pass an exam related to the course”, “a personalization database comprising personal attributes of the student”); conducting input/output interaction (e.g., “a dialogue module which is configured to carry out an interactive dialogue . . . the interactive dialogue includes a first answer given in response to a first question”); analyzing collected/stored data using an algorithm and generate one or more relevant results (e.g., “an AI Bot module which is configured to: obtain first data from the course database; obtain second data from the question database . . . generate a new question . . . based on at least the first data and the second data”, “the AI Bot module generates the interactive dialogue . . . covers the course material . . . and takes the exam relevant questions into account”; “an AI Bot module which is configured to use the data to set up multiple problems . . . a knowledge state module which is configured to determine the actual knowledge state the student; a problem selection module which is configured to select a problem . . . a quantitative feedback module configured to give a quantitative feedback . . . a qualitative feedback module adapted to give a qualitative feedback . . . obtain a solution input from the student and provide quantitative feedback and qualitative feedback in response”; “an AI Bot module which is configured to: obtain the data; use the data to generate artificial questions . . . use the data to generate a respective expected responses . . . create a data set where each artificial question is assigned to a respective expected response . . . the answers to the questions in the data set are reviewed by a human . . . so that the dataset comprises pairs of artificial questions and related approved or rewritten answers; a paraphrase detector module . . . analyze a question input from the student . . . a response generation module which generates, if the similarity is equal to or above the predetermined amount of similarity to at least one of the artificial questions, a response to the question . . . the system in configured to provide the answer to the student”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a patent-eligible practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Accordingly, when each of the claims is considered as a whole, the additional element(s) fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims, when considered as a whole, recites an element—or a combination of elements—directed to an inventive concept.
Note also that the utilization of the conventional computer/network technology to facilitate the presentation pertinent content/information to one or more users, including the process of implementing one or more artificial intelligence-based chatbots, which facilitate—via one or more dialogues—the presentation of pertinent educational materials to the user (e.g. a student), and/or the evaluation of the user’s skills with respect to one or more course materials, etc., is already directed to a well-understood, routine, conventional activity in the art (e.g., see US 2018/0130156; US 2017/0206797, etc.).
It is further worth noting—per the original disclosure—that the claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, per the original specification, the claimed interactive dialog is facilitated by a system that implements one or more commercially available conventional computing devices (e.g., a personal computer, a cellphone, etc.); and such computing device(s) communicates—over the conventional communication network (e.g., the Internet)—with one or more online servers (e.g., see [0128]; [0133], etc.).
In addition, the original specification already admits that the process of setting one or more computers with an artificial AI Bot is generally known; and thus, the skilled artisan can implement the software configuration using his/her technical skills ([0135]).
The observations above confirm that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-6, 8-13 and 15-17). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C.112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C.112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
● Claims 8-17 are rejected under 35 U.S.C.112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
(a) Regarding claims 8-13, claim 8 recites, “the system is configured so that the problems are selected by a human person having knowledge in the file of the specific course” (emphasis added).
However, given the context of the claim, it is unclear what the term, “knowledge in the file of the specific course”, is implying. In particular, it is unclear whether the above is intended to mean, --knowledge in field of the specific course--.
Note that claims 9-13 are also subjected to the above deficiency given their direct or indirect dependency on claim 8.
(b) Regarding claims 14-17, claim 14 recites, “wherein the answers to the questions in the data set are reviewed by a human person” (emphasis added).
However, there is insufficient antecedent basis for the term “the answers” in the claim.
(c) Claim 14 further recites, “wherein the is based on an approved or rewritten answer of the pairs of artificial questions and related approved or rewritten answers” (emphasis added).
However, it is unclear what the term “wherein the is based on” is implying (e.g., it is not clear whether the above is intended to say, --wherein the response is based on--, emphasis added). Accordingly, claims 14-17 are further ambiguous due to the above discrepancy.
Applicant is further advised to evaluate each of the current clams and make appropriate corrections if additional discrepancies are discovered.
35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph
8. The following is a quotation of 35 U.S.C.112(f):
(f) ELEMENT IN CLAIM FOR A COMBINATION.—An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C.112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
● Claims 1-17 invoke 35 U.S.C.112(f) or pre AIA 35 U.S.C.112, sixth paragraph for the following reasons.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a dialog module”, “an AI BOT module”, “a self-improvement module”, “a derail-detection module”, “a grading module”, “a knowledge state module”, “a problem selection module”, “a quantitative feedback module”, “a qualitative feedback module”, “a knowledge state monitoring and matching module”, “a paraphrase detector module”, “a response generation module”, “a content filter module”, etc.
Accordingly, each of the modules above is considered to be computer program that a processor executes since the specification describes that “[a] module in this sense is a building block of a software system which represents a functionally closed unit and provides a specific service” (see [0065], emphasis added).
Claim Rejections - 35 USC § 103
9. 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Note that the one or more citations (paragraphs or columns) presented in this office action regarding the teaching of a cited reference(s) are exemplary only. Accordingly, such citation(s) are not intended to limit/restrict the teaching of the reference(s) to the cited portion(s) only. Applicant is required to evaluate the entire disclosure of each reference; such as additional portions that teach or suggest the claimed limitations.
● Claims 7-10 are rejected under 35 U.S.C.103 as being unpatentable over Solomon 2017/0206797 in view of Lan 2016/0171902.
Regarding claim 7, Solomon teaches the following claimed limitations: a system for training a student comprising: a course database comprising data of a course material to be studied by the student for a specific course ([0003]; [0019] lines 1-9; [0033] lines 23-26: e.g., a system/method for teaching courses consisting of lessons; wherein the system implements one or more knowledge bases—such as, a knowledge base populated with subject of forces and motion that Isac Newton discovered, etc.); an AI Bot module which is configured to use the data to set up multiple problems to be solved in an exam ([0032] lines 1-10; 0033] lines 1-22: e.g., the system incorporates an Expert Avatar, which implements an artificial intelligence; and wherein the Expert Avatar creates anticipated queries and responses, and/or questions for assessing the student’s understanding the subject that the student is learning. Accordingly, such queries/questions correspond to the multiple problems to be solved in an exam); a knowledge state module which is configured to determine the actual knowledge state [of] the student; a problem selection module which is configured to select a problem generated by the AI bot dependent on the actual knowledge state the student ([0020]; [0033] lines 26-31; [0049]; [0050]; [0056]: e.g., the system already incorporates at least a “Student Profile DB”, see FIG 1, which stores the student’s grade; and furthermore, the Expert Avatar interacts with a learning management function that tracks and determines, based on data related to the user, the student’s progress regarding a learning goal; and thereby, identifies appropriate queries/questions for the student, etc. Thus, the process of tracking or determining the student’s progress indicates the implementation of knowledge state module, which is utilized to determine the actual knowledge state of the student; and the quires/questions, which the system identifies for the student based on the student’s progress, corresponds to the process of selecting a problem generated by the AI bot dependent on the actual knowledge state the student); a qualitative feedback module adapted to give a qualitative feedback to the student in relation to his input solution of the problem ([0033] lines 33-34: e.g., besides (i) testing the student regarding the student’s understanding about the subject and (ii) tracking the student’s achievement of the learning goals, the Expert Avatar with the learning management also provides the student with feedback—such as, an assessment of the student’s progress in achieving the goals, etc. The above indicates the implementation of a qualitative feedback module, which is already adapted to give qualitative feedback to the student in relation to his input solution of the problem); wherein the system is configured to obtain a solution input from the student and provide qualitative feedback in response ([0033] lines 33-34: e.g., as already pointed out above, the system provides, based on testing the student’s understanding regarding the subject, feedback regarding the student’s progress in achieving learning goals; and wherein, such feedback is presented to the student and the instructor. Thus, the system is already configured to obtain a solution input from the student and provide qualitative feedback in response).
Solomon does not expressly describe a quantitative feedback module configured to give quantitative feedback to the student in relation to his input solution of the problem; and further providing the quantitative feedback.
However, Solomon already stores—in the student’s profile database—the results of the questions, which the Expert Avatar asked the student when judging the student’s understanding (see [0032] lines 1-10); and furthermore, Solomon implements a scoring scheme that scores the student’s progress in (see [0050).
In addition, Lan discloses an automatic grading system/method (see [0003]); wherein the system allows an instructor to select one or more desired questions or problems and draft one or more answers/solutions to each of the questions/problems ([0044] to [0047]); and wherein, the system implements an algorithm(s) that determines a corresponding score—such as a partial score or a full score—to a solution that the student is providing; and furthermore, besides presenting the score to the student, the system further provides—based on assessing the student’s solution—the student with relevant feedback; such as, explaining the location of the student’s error and/or the correct expression, etc. ([0057]; [0060]; [0220] to [0224]).
Accordingly, given the above teaching, 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 invention of in view of Solomon in view of Lan; for example, by providing an option for the instructor(s) to select/draft one or more desired questions/problems to be solved, wherein the instructor also provides one or more relevant answers/solutions applicable to each of the questions/problems; and the system’s algorithm(s) is also upgraded; so that, besides evaluating each of the answers/solutions that the student is providing in response to each question/problem being presented to the student, the Expert Avatar also determines a corresponding score (e.g., a partial score, a full score, etc.) to each student’s answer/solution based on the degree of accuracy of the answer/solution that the student is providing; and subsequently presents to the student (including the instructor if needed) a more comprehensive feedback, which includes the score that the student has achieved with respect to each of his/her answers/solutions, along with relevant explanation regarding some issues (if any) noted regarding each of the student’s answers/solutions—such as, an explanation that indicates the location where the student made the error, including the expected correct answer/solution, etc., so that the student would have a better chance to easily improve his/her skills based on such comprehensive feedback he/she is getting from the system.
Regarding claim 8, Solomon in view of Lan teaches the claimed limitations as discussed above per claim 7.
The limitation, “the problems are selected by a human person having knowledge in the file of the specific course”, is already addressed per the modification discussed with respect to claim 7. In particular, per the modified system discussed above, the instructor—i.e., a person having knowledge in the field of the course—already selects one or more of the questions/problems.
Regarding claim 9, Solomon in view of Lan teaches the claimed limitations as discussed above per claim 8.
Here also the limitation, “the quantitative feedback is a score given to the solution of the student and the AI Bot module is configured to take the selected problem, the solution input by the student, a reference answer and the maximum score for solving said problem into account”, is already addressed per the modification discussed above with respect to claim 7 (or claim 8). For instance, when the Expert Avatar (i.e., the AI Bot) is providing a partial score—as opposed to a full score—to the student’s solution, the Expert Avatar already recognizes (i) which question/problem that the student is answering/solving and also (ii) the student’s specific answer/solution (e.g., the answer/solution that caused the partial score). Of course, awarding only a partial score to the student’s answer/solution also indicates that the algorithm already (iii) compares the student’s answer/solution to a correct (i.e., a reference) answer/solution (e.g., to determine the value of the partial score based on the deviation of the student’s answer from the correct answer); and (iv) the correct answer/solution is a form of answer/solution that is normally assigned the full score (i.e., the maximum score).
Regarding claim 10, Solomon in view of Lan teaches the claimed limitations as discussed above per claim 9.
Of course, the limitation, “the qualitative feedback is a description explaining to the student what would have been needed to obtain a higher score or the maximum score for solving said problem”, is also already addressed per the modification discussed with respect to claim 7 (or claim 9). In particular, part of the comprehensive feedback being presented to the student includes an explanation that indicates the location where the student made the error, including the expected correct answer/solution, etc.
Thus, the feedback is already a description explaining to the student what would have been needed to obtain a higher score or the maximum score for solving the problem.
Nevertheless, it is worth noting that the content (e.g., the topic) of the feedback is merely nonfunctional descriptive matter; and thus, it does not patentably distinguish the claim from the prior art.
● Considering each of the claims as a whole, the prior art does not teach or suggest claims 1-6 and 11-17 (regarding the state of the prior art, see the obviousness analysis presented above regarding claims 7-10).
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached on 7:00AM-3:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached on (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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