DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 1, 2, 5, 8, 9, 11, 12, 15, and 16 objected to because of the following informalities:
Claim 1, line 18, “a learning question output completion message” should read “the learning question output completion message”.
Claim 1, line 27, “a learner answer detection completion message” should read “the learner answer detection completion message”.
Claim 1, line 31, “a scoring completion message” should read “the scoring completion message”.
Claim 1, line 37, “the occurrence” should read “an occurrence”.
Claim 2, line 2, “a learning question output screen” should read “the learning question output screen”.
Claims 5, 11, and 15, have multiple instances of “a leaner's answer”, which should read “a learner's answer”.
Claim 8, line 4, “an evaluation output request message” should read “the evaluation output request message”.
Claim 8, line 9, “an error occurrence area” should read “an error occurrence area”.
Claim 9, line 4, “an evaluation output request message” should read “the evaluation output request message”.
Claim 9, line 9, “an error occurrence area” should read “the error occurrence area”.
Claim 11, line 9, “the leaner answer area” should read “the learner answer area”.
Claim 12, line 2, “a learning question output screen” should read “the learning question output screen”.
Claim 16, line 5, “a scoring prompt” should read “the scoring prompt”.
Claim 16, line 6, “the learning's answer” should read “the learner's answer”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “a question output request message” in line 13. Since the claim language does not use antecedent basis (e.g. “the” or “said”), it is unclear if applicant is referring to the same ones of “a learning question output request message” of claim 1 line 2 or a different question output request message. For purposes of examination, it is assumed that “a question output request message” refers to the same ones of “a learning question output request message” found in claim 1 line 2.
Claim 6 recites the limitation “a scoring prompt” in line 13. Since the claim language does not use antecedent basis (e.g. “the” or “said”), it is unclear if applicant is referring to the same ones of “a scoring prompt” of claim 1 from which claim 6 depends or a second scoring prompt. For purposes of examination, it is assumed that “a scoring prompt” refers to the same ones of “a scoring prompt” found in claim 1.
Claims 2-10 are rejected to as being dependent upon a rejected base claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to at least one of abstract idea groupings, according to the 2019 Revised Patent Subject Matter Guidelines (Mathematical Concepts, Mental Processes and/or Certain Methods of Organizing Human Activity). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
More specifically, regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a system and/or process, which is are statutory categories of invention.
Step 2A-1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim 1 recites the following, with the abstract ideas highlighted in bold, including an indication as to the abstract idea grouping(s) to which the indicated limitations belong to, according to the 2019 Revised Patent Subject Matter Guidelines. Independent claim 11, having substantially similar features, were also analyzed and to which the following conclusion is also applicable:
A question learning support device comprising: a control module configured to output a learning question output request message in response to a question learning start request, output a learner answer detection request message in response to a learning question output completion message, which is a response to the learning question output request message, output a learning question scoring request message in response to a learner answer detection completion message, which is a response to the learner answer detection request message, and output an evaluation output request message when it is determined that a learner's answer is incorrect based on a scoring completion message, which is a response to the learning question scoring request message; a question information detection module configured to detect learning question information in response to the learning question output request message of the control module and output a question output request message including the learning question information; a learning question output module configured to generate a learning question output screen including the learning question information in response to the question output request message of the question information detection module and transmit a learning question output completion message after outputting the learning question output screen; a handwriting area detection module configured to detect a part of the learning question output screen as a learner answer area in response to a learner answer detection request message of the control module and output a handwriting area detection completion message including the learner answer area; a handwriting recognition module configured to detect a learner's answer by performing handwriting recognition on the learner answer area in response to the handwriting area detection completion message of the handwriting area detection module and transmit a learner answer detection completion message including the learner's answer; a scoring module configured to generate a scoring prompt including the learner's answer in response to the learning question scoring request message of the control module to transmit the scoring prompt to an artificial intelligence model and transmit a scoring completion message including a result of the scoring to the control module when receiving the result of the scoring, which is a response to the scoring prompt, from the artificial intelligence model; and a scoring result output module configured to detect a part of the learner answer area as an error occurrence area based on the result of the scoring in response to the evaluation output request message of the control module and overlap and output the occurrence of an error in the error occurrence area.
The limitations in claim 1 (as well as claim 11) recites an abstract idea included in the groupings of mental processes, connected to technology only through application thereof using generic computing elements (e.g., computer, artificial intelligence, etc.) and/or insignificant extra-solution activity. According to the 2019 Revised Patent Subject Matter Guidelines:
Mental Processes include concepts performed in the human mind (including an observation, evaluation, judgement, opinion);
Specifically, the instant claims include functions/limitations, as highlighted in the independent claim above, that constitute at least:
D. Concepts performed in the human mind (e.g., “determined that a learner's answer is incorrect, detect learning question information, detect a part of the learning question output screen, detect a learner's answer by performing handwriting recognition on the learner answer area, generate a scoring prompt, detect a part of the learner answer area as an error occurrence area, etc.”), which is an abstract idea included in the grouping of Mental Processes. These limitations are interpreted as at least Mental Processes insomuch as the claim limitations are directed to steps/concepts which are capable of being performed in the human mind, while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity as set forth in the claims.
Regarding dependent claims 2-10 and 12-20:
Each claim is dependent either directly or indirectly from the independent claim identified above and includes all the limitations of said independent claim. Therefore, each dependent claim recites the same abstract idea as identified above. Each of the dependent claim further describes additional aspects of the abstract idea, i.e., additional aspects to the Mental Processes. For example, some dependent claims merely provide additional Mental Processes to be performed and/or additional insignificant extra-solution activity, without anything more significant to establish eligibility under 35 U.S.C. 101.
Step 2A-2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
-Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Claims 1-20 clearly do not improve the functioning of a computer, as they only incorporate generic computing elements, do not effect a particular treatment, and do not transform or reduce a particular article to a different state or thing. Similarly, there is no improvement to a technical field. In addition the claims do not apply the judicial exception with, or by use of a particular machine. The claims do not apply or use the judicial exception in a meaningful way. The claimed invention does not suggest improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05 (a)).
This judicial exception is not integrated into a practical application because the claimed invention merely applies the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea (MPEP 2106.05 (f)) and/or generally links the use of the judicial exception to a particular technology or field of use (MPEP 2106.05 (h)). The claimed computer components are recited at a level of generality and are merely invoked as tool to perform the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
For the reasons as discussed above, the claim limitations are not integrated to a practical application.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above. For example, the recitations of utilization of “computer, artificial intelligence”, etc. used to apply the abstract idea merely implements the abstract idea at a low level of generality and fail to impose meaningful limitations to impart patent-eligibility. These elements and the mere processing of data using these elements do not set forth significantly more than the abstract idea itself applied on general purpose computing devices. The recited generic elements are a mere means to implement the abstract idea. Thus, they cannot provide the “inventive concept” necessary for patent-eligibility. “[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implement]’ an abstract idea ‘on ... a computer,’... that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132 S. Ct. at 1301). As such, the significantly more required to overcome the 35 U.S.C. 101 hurdle and transform the claimed subject matter into a patent-eligible abstract idea is lacking. Accordingly, the claims are not patent-eligible.
Further, in order to be eligible the claims would require structure that is beyond generic. See Alice Corp. v. CLS Bank International, 134 S. Ct. at 2358-59. The elements of computer and artificial intelligence are well known conventional devices used to manage data (detecting, analyzing, etc.) input from users as evidenced by Daigo YAMAGISHI (US 20190312992 A1; hereinafter Yamagishi). Yamagishi discloses that there are well-known analysis methods used to analyze data through the use of AI (¶31). See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
The dependent claims do not add “significantly more” for at least the same reasons as directed to their respective independent claims, at least based on the position, as discussed above, that each of the dependent claims merely provide additional limitations to further expand the abstract idea of the independent claims, without adding anything which would establish eligibility under 35 U.S.C. 101.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, 7, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over CHA KILL YOUNG (KR20230034802A; hereinafter Young) in view of Kilari et al. (US 20230120965 A1; hereinafter Kilari).
Regarding claim 1, Young discloses a question learning support device (tablet laptop or tablet device; ¶8) comprising: a control module configured to output a learning question output request message in response to a question learning start request (the requests messages mentioned are internal requests, the result of these internal messages will result in the question/problem being asked to the user, in this case it will be mathematical problems; ¶8), output a learner answer detection request message in response to a learning question output completion message, which is a response to the learning question output request message (again, these are internal requests that will naturally happen, to acquire the STEM answer data from the learner; ¶5), output a learning question scoring request message in response to a learner answer detection completion message, which is a response to the learner answer detection request message (again, these are internal requests that will naturally happen, to acquire the STEM answer data from the learner; ¶5), and output an evaluation output request message when it is determined that a learner's answer is incorrect based on a scoring completion message, which is a response to the learning question scoring request message (again, this internal message happens in response to finding errors in the answer; ¶3); a question information detection module configured to detect learning question information in response to the learning question output request message of the control module and output a question output request message including the learning question information (again, these internal messages naturally happen because the system will include all data related to STEM questions; ¶8); a learning question output module configured to generate a learning question output screen including the learning question information in response to the question output request message of the question information detection module and transmit a learning question output completion message after outputting the learning question output screen (again, internal messages will happen naturally in the backend, the display of the question with information about the question will be shown to the user in order for the user to answer it; ¶8); a handwriting area detection module configured to detect a part of the learning question output screen as a learner answer area in response to a learner answer detection request message of the control module and output a handwriting area detection completion message including the learner answer area (again, internal requests will happen naturally, the system will detect handwriting in the answer area; ¶3); a handwriting recognition module configured to detect a learner's answer by performing handwriting recognition on the learner answer area in response to the handwriting area detection completion message of the handwriting area detection module and transmit a learner answer detection completion message including the learner's answer (again, internal requests will happen naturally, the system recognizes handwritten data from the learner and analyzes it; ¶6); a scoring module configured to generate a scoring prompt including the learner's answer in response to the learning question scoring request message of the control module to transmit the scoring prompt to an artificial intelligence model and transmit a scoring completion message including a result of the scoring to the control module when receiving the result of the scoring, which is a response to the scoring prompt (again, requests messages naturally happen in the backend, and the system scores the answer from the learner; ¶8), from the artificial intelligence model (this process is done through AI; ¶8); and a scoring result output module configured to detect a part of the learner answer area as an error occurrence area based on the result of the scoring in response to the evaluation output request message of the control module (system analyzes errors in the learner's handwriting area; ¶8).
Young does not explicitly disclose overlap and output the occurrence of an error in the error occurrence area.
However, Kilari focuses in grading answers from users and providing feedback to students based on positive or negative attributions from their answers. Kilari teaches overlap and output the occurrence of an error in the error occurrence area (highlights words in red for negative attribution in the user's answer; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error will provide the benefit of visual feedback to the user about their answer.
Regarding claim 3, Young discloses wherein the handwriting area detection module detects an area, in which a solution process is handwritten (the solution is handwritten; ¶8), of a question solution area of the learning question output screen as the learner answer area (solution area shown in Fig 2).
Regarding claim 5, Young discloses wherein the handwriting recognition module detects a leaner's answer composed of text and detects a learner's answer composed of LaTex code when a learning question is a math question (detects text and will convert to LaTex code; ¶8).
Regarding claim 7, Young discloses wherein the scoring module receives the result of the scoring including an error location and an error cause from the artificial intelligence model and transmits the scoring completion message including the result of the scoring to the control module (shows error and where the user made the error shown in Fig 5).
Regarding claim 8, Young does not explicitly disclose wherein the control module determines that the learner's answer is incorrect when the result of the scoring detected from the scoring completion message includes an error location and transmits an evaluation output request message including the error location to the scoring result output module, and the scoring result output module detects the error location from the evaluation output request message of the control module, detects an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area and displays an error occurrence symbol in the error occurrence area.
However, Kilari teaches wherein the control module determines that the learner's answer is incorrect when the result of the scoring detected from the scoring completion message includes an error location (reference answer shows the incorrect location of the error as shown in Fig 4b) and transmits an evaluation output request message including the error location to the scoring result output module (explanations with corrective feedback; ¶59), and the scoring result output module detects the error location from the evaluation output request message of the control module (error has been located as shown in fig 4B), detects an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area and displays an error occurrence symbol in the error occurrence area (the area where the error is located is shown in Fig 4b and red highlighting is used as an error occurrence symbol in the error occurrence area; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error and showing the location of the error will provide the benefit of visual feedback to the user about their answer.
Regarding claim 9, Young does not explicitly disclose wherein the control module determines that the learner's answer is incorrect when the result of the scoring detected from the scoring completion message includes an error location and an error cause and transmits an evaluation output request message including the error location and the error cause to the scoring result output module, and the scoring result output module detects the error location and the error cause from the evaluation output request message of the control module, detects an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area, displays an error occurrence symbol in the error occurrence area, and displays the error cause near the error occurrence area.
However, Kilari teaches wherein the control module determines that the learner's answer is incorrect when the result of the scoring detected from the scoring completion message includes an error location and an error cause (reference answer shows the incorrect location of the error as shown in Fig 4b and the cause of the error is highlighted in ¶80) and transmits an evaluation output request message including the error location and the error cause to the scoring result output module (explanations with corrective feedback; ¶59), and the scoring result output module detects the error location and the error cause from the evaluation output request message of the control module (error has been located as shown in fig 4B), detects an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area, displays an error occurrence symbol in the error occurrence area, and displays the error cause near the error occurrence area (the area where the error is located is shown in Fig 4b and red highlighting is used as an error occurrence symbol in the error occurrence area; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error and showing the location of the error will provide the benefit of visual feedback to the user about their answer.
Regarding claim 10, Young discloses wherein the control module determines that the learner's answer is correct when the result of the scoring detected from the scoring completion message does not include an error location and an error cause and transmits a positive feedback output request message to the scoring result output module (system does not include error location or error cause in one of the examples as shown in Fig 5 and positive feedback as shown in Fig 5).
Young does not disclose the scoring result output module overlaps and outputs positive feedback on the learning question output screen in response to the positive feedback output request message of the control module.
However, Kilari teaches the scoring result output module overlaps and outputs positive feedback on the learning question output screen in response to the positive feedback output request message of the control module (highlights words in green for positive attribution in the user's answer; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight a right answer will provide the benefit of visual feedback to the user about their answer.
Claims 11-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over CHA KILL YOUNG (KR20230034802A; hereinafter Young) in view of Ho Jung KANG US (US 20230020145 A1; hereinafter Kang).
Regarding claim 11, Young discloses a question learning support method performed by a question learning support device (tablet laptop or tablet device; ¶8), comprising: detecting learning question information in response to a question learning start request (the requests messages mentioned are internal requests, the result of these internal messages will result in the question/problem being asked to the user, in this case it will be mathematical problems; ¶8); detecting a part of the learning question output screen as a learner answer area (this area has the answer area for the learner; Fig 2A); detecting a learner's answer through handwriting recognition on a learner answer area detected in the detecting of the leaner answer area (the system will detect handwriting in the answer area; ¶3); generating a scoring prompt including the learner's answer and outputting the scoring prompt to an artificial intelligence model (the system scores the answer from the learner through AI; ¶8); and outputting a result of scoring of the artificial intelligence model, which is a response to the scoring prompt (result shown in Fig 5).
Young does not disclose outputting a learning question output screen including the learning question information detected in the detecting of the learning question information.
However, Kang teaches outputting a learning question output screen including the learning question information detected in the detecting of the learning question information (question display area shown in Fig 2A where the question is detected).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kang because by keeping the question and the answer in proximity it allows the AI model to process and verify information step by step. Going from question to answer allows for a smooth transition.
Regarding claim 12, Young does not disclose wherein the outputting of the learning question output screen includes outputting a learning question output screen including a learning question display area in which the learning question information is displayed and a question solution area in which a question solution process of a learner to the learning question is handwritten.
However, Kang teaches wherein the outputting of the learning question output screen includes outputting a learning question output screen including a learning question display area in which the learning question information is displayed and a question solution area in which a question solution process of a learner to the learning question is handwritten (Fig 2A has both the question and the question solution area where the process of the learner is handwritten).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kang because by keeping the question and the answer in proximity it allows the AI model to process and verify information step by step. Going from question to answer allows for a smooth transition.
Regarding claim 13, Young discloses wherein the detecting of the learner's answer includes detecting an area, in which a solution process is handwritten (the system will detect handwriting in the answer area; ¶3), of a question solution area.
Young does not disclose a question solution area of the learning question output screen as the learner answer area.
However, Kang teaches a question solution area of the learning question output screen as the learner answer area (Fig 2A has both the question and the question solution area where the process of the learner is handwritten).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kang because by keeping the question and the answer in proximity it allows the AI model to process and verify information step by step. Going from question to answer allows for a smooth transition.
Regarding claim 15, Young discloses wherein the detecting of the learner's answer includes detecting a leaner's answer composed of text and detecting a learner's answer composed of LaTex code when a learning question is a math question (detects text and will convert to LaTex code; ¶8).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kilari as applied to claim 1 above, and further in view of Ho Jung KANG US (US 20230020145 A1; hereinafter Kang).
Regarding claim 2, Young discloses wherein the learning question output module generates a question solution area (this is where the user writes their answer; Fig 2), the question solution area is an area in which a question solution process of a learner to the learning question is input through handwriting (handwritten data is analyzed, which has a an area in order to exist; ¶8).
Young does not disclose the learning question output module generates a learning question output screen including a learning question display area, the learning question display area is an area, in which a learning question detected from the learning question information, of the learning question output screen.
However, Kang teaches the learning question output module generates a learning question output screen including a learning question display area (question display area shown in Fig 2A), the learning question display area is an area, in which a learning question detected from the learning question information, of the learning question output screen (this display area is simply where the question is located; Fig 2A).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kang because by keeping the question and the answer in proximity it allows the AI model to process and verify information step by step. Going from question to answer allows for a smooth transition.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kilari as applied to claim 1 above, and further in view of Hao et al. (CN110298250A; hereinafter Hao).
Regarding claim 4, Young discloses wherein the handwriting area detection module detects the learner answer area (detection of handwritten answer of learner; ¶8).
Young does not disclose which includes vertex coordinates of an area in which a solution process is handwritten, of a question solution area of the learning question output screen.
However, Hao focuses on a writing scoring and error correction method when analyzing calligraphy, which relates to Young because they are both focused on analyzing input and determining if there was an error.
Hao teaches vertex coordinates of an area in which a solution process is handwritten, of a question solution area of the learning question output screen (vertex coordinates being used in an area of a solution from a user; ¶22).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Hao because through vertex coordinates the system can locate the exact location where a user made an error.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kilari as applied to claim 1 above, and further in view of White et al. (A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT; hereinafter White).
Regarding claim 6, Young does not disclose wherein the scoring module sets a prompt including a code area into which the learner's answer is inserted and generates a scoring prompt that inserts the learner's answer detected from the handwriting recognition module into the code area.
However, White teaches wherein the scoring module sets a prompt including a code area into which the learner's answer is inserted and generates a scoring prompt that inserts the learner's answer detected from the handwriting recognition module into the code area (generating and customizing prompts for AI, such as LLM systems, is used to customize the output and interactions of LLMs; Page 2 - Overview of Prompt Patterns).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of White because customizing prompts allows for more variability with consistency. After creating a prompt that works through trial and error, all the user has to do is fill in the blanks and re-use the prompt in order to get consistent results. Furthermore, it defines exactly what the user wants for all future iterations.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kang as applied to claim 11 above, and further in view of Hao et al. (CN110298250A; hereinafter Hao).
Regarding claim 14, Young does not disclose wherein the detecting of the learner's answer includes detecting the learner answer area including vertex coordinates of an area, in which a solution process is handwritten, of a question solution area of the learning question output screen.
However, Hao teaches wherein the detecting of the learner's answer includes detecting the learner answer area including vertex coordinates of an area, in which a solution process is handwritten, of a question solution area of the learning question output screen (vertex coordinates being used in an area of a solution from a user; ¶22).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Hao because through vertex coordinates the system can locate the exact location where a user made an error.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kang as applied to claim 11 above, and further in view of White et al. (A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT; hereinafter White).
Regarding claim 16, Young does not disclose wherein the outputting of the scoring prompt includes: setting a prompt including a code area into which the learner's answer is inserted; and outputting a scoring prompt that inserts the learner's answer detected in the detecting of the learning's answer into the code area.
However, White teaches wherein the outputting of the scoring prompt includes: setting a prompt including a code area into which the learner's answer is inserted; and outputting a scoring prompt that inserts the learner's answer detected in the detecting of the learning's answer into the code area (generating and customizing prompts for AI, such as LLM systems, is used to customize the output and interactions of LLMs; Page 2 - Overview of Prompt Patterns).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of White because customizing prompts allows for more variability with consistency. After creating a prompt that works through trial and error, all the user has to do is fill in the blanks and re-use the prompt in order to get consistent results. Furthermore, it defines exactly what the user wants for all future iterations.
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Young in view of Kang as applied to claim 11 above, and further in view of Kilari et al. (US 20230120965 A1; hereinafter Kilari).
Regarding claim 17, Young does not disclose wherein the outputting of the result of the scoring includes detecting a part of the learner answer area as an error occurrence area and overlapping and outputting occurrence of an error in the error occurrence area.
However, Kilari teaches wherein the outputting of the result of the scoring includes detecting a part of the learner answer area as an error occurrence area and overlapping and outputting occurrence of an error in the error occurrence area (highlights words in red for negative attribution in the user's answer; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error will provide the benefit of visual feedback to the user about their answer.
Regarding claim 18, Young discloses wherein the outputting of the result of the scoring includes: determining that the learner's answer is correct when the result of the scoring of the artificial intelligence model does not include an error location and an error cause (system does not include error location or error cause in one of the examples as shown in Fig 5).
Young does not disclose overlapping and outputting positive feedback on the learning question output screen.
However, Kilari teaches overlapping and outputting positive feedback on the learning question output screen (highlights words in green for positive attribution in the user's answer; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight a right answer will provide the benefit of visual feedback to the user about their answer.
Regarding claim 19, Young does not explicitly disclose wherein the outputting of the result of the scoring includes: determining that the learner's answer is incorrect when the result of the scoring received from the artificial intelligence model includes an error location; detecting an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area; and displaying an error occurrence symbol in the error occurrence area.
However, Kilari teaches wherein the outputting of the result of the scoring includes: determining that the learner's answer is incorrect when the result of the scoring received from the artificial intelligence model includes an error location (reference answer shows the incorrect location of the error as shown in Fig 4b); detecting an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area (error has been located as shown in fig 4B); and displaying an error occurrence symbol in the error occurrence area (the area where the error is located is shown in Fig 4b and red highlighting is used as an error occurrence symbol in the error occurrence area; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error and showing the location of the error will provide the benefit of visual feedback to the user about their answer.
Regarding claim 20, Young does not explicitly disclose wherein the outputting of the result of the scoring includes: determining that the learner's answer is incorrect when the result of the scoring received from the artificial intelligence model includes an error location and an error cause; detecting an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area; displaying an error occurrence symbol in the error occurrence area; and displaying the error cause near the error occurrence area.
However, Kilari teaches wherein the outputting of the result of the scoring includes: determining that the learner's answer is incorrect when the result of the scoring received from the artificial intelligence model includes an error location and an error cause (reference answer shows the incorrect location of the error as shown in Fig 4b and the cause of the error is highlighted in ¶80); detecting an area, which corresponds to the error location, of the learner answer area of the learning question output screen as an error occurrence area (error has been located as shown in fig 4B); displaying an error occurrence symbol in the error occurrence area; and displaying the error cause near the error occurrence area (the area where the error is located is shown in Fig 4b and red highlighting is used as an error occurrence symbol in the error occurrence area; ¶80).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Young to implement the teachings of Kilari because overlapping to highlight an error and showing the location of the error will provide the benefit of visual feedback to the user about their answer.
Conclusion
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/JOSE ANGELES/Examiner, Art Unit 3715
/Jay Trent Liddle/Primary Examiner, Art Unit 3715
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