DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
2. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
3. Claims 1, 2, 9, 10, 17 and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kataoka et al. (US 2022/0130270 A1).
Regarding claims 1, 2, 9, 10, 17 and 18, Kataoka discloses
a method (as per claim 1) and corresponding system (as per claim 9) and computer readable medium (as per claim 17) for artificial intelligence-based grading and feedback of digitally entered handwritten characters into a device, the method comprising:
converting, using a first device, a computer-readable document comprising questions into a digital worksheet comprising teacher layers (base of the answer/entry field – Par’s. 54-55) and student layers (answers), wherein the teacher layers and the student layers each comprise the questions (Par. 53 – handwriting input is superimposed on an image of the question, forming the teacher layer and student layer);
detecting, using a first machine learning model trained to categorize input questions and generate answer zones for the input questions, answer zones whose sizes in the student layers are based on the categorization of the questions of the digital worksheet (Par. 48 – performing machine learning of a relationship between an answer to a question and grading to the answer);
generating first updated student layers comprising the questions and the answer zones; and
receiving second updated student layers comprising the first updated student layers and respective answers digitally handwritten into the answer zones (Par. 67 – extract information of an answer and update examination identification information);
generating, using a second machine learning model, clusters of the respective answers based on hand stroke similarities in the respective answers and based on content similarities in the respective answers; and
presenting, using the first device and the teacher layers, the respective answers based on the clusters (Par’s. 53, 89-92 – generate and display grading data based in part on handwriting similarities) (as per claims 1, 9 and 17), and
receiving, at the first device, digitally handwritten annotations to the answers presented using the teacher layers; generating third updated student layers comprising the second updated student layers and the annotations; and sending the third updated student layers for presentation (Par. 70) (as per claims 2, 10 and 18).
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kataoka et al. (US 2022/0130270 A1) in view of Panuganty et al. (US 2021/0383711 A1).
Regarding claims 3, 11 and 19, to the extent that Kataoka does not explicitly disclose the first machine learning model categorizes the questions in the digital worksheet as true or false questions, multiple choice questions, free-form answer questions, and fill-in-the-blank questions, wherein respective sizes of the answer zones are the same when respective answer zones correspond to a same category of questions, and wherein respective sizes of the answer zones differ when respective answer zones correspond to a different category of questions, Panuganty discloses identifying and categorizing question types based on size areas from scanned worksheets (Par’s. 39, 117), including multiple choice, free-form and fill-in-the-blank (see e.g. Par’s. 92, 107). Panuganty does not appear to explicitly disclose detecting true or false questions, however such a modification would be obvious to try because it would involve choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success. Accordingly, it would have been obvious to one skilled in the art before the effective filing date of the invention to modify the teachings of Kataoka by categorizing questions types as suggested by Panuganty, to obtain predictable results of automatically generating new testing content based on analysis of previous contents.
7. Claims 4-7, 12-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kataoka et al. (US 2022/0130270 A1) in view of Ward (US Patent No. 8,187,005 B1).
Regarding claims 4, 12 and 20, to the extent that Kataoka does not explicitly disclose one or more first student layers of the student layers is for a first student and is not viewable by any other students, Ward discloses a student response system for multiple students with a private mode where student responses (layers written over the teacher layer) are not viewable by other students (column 2, lines 20-45). It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the teachings of Kataoka by including this feature of Ward, in order to provide privacy and prevent cheating.
Regarding claims 5-7, 13-15 and 20, Kataoka in view of Ward further discloses
presenting the respective answers based on the clusters comprises presenting first respective answers, from the student layers, to a first respective questions in sequential order with second respective answers, from the student layers, to a second respective question (Par. 95) (as per claims 5 and 13),
presenting the respective answers based on the clusters comprises presenting a first cluster of the first respective answers prior to a second cluster of the first respective answers (Par. 95) (as per claims 6 and 14), and
the first cluster consist of correct answers, and wherein the second cluster consist of incorrect answers (Par. 95) (as per claims 7 and 15).
8. Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kataoka et al. (US 2022/0130270 A1) in view of Rath et al. (US 2017/0025033 A1).
Regarding claims 8 and 16, to the extent that Kataoka does not explicitly disclose the hand stroke similarities comprise at least one of text direction, curvature, and pressure used to digitally enter the respective answers, Rath discloses grading hand stroke similarities based on pressure applied while retracing an object (Par’s. 61, 63). It would have been obvious to one skilled in the art before the effective filing date of the invention to modify the teachings of Kataoka by basing similarities on pressure, to obtain predictable results of judging the user’s technique while performing the hand stroke.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gedlinske et al. (US Patent No. 8,385,811 B1) discloses a system for processing forms using color, including recognizing answer areas and extracting handwritten responses. Lin et al. (US Patent No. 12,548,459 B1) discloses preparing practice tests using machine learning models.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER EGLOFF whose telephone number is (571) 270-3548. The examiner can normally be reached 9:00 AM – 5:00 PM, Monday through Friday Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached at 571-272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Peter R Egloff/
Primary Examiner, Art Unit 3715
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