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 . This Office Action is in response to Applicant’s reply filed 7/10/26. Claims 1, 10, and 16 are amended. Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/10/26 has been entered.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/339,976. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the pending application are encompassed by the claims of the copending provisional.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) s 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Publication No. 2023/0215283 A1 to Sha et al. (hereinafter “Sha”).
Concerning claim 1, Sha discloses a computer-implemented method, comprising:
accessing, by a content server, historical consumption information and historical comprehension information, wherein the historical consumption information describes historical educational content consumption behaviors of a set of historical users, wherein the historical comprehension information comprises a historical measure of comprehension of historical educational content by the set of historical users (paragraphs [0017], [0030], [0044], [0046] – historical consumption behaviors relating to education and comprehension of users are stored and accessed by a machine learning engine);
generating, by the content server, a training dataset based on the historical consumption information and the historical comprehension information (paragraphs [0046]-[0049]) – training dataset is generated);
training, by the content server, a machine-learned model configured to predict a measure of comprehension for a user based on behaviors of the user as the user consumes educational content (paragraphs [0057]-[0061] – patterns in learning effectiveness are determined for the set of users);
obtaining, by the content server, interaction data of a target user consuming target educational content according to interactions with interactive tag elements, wherein the interactive tag elements are displayed on an educational content interface, wherein obtaining the interaction data comprises: (paragraphs [0042]-[0044], [0057]-[0059], [0065]-[0072] – presentation parameters of the content may be adjusted based on the user interaction with the various elements);
in response to progression through the target educational content and the interactions with a first interactive tag element, adding a second interactive tag element on the educational content interface to obtain at least a portion of the interaction data, wherein the first interactive tag element and the second interactive tag element are associated with a same content tag for the target educational content (Fig. 2, paragraphs [0042]-[0044], [0057]-[0059]- elements may have multiple tags and the education content may be varied dues to interactions with content, and multiple interactive tag elements associated with the content);
applying, by the content server, the machine-learned model to the interaction data to determine a target measure of comprehension for each of a plurality of portions of the target educational content (paragraphs [0063]-[0065] – personalized materials are generated for users based on a collective target measure of comprehension); and
for a portion of the target educational content corresponding to a below-threshold target measure of comprehension: identifying, by the content server, one or more characteristics of the portion of the target educational content (paragraphs [0065], [0066] – relevant content delivery adaptations may be provided for underrepresented learning styles);
applying, by the content server, a content identification model to the one or more characteristics of the portion of the target educational content to identify supplemental educational content related to the portion of the target educational content (paragraphs [0070]-[0076] - additional content is adjusted and provided to the user based on their personal learning style); and
generating, by the content server, a supplemental content display in the educational content interface to present the supplemental educational content to the target user (paragraphs [0038], [0051], [0070]-[0076] - additional content is adjusted and provided to the user on a display).
Concerning claims 2, 11, and 17, Sha discloses wherein the behaviors include one or more of reading rates, pause time, number of re-read times, delays in content consumption, highlighting, types of highlighting, highlight coverage, time of day, switch outs, and switch duration (paragraphs [0055], [0063]-[0067] – content consumption includes reading rates and switch outs).
Concerning claims 3, 12, and 18, Sha discloses wherein the measure of comprehension includes one or more of test scores, user evaluations of comprehension, types of highlighting, post- comprehension quiz results (paragraphs [0017], [0030], [0044], [0046] – past learning records measure comprehension of the users).
Concerning claim 4, Sha discloses wherein the content identification model is trained with a second dataset comprising historical educational content consumed by historical users, and the historical educational content includes one or more target educational content and supplemental educational content associated with each target educational content consumed by the historical users (paragraphs [0017], [0030], [0044], [0046] – historical consumption behaviors relating to education and comprehension of users are stored and accessed by a machine learning engine).
Concerning claim 5, Sha discloses further comprising: accessing the second dataset; and training the content identification model to predict a likelihood of supplemental educational content that improves a user's measure of comprehension for the portion of the target educational content (paragraphs [0057]-[0061] – patterns in learning effectiveness are determined for the set of users).
Concerning claims 6, and 19, Sha discloses wherein applying the machine-learned model to determine a measure of comprehension comprises: determining the measure of comprehension in real time as the target user consumes the portion of the target educational content (paragraphs [0047], [0051], [061], [0063], [0065], [0067] – real time measures of comprehension of the user are determined).
Concerning claims 7, and 14, Sha discloses wherein generating the educational content interface to present the supplemental educational content to the target user comprises: displaying the supplemental educational content to the target user in real time as the target user consumes the portion of the target educational content (paragraphs [0047], [0051], [061], [0063], [0065], [0067], [0070]-[0076] – content is displayed in real time).
Concerning claim 8, Sha discloses wherein generating the educational content interface to present the supplemental educational content to the target user comprises: displaying the supplemental educational content to the target user after the target user consumes the portion of the target educational content (paragraphs [0047], [0051], [061], [0063], [0065], [0067], [0070]-[0076] – content is displayed based on user interaction).
Concerning claims 9, 15, and 20, Sha discloses further comprising: identifying second supplemental educational content related to a second portion of the target educational content, wherein the second portion and the portion of the target educational content are related to a low-comprehension dedicated section; and modifying the educational content interface to add the second supplemental educational content for display (paragraphs [0065], [0066] – relevant content delivery adaptations may be provided for underrepresented learning styles).
Concerning claims 10 and 16, see the rejection of claim 1.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered and the Office Action cites additional explanations and citations with respect to the amended subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MALINA D BLAISE whose telephone number is (571)270-3398. The examiner can normally be reached Mon. - Thurs. 7:00 am - 5:00 pm (PT).
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, 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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MALINA D. BLAISE
Primary Examiner
Art Unit 3715
/MALINA D. BLAISE/Primary Examiner, Art Unit 3715
