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 .
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.
Claim Objections
Claim 11 is objected to because of the following informalities:
In the third line of claim 11, please replace “non-tangible” with --non-transitory--. Based on Applicant’s specification, this appears to be a typographical error.
Appropriate correction is required.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Claim(s) 1, 11, and 20, Claim(s) 1, 11, and 20 recite(s):
- identifying a course to be output to a user, the course having at least a first portion and a second portion;
- outputting the first portion to the user;
- receiving, from the user, at least one user response based on the outputting of the first portion;
- evaluating the at least one user response, resulting in a user evaluation;
- modifying the second portion, resulting in a modified second portion;
- outputting the modified second portion to the user.
Each of the above limitations falls within the abstract-idea category of “Certain methods of organizing human activity.” Specifically, those limitations relate to the following subject matter that is grouped into the category of “Certain methods of organizing human activity”:
- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions): manages learning of a user; manages interactions between a course user and a course provider, both of which may involve people.
To the extent that any of these limitations are recited alongside recitations of generic computer components, as described below in this rejection: If a claim limitation, under its broadest reasonable interpretation, covers subject matter recognized as certain methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain method of organizing human activity” grouping of abstract ideas. Accordingly, the claim(s) recite an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements when considered both individually and as an ordered combination do not integrate the abstract idea into a practical application. The claim(s) recite the following additional elements/limitations, each of which are addressed in the list below with the reason(s) why they do not integrate the abstract idea into a practical application:
- at least one processor of a computer system; outputting via presenting; a system comprising: at least one processor; a non-tangible computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations; a non-tangible computer-readable storage medium having instructions stored which, when executed by a processor, cause the processor to perform operations: These element(s)/limitation(s) amount to mere instructions to apply an exception. See MPEP 2106.05(f). In making this determination, examiners may consider whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Mere instructions to apply an exception is a consideration with respect to both integration of an abstract idea into a practical application and significantly more. MPEP 2106.05(f)(2) states: “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).” This is the case with these particular claim element(s)/limitation(s). Those elements/limitations do not meaningfully limit the claim because implementing an abstract idea on a generic computer does not integrate the abstract idea into a practical application, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. Therefore, these particular claim element(s)/limitation(s) do not integrate the abstract idea into a practical application for at least this reason.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim(s) are directed to an abstract idea.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either individually or as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of computer-related components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The claim(s) are not patent eligible.
As per dependent claim(s) 2-10 and 12-19, these claim(s) incorporate the above abstract idea via their dependencies on the respective independent claim(s). The additional element(s)/limitation(s) of the respective independent claim(s) do not integrate the abstract idea into a practical application, nor do they add significantly more, with respect to those dependent claim(s), under the same reasoning as above with respect to the respective independent claim(s).
Those dependent claim(s) add the following generic computer components, which do not integrate the abstract idea into a practical application, nor add significantly more, under the same reasoning as given above with respect to generic computer components in the independent claim(s). Those additional generic computer components and their corresponding dependent claim(s) are as follows:
- machine-learning (claims 2-3, 5-6, 12-13, and 15-16);
- Natural Language Processing (NLP) (claims 5 and 15);
- a display (claim 10);
- a speaker (claim 10).
The remaining added elements/limitations of those dependent claim(s) do not integrate the abstract idea into a practical application nor add significantly more because they all merely add further functional step(s) and/or detail to the abstract idea; as part of the abstract idea, they cannot integrate into a practical application or be significantly more than the abstract idea of which they are a part. For example, claim 4 merely adds detail to the at least one user response.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application, nor add significantly more. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim(s) 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea that is not integrated into a practical application and is without significantly more.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because: According to the preamble of claim 20, claim 20 is directed to a non-tangible computer-readable storage medium. (It appears that Applicant might have accidentally used “non-tangible” here instead of “non-transitory”.) A non-tangible computer-readable storage medium encompasses a signal per se, which does not fall within a statutory class under 101. See MPEP 2106.03(I). Therefore, the claim is rejected under 35 U.S.C. 101.
To attempt to overcome this particular rejection under 35 U.S.C. 101, Examiner suggests amending the first line of claim 20 to replace “non-tangible” with --non-transitory-- and insert -- thereon-- immediately after the word “stored”.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1, 7-11, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gal, US 20140335497 A1.
As per Claims 1, 11, and 20, Gal discloses:
- a method (paragraph [0004] (“Some embodiments of the invention include, for example, devices, systems, and methods of adaptive teaching and learning.”));
- identifying, via at least one processor of a computer system, a course to be presented to a user, the course having at least a first portion and a second portion (Figure 3; paragraphs [0028]-[0029] (evaluating student performance); paragraph [0045] (“In some embodiments, the lesson planning module is to dynamically perform a modification of the lesson plan, in accordance with one or more predefined rules, based on performance of one or more digital learning objects through one or more student stations.”); paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like. In some embodiments, portions of the content of educational learning objects may be automatically modified, removed or added, based on characteristics of the student utilizing the learning object, thereby providing to each student a learning object accommodating the student's characteristic and record of progress.”); paragraph [0111] (whole paragraph); paragraph [0120] (whole paragraph); paragraphs [0121]-[0123] (computing system); paragraph [0244] (“The planning module allows planning, presenting, editing and preparing items for delivery to a class of students, the items including, for example, integrated teaching-learning-assessment plans having flow and content components. The deliverable items may correspond to an entire yearly curricular program, for each subject matter. The planning may include selecting learning objects and/or learning activities and arranging them in accordance with a teacher's preferred order, optionally allowing differential and/or pre-defined and/or conditional order, layout, and allocation of content and/or activities to different students or groups of students.”));
- presenting, via the computer system, the first portion to the user (Figure 3; paragraphs [0028]-[0029] (evaluating student performance); paragraph [0045] (“In some embodiments, the lesson planning module is to dynamically perform a modification of the lesson plan, in accordance with one or more predefined rules, based on performance of one or more digital learning objects through one or more student stations.”); paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like. In some embodiments, portions of the content of educational learning objects may be automatically modified, removed or added, based on characteristics of the student utilizing the learning object, thereby providing to each student a learning object accommodating the student's characteristic and record of progress.”); paragraph [0111] (whole paragraph); paragraph [0120] (whole paragraph); paragraphs [0121]-[0123] (computing system); paragraph [0127] (“The student stations 301-303 are used by students (e.g., individually such that each student operates a station, or that two students operate a station, or the like) to perform personal learning activities, to conduct personal assignments, to participate in learning activities in-class, to participate in assessment activities, to access rich digital content in various educational subject matters in accordance with the lesson plan, to collaborate in group assignments, to participate in discussions, to perform exercises, to participate in a learning community, to communicate with the teacher station 310 or with other student stations 301-303, to receive or perform personalized learning activities, or the like.”); here, the first portion is digital educational content presented to a student, before evaluation and further modified content is presented);
- receiving, at the computer system from the user, at least one user response based on the presenting of the first portion (Figure 3; paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like.”); paragraph [0107] (“learning object 215 is a JavaScript program in which the student selects answers in a multiple-choice quiz”); paragraphs [0121]-[0123] (computing system); paragraph [0127] (“The student stations 301-303 are used by students (e.g., individually such that each student operates a station, or that two students operate a station, or the like) to perform personal learning activities, to conduct personal assignments, to participate in learning activities in-class, to participate in assessment activities, to access rich digital content in various educational subject matters in accordance with the lesson plan, to collaborate in group assignments, to participate in discussions, to perform exercises, to participate in a learning community, to communicate with the teacher station 310 or with other student stations 301-303, to receive or perform personalized learning activities, or the like.”); paragraph [0133] (“System 300 may determine and report that at least 80 percent of the students in the first group successfully completed at least 75 percent of their allocated learning activity, or that at least 50 percent of the students in the second group failed to correctly answer at least 30 percent of questions allocated to them.”));
- evaluating, via the at least one processor of the computer system, the at least one user response, resulting in a user evaluation (Figure 3; paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like.”); paragraph [0107] (“learning object 215 is a JavaScript program in which the student selects answers in a multiple-choice quiz”); paragraphs [0121]-[0123] (computing system); paragraph [0133] (“System 300 may determine and report that at least 80 percent of the students in the first group successfully completed at least 75 percent of their allocated learning activity, or that at least 50 percent of the students in the second group failed to correctly answer at least 30 percent of questions allocated to them.”); paragraph [0151] (whole paragraph); paragraph [0187] (“A saved state may include, for example, a screen-shot, textual component, graphical components, audio/video, animations, results, achievements, answers, correct answers, incorrect answers, or other information.”); paragraph [0233] (“indication of the number or percentage of correct answers that the student provided in the current lesson”));
- modifying, via the at least one processor, the second portion, resulting in a modified second portion (Figure 3; paragraphs [0028]-[0029] (evaluating student performance); paragraph [0045] (“In some embodiments, the lesson planning module is to dynamically perform a modification of the lesson plan, in accordance with one or more predefined rules, based on performance of one or more digital learning objects through one or more student stations.”); paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like. In some embodiments, portions of the content of educational learning objects may be automatically modified, removed or added, based on characteristics of the student utilizing the learning object, thereby providing to each student a learning object accommodating the student's characteristic and record of progress.”); paragraph [0111] (whole paragraph); paragraph [0120] (whole paragraph); paragraphs [0121]-[0123] (computing system); here, the modified second portion is the portion of the digital educational content that is presented that was modified based on the student’s performance);
- presenting, via the computer system, the modified second portion to the user (Figure 3; paragraphs [0028]-[0029] (evaluating student performance); paragraph [0045] (“In some embodiments, the lesson planning module is to dynamically perform a modification of the lesson plan, in accordance with one or more predefined rules, based on performance of one or more digital learning objects through one or more student stations.”); paragraph [0098] (“Additionally or alternatively, within the flow of a learning object, personalized feedback or support may be provided to the student, taking into account the specific needs or skills of the student, his prior performance and answers, his specific strengths and weaknesses, his progress and decisions, or the like. In some embodiments, portions of the content of educational learning objects may be automatically modified, removed or added, based on characteristics of the student utilizing the learning object, thereby providing to each student a learning object accommodating the student's characteristic and record of progress.”); paragraph [0111] (whole paragraph); paragraph [0120] (whole paragraph); paragraphs [0121]-[0123] (computing system); paragraph [0127] (“The student stations 301-303 are used by students (e.g., individually such that each student operates a station, or that two students operate a station, or the like) to perform personal learning activities, to conduct personal assignments, to participate in learning activities in-class, to participate in assessment activities, to access rich digital content in various educational subject matters in accordance with the lesson plan, to collaborate in group assignments, to participate in discussions, to perform exercises, to participate in a learning community, to communicate with the teacher station 310 or with other student stations 301-303, to receive or perform personalized learning activities, or the like.”); here, the modified second portion is the portion of the digital educational content that is presented that was modified based on the student’s performance);
- a system comprising: at least one processor; a non-tangible computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations (Figure 3; paragraph [0095]; paragraphs [0121]-[0123]);
- a non-tangible computer-readable storage medium having instructions stored which, when executed by a processor, cause the processor to perform operations (Figure 3; paragraph [0095]; paragraphs [0121]-[0123]).
As per Claims 7 and 17, Gal further discloses wherein the modifying of the second portion comprises inserting an example into the second portion, where the example was not previously included in the second portion (paragraph [0290]; paragraph [0294]).
As per Claims 8 and 18, Gal further discloses wherein the modifying of the second portion comprises inserting an additional question into the second portion, where the additional question was not previously included in the second portion (paragraph [0294]).
As per Claims 9 and 19, Gal further discloses wherein the modifying of the second portion comprises dynamically adjusting the content of the second portion based on the user evaluation (paragraphs [0028]-[0029]; paragraph [0045]; paragraph [0098]).
As per Claim 10, Gal further discloses wherein the presenting of the first portion and the presenting of the modified second portion are performed using a display of the computer system (paragraph [0123]; paragraph [0127]; paragraph [0135]; paragraph [0265]; paragraph [0274]).
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.
Claim(s) 2, 4-6, 12, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vleugels, US 20240274025 A1.
As per Claims 2 and 12, Gal fails to disclose wherein the evaluating of the at least one user response via the at least one processor occurs by executing at least one machine learning model using the at least one user response as an input. Vleugels discloses wherein the evaluating of the at least one user response via the at least one processor occurs by executing at least one machine learning model using the at least one user response as an input (paragraph [0101]; paragraph [0127]; paragraph [0132]). 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 Gal such that the evaluating of the at least one user response via the at least one processor occurs by executing at least one machine learning model using the at least one user response as an input, as disclosed by Vleugels, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claims 4 and 14, Gal further discloses wherein the at least one user response comprises data indicative of user comprehension of the first portion (paragraph [0107]; paragraph [0133]; paragraph [0151]; paragraph [0187]; paragraph [0233]).
As per Claims 5 and 15, the modified Gal fails to disclose wherein: the at least one machine learning model utilizes Natural Language Processing (NLP); and wherein the NLP performs the modifying of the second portion using a prompt, the prompt based on the user evaluation. Vleugels further discloses wherein: the at least one machine learning model utilizes Natural Language Processing (NLP); and wherein the NLP performs the modifying of the second portion using a prompt, the prompt based on the user evaluation (paragraph [0022]; paragraph [0024]; paragraph [0042]; paragraph [0043]; paragraph [0045]; paragraph [0046]; paragraphs [0056]-[0057]; paragraph [0086]; paragraph [0133]). 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 the modified Gal such that the at least one machine learning model utilizes Natural Language Processing (NLP); and wherein the NLP performs the modifying of the second portion using a prompt, the prompt based on the user evaluation, as disclosed by Vleugels, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per Claims 6 and 16, the modified Gal fails to disclose wherein the at least one machine learning model comprises a reinforcement learning model that selects content for the modified second portion based on a reward function that optimizes user comprehension. Vleugels further discloses wherein the at least one machine learning model comprises a reinforcement learning model that selects content for the modified second portion based on a reward function that optimizes user comprehension (paragraph [0022]; paragraph [0024]; paragraph [0045]; paragraph [0046]; paragraph [0084]; paragraph [0086]; paragraph [0122]; paragraph [0133]). 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 the modified Gal such that the at least one machine learning model comprises a reinforcement learning model that selects content for the modified second portion based on a reward function that optimizes user comprehension, as disclosed by Vleugels, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vleugels in further view of Keen, US 20240331561 A1.
As per Claims 3 and 13, the modified Vleugels fails to disclose wherein the at least one machine learning model comprises a neural network, the neural network having been trained on a plurality of training pairs, each pair in the plurality of training pairs comprising: a previous user response; and a previous user comprehension determined after the previous user response. Keen discloses wherein the at least one machine learning model comprises a neural network, the neural network having been trained on a plurality of training pairs, each pair in the plurality of training pairs comprising: a previous user response; and a previous user comprehension determined after the previous user response (paragraph [0048]; paragraph [0050]; paragraph [0058]). 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 the modified Vleugels such that the at least one machine learning model comprises a neural network, the neural network having been trained on a plurality of training pairs, each pair in the plurality of training pairs comprising: a previous user response; and a previous user comprehension determined after the previous user response, as disclosed by Keen, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a. Nel, US 20200082735 A1 (neuroadaptive intelligent virtual reality learning system and method);
b. Zaslavsky, US 20150242975 A1 (self-construction of content in adaptive e-learning datagraph structures);
c. Van Schaack, US 20050277099 A1 (system, apparatus and method for maximizing effectiveness and efficiency of learning, retaining and retrieving knowledge and skills).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, NATHAN UBER can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATHAN ERB/Primary Examiner, Art Unit 3628