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 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.
Claim 2 is 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. Regarding claim 2, the phrase "for example" or “e.g.” or “may be” renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 judicial exception(s) without significantly more.
[STEP 1] The claim recites at least one step or structure. Thus, the claim is to a process or product, which is one of the statutory categories of invention (Step 1: YES).
[STEP2A PRONG I] The claim(s) 1, 11 and 20 recite(s):
A method comprising:
obtaining a set of markers indicative of a progression of a student through a first challenge corresponding to a first state of a set of educational states;
generating an input vector comprising embeddings representative of the set of markers;
inputting the input vector into an unsupervised machine learning model and receiving, as output from the unsupervised machine learning model, an indication of a coordinate in vector space representative of the progression;
determining a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification;
determining a next state of the set of educational states for traversal from the first state based on the progression classification; and
generating, for output to the student, educational programming corresponding to the next state.
11. A non-transitory computer-readable storage medium storing computer instructions, the computer instructions, when executed by one or more processors, cause the one or more processors to perform operations, the instructions comprising instructions to:
obtain a set of markers indicative of a progression of a student through a first challenge corresponding to a first state of a set of educational states;
generate an input vector comprising embeddings representative of the set of markers;
input the input vector into an unsupervised machine learning model and receiving, as output from the unsupervised machine learning model, an indication of a coordinate in vector space representative of the progression;
determine a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification;
determine a next state of the set of educational states for traversal from the first state based on the progression classification; and
generate, for output to the student, educational programming corresponding to the next state.
20. A computer system comprising:
one or more processors; and
a non-transitory computer readable storage medium storing instructions that, when
executed by the one or more processors, cause the one or more processors to
perform operations comprising:
obtaining a set of markers indicative of a progression of a student through a first
challenge corresponding to a first state of a set of educational states;
generating a input vector comprising embeddings representative of the set of
markers;
inputting the input vector into an unsupervised machine learning model and
receiving, as output from the unsupervised machine learning model, an indication of a coordinate in vector space representative of the progression;
determining a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification;
determining a next state of the set of educational states for traversal from the first
state based on the progression classification; and
generating, for output to the student, educational programming corresponding to the next state.
The non-highlighted aforementioned limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation between people but for the recitation of generic computer components. That is, other than reciting “machine learning model”, “A non-transitory computer-readable storage medium”, “computer”, “one or more processors” nothing in the claim element precludes the step from practically being performed between people. For example, but for the recited language, the step in the context of this claim encompasses a teacher observing students’ behaviors and adjusting its instruction/lecture level accordingly.
If a claim limitation, under its broadest reasonable interpretation, covers managing interactions between people, then it falls within the “Organization of Human Activity” grouping of abstract ideas.
Accordingly, the claim recites a judicial exception, and the analysis must therefore proceed to Step 2A Prong Two.
[STEP2A PRONG II] This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional element(s) – “machine learning model”, “A non-transitory computer-readable storage medium”, “computer”, “one or more processors”.
The “machine learning model”, “A non-transitory computer-readable storage medium”, “computer”, “one or more processors”, the aforementioned steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
[STEP2B] The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the aforementioned steps amounts to no more than mere instructions to apply the exception using a generic computer component, which cannot provide an inventive concept (for example, see paragraph 13, 15).
As noted previously, the claim as a whole merely describes how to generally “apply” the aforementioned concept in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
The claim is not patent eligible. (Step 2B: NO).
Claim(s) 2-10 and 12-19 are dependent on supra claim(s) and includes all the limitations of the claim(s). Therefore, the dependent claim(s) recite(s) the same abstract idea. For example, claims 2-10 and 12-19 are directed to the type of data being offered to the machine learning machine and how the state machine behaves (an abstract idea), the specific labeling steps being done to the data (abstract ideas) and using the data to provide an alert to the teacher (abstract ideas). The claims recite no additional limitations. Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. 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. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claim Rejections - 35 USC § 103
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 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 1-4, 11-13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baphna US 20210027647 and in view of Banerjee US 20180101535
Claims 1, 11 and 20: The Baphna reference provides a teaching of a method comprising:
obtaining a set of markers indicative of a progression of a student through a first challenge corresponding to a first state of a set of educational states (see paragraph 77);
generating a input vector comprising embeddings representative of the set of markers (see paragraph 84); and
inputting the input vector into an unsupervised machine learning model and receiving, as output from the unsupervised machine learning model, an indication of a coordinate in vector space representative of the progression (see paragraph 85 and 93).
The Baphna reference is silent on the teaching of determining a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification; determining a next state of the set of educational states for traversal from the first state based on the progression classification; and generating, for output to the student, educational programming corresponding to the next state.
However, the Banerjee reference provides a teaching of:
determining a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification (see paragraph 51);
determining a next state of the set of educational states for traversal from the first state based on the progression classification (see paragraph 67) ; and
generating, for output to the student, educational programming corresponding to the next state (see paragraph 43 and 47).
Therefore, 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 the with the feature of determining a progression classification based on a mapping of the coordinate to a plurality of clusters, each cluster of the plurality of clusters representative of a different progression classification; determining a next state of the set of educational states for traversal from the first state based on the progression classification; and generating, for output to the student, educational programming corresponding to the next state; as taught by the Banarjee reference, in order to provide more efficient and flexible delivery that is adapted to the learning styles of different learners (see paragraph 7).
With respect to claim 11, the Baphna reference provide a teaching of a non-transitory computer readable storage medium storing computer instruction (see paragraph 44)
With respect to claim 20, the Baphna reference provide a teaching of one or more processor (see paragraph 46) and a non-transitory computer readable storage medium storing computer instruction (see paragraph 44)
Claim 2: The Baphna reference provides a teaching of wherein the output may be one or more of a new
challenge, a suggestion to the student (see paragraph 100 providing a different challenge to the student or hints)
Claim 3 and 12: The Baphna reference provides a teaching of wherein the educational states are connected by a state machine (see FIG. 2E as an example with state machine), the state machine comprising:
state nodes representative of corresponding educational programming (see paragraph 91) , and directional edges, each edge of the edges pointing to a next state node and annotated with
one or more conditions that, when satisfied, cause the state machine to select the next state node corresponding to the satisfied edge (see FIG. 2E for example the directional edge between H11 and H22).
Claim 4 and 13: The Baphna reference is silent on the teaching of inputting historical examples of a plurality of students' progress through the first challenge into the unsupervised machine learning model; and receiving, as output from the unsupervised machine learning model, indications of clusters of coordinates corresponding to the historical examples.
However, the Banarjee reference provides a teaching of inputting historical examples of a plurality of students' progress through the first challenge into the unsupervised machine learning model (see paragraph 47) ; and receiving, as output from the unsupervised machine learning model, indications of clusters of coordinates corresponding to the historical examples (see paragraph 67).
Therefore, 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 the Baphna referene with the feature of inputting historical examples of a plurality of students' progress through the first challenge into the unsupervised machine learning model; and receiving, as output from the unsupervised machine learning model, indications of clusters of coordinates corresponding to the historical examples; as taught by the Banarjee reference, in order to provide more efficient and flexible delivery that is adapted to the learning styles of different learners (see paragraph 7).
Claims 5-8 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Baphna US 20210027647, in view of Banerjee US 20180101535 and further in view of Ardel US 20230325292
Claims 5 and 14: The Baphna reference is silent on the teaching of wherein a model is trained to output the progression classification by receiving, from an expert user, a label of a given progression classification for each cluster of the plurality of clusters.
However, the Ardel reference provide s teaching of wherein a model is trained to output the progression classification by receiving, from an expert user, a label of a given progression classification for each cluster of the plurality of clusters (see paragraph 49 data labeled by subject matter expert). The Ardel reference provides a teaching of well-known data processing technique. As such, it would have been obvious to one of ordinary skill in the art at the time of the invention to have incorporated the feature of a model is trained to output the progression classification by receiving, from an expert user, a label of a given progression classification for each cluster of the plurality of clusters, as in the improvement discussed in the Ardel reference. As taught in Ardel, it is within the capabilities of one of ordinary skill in the art to attach and incorporate the feature of wherein a model is trained to output the progression classification by receiving, from an expert user, a label of a given progression classification for each cluster of the plurality of clusters.
Claims 6 and 15: The Baphna reference is silent on the teaching of wherein the progression classification is determined to be a given label based on the coordinate being within a cluster having the given label as labeled by the expert user.
However, the Ardel reference provides a teaching of wherein the progression classification is determined to be a given label based on the coordinate being within a cluster having the given label as labeled by the expert user. (see paragraph 164). The Ardel reference provides a teaching of well-known data processing technique. As such, it would have been obvious to one of ordinary skill in the art at the time of the invention to have incorporated the feature of wherein the progression classification is determined to be a given label based on the coordinate being within a cluster having the given label as labeled by the expert user, as in the improvement discussed in the Ardel reference. As taught in Ardel, it is within the capabilities of one of ordinary skill in the art to attach and incorporate the feature of wherein the progression classification is determined to be a given label based on the coordinate being within a cluster having the given label as labeled by the expert user.
Claims 7 and 16: The Baphna is silent on the teaching of wherein the progression classification is determined to be unknown based on the coordinate not being within any cluster of the plurality of clusters.
However, the Ardel reference provides a teaching of wherein the progression classification is determined to be unknown based on the coordinate not being within any cluster of the plurality of clusters (see paragraph 48). The Ardel reference provides a teaching of well-known data processing technique. As such, it would have been obvious to one of ordinary skill in the art at the time of the invention to have incorporated the feature of wherein the progression classification is determined to be unknown based on the coordinate not being within any cluster of the plurality of clusters, as in the improvement discussed in the Ardel reference. As taught in Ardel, it is within the capabilities of one of ordinary skill in the art to attach and incorporate the feature of wherein the progression classification is determined to be unknown based on the coordinate not being within any cluster of the plurality of clusters.
Claims 8 and 17: The Baphna reference is silent on the teaching of wherein responsive to detecting that the progression classification is unknown, the method further comprises alerting an administrator that a new coordinate space needs review by an expert for labeling.
However, the Ardel reference provides a teaching of wherein responsive to detecting that the progression classification is unknown, the method further comprises alerting an administrator that a new coordinate space needs review by an expert for labeling (see paragraph 144). The Ardel reference provides a teaching of well-known data processing technique. As such, it would have been obvious to one of ordinary skill in the art at the time of the invention to have incorporated the feature of wherein responsive to detecting that the progression classification is unknown, the method further comprises alerting an administrator that a new coordinate space needs review by an expert for labeling, as in the improvement discussed in the Ardel reference. As taught in Ardel, it is within the capabilities of one of ordinary skill in the art to attach and incorporate the feature of wherein responsive to detecting that the progression classification is unknown, the method further comprises alerting an administrator that a new coordinate space needs review by an expert for labeling.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Baphna US 20210027647, in view of Banerjee US 20180101535, in view of Ardel US 20230325292 and further in view of Gal US 20100190142
Claims 9 and 18: The Baphna reference is silent on the teaching of wherein responsive to detecting that the progression classification is unknown, the method further comprises transmitting an alert to a teacher of the student to provide an intervention.
However, the Gal reference provides a teaching of wherein responsive to detecting that the progression classification is unknown, the method further comprises transmitting an alert to a teacher of the student to provide an intervention (see paragraph 73 and 87).
Therefore, 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 the with the feature of wherein responsive to detecting that the progression classification is unknown, the method further comprises transmitting an alert to a teacher of the student to provide an intervention, as taught by the Gal reference, in order to fulfill the specific need of the student.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Baphna US 20210027647, in view of Banerjee US 20180101535, in view of Ardel US 20230325292 and further in view of Zhang US 10964224
Claim 10 and 19: The Baphna reference is silent on the teaching of wherein the method further comprises responsive to detecting that the progression classification is unknown, classifying the unknown progression classification to have a given progression classification of a nearest cluster.
However, the Zhang reference provides a teaching of wherein the method further comprises responsive to detecting that the progression classification is unknown, classifying the unknown progression classification to have a given progression classification of a nearest cluster (see paragraph 36).
Therefore, 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 the Baphna reference with the feature of wherein the method further comprises responsive to detecting that the progression classification is unknown, classifying the unknown progression classification to have a given progression classification of a nearest cluster, as taught by the Zhang reference, since it would allows the user to maintain focus during the learning session by positively reinforcing the target learning material (see paragraph 36).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dalli US 20220147876
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT J UTAMA whose telephone number is (571)272-1676. The examiner can normally be reached 9:00 - 17:30 Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at (571)270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT J UTAMA/Primary Examiner, Art Unit 3715
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