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 action is in response to Application filed 06/05/2025.
Claims 1-27 are pending.
Priority
This application is claimed as a continuation of U.S. Patent Application No. 17/930,643 filed 09/08/2022 and a continuation in part of U.S. Patent Application No. 18/415,148 filed 01/17/2024, which is a continuation of U.S. Patent Application No. 17/012,259 filed 09/04/2020, which claims priority from U.S. Provisional Patent Application No. 62/896,458 filed 09/05/2019.
The application 17/930,643 provides sufficient support for the claimed invention of this application as requirement under 35 U.S.C. § 112(a) or (pre-AIA ) 35 U.S.C. § 112, first paragraph. Therefore, all claims of this application entitle to the filing date of application 17/930,643, which is 09/08/2022.
In addition, other parent applications provides sufficient support for claims 1-4, 6-7, 18, 21 and 24-27 of this application as currently presented as requirements under 35 U.S.C. § 112(a) or (pre-AIA ) 35 U.S.C. § 112, first paragraph. Therefore, claims 1-4, 6-7, 18, 21 and 24-27 as currently presented entitle to the earliest filing date of these parent application, which is 09/05/2019.
In short, claims 1-4, 6-7, 18, 21 and 24-27 as currently presented have the effective filing date of 09/05/2019 and claims 5, 8-17, 19-20 and 22-23 as currently presented have the effectively filing date of 09/08/2022.
Specification
The disclosure is objected to because of the following informalities:
Regarding paragraph [0001], U.S. Patent Application No. 18/415,148 has been patented, its information should be supplemented with its patent information (e.g., Patent No.).
Appropriate correction is required.
Claim Objections
Claims 1-3, 8-9 and 14-15 are objected to because of the following informalities:
Regarding claim 1, the term “comprise” in line 9 should be “comprises”.
Other dependent claims 2-3 are objected as incorporating the informality of the objected independent claim 1 upon which they depend.
Regarding claim 8, the recited “wherein partitioning the text representation into the one or more portions of text” in line 2 should be “wherein partitioning the text representation into the multiple portions of text” (see claim 4).
Regarding claim 9, the recited “wherein partitioning the text representation into the one or more portions of text” in line 1 should be “wherein partitioning the text representation into the multiple portions of text” (see claim 4).
Regarding claim 14, the recited “one or more portions of text” in line 5 should be “the one or more portions of text” (see line 1). In addition, the term “a first cluster” in line 9 should be “the first cluster” (see claim 1).
Regarding claim 15, the term “an embedding of noun” in line 4 should be “an embedding of the noun”.
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 1-27 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 "the educational content item" in line 16. There is insufficient antecedent basis for this limitation in the claim.
Claim 3 recites the limitation "generating the educational content item based on the cluster of one or more portions of text" in line 1 and/or the limitation “the cluster of one or more portions of text” in line 2 and line 4 and/or the limitation “the educational content item” in line 8. There is insufficient antecedent basis for these limitations in the claim.
Claim 4 recites the limitation "the one or more portions of text" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the probabilistic coherence" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation “the LDA model” in line 4 and the limitation "the probabilistic coherence" in line 5. There is insufficient antecedent basis for these limitations in the claim.
Claim 27 recites the limitation "the one or more portions of text" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Other dependent claims are rejected as incorporating and failing to resolve the deficiency of rejected independent claims 1 and 4 upon which they depend correspondingly.
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 1-10, 16-18, 21-22 and 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing and generating content items.
The claims recite an abstract idea of processing and generating content items based on broadly recited steps of converting, partitioning, extracting, determining, identifying and generating, which are broadly recited steps/concepts that can be performed in the human mind or with the aid of pencil and paper and directed to mental processes grouping of abstract ideas . This judicial exception is not integrated into a practical application because other additional elements including genetic computer components and common computer functionality (e.g., accessing, storing, displaying, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering and displaying) for implementing the abstract idea are not sufficient to integrate the abstract idea into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements include only generic/common computer components (e.g., memory, processor, program instructions, etc.) and generic/common computer functions (e.g., accessing, storing, displaying, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering and displaying), which are not sufficient to amount to significantly more than the recited abstract idea.
Abstract idea analysis as follows:
Step 1:
According to the first part of the analysis, in the instant claims, claims 1-10, 16-18 and 21-22 are directed to a method (i.e. a process) and claim 27 is directed to one or more non-transitory computer readable media encoded with instructions (i.e., an article of manufacture). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture or composition of matter).
Step 2a Prong 1 (claims 1, 4 and 27):
The following limitations recited in claim 1 are abstract ideas that fall under mental processes:
converting, via a processor, a multimedia document into a text representation of the multimedia document (the step of converting as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
partitioning, via the processor, the text representation into multiple portions of text based on a text characteristic of the text representation (the step of partitioning as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
determining, via the processor, one or more educational concepts associated with each portion of the multiple portions of text (the step of determining as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
generating, via the processor, at least a first cluster and a second cluster, wherein each of the first and second clusters comprise one or more portions of text of the multiple portions of text, wherein each portion of respective text in the first cluster is associated with a first educational concept of the one or more educational concepts and each portion of respective text in the second cluster is associated with a second education concept of the one or more educational concepts (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
generating, via the processor, a first educational content item based on the first cluster and a second educational content item based on the second cluster (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
All the limitations above are mental steps that can be performed in the human mind or with the aid of pencil and paper.
The following limitations recited in claims 4 and 27 are abstract ideas that fall under mental processes:
converting, via a processor, a multimedia document into a text representation of the multimedia document (the step of converting as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
partitioning, via the processor, the text representation into multiple portions of text based on a text characteristic of the text representation (the step of partitioning as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
extracting, via the processor, a first feature of a first portion of text of the one or more portions of text, wherein the first feature comprises a representation of a semantic meaning of the first portion of text (the step of extracting as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
determining, via the processor, a first educational concept associated with the first portion of text based on the first feature (the step of determining as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
identifying, via the processor, a first cluster of one or more portions of text of the multiple portions of text based on the first educational concept, wherein the first cluster comprises the first portion of text associated with the first educational concept (the step of identifying as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
generating, via the processor, a first educational content item based on the first cluster (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
generating, via the processor, a first node in a relational knowledge base, wherein the first node comprises the first educational concept and the first educational content item, and wherein the relational knowledge base comprises linking structure linking one or more nodes based on a semantic relationship between one or more educational concepts of the one or more nodes (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
All the limitations above are mental steps that can be performed in the human mind or with the aid of pencil and paper.
Step 2a Prong 2 (Claims 1, 4 and 27):
The following limitations in claims 1, 4 and 27 are additional elements:
generating, via the processor, a user interface configured to display the educational content item (this step of generating a user interface to display as broadly recited being directed to mere data outputting/displaying or insignificant extra-solution activity);
via processor (this element is directed to generic computer or computer component and/or mere instructions for implementing the abstract idea); and
One or more non-transitory computer readable media encoded with instructions which, when executed by one or more processors, cause the one or more processors to (these elements are directed to generic computer or computer components and/or mere instructions for implementing the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions or insignificant extra-solution activity for implementing or applying the abstract. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2b (Claims 1, 4 and 27):
The following limitations in claims 1, 4 and 27 are additional elements:
generating, via the processor, a user interface configured to display the educational content item (this step of generating a user interface to display as broadly recited being directed to mere data outputting/displaying or insignificant extra-solution activity);
via processor (this element is directed to generic computer or computer component and/or mere instructions for implementing the abstract idea); and
One or more non-transitory computer readable media encoded with instructions which, when executed by one or more processors, cause the one or more processors to (these elements are directed to generic computer or computer components and/or mere instructions for implementing the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions or well-understood, routine, conventional activity, and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 2, claim 2 depends on claim 1. As such, claim 2 recites the abstract idea as presented in claim 1.
In addition, claim 2 includes additional elements:
wherein determining the one or more educational concepts associated with each portion of the multiple portions of text comprises:
extracting, via the processor, a feature of each portion, wherein the feature comprises a representation of a semantic meaning of each portion (the step of extracting as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
determining the one or more educational concepts associated with each portion based on the feature of each portion (the step of determining as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
These are additional elements directed to mental processes or the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 3, claim 3 depends on claim 1. As such, claim 3 recites the abstract idea as presented in claim 1.
In addition, claim 3 includes additional elements:
wherein generating the educational content item based on the cluster of one or more portions of text comprises:
generating a node in a relational knowledge base, wherein the node comprises the first educational concept and the cluster of one or more portions of text, and wherein the relational knowledge base comprises linking structure linking one or more nodes based on a semantic relationship between one or more educational concepts of the one or more nodes (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion); and
generating the educational content item based on the node (the step of generating as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion).
These are additional elements directed mental processes or the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 5, claim 5 depends on claim 4. As such, claim 5 recites the abstract idea as presented in claim 4.
In addition, claim 5 includes additional elements:
wherein the multimedia document comprises at least one of an image or video (this limitation specifying the multimedia document, which is directed to mere additional data), and
wherein converting the multimedia document into the text representation comprises utilizing a machine learning image classification model to generate a text description of the at least one of the image or video (the step of converting multimedia into text representation as broadly recited can be mentally performed in the human mind (i.e., abstract idea), this element further recites the converting using a machine learning image classification model, however, the machine learning image classification model recited broadly without specifying how it functions to generate the text description can be interpreted as being directed to generic computer and/or mere instructions for implementing the abstract idea).
These are additional elements directed to mental process (i.e., abstract idea) and/or generic computer and/or mere instructions for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 6, claim 6 depends on claim 4. As such, claim 6 recites the abstract idea as presented in claim 4.
In addition, claim 6 includes additional elements:
wherein the multimedia document comprises at least one of an image or video (this limitation specifying the multimedia document, which is directed to mere additional data), and
wherein converting the multimedia document into the text representation comprises extracting text portrayed in the at least one of the image or video utilizing an optical character recognition method (the step of extracting as broadly recited can be mentally performed in the human mind (i.e., abstract idea), this element further recites using an optical character recognition method, however, an optical character recognition method as broadly recited can be performed by the human mind through mental processes such as observation, evaluation, judgment and opinion).
These are additional elements directed to mental process (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 7, claim 7 depends on claim 4. As such, claim 7 recites the abstract idea as presented in claim 4.
In addition, claim 7 includes additional elements:
wherein converting the multimedia document into the text representation comprises extracting metadata associated with the multimedia document, the metadata comprising at least one of a timestamp, heading level, or location of the text representation relative to the multimedia document (the step of converting and/or extracting as broadly recited without specifying “how” can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion).
These are additional elements directed to mental process (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 8, claim 8 depends on claim 4. As such, claim 8 recites the abstract idea as presented in claim 4.
In addition, claim 8 includes additional elements:
wherein the multimedia document comprises text and other media (this limitation specifying the multimedia document, which is directed to mere additional data), and
wherein partitioning the text representation into the one or more portions of text comprises partitioning the text representation based on at least one of a heading, paragraph, a page coordinate, or section of the multimedia document (the step of partitioning as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion).
These are additional elements directed to mental process (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 9, claim 9 depends on claim 4. As such, claim 9 recites the abstract idea as presented in claim 4.
In addition, claim 9 includes additional elements:
wherein partitioning the text representation into the one or more portions of text comprises:
partitioning, via the processor, one or more words of the text representation into a portion of text based on a threshold number of words (the step of partitioning as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
partitioning, via the processor, an additional word of the text representation into the portion of text until a punctuation is reached (the step of partitioning as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
These are additional elements directed to mental process (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 10, claim 10 depends on claim 4. As such, claim 10 recites the abstract idea as presented in claim 4.
In addition, claim 10 includes additional elements:
wherein extracting the first feature of the first portion of text comprises:
processing, via the processor, the first portion of text to remove a stopword and punctuation from the first portion of text (the step of processing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
processing, via the processor, the first portion of text to remove a first word from the first portion of text based on a high word frequency of the first word in the first portion of text (the step of processing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea);
extracting, via the processor, a second word from the first portion of text based on a low word frequency of the second word in the first portion of text (the step of extracting as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
generating, via the processor, a corpus comprising the second word and the low word frequency of the second word, wherein the corpus is associated with the first educational concept (the step of generating as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
These are additional elements directed to mental processes (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 16, claim 16 depends on claim 4. As such, claim 16 recites the abstract idea as presented in claim 4.
In addition, claim 16 includes additional elements:
wherein the first educational content item comprises the first cluster, a title of the first cluster, and a keyword of the first cluster (this limitation specifying the first education content item, which is directed to mere additional data).
These are additional elements directed to mere additional data, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 17, claim 17 depends on claim 16. As such, claim 17 recites the abstract idea as presented in claim 16.
In addition, claim 17 includes additional elements:
wherein the keyword comprises a set of one or more keywords (this element specifying keyword, which directed to mere additional data), and
wherein generating the first educational content item based on the first cluster further comprises joining, via the processor, the set of one or more keywords to generate the title of the first cluster (the step of generating and joining as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes such as observation, evaluation, judgment and opinion, wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea)
These are additional elements directed to mere additional data and mental process (i.e., abstract idea), which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 18, claim 18 depends on claim 4. As such, claim 18 recites the abstract idea as presented in claim 4.
In addition, claim 18 includes additional elements:
wherein the relational knowledge base comprises the first node and a second node, and wherein the first node and second node are linked by a weighted edge, wherein a weight of the weighted edge represents a probability that an educational concept of the first node is related to an educational concept of the second node (this element specifying the relational knowledge base, which is directed to mere descriptive data/structure).
These are additional elements directed to mere additional data, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 21, claim 21 depends on claim 4. As such, claim 21 recites the abstract idea as presented in claim 4.
In addition, claim 21 includes additional elements:
“receiving, via the processor, a user input directing a revision of the first node” (this step of receiving as broadly recited being directed to mere data gathering recited at high level of generality or insignificant extra-solution activity);
modifying, via the processor, the first node based on the user input (this step of modifying as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper; wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea); and
placing, via the processor, the modified first node in the relational knowledge base based on an educational concept of the modified first node (this step of placing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper; wherein the processor as recited is directed to generic computer or mere instructions for implementing the mental process or abstract idea).
These are additional elements directed to mental processes and insignificant extra-solution activity for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claim 22, claim 22 depends on claim 4. As such, claim 22 recites the abstract idea as presented in claim 4.
In addition, claim 22 includes additional elements:
wherein the user interface is further configured to display:
a title of the first educational content item representing a summary of the first educational content item;
a first keyword representing a dominant educational concept of the first educational content item; and
a second keyword representing a secondary educational concept of the first educational content item (these elements are directed to information to be displayed, which directed to mere data outputting or displaying or being insignificant extra-solution activity).
These are additional elements directed to insignificant extra-solution activity for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
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.
Claims 1-2 (effective filing date 09/05/2019) are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018).
As to claim 1, Zavesky et al. teaches:
“A method for presenting educational content” (see Zavesky et al., Abstract), the method comprising:
“converting, via a processor, a multimedia document into a text representation of the multimedia document” (see Zavesky et al., [0026] for speech-to-text conversion);
“partitioning, via the processor, the text representation into multiple portions of text based on a text characteristic of the text representation” (see Zavesky et al., [0027] for performing content segmentation to generate content segments; also see [0030]);
“determining, via the processor, one or more educational concepts associated with each portion of the multiple portions of text” (see Zavesky et al., [0030]-[0031] for identifying topics (i.e., concepts) in the context sources and/or content segments; also see [0029] for various topics/concepts);
“generating, via the processor, at least a first cluster and a second cluster, wherein each of the first and second clusters comprise one or more portions of text of the multiple portions of text, wherein each portion of respective text in the first cluster is associated with a first educational concept of the one or more educational concepts and each portion of respective text in the second cluster is associated with a second education concept of the one or more educational concepts” (see Zavesky et al., [0032] for clustering of similar content segments based on correspondence or similarities of the topic(s) into clusters; also see [0034]);
“generating, via the processor, a first educational content item based on the first cluster and a second educational content item based on the second cluster” (see Zavesky et al., [0035]-[0036] for selecting/generating content segments from clusters and generating a course based on content segments from a plurality of clusters, wherein each content segment or each course can be interpreted a content item); and
“generating, via the processor, a user interface configured to display the educational content item” (see Zavesky et al., [0037] for displaying the course or content segments to the user; also see [0049]).
As to claim 2, this claim is rejected based on the same arguments as above to reject claim 1 and is similarly rejected including the following:
Zavesky et al. teaches:
“wherein determining the one or more educational concepts associated with each portion of the multiple portions of text comprises” (see Zavesky et al., [0026] and [0030]-[0031] for identifying topics/concepts associated with content):
“extracting, via the processor, a feature of each portion, wherein the feature comprises a representation of a semantic meaning of each portion” (see Zavesky et al., [0026] for word/phrase extraction, image feature extraction and audio feature extraction to identify concepts/topics; also see [0031]); and
“determining the one or more educational concepts associated with each portion based on the feature of each portion” (see Zavesky et al., [0026] and [0030]-[0031] for identifying topics/concepts based on extracted features (e.g., word/phrase, image feature or audio feature, etc.)).
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 3-8, 16, 18, 21-22, and 24-27 (effective filing date 09/05/2019 or 09/08/2022) are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), and further in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018).
As to claim 3, Zavesky et al. teaches all limitation as recited in claim 1 including generating the educational content item based on the cluster of one or more portions of text (see Zavesky et al., [0035]-[0036] for generating a course based on content segments; also see [0031] for tagging content segments with the topic(s) and storing the content segments in a database).
In addition, Zavesky et al. teaches a knowledge base including a lexical database and/or a plurality of lexicons used by the system to determine concepts/topics presented in the content (see [0029]).
However, Zavesky et al. does not explicitly teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding node(s) to the knowledge base as recited as follows:
“wherein generating the educational content item based on the cluster of one or more portions of text comprises:
generating a node in a relational knowledge base, wherein the node comprises the first educational concept and the cluster of one or more portions of text, and wherein the relational knowledge base comprises linking structure linking one or more nodes based on a semantic relationship between one or more educational concepts of the one or more nodes; and
generating the educational content item based on the node”.
On the other hand, Subramanian et al. teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding node(s) to the knowledge base (see Subramanian et al., Abstract and [0074] for a knowledge graph representing a semantic relationship between concepts; also see [0075] for allowing to add node/concept to the knowledge graph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramanian et al.'s teaching to Zavesky et al.’s by implementing a knowledge graph which can be modified/updated to include new concepts. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to manage content sources and/or content segments based on topics/concepts. In addition, both of the references (Zavesky et al. and Subramanian et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, managing content (e.g., learning courses) based on topics/concepts using knowledge base or database of topics/concepts. This close relation between both of the references highly suggests an expectation of success when combined.
As to claim 4, Zavesky et al. teaches:
“A method for presenting educational content” (see Zavesky et al., Abstract), the method comprising:
“converting, via a processor, a multimedia document into a text representation of the multimedia document” (see Zavesky et al., [0026] for speech-to-text conversion);
“partitioning, via the processor, the text representation into multiple portions of text based on a text characteristic of the text representation” (see Zavesky et al., [0027] for performing content segmentation to generate content segments);
“extracting, via the processor, a first feature of a first portion of text of the one or more portions of text, wherein the first feature comprises a representation of a semantic meaning of the first portion of text” (see Zavesky et al., [0026] and [0030]-[0031] for extracting words/phrases, image features or audio features to identify topics/concepts);
“determining, via the processor, a first educational concept associated with the first portion of text based on the first feature” (see Zavesky et al., [0030]-[0031] for identifying topics/concepts associated with content segments; also see [0029]);
“identifying, via the processor, a first cluster of one or more portions of text of the multiple portions of text based on the first educational concept, wherein the first cluster comprises the first portion of text associated with the first educational concept” (see Zavesky et al., [0032] for clustering content segments based on similar topics/concepts into clusters of content segments, wherein each cluster is associated with a topic/concept);
“generating, via the processor, a first educational content item based on the first cluster” (see Zavesky et al., [0032] for tagging content segments with the topic(s) and storing the content segments in the database, wherein each content segment can be interpreted as a content item as recited; also see [0035]-[0036] for generating a course based on content segments from a plurality of clusters, wherein each content segment or each course can be interpreted a content item); and
“generating, via the processor, a user interface configured to display the first educational content item” (see Zavesky et al., [0037] for displaying the course or content segments to the user; also see [0049]).
In addition, Zavesky et al. teaches a knowledge base including a lexical database and/or a plurality of lexicons used by the system to determine concepts/topics presented in the content (see [0029]).
However, Zavesky et al. does not explicitly teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding/generating node(s) to/in the knowledge base as recited as follows:
“generating, via the processor, a first node in a relational knowledge base, wherein the first node comprises the first educational concept and the first educational content item, and wherein the relational knowledge base comprises linking structure linking one or more nodes based on a semantic relationship between one or more educational concepts of the one or more nodes”.
On the other hand, Subramanian et al. teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding/generating node(s) to/in the knowledge base (see Subramanian et al., Abstract and [0074] for a knowledge graph representing a semantic relationship between concepts; also see [0075] for allowing to add node/concept to the knowledge graph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramanian et al.'s teaching to Zavesky et al.’s by implementing a knowledge graph which can be modified/updated to include new concepts. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to manage content sources and/or content segments based on topics/concepts. In addition, both of the references (Zavesky et al. and Subramanian et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, managing content (e.g., learning courses) based on topics/concepts using knowledge base or database of topics/concepts. This close relation between both of the references highly suggests an expectation of success when combined.
As to claim 5, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the multimedia document comprises at least one of an image or video, and wherein converting the multimedia document into the text representation comprises utilizing a machine learning image classification model to generate a text description of the at least one of the image or video” (see Zavesky et al., [0025] for different types of content sources (e.g., text documents, slide representation, audio programs, videos, images, etc.); also see [0031] for using classifiers to identify topics in scenes, wherein any text to identify a topic in a scene can be interpreted as a text description as recited).
As to claim 6, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the multimedia document comprises at least one of an image or video, and wherein converting the multimedia document into the text representation comprises extracting text portrayed in the at least one of the image or video utilizing an optical character recognition method” (see Zavesky et al., [0025] for different types of content sources (e.g., text documents, slide representation, audio programs, videos, images, etc.); also see [0026] for using optical character recognition (OCR) image processing to extract text from image).
As to claim 7, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein converting the multimedia document into the text representation comprises extracting metadata associated with the multimedia document, the metadata comprising at least one of a timestamp, heading level, or location of the text representation relative to the multimedia document” (see Zavesky et al., [0026] for speech-to-text conversion; also see [0027] for extracting metadata from content sources; also see [0032] for metadata of content segments including title (i.e., heading)).
As to claim 8, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the multimedia document comprises text and other media, and wherein partitioning the text representation into the one or more portions of text comprises partitioning the text representation based on at least one of a heading, paragraph, a page coordinate, or section of the multimedia document” (see Zavesky et al., [0025] for different types of content sources (e.g., text documents, slide representation, audio programs, videos, images, etc.); also see [0027] for content segmentation using metadata of the content sources, e.g., explicit chaptering of lessons (i.e., representing section of the content source)).
As to claim 16, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the first educational content item comprises the first cluster, a title of the first cluster, and a keyword of the first cluster” (see Zayesky et al., [0031]-[0032] for tagging and clustering content segments based on topics, wherein each content segment is associated with a topic/concept, wherein the topic/concept can be interpreted as a title or a keyword of a cluster).
As to claim 18, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the relational knowledge base comprises the first node and a second node, and wherein the first node and second node are linked by a weighted edge, wherein a weight of the weighted edge represents a probability that an educational concept of the first node is related to an educational concept of the second node” (see Zavesky et al., [0029] for a lexical database as knowledge base; also see Subramanian et al., Abstract, [0046]-[0047] and Fig. 10 for a knowledge graph including concepts as nodes/vertices and relationships between concepts as edges between nodes/vertices of the knowledge graph (i.e., a relationship knowledge base)).
As to claim 21, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“ generating the first node in a relational knowledge base comprises” (see Zavesky et al., [0029] for a lexical database as a relational knowledge base; also see Subramanian et al., Fig. 10 and [0046]-[0047] for generating a knowledge graph by adding node(s)/concept(s) to the knowledge graph):
“receiving, via the processor, a user input directing a revision of the first node” (see Zavesky et al., [0037] for user interface; also see Subramanian et al., [0074] for generating a knowledge graph based on input to the knowledge graph generator);
“modifying, via the processor, the first node based on the user input” (see Subramanian et al., [0074] for generating a knowledge graph based on input to the knowledge graph generator; also see [0081] for modifying the learner journal map (i.e., a portion of knowledge graph) based on the learner’s history/input); and
“placing, via the processor, the modified first node in the relational knowledge base based on an educational concept of the modified first node” (see Subramanian et al., [0081] for modifying the learner journal map (i.e., a portion of knowledge graph) by highlighting particular nodes/concepts based on the learner’s history/input).
As to claim 22, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the user interface is further configured to display” (see Zavesky et al., [0037] for displaying content segments (i.e., content items) to a user):
“a title of the first educational content item representing a summary of the first educational content item” (see Zavesky et al., [0037] presenting to a user a list of content segment (i.e. content items) including titles of the content segments);
“a first keyword representing a dominant educational concept of the first educational content item” (see Zavesky et al., [0037] presenting to a user a list of content segment (i.e. content items) including titles of the content segments and other information including the core topic (i.e., a first keyword)); and
“a second keyword representing a secondary educational concept of the first educational content item” (see Zavesky et al., [0037] presenting to a user a list of content segment (i.e. content items) including titles of the content segments and other information including other topics (i.e., second keywords)).
As to claim 24, this claim is rejected based on the same arguments as above to reject claim 4 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“evaluating, via the processor, a user understanding of the first educational concept based on a user response related to the first educational content item” (see Zavesky et al., [0045] for determining whether a user has knowledge regarding core topics/concepts based on user access history);
“identifying, via the processor, a second node in the relational knowledge base related to the first educational concept based on the user understanding of the first educational concept, wherein the second node comprises a second educational concept and a second educational content item associated with the second educational concept” (see Zavesky et al., [0045] for selecting content segments related to other core topics/concepts); and
“configuring, via the processor, the user interface to display the second educational content item” (see Zavesky et al., [0037] for presenting selected content segments (i.e., content items) to a user).
As to claim 25, this claim is rejected based on the same arguments as above to reject claim 24 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the user understanding of the first educational concept is above a threshold, and wherein identifying the second node based on the user understanding of the first educational concept comprises identifying the second node wherein the second educational concept is substantially distinct from the first educational concept” (see Zavesky et al., [0045]-[0047] for determining that a user has knowledge relating to a topic/concept or user is confused relating a topic/concept based on a number of repeated accesses, and selecting content segments of other topics/concepts if user has knowledge relating to a particular topic/concept).
As to claim 26, this claim is rejected based on the same arguments as above to reject claim 24 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. teaches:
“wherein the user understanding of the first educational concept is below a threshold, and wherein identifying the second node based on the user understanding of the first educational concept comprises identifying the second node wherein the second educational concept is substantially similar to the first educational concept” ” (see Zavesky et al., [0045]-[0047] for determining that a user has knowledge relating to a topic/concept or user is confused relating a topic/concept based on a number of repeated accesses, and selecting content segments of other topics/concepts if user has knowledge relating to a particular topic/concept, otherwise, selecting content segments form the same topic/concept).
As to claim 27, Zavesky et al. teaches:
“One or more non-transitory computer readable media encoded with instructions which, when executed by one or more processors, cause the one or more processors to” (see Zavesky et al., Abstract):
“convert a multimedia document into a text representation of the multimedia document” (see Zavesky et al., [0026] for speech-to-text conversion);
“partition the text representation into multiple portions of text based on a text characteristic of the text representation” (see Zavesky et al., [0027] for performing content segmentation to generate content segments);
“extract a first feature of a first portion of text of the one or more portions of text, wherein the first feature comprises a representation of a semantic meaning of the first portion of text” (see Zavesky et al., [0026] and [0030]-[0031] for extracting words/phrases, image features or audio features to identify topics/concepts);
“determine a first educational concept associated with the first portion of text based on the first feature” (see Zavesky et al., [0030]-[0031] for identifying topics/concepts associated with content segments; also see [0029]);
“identify a first cluster of one or more portions of text of the multiple portions of text based on the first educational concept, wherein the first cluster comprises the first portion of text associated with the first educational concept” (see Zavesky et al., [0032] for clustering content segments based on similar topics/concepts into clusters of content segments, wherein each cluster is associated with a topic/concept);
“generate a first educational content item based on the first cluster” (see Zavesky et al., [0032] for tagging content segments with the topic(s) and storing the content segments in the database, wherein each content segment can be interpreted as a content item as recited; also see [0035]-[0036] for generating a course based on content segments from a plurality of clusters, wherein each content segment or each course can be interpreted a content item); and
“generate a user interface configured to display the first educational content item” (see Zavesky et al., [0037] for displaying the course or content segments to the user; also see [0049]).
In addition, Zavesky et al. teaches a knowledge base including a lexical database and/or a plurality of lexicons used by the system to determine concepts/topics presented in the content (see [0029]).
However, Zavesky et al. does not explicitly teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding/generating node(s) to/in the knowledge base as recited as follows:
“generate a first node in a relational knowledge base, wherein the first node comprises the first educational concept and the first educational content item, and wherein the relational knowledge base comprises linking structure linking one or more nodes based on a semantic relationship between one or more educational concepts of the one or more nodes”.
On the other hand, Subramanian et al. teaches a knowledge base including linking structure that link nodes based on semantic relationship and a feature for adding/generating node(s) to/in the knowledge base (see Subramanian et al., Abstract and [0074] for a knowledge graph representing a semantic relationship between concepts; also see [0075] for allowing to add node/concept to the knowledge graph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Subramanian et al.'s teaching to Zavesky et al.’s by implementing a knowledge graph which can be modified/updated to include new concepts. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to manage content sources and/or content segments based on topics/concepts. In addition, both of the references (Zavesky et al. and Subramanian et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, managing content (e.g., learning courses) based on topics/concepts using knowledge base or database of topics/concepts. This close relation between both of the references highly suggests an expectation of success when combined.
Claim 9 (effective filing date 09/08/2022) is rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), and further in view of Golden et al. (U.S. Publication No. 2003/0018470, Publication date 01/23/2003).
As to claim 9, Zavesky et al. as modified by Subramanian et al. teaches all limitations as recited in claim 4 including segmenting/partitioning content source into content segments (see Zayesky et al., [0027] and [0030]).
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature for separating text into sub-portions based on a minimum text length threshold and a maximum text length threshold as recited as follows:
“wherein partitioning the text representation into the one or more portions of text comprises:
partitioning, via the processor, one or more words of the text representation into a portion of text based on a threshold number of words; and
partitioning, via the processor, an additional word of the text representation into the portion of text until a punctuation is reached”.
On the other hand, Golden et al. explicitly teaches a feature for separating text into sub-portions based on segment delimiter, a minimum text length threshold and a maximum text length threshold as recited as follows:
“partitioning, via the processor, one or more words of the text representation into a portion of text based on a threshold number of words” (see Golden et al., Fig. 6, element 200 for defining current text segment as word sequence of minimum length (i.e., minimum text length threshold); also see [0051]); and
“partitioning, via the processor, an additional word of the text representation into the portion of text until a punctuation is reached” (see Golden et al., Fig. 6, element 214 for adding next word to current text segment until maximum text segment length (i.e., maximum text length threshold) has been reached; also see [0051] for adding an additional word, at a time, to the text segment and see [0016] for segmenting based on segment delimiter (e.g., a punctuation)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Golden et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of partitioning text into segments based on a minimum/maximum text length threshold and segment delimiter. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to identify segments from a text. In addition, both of the references (Zavesky et al. and Golden et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, identifying different portions/topics within a document/file. This close relation between both of the references highly suggests an expectation of success when combined.
Claims 10 and 13 (effective filing date 09/08/2022) are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), and further in view of Walthers et al. (U.S. Publication No. 2018/0322508, Publication date 11/08/2018).
As to claim 10, Zavesky et al. as modified by Subramanian et al. teaches all limitation as recited in claim 4 including extracting the first feature of the first portion of text (see Zavesky et al., [0039] for extracting word/phrase (i.e., first feature) of the converted text).
In addition, Zavesky et al. as modified by Subramanian et al. teach wherein extracting the first feature of the first portion of text comprises:
“extracting, via the processor, a second word from the first portion of text based on a low word frequency of the second word in the first portion of text” (see Zavesky et al., [0041] for extracting word/phrase based on a word frequency or a phrase frequency); and
“generating, via the processor, a corpus comprising the second word and the low word frequency of the second word, wherein the corpus is associated with the first educational concept” (see Zavesky et al., [0041] for storing the word or the phrase in association with a topic in a database (i.e. a corpus)).
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature of preprocessing the text to remove unnecessary items as recited as follows:
“processing, via the processor, the first portion of text to remove a stopword and punctuation from the first portion of text; and
processing, via the processor, the first portion of text to remove a first word from the first portion of text based on a high word frequency of the first word in the first portion of text”.
In the other hand, Walthers et al. explicitly teaches a feature of preprocessing the text to remove unnecessary items as recited as follows:
“processing, via the processor, the first portion of text to remove a stopword and punctuation from the first portion of text” (see Walthers et al., [0038] for ignoring/removing punctuation, stopwords); and
processing, via the processor, the first portion of text to remove a first word from the first portion of text based on a high word frequency of the first word in the first portion of text” (see [0038] for ignoring/removing frequent words (i.e., words with high frequency of occurrence)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Walthers et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of pre-processing text to remove unnecessary character/word items form text. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to identify and extract keywords. In addition, both of the references (Zavesky et al. and Walthers et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, processing text portions to extract data and cluster the same according to concept/category. This close relation between both of the references highly suggests an expectation of success when combined when combined.
As to claim 13, Zavesky et al. as modified by Subramanian et al. teaches all limitation as recited in claim 4 including extracting the first feature of the first portion of text (see Zavesky et al., [0039] for extracting word/phrase (i.e., first feature) of the converted text).
In addition, Zavesky et al. as modified by Subramanian et al. teach:
“utilizing, via the processor, a machine learning transformer model to generate an embedding representing a semantic meaning of the first portion of text, wherein the embedding comprises a high-dimensional vector encoding of the semantic meaning of the first portion of text” (see Zavesky et al., [0040] for the disclosure of an n-dimensional feature space in identifying/clustering similar content segments, which suggests that each content segment can be represented with a representation based on n-features (e.g., embedding or vector)); and
“placing, via the processor, the embedding in a high-dimensional semantic space based on the high-dimensional vector encoding” (see Zavesky et al., [0040] for the disclosure of an n-dimensional feature space in identifying/clustering similar content segments, which suggests that each content segment can be represented with a representation based on n-features (e.g., embedding or vector) in the n-dimensional feature space).
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature of preprocessing the text to remove unnecessary items as recited as follows:
“processing, via the processor, the first portion of text to remove a punctuation from the first portion of text”.
In the other hand, Walthers et al. explicitly teaches a feature of preprocessing the text to remove unnecessary items as recited as follows:
“processing, via the processor, the first portion of text to remove a punctuation from the first portion of text” (see Walthers et al., [0038] for ignoring/removing punctuation from text).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Walthers et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of pre-processing text to remove unnecessary character/word items form text. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to identify and extract keywords. In addition, both of the references (Zavesky et al. and Walthers et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, processing text portions to extract data and cluster the same according to concept/category. This close relation between both of the references highly suggests an expectation of success when combined when combined.
Claims 11-12 and 14-15 (effective filing date 09/08/2022) are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), in view of Walthers et al. (U.S. Publication No. 2018/0322508, Publication date 11/08/2018), and further in view of Jacob et al. (U.S. Publication No. 2021/0397639, Publication date 12/23/2021).
As to claim 11, Zavesky et al. as modified by Subramanian et al. and Walther et al. teaches all limitation as recited in claim 10 including identifying the first cluster of one or more portions of text based on the first educational concept (see Zavesky et al., [0032] and [0034] for identifying a cluster of similar content segments).
However, Zavesky et al. as modified by Subramanian et al. and Walthers et al. does not explicitly teach a feature of using a latent Dirichlet allocation (LDA) model to identify cluster(s) as recited as follows:
“wherein identifying the first cluster of one or more portions of text based on the first educational concept comprises:
generating, via the processor, a coherence score of a cluster of one or more portions of text of the multiple portions of text using a latent Dirichlet allocation (LDA) model, wherein the LDA model evaluates the probabilistic coherence of the cluster of one or more portions of text with the first educational concept based on the corpus;
iteratively varying, via the processor, the size of the cluster by adding a portion of text of the multiple portions of text to the cluster or removing a portion of text from the cluster and generating a coherence score for each variation using the LDA model; and
assigning, via the processor, the cluster with the maximum coherence score as the first cluster of one or more portions of text associated with the first educational concept”.
On the other hand, Jacob et al. explicitly teaches a feature of using a latent Dirichlet allocation (LDA) model to identify cluster(s) as recited as follows:
“wherein identifying the first cluster of one or more portions of text based on the first educational concept comprises” (see Jacob et al., [0019] for using a LDA model to identify similar texts (i.e., a cluster)):
“generating, via the processor, a coherence score of a cluster of one or more portions of text of the multiple portions of text using a latent Dirichlet allocation (LDA) model, wherein the LDA model evaluates the probabilistic coherence of the cluster of one or more portions of text with the first educational concept based on the corpus” (see Jacob et al., [0019]-[0020] and [0025] for using a LDA model to generate a topic score indicating the strength of match between a particular case record (i.e., a portion of text) and a topic/cluster, wherein the score is generated based on a set of topics/words (i.e., corpus of words/topics));
“iteratively varying, via the processor, the size of the cluster by adding a portion of text of the multiple portions of text to the cluster or removing a portion of text from the cluster and generating a coherence score for each variation using the LDA model” (see Jacob et al., [0039] for allowing a user to change the cluster size by selecting a new cluster size to regenerate the clusters); and
“assigning, via the processor, the cluster with the maximum coherence score as the first cluster of one or more portions of text associated with the first educational concept” (see Jacob et al., [0034]-[0035] and [0048] for assigning each case record (i.e., portion of text) to topic/cluster based on the highest topic score for each case record; also see [0019]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jacob et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al. and Walthers et al.) by adding a feature of clustering portions of text using a LDA model. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to cluster similar portions of text according to topics/concepts. In addition, both of the references (Zavesky et al. and Jacob et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, processing text portions to extract data and cluster the same according to concept/category. This close relation between both of the references highly suggests an expectation of success when combined when combined.
As to claim 12, Zavesky et al. as modified by Subramanian et al. and Walther et al. teaches all limitation as recited in claim 10 including generating the first education content item based on the first cluster (see Zavesky et al., [0036] for selecting/generating a content segment from each of clusters).
However, Zavesky et al. as modified by Subramanian et al. and Walthers et al. does not explicitly teach a feature of using a latent Dirichlet allocation (LDA) model to identify topics and/or cluster(s) as recited as follows:
“extracting, via the processor, a noun from the first cluster;
generating, via the processor, a coherence score of the noun using the LDA model, wherein the LDA model evaluates the probabilistic coherence of the noun with the first educational concept based on the corpus; and
selecting, via the processor, the noun as a keyword representing the first cluster based on the coherence score”.
On the other hand, Jacob et al. explicitly teaches a feature of using a latent Dirichlet allocation (LDA) model to identify topics and/or cluster(s) as recited as follows:
“extracting, via the processor, a noun from the first cluster” (see Jacob et al., [0048] for extracting words from a case record);
“generating, via the processor, a coherence score of the noun using the LDA model, wherein the LDA model evaluates the probabilistic coherence of the noun with the first educational concept based on the corpus” (see Jacob et al., [0048] for generating a topic score of those extracted word with respect to a topic using LDS model); and
“selecting, via the processor, the noun as a keyword representing the first cluster based on the coherence score” (see Jacob et al., [0048]-[0050] for selecting the topic (i.e., matching word) as the topic for each case report or cluster of similar case records).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jacob et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al. and Walthers et al.) by adding a feature of clustering portions of text using a LDA model. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to cluster similar portions of text according to topics/concepts. In addition, both of the references (Zavesky et al. and Jacob et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, processing text portions to extract data and cluster the same according to concept/category. This close relation between both of the references highly suggests an expectation of success when combined when combined.
As to claim 14, Zavesky et al. as modified by Subramanian et al. and Walther et al. teaches all limitation as recited in claim 13 including identifying the first cluster of one or more portions of text based on the first educational concept (see Zavesky et al., [0032] and [0034] for identifying a cluster of similar content segments).
However, Zavesky et al. as modified by Subramanian et al. and Walthers et al. does not explicitly teach a feature of clustering using a k-means clustering method to identify topics and/or cluster(s) as recited as follows:
“wherein identifying the first cluster of one or more portions of text of the multiple portions of text based on the first educational concept comprises:
generating, via the processor, a cluster of one or more embeddings of the high- dimensional semantic space via a k-means clustering method, wherein the one or more embeddings are associated with one or more portions of text of the multiple portions of text;
determining, via the processor, an optimal number of clusters of one or more embeddings by iteratively varying the number of clusters and maximizing a silhouette score for the one or more embeddings of the number of clusters; and
identifying, via the processor, a first cluster based on the optimal number of clusters”.
On the other hand, Jacob et al. explicitly teaches a feature of clustering using a k-means clustering method to identify topics and/or cluster(s) as recited as follows:
“wherein identifying the first cluster of one or more portions of text of the multiple portions of text based on the first educational concept comprises” (see Jacob et al., [0021] for the clustering module)
“generating, via the processor, a cluster of one or more embeddings of the high- dimensional semantic space via a k-means clustering method, wherein the one or more embeddings are associated with one or more portions of text of the multiple portions of text” (see Jacob et al., [0021] for converting each topic into a feature vector and clustering the feature vectors using a clustering algorithm (e.g., K-Means); also see [0035]);
“determining, via the processor, an optimal number of clusters of one or more embeddings by iteratively varying the number of clusters and maximizing a silhouette score for the one or more embeddings of the number of clusters” (see Jacob et al., [0039] for allowing a user to change the cluster size to optimize the clustering; also see [0037]); and
“identifying, via the processor, a first cluster based on the optimal number of clusters” (see Jacob et al., [0037] for selecting a cluster based on cluster size).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jacob et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al. and Walthers et al.) by adding a feature of clustering using a k-means clustering method. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to cluster similar portions of text according to topics/concepts. In addition, both of the references (Zavesky et al. and Jacob et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, processing text portions to extract data and cluster the same according to concept/category. This close relation between both of the references highly suggests an expectation of success when combined when combined.
As to claim 15, this claim is rejected based on the same arguments as above to reject claim 14 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al., Walthers et al. and Jacob et al. teaches:
“wherein generating the first educational content item based on the first cluster comprises” (see Zavesky et al., [0036] for selecting/generating a content segment from each of the clusters):
“extracting, via the processor, a noun from the first cluster” (see Zavesky et al., [0031] for extracting keywords or phrases from content segments; also see Jacob et al., [0048] for extracting word from a case record);
“generating, via the processor, an embedding of noun via the machine learning transformer model, wherein the embedding of the noun comprises a high-dimensional vector encoding of the semantic meaning of the noun” (see Zavesky et al., [0040] for representing a content item (e.g., a word) in an n-dimensional feature space; also see Jacob et al., [0021] for representing a word/topic as a feature vector);
“generating, via the processor, an embedding of the first cluster via the machine learning transformer model, wherein the embedding of the first cluster comprises a high- dimensional vector encoding of the semantic meaning of the first cluster” (see Zavesky et al., [0040] for representing a content item (e.g., a word) in an n-dimensional feature space; also see Jacob et al., [0021] for representing a word/topic as a feature vector);
“generating, via the processor, a similarity score based on a comparison of the embedding of the noun and the embedding of the first cluster” (see Zavesky et al., [0040] for comparing content segments in the n-dimensional feature space using a distance within the n-dimensional feature space, wherein the distance as disclosed can be interpreted as a similarity score as recited; also see Jacob et al., [0021] and [0035] for clustering similar topics (i.e., similar feature vectors) based on similar feature vectors in a feature vector space); and
“selecting, via the processor, the noun as a keyword representing the first cluster based on the similarity score” (see Zavesky, [0040] and [0031] for clustering and tagging content segments with topic(s); Jacob et al., [0034]).\
Claim 17 (effective filing date 09/08/2022) is rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), and further in view of Lagad et al. (U.S. Publication No. 2008/0222140, Publication date 09/11/2008).
As to claim 17, Zavesky et al. as modified by Subramanian et al. teaches all limitations as recited in claim 16.
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature of generating a title for a cluster by joining keywords associated with the cluster as equivalently recited as follows:
“wherein the keyword comprises a set of one or more keywords, and wherein generating the first educational content item based on the first cluster further comprises joining, via the processor, the set of one or more keywords to generate the title of the first cluster”
On the other hand, Lagad et al. explicitly teaches a feature of generating a title for a cluster by joining keywords associated with the cluster (see Lagad et al., [0109] wherein the title of the merged cluster is a set of all keywords common to both cluster titles; also see [0061] wherein cluster title consists of the most suitable keywords selected from the abstract and/or title of the returned web snippets).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lagad et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of generating a title for a cluster based on keywords associated with the cluster. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to generate a title for a cluster. In addition, both of the references (Zavesky et al. and Lagad et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, clustering content items. This close relation between both of the references highly suggests an expectation of success when combined.
Claims 19-20 (effective filing date 09/08/2022) are rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), and further in view of Mohassel et al. (CN-114730389-A, Publication date 07/08/2022).
As to claim 19, Zavesky et al. as modified by Subramanian et al. teaches all limitations as recited in claim 4.
In addition, Zavesky et al. as modified by Subramanian et al. teaches:
“wherein generating the first node in the relational knowledge base comprises” (see Subramanian et al., Fig. 10 and [0047] for adding each concept as a vertex of the knowledge graph (i.e., the relational knowledge base)):
“placing, via the processor, the first node in the relational knowledge base based on an educational concept of the first node” (see Subramanian et al., Fig. 10 and [0047] for adding each concept of a course as a vertex of the knowledge graph (i.e., the relational knowledge base)); and
“grouping, via the processor, the first node and a second node in the relational knowledge base into a content grouping based on a probability that the educational concept of the first node is related to an educational concept of the second node” (see Zavesky et al., [0032] for clustering similar content segments into clusters; also see Subramanian et al., Fig. 10 and [0047] for arranging concepts/nodes based on concept similarity).
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature for calculating centroid of the cluster based on average location of nodes/members recited as follows:
“determining, via the processor, a centroid of the content grouping based on an average location of the location of the first node and the second node; and
iteratively adding, via the processor, one or more additional nodes to the content grouping and re-calculating the centroid of the content grouping”.
On the other hand, Mohassel et al. explicitly teaches a feature for calculating centroid of the cluster based on average location of nodes/members recited as follows:
“determining, via the processor, a centroid of the content grouping based on an average location of the location of the first node and the second node” (see Mohassel et al., [page 11, lines 16-20] for calculating a center/centroid of a cluster based on an average location of all data points/nodes in the cluster); and
“iteratively adding, via the processor, one or more additional nodes to the content grouping and re-calculating the centroid of the content grouping” (see Mohassel et al., [page 11, lines 16-20] for recalculating a center/centroid of a cluster based on an average location of all data points/nodes in the cluster as new point(s) added to the cluster).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mohassel et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of generating a center/centroid of a cluster. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to manage a cluster with a center/centroid. In addition, both of the references (Zavesky et al. and Mohassel et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, clustering content items. This close relation between both of the references highly suggests an expectation of success when combined.
As to claim 20, this claim is rejected based on the same arguments as above to reject claim 19 and is similarly rejected including the following:
Zavesky et al. as modified by Subramanian et al. and Mohassel et al. teaches:
“wherein the user interface is further configured to display a second educational content item of the second node of the content grouping” (see Zavesky et al., [0037] for displaying a list of content items wherein each content item can be selected from different clusters (see [0036]) wherein each content segment is a content item as recited).
Claim 23 (effective filing date 09/08/2022) is rejected under 35 U.S.C. 103 as being unpatentable over Zavesky et al. (U.S. Publication No. 2019/0287415, effectively filed date 03/14/2018), in view of Subramanian et al. (U.S. Publication No. 2019/0354887, effectively filed date 05/18/2018), and further in view of Brennan et al. (U.S. Publication No. 2015/0088888, Publication date 03/26/2015).
As to claim 23, Zavesky et al. as modified by Subramanian et al. teaches all limitations as recited in claim 22 including a user interface for viewing content segments (i.e., content items) (see Zavesky et al., [0037]).
However, Zavesky et al. as modified by Subramanian et al. does not explicitly teach a feature of updating a file/document with a key/label and concept indicators to identify sub-portions within the file and displaying the updated file as equivalently recited as follows:
“first visual emphasis in the first educational content item indicating a sub -portion of text of the first educational content item related to the first keyword; and
a second visual emphasis in the first educational content item indicating a sub- portion of text of the first educational content item related to the second keyword”.
On the other hand, Brennan et al. explicitly teaches a feature of updating a file with a key/label and concept indicators to identify sub-portions within the file and displaying the updated file (see Brennan et al., Fig. 6 and [0124] wherein information in the box at the top defining different types of dashed lines (i.e., concept indicators) associated with different concepts/topics can be interpreted as a key) as equivalently recited as follows:
“first visual emphasis in the first educational content item indicating a sub -portion of text of the first educational content item related to the first keyword” (see Brennan et al., Fig. 6 and [0124] wherein different types of dashed lines represent different visual emphasis); and
“a second visual emphasis in the first educational content item indicating a sub- portion of text of the first educational content item related to the second keyword” (see Brennan et al., Fig. 6 and [0124]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brennan et al.'s teaching to Zavesky et al.’s system (as modified by Subramanian et al.) by adding a feature of updating a file with concept indicators to identify sub-portions within the file and displaying the updated file. Ordinarily skilled artisan would have been motivated to do so to provide Zavesky et al.’s system with an effective way to display a file/document including portions associated with different topics/concepts to a user. In addition, both of the references (Zavesky et al. and Brennan et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, identifying different portions and topics/concepts associated with different portions of a document/file. This close relation between both of the references highly suggests an expectation of success when combined.
Conclusion
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/Phuong Thao Cao/Primary Examiner, Art Unit 2164
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