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 § 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 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 of this title, 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 1-3, 7-11, 15-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11321538 to Fontecilla (“Fontecilla”) in view of U.S. Patent Application Publication No. 20170178264 to Cosker et al. (“Cosker”).
As to claims 1 and 9, Fontecilla discloses a system, a method for evaluating and comparing credentials from separate institutions, the system comprising: a server platform [column 5 lines 6-15, column 34 lines 54-61], wherein the server platform includes a processor [Fig. 11: 1110] and a memory [Fig. 11: 1120]; a data collection module of the server platform configured to receive one or more text documents from a user device [Fig. 1B: 164, column 6 lines 24-37]; a machine learning module [column 4 lines 38-34, column 5 lines 16-25] of the server platform configured to break up each of the one or more text documents into a plurality of sections based on structural and/or semantic qualities of the one or more text documents [column 4 lines 41-51, column 7 lines 8-30:, column 8 lines 44-53, also see Figs 3, 4B] ; a natural language processing (NLP) module of the server platform configured to perform semantic analysis on the one or more text documents [column 4 lines 38-34, column 8 lines 44-53]; wherein the data collection module is configured to retrieve one or more additional text documents from one or more field knowledge databases [Fig. 1B: 162, column 5 lines 26-41, also see Fig. 1A: 134 (Document database), column 9 line 62 to column 10 line 11]; wherein an assessment scale generator of the server platform generates similarity scores between the plurality of sections of the one or more text documents and a plurality of sections of the one or more additional text documents [column 4 lines 44-51, column 8 lines 32-53: “NLP model 178 may be configured to determine how semantically similar two text tokens are, two sentences are, two sections are, two documents are, or a combination thereof… Section semantic scores 182 representing the semantic similarity score of each section of first document 162, second document 164, or both”];
Fontecilla does not expressly disclose evaluating and comparing credentials from separate institutions and wherein the one or more text documents include one or more academic transcripts. However, it is known to apply machine-learning and natural language processing techniques to determine semantic similarity between documents (as taught by Fontecilla), and such techniques can be adapted for use in evaluating and comparing academic transcripts from different institutions.
In the same or similar field of invention, Cosker discloses evaluating and comparing credentials from separate institutions [Cosker Abstract, paragraphs 0021, and wherein the one or more text documents include one or more academic transcripts [Cosker Fig. 3, Fig. 4, Abstract, paragraphs 0006, 0036-0037].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Fontecilla to have features of evaluating and comparing credentials from separate institutions and wherein the one or more text documents include one or more academic transcripts as taught by Cosker. The suggestion/motivation would have been to provide systems and methods for evaluating a student transcript to determine the eligibility of courses taken at other universities for credits toward a degree program offered by the admitting university [Cosker paragraph 0006].
As to claims 2 and 10, Fontecilla discloses wherein the assessment scale generator receives user-defined criteria defining one or more types of sections of the one or more text documents are high relevance, low relevance, or no relevance [column 19 lines 15-20, lines 47-65 (“threshold score may be a numerical value between 0.0 and 1.0, such as 0.7, 0.8, 0.9, 0.95, or other values…” “whether the text token similarity score is greater than or equal to the text token similarity threshold score” (e.g. if similarity score is greater than threshold then “high relevance”), “if the text token similarity score is less than the text token similarity threshold score “ (e.g. if similarity score is lower than threshold then “low relevance”, and if similarity score is 0 then “no relevancy”), column 20 lines 20-32, also see column 21 lines 43-56, column 22 lines 132, 32-36, 43-65], and wherein the assessment scale generator generates the similarity scores in part based on the received user-defined criteria [column 19 lines 54-65: “a text token similarity threshold score for a text token may be compared to the text token similarity threshold score to determine whether the text token similarity score is greater than or equal to the text token similarity threshold score. If so, then that text token may be classified as being “well represented” within a corresponding section of second document 300. However, if the text token similarity score is less than the text token similarity threshold score, then that text token may be classified as being “not well represented” within the corresponding section of second document 300“, column 20 lines 20-32, also see column 22 line 66 to column 23 line 13].
As to claims 3 and 11, Cosker discloses wherein the one or more text documents and the one or more additional text documents include one or more course descriptions and/or one or more syllabi for one or more academic courses [paragraphs 0045-47, 0050]. In addition, the same motivation is used as the rejection of claims 1 and 9.
As to claims 7, 15 and 19, Fontecilla discloses wherein the similarity scores are percentage values [column 9 lines 13-18, column 19 lines 12-20 (“The semantic similarity scores may be numerical values between 0.0 and 1.0…”, these scores can be easily converted into percentage value such as 0% to 100% (see Table 2), column 31 lines 25-41].
As to claims 8, 16 and 20, Fontecilla discloses wherein the assessment scale generator further generates a plurality of subscores analyzing similarity of one or more specific lines or one or more specific paragraphs of the one or more text documents relative to the one or more additional text documents [column 19 lines 21-36: “the semantic similarity scores for each text token in each requirement sentence (e.g., a sentence including the predefined keyword) of a section of first document 200 may be combined to generate a section semantic score for a given section…”].
As to claim 17, Fontecilla discloses a system for evaluating and comparing credentials from separate institutions, comprising: a server platform [column 5 lines 6-15, column 34 lines 54-61], wherein the server platform includes a processor [Fig. 11: 1110] and a memory [Fig. 11: 1120]; a data collection module of the server platform configured to receive one or more text documents from a user device [Fig. 1B: 164, column 6 lines 24-37]; a machine learning module [column 4 lines 38-34, column 5 lines 16-25] of the server platform configured to break up each of the one or more text documents into a plurality of sections based on structural and/or semantic qualities of the one or more text documents [column 4 lines 41-51, column 7 lines 8-30:, column 8 lines 44-53, also see Figs 3, 4B] ; a natural language processing (NLP) module of the server platform configured to perform semantic analysis on the one or more text documents [column 4 lines 38-34, column 8 lines 44-53]; wherein the data collection module is configured to retrieve one or more additional text documents from one or more field knowledge databases [Fig. 1B: 162, column 5 lines 26-41, also see Fig. 1A: 134 (Document database), column 9 line 62 to column 10 line 11]; wherein an assessment scale generator of the server platform generates similarity scores between the plurality of sections of the one or more text documents and a plurality of sections of the one or more additional text documents [column 4 lines 44-51, column 8 lines 32-53: “NLP model 178 may be configured to determine how semantically similar two text tokens are, two sentences are, two sections are, two documents are, or a combination thereof… Section semantic scores 182 representing the semantic similarity score of each section of first document 162, second document 164, or both”];
wherein the assessment scale generator receives user-defined criteria defining one or more types of sections of the one or more text documents are high relevance, low relevance, or no relevance [column 19 lines 15-20, lines 47-65 (“threshold score may be a numerical value between 0.0 and 1.0, such as 0.7, 0.8, 0.9, 0.95, or other values…” “whether the text token similarity score is greater than or equal to the text token similarity threshold score” (e.g. if similarity score is greater than threshold then “high relevance”), “if the text token similarity score is less than the text token similarity threshold score “ (e.g. if similarity score is lower than threshold then “low relevance”, and if similarity score is 0 then “no relevancy”), column 20 lines 20-32, also see column 21 lines 43-56, column 22 lines 132, 32-36, 43-65], and wherein the assessment scale generator generates the similarity scores in part based on the received user-defined criteria [column 19 lines 54-65: “a text token similarity threshold score for a text token may be compared to the text token similarity threshold score to determine whether the text token similarity score is greater than or equal to the text token similarity threshold score. If so, then that text token may be classified as being “well represented” within a corresponding section of second document 300. However, if the text token similarity score is less than the text token similarity threshold score, then that text token may be classified as being “not well represented” within the corresponding section of second document 300“, column 20 lines 20-32, also see column 22 line 66 to column 23 line 13].
Fontecilla does not expressly disclose evaluating and comparing credentials from separate institutions. However, it is known to apply machine-learning and natural language processing techniques to determine semantic similarity between documents (as taught by Fontecilla), and such techniques can be adapted for use in evaluating and comparing academic transcripts from different institutions.
In the same or similar field of invention, Cosker discloses evaluating and comparing credentials from separate institutions [Cosker Abstract, paragraphs 0021].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Fontecilla to have features of evaluating and comparing credentials from separate institutions and wherein the one or more text documents include one or more academic transcripts as taught by Cosker. The suggestion/motivation would have been to provide systems and methods for evaluating a student transcript to determine the eligibility of courses taken at other universities for credits toward a degree program offered by the admitting university [Cosker paragraph 0006].
Claims 4-5, 12-13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11321538 to Fontecilla (“Fontecilla”) in view of U.S. Patent Application Publication No. 20170178264 to Cosker et al. (“Cosker”) in further view of U.S. Patent Application Publication No. 20230023630 to Hamilton et al. (“Hamilton”) .
As to claims 4 and 12, Fontecilla and Cosker disclose the system of claim 1 and the method of claim 9 [See rejection of claims 1 and 9].
Fontecilla and Cosker do not expressly disclose wherein the machine learning module utilizes an unsupervised learning module in breaking up each of the one or more text documents into the plurality of sections.
In the same or similar field of invention, Hamilton discloses wherein the machine learning module utilizes an unsupervised learning module in breaking up each of the one or more text documents into the plurality of sections [Hamilton paragraphs 0048-0049, 0052, 0054].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Fontecilla and Cosker to have feature of wherein the machine learning module utilizes an unsupervised learning module in breaking up each of the one or more text documents into the plurality of sections as taught by Hamilton. The suggestion/motivation would have been to improve the functionality of software development tools for building machine learning programs or other predictive modeling programs by applying particular rules that transform unstructured data into a training dataset usable for configuring a machine learning program [Hamilton paragraph 0016].
As to claims 5, 13 and 18, Fontecilla and Cosker disclose the system of claim 1, the method of claim 9 and the system of claim 17 [See rejection of claims 1,9 and 17]. Further, Hamilton discloses wherein the data collection module includes at least one web crawler configured to automatically retrieve additional documentation from one or more online sources [Hamilton paragraphs 0020]. In addition, the same motivation is used as the rejection of claims 4 and 12.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11321538 to Fontecilla (“Fontecilla”) in view of U.S. Patent Application Publication No. 20170178264 to Cosker et al. (“Cosker”) in further view of U.S. Patent Application Publication No. 20110178791 to Stymne et al. (“Stymne”) .
As to claims 6 and 14, Fontecilla and Cosker disclose the system of claim 1 and the method of claim 9 [See rejection of claims 1 and 9].
Fontecilla and Cosker do not expressly disclose wherein the one or more text documents and the one or more additional text documents are not all the same language, and wherein the NLP module automatically translates at least one of the one or more text documents and/or the one or more additional text documents to ensure all text documents are in the same language.
In the same or similar field of invention, Stymne discloses wherein the one or more text documents and the one or more additional text documents are not all the same language, and wherein the NLP module automatically translates at least one of the one or more text documents and/or the one or more additional text documents to ensure all text documents are in the same language [Stymne Abstract, paragraphs 0012, 0017, 0029-30, Fig. 2, also see paragraph 0036].
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Fontecilla and Cosker to have feature of wherein the one or more text documents and the one or more additional text documents are not all the same language, and wherein the NLP module automatically translates at least one of the one or more text documents and/or the one or more additional text documents to ensure all text documents are in the same language as taught by Hamilton. The suggestion/motivation would have been to improve translation scores (such as NIST and BLEU scores) when translating into a compounding language [Hamilton paragraph 0027].
Conclusion
The following prior art is made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. Patent No. 11321538 to Liang et al. (See Abstract, Figs. 3-5, 7-8 and corresponding columns).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTIM G SHAH whose telephone number is (571)270-5214. The examiner can normally be reached Mon-Fri 7:30am-4pm.
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/ANTIM G SHAH/Primary Examiner, Art Unit 2693
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