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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/01/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims recite extracting data from surveys, including questions and answers, identifying answers of previously filled surveys to predict answers to subsequent surveys without significantly more. The abstract idea further classifies regions of the survey to improve accuracy of predicted answers correlating to predefined questions. This judicial exception is not integrated into a practical application because there is no meaningful limitations beyond generally linking the use of an abstract idea to a particular technical environment. Furthermore, the process or method steps performed are not enough to qualify as “significantly more” than the abstract idea itself as the steps may be performed in the human mind and/or displayed on with pen and paper.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims recite applying a machine learning model to predict answers. Machine learning is known in the art as simply applying mathematical algorithms using generic computer components to determine an output given a series of inputs. Generic computer components recited as performing generic functions that are well-understood, routine and conventional amount to no more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional element does not amount to significantly more than the above-identified abstract idea. There is no indication that the elements improve the functioning of a computer or improves any other technology.
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, 7 and 8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dontá Lamar Wilson et al (US 20240037585 A1).
Regarding claim 1, Wilson et al discloses a computer implemented method of extracting data from surveys (¶ [88]), the method comprising:
obtaining completed surveys (¶ [88-89]), each completed survey comprising answers to preconfigured questions (¶ [88] and ¶ [91]);
identifying portions of each completed survey corresponding to answers (¶ [131-132]);
applying a machine learning model to the portions identified as answers to predict answers (¶ [132] and ¶ [135]); and
accumulating a response to the survey based on the predicted answers (¶ [143]).
Regarding 2, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1), wherein accumulating a response to the survey based on the identified answers comprises matching the identified answers to the preconfigured questions (¶ [98]).
Regarding claim 7, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1), wherein obtaining completed surveys comprises obtaining an image of completed surveys (¶ [66] image of the completed survey obtained for machine learning algorithm; ¶ [88] learning program receives completed surveys).
Regarding claim 8, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1), further comprising obtaining from a user an indication from a list of preconfigured surveys of the survey being obtained (¶ [83-84] user initiates/selects survey to be input).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 4, 10, 14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al in view of Lance Parker et al (US 20020052774 A1).
Regarding claim 3, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1).
Wilson et al fails to explicitly disclose outputting a display of the answers to each question in a graphical user interface to the user, along with an indication of a location of the answer in the survey and a confidence level that the answer has been correctly predicted by a second machine learning model.
Parker et al, in the same field of endeavor of formulating questions, obtaining responses and analyzing the responses mathematically to obtain desired information (¶ [30]), teaches outputting a display of the answers to each question in a graphical user interface to the user (¶ [72]), along with an indication of a location of the answer in the survey (¶ [41]) and a confidence level that the answer has been correctly predicted by a second machine learning model (¶ [52] and ¶ [65]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Wilson et al comprising obtaining completed surveys, identifying portions of each completed survey corresponding to answers, and applying a machine learning model to the portions identified as answers to predict answers to utilize the teachings of Parker et al which teaches outputting a display of the answers to each question in a graphical user interface to the user, along with an indication of a location of the answer in the survey and a confidence level that the answer has been correctly predicted by a second machine learning model to conduct surveys more quickly and efficiently than conventional manual methods.
Regarding claim 4, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1).
Wilson et al fails to explicitly disclose classifying sections of the survey into one of a selected number of different types; wherein identifying portions of the survey corresponding to answers comprises identifying a portion corresponding to answers for each classified section of the survey.
Parker et al teaches classifying sections of the survey into one of a selected number of different types (¶ [29]); wherein identifying portions of the survey corresponding to answers comprises identifying a portion corresponding to answers for each classified section of the survey (¶ [29]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Wilson et al comprising obtaining completed surveys, identifying portions of each completed survey corresponding to answers, and applying a machine learning model to the portions identified as answers to predict answers to utilize the teachings of Parker et al which teaches classifying sections of the survey into one of a selected number of different types; wherein identifying portions of the survey corresponding to answers comprises identifying a portion corresponding to answers for each classified section of the survey to elicit attitude, behavior and demographic of a respondent thus providing a more in-depth understanding of a user’s profile.
Regarding claim 10, Wilson et al discloses the computer implemented method of claim 1 (see rejection of claim 1).
Wilson fails to explicitly disclose further comprising identifying portions of the survey corresponding to questions.
Parker et al teaches identifying portions of the survey corresponding to questions (¶ [80-83]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Wilson et al comprising obtaining completed surveys, identifying portions of each completed survey corresponding to answers, and applying a machine learning model to the portions identified as answers to predict answers to utilize the teachings of Parker et al which teaches identifying portions of the survey corresponding to questions to conduct surveys more quickly and efficiently than conventional manual methods.
Regarding claim 14, Wilson et al discloses a computer-implemented method of extracting data from surveys (see rejection of claim 1), the method comprising:
obtaining completed surveys, each completed survey comprising answers to preconfigured questions (see rejection of claim 1);
applying a question machine learning model to identify and predict instances of questions in each completed survey (see rejection of claim 10);
applying a separate answer machine learning model to identify and predict answers in each completed survey (see rejection of claim 3); and
accumulating a response to the survey based on the predicted answers (see rejection of claim 1).
Regarding claim 15, Wilson et al discloses the computer implemented method of claim 14 (see rejection of claim 14), wherein obtaining completed surveys comprises obtaining an image of each completed survey (see rejection of claim 7).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al in view of Parker et al as applied to claim 4 above, and further in view of Adam Votava et al (US 20200402080 A1).
Regarding claim 5, Wilson et al discloses the computer implemented method of claim 4 (see rejection of claim 4).
Wilson et al fails to explicitly disclose wherein the selected number of different types comprise: option, table, scale, image, and text.
Votava et al, in the same field of endeavor of gathering evaluation information from a user in the form of a survey (¶ [6]), teaches the selected number of different types comprise: option, table, scale, image, and text (¶ [197-199]).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Wilson et al comprising obtaining completed surveys, identifying portions of each completed survey corresponding to answers, and applying a machine learning model to the portions identified as answers to predict answers to utilize the teachings of Votava et al which teaches the selected number of different types comprise: option, table, scale, image, and text as providing predetermined response options afford for linkage with a value of an evaluation score, thereby determining comparative evaluation scores easily without extensive analyses or computations.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al in view of Parker et al as applied to claim 4 above, and further in view of Milind Kopikare et al (US 20210035132 A1).
Regarding claim 6, Wilson et al discloses the computer implemented method of claim 4 (see rejection of claim 4).
Wilson et al fails to explicitly disclose wherein applying a machine learning model to the portions identified as answers to predict answers comprises applying a machine learning model to the portions identified as answers to predict answers for each classified section based on the classified type of the section.
Kopikare et al, in the same field of endeavor of response prediction system to optimize responses to surveys (Abstract), teaches applying a machine learning model to the portions identified as answers to predict answers comprises applying a machine learning model to the portions identified as answers to predict answers for each classified section based on the classified type of the section (¶ [33-34] and ¶ [65] wherein the prediction model predicts answers based on question characteristics).
It would have been obvious to one of ordinary skill in the art before the invention was effectively filed for the method as disclosed by Wilson et al comprising obtaining completed surveys, identifying portions of each completed survey corresponding to answers, and applying a machine learning model to the portions identified as answers to predict answers to utilize the teachings of Kopikare et al which teaches applying a machine learning model to the portions identified as answers to predict answers comprises applying a machine learning model to the portions identified as answers to predict answers for each classified section based on the classified type of the section to improve the accuracy of survey response quality predictions.
Conclusion
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/JAMARES Q WASHINGTON/Primary Examiner, Art Unit 2681
November 1, 2025