DETAILED ACTION
This non-final rejection is responsive to communication filed March 18, 2024. Claims 1-10 are pending in this application.
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 November 4, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1-4 are objected to because of the following informalities: The claims include limitations that are not separated by semicolons or commas, which makes it hard to interpret the limitations. There should be a semicolon or comma after each limitation. For example, in claim 1, the punctuation is missing at the end of line 9; in claim 2, the punctuation is missing at the end of line 5; in claim 3, the punctuation is missing at the end of line 2; in claim 4, there may be punctuation missing at the end of line 2. Appropriate correction is required.
Further, the word “and” is repeated between lines 3-4 of claim 3 and between lines 2-3 of claim 4. 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 1-10 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 appears to be a method claim, but the second line of the claim recites “an end-to-end system” and the fourth line recites “systems for storing data.” Claim 3 recites “a custom screening system.” Claims 4 and 5 recite “wherein the system…” Therefore, it is unclear as to whether applicant intends to claim a method or system. Further, it is unclear as to what system the term “the system” refers to in claims 4 and 5. Claims 2-10 are rejected as being dependent upon rejected claim 1.
Claim 1 recites the limitation "the series" in line 12 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation "the extracted features" in line 6 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the predictive models" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "the time", “the optimized time period” and “the value” in lines 1-2 of the claim. There is insufficient antecedent basis for these limitations in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites starting from user inputted data to deliver customized predictions for when a young child will next sleep comprising: systems for storing data; model serving of customized wake window predictions for young children; receiving inputs from a user; receiving nap times and wake times; inputting the series of nap times and wake times into a data storage medium to analyze various aspects of the data, thereby extracting a plurality of features from the series of inputted data to generate internal prediction of optimal sleep window predictions based on the value of recent history. These limitations represent methods of organizing human activity, particularly the limitations appear to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application. The limitations of an end-to-end system; feature engineering; model training; data drift detection; model retraining; an app input device having a screen; a data storage medium that is pre trained; and train and analyze a machine learning model. The end-to-end system, app input device, and data storage medium are generic computer components that are recited at a high level of generality, such that they amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Further, the elements of feature engineering; model training; data drift detection; model retraining; a data storage medium that is pre trained; and train and analyze a machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
The recitations of feature engineering; model training; data drift detection; model retraining; a data storage medium that is pre trained; and train and analyze a machine learning model also merely indicates a field of use or technological environment in which the judicial exception is performed. As such, these type of limitations merely confine the use of the abstract idea to a particular technological environment (neural networks/machine learning) and thus fail to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitation of an end-to-end system; feature engineering; model training; data drift detection; model retraining; an app input device having a screen; a data storage medium that is pre trained; and train and analyze a machine learning model amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
With respect to claim 2, the limitations “receiving a individualized dataset of wake and sleep windows; receiving a ground truth wake and sleep windows determined by user input; parsing each individualized features of into a series of dataset wake and sleep windows” also describe and represent methods of organizing human activity, particularly the limitations appear to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of a machine learning model that is pre-trained and Inputting the extracted features and the ground truth to the machine learning model, LightGBM, using industry standard techniques provide nothing more than mere instructions to implement an abstract idea on a generic computer or merely confine the use of the abstract idea to a particular technological environment (neural networks/machine learning).
With respect to claim 3, the limitations “wherein the system receives a individualized dataset of wake and sleep windows inputting such data into the data storage medium and and a custom screening system designed by sleep experts is used to determine whether the individualized inputted dataset is within acceptable ranges and parsing and selecting the inputted data to determine appropriate values and features for predicted sleep windows” also describe and represent methods of organizing human activity, particularly the limitations appear to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the system and data storage medium are generic computer components.
With respect to claim 4, the limitations “wherein the system receives a individualized dataset of wake and sleep windows inputting such data into the data storage medium and and a custom screening system designed by sleep experts is used to determine whether the individualized inputted dataset is within acceptable ranges and if not in acceptable ranges parsing and selecting expert determined wake window standards to replace output generated from the inputted data to determine appropriate values and features for predicted sleep windows” also describe and represent methods of organizing human activity, particularly the limitations appear to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the system and data storage medium are generic computer components.
With respect to claim 5, the limitation “wherein the system is able to analyze various features of sleep and wake windows based on timing, trends, durations, and wake times” represents methods of organizing human activity, particularly the limitation appears to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the system is a generic computer component.
With respect to claims 6 and 7, the limitations “further comprising the determination of at least one pattern between extracted features of sleep and wake windows; and associating each extracted feature with a labeled predicted sleep window based on each feature set” and “wherein patterns are determined across the series of imputed datasets and normalized sleep and wake window” represent methods of organizing human activity, particularly the limitations appear to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
With respect to claim 8, the limitation “where certain inputted data features are given higher relevance with respect to inputted data features that are outside of accepted values” represents methods of organizing human activity, particularly the limitation appears to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
With respect to claim 9, the limitation “where a rule to use the predictive models with current sleep values to present wake window predictions if the user has not been online for up to and including a certain minimum period of days” represents methods of organizing human activity, particularly the limitation appears to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of predictive models and a user being online provide nothing more than mere instructions to implement an abstract idea on a generic computer or merely confine the use of the abstract idea to a particular technological environment (neural networks/machine learning).
With respect to claim 10, the limitation “where if the time the user has not logged in is greater than the optimized time period for inputted values, the value for the wake window determined by sleep experts is used as the prediction” represents methods of organizing human activity, particularly the limitation appears to describe managing personal behavior such as managing sleep and wake times.
This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (US 2025/0205556 A1) (‘Shi’) in view of Rhodes et al. (US 2024/0028876 A1) (‘Rhodes’).
With respect to claim 1, Shi teaches a method, comprising:
an end-to-end system starting from user inputted data (paragraphs 20, 217, and 218) to deliver customized predictions for when a young child will next sleep (Fig. 1; paragraphs 77, 157, 338, 341, and 380) comprising:
systems for storing data (paragraph 147);
feature engineering (paragraphs 165 and 196);
model training (paragraphs 196 and 202);
model serving of customized wake window predictions for young children (paragraphs 342-343, 371-372, 375, 387, and 422)
receiving inputs from a user taken by a app input device having a screen (paragraph 149);
receiving nap times and wake times (paragraphs 104, 195, 404, 420-421);
inputting the series of nap times and wake times into a data storage medium that is pre trained to analyze various aspects of the data, thereby extracting a plurality of features from the series of inputted data to train and analyze a machine learning model to generate internal prediction of optimal sleep window predictions based on the value of recent history (Figs. 7-9; paragraphs 169, 371, 387, 422, and 490).
Shi does not explicitly teach data drift detection; or model retraining.
Rhodes teaches data drift detection; and model retraining (paragraph 37).
It would have been obvious to a person having skill in the art prior to the effective filing of the invention to have modified Shi to detect data drift and perform model retraining as taught by Rhodes to enable early detection of changing data to prompt model retraining to keep predictions accurate, prevent faulty decision-making, and maintain a relevant machine learning model. Further Shi teaches model training and thus it is obvious Shi would want to maintain relevant models by re-training as necessary.
With respect to claim 2, Shi in view of Rhodes teaches method of claim 1, wherein the machine learning model is pre trained by:
receiving a individualized dataset of wake and sleep windows (Shi, paragraphs 146, 164, 341, and 386);
receiving a ground truth (Rhodes, paragraphs 23 and 29) wake and sleep windows determined by user input (Shi, paragraphs 165, 195, and 341);
parsing each individualized features of into a series of dataset wake and sleep windows (paragraphs 165 and 96)
Inputting the extracted features and the ground truth to the machine learning model, LightGBM, using industry standard techniques (Rhodes, paragraph 68).
With respect to claim 3, Shi in view of Rhodes teaches the method of claim 1, wherein the system receives a individualized dataset of wake and sleep windows (Shi, paragraphs 146, 164, 341, and 386)
inputting such data into the data storage medium (Shi, paragraphs 146, 164, 341, and 386) and
and a custom screening system designed by sleep experts (paragraphs 354, 357, and 427) is used to determine whether the individualized inputted dataset is within acceptable ranges and parsing and selecting the inputted data to determine appropriate values and features for predicted sleep windows (Shi, paragraphs 71, 82, 105, 297, and 352).
With respect to claim 4, Shi in view of Rhodes teaches the method of claim 1, wherein the system receives a individualized dataset of wake and sleep windows inputting such data into the data storage medium and
and a custom screening system designed by sleep experts is used to determine whether the individualized inputted dataset is within acceptable ranges (Shi, paragraphs 71, 82, 105, 297, and 352) and if not in acceptable ranges parsing and selecting expert determined wake window standards to replace output generated from the inputted data to determine appropriate values and features for predicted sleep windows (Shi, paragraphs 82-83, 85, 352, and 355).
With respect to claim 5, Shi in view of Rhodes teaches the method of claim 1, wherein the system is able to analyze various features of sleep and wake windows based on timing, trends, durations, and wake times (Shi, paragraphs 165, 195, 216, 341, and 386).
With respect to claim 6, Shi in view of Rhodes teaches the method of claim 1, further comprising the determination of at least one pattern between extracted features of sleep and wake windows; and associating each extracted feature with a labeled predicted sleep window based on each feature set (Shi, paragraphs 19, 21, and 216-218).
With respect to claim 7, Shi in view of Rhodes teaches the method of claim 6, wherein patterns are determined across the series of imputed datasets and normalized sleep and wake windows (Shi, paragraphs 19, 21, and 216-218).
With respect to claim 8, Shi in view of Rhodes teaches the method of claim 1, where certain inputted data features are given higher relevance with respect to inputted data features that are outside of accepted values (Shi, paragraphs 202 and 349).
With respect to claim 9, Shi in view of Rhodes teaches the method of claim 1, where a rule to use the predictive models with current sleep values to present wake window predictions if the user has not been online for up to and including a certain minimum period of days (This limitation is optionally recited. Using the predictive models with current sleep values to present wake window predictions only occurs if the user has not been online for up to and including a certain minimum period of days.) (Shi, paragraphs 75-76, 89, 91, 162, and 391).
With respect to claim 10, Shi in view of Rhodes teaches the method of claim 1, where if the time the user has not logged in is greater than the optimized time period for inputted values, the value for the wake window determined by sleep experts is used as the prediction (This limitation is optionally recited. The value for the wake window determined by sleep experts is used as the prediction only occurs if the time the user has not logged in is greater than the optimized time period for inputted values.) (Shi, paragraphs 352, 358, and 384).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 July 2, 2026
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