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 .
Claims 1-20 are presented for examination.
Examiner attempted to contact Applicant to discuss 35 U.S.C. 112(b) issues and for claim interpretation, but was unsuccessful.
Applicant is encouraged to contact Examiner to discuss claim issues.
Allowable Subject Matter
Claims 1-6, 8-13, 15-20 are allowed.
The prior arts made of record do not teach
generating, ... based at least in part on the input feature data object, an input deidentified three-dimensional model;
generating, ... based at least in part on the input deidentified three-dimensional model, and using a dynamically-deployed risk prediction machine learning model, ... predicted risk score, wherein:
the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold,
the dynamically-deployed risk prediction machine learning model is trained using the one or more dynamic deployment training entries,
each dynamic deployment training entry comprises a corresponding training entry deidentified three-dimensional model that is associated with a corresponding training entry feature data object and a corresponding dynamic deployment ground-truth indicator,
each corresponding dynamic deployment ground-truth indicator of a corresponding dynamic deployment training entry is determined based at least in part on a corresponding recommendation validation indicator for a corresponding predicted recommendation that is generated based at least in part on an initial risk score, and
each initial risk score is generated by an initially-deployed risk prediction machine learning model based at least in part on an identifiable feature data object for the corresponding dynamic deployment training entry; and
performing, by the one or more processors, one or more prediction-based actions based at least in part on the predicted risk score.
The prior art search revealed
- prior art that discloses using knowledge distillation and Dark Knowledge for three-dimensional data to predict health risks, but is silent regarding using deidentified information,
- prior art that discloses a machine learning model (MLM) to determine health risk using user movement data,
- prior art that discloses sending model parameters from multiple MLMs to a central MLM and deeming a model stable after it has been trained on at least a threshold number of data points, but is silent regarding sending a predictive output,
- prior art that discloses training student MLM(s) using knowledge distillation and comparing output with ground truth data, but is silent regarding deidentification of information,
- prior art that discloses generating ground truth based on output of an MLM trained on deidentification information, and
- prior art that discloses sending encrypted information or parameter information to a central MLM, but is silent regarding deidentification of information.
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 7 and 14 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 pre-AIA the applicant regards as the invention.
Claim(s) 7 and 14 recite(s) “wherein the comprises a convolutional neural network and an image-based classification machine learning component”. Applicant’s disclosure states
(a) “An example of an initially-deployed risk determination machine learning model is a trained supervised machine learning model (e.g., a trained supervised regression model, a convolutional neural network model, and/or the like) and/or an image-based classification machine learning component” in [0029], and
(b) “An example of a dynamically-deployed risk determination machine learning model is a trained supervised machine learning model (e.g., a trained supervised regression model, a convolutional neural network model, and/or the like) and/or an image-based classification machine learning component” in [0033].
However the terms “initially-deployed risk determination machine learning model” and “dynamically-deployed risk determination machine learning model” are absent in the claims.
Further the terms from claims 1 and 8
“initially-deployed risk prediction machine learning model” and
“dynamically-deployed risk prediction machine learning model”,
are not associated with convolutional neural network or image-based classification machine learning component in applicant’s disclosure.
Therefore it is unclear what comprises “a convolutional neural network and an image-based classification machine learning component”, rendering the claim(s) indefinite.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lai et al “Capturing causality and bias in human action recognition” discloses using knowledge distillation and Dark Knowledge for three-dimensional data to predict health risks, rather than using deidentified information, retrieved from Pattern Recognition Letters, Volume 147, 2021, Pages 164-171, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2021.04.008 ,
https://www.sciencedirect.com/science/article/pii/S0167865521001380 , ISSN 0167-8655.
Shah (US 20220399132 A1) discloses a machine learning model (MLM) to determine health risk.
Cheng (US 20200218974 A1) discloses MLM predicting health risk using user movement data.
Sprague (US 20200334524 A1) discloses sending model parameters (rather than a predictive output) from multiple MLMs to a central MLM, and deeming a model stable after it has been trained on at least a threshold number of data points.
Hall (US 20220344049 A1) discloses training student MLM(s) using knowledge distillation and comparing output with ground truth data, but is silent regarding deidentification of information.
Aghaei (US 20220351860 A1) discloses generating ground truth based on output of an MLM trained on deidentification information.
Hassanzadeh (US 20230025754 A1) discloses sending encrypted information or parameter information to a central MLM, but is silent regarding deidentification of information.
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146