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 .
Status of the Application
This action is a first action on the merits in response to the application filed on 04/04/2023.
Status of Claims
Claims 1-20 filed on 04/04/2023 are currently pending and have been examined in this application.
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 non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-20 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of finding a relationship between two sets of data with different dimensions.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method for training a machine learning model, the method comprising: a first set of training data from a first dataset, wherein: the first dataset has a greater number of samples than a second dataset; the first dataset has a fewer number of dimensions than the second dataset; samples of the first dataset are mapped to a plurality of ground truth states; samples of the second dataset are mapped to the plurality of ground truth states; creating a second set of training data from the second dataset, wherein the second set of training data has at least one additional dimension of data than the first set of training data; simulating an additional dimension of data for the first set of training data; incorporating the simulated additional dimension of data with the first set of training data; training a first machine learning model based on the first set of training data that comprises the simulated additional dimension of data; obtaining a plurality of weights from the first trained machine learning model; and training a second machine learning model based on the second set of training data and the obtained plurality of weights from the first trained machine learning model”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite Mental Process because an ordinary person can reasonably find a relationship between data from radar sensors and non-radar sensors. The above limitations also recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for finding a relationship between two sets of data with different dimensions. Specifically, samples of datasets are mapped to a plurality of ground truth states by sleep experts. See original disclosure para. 0023 “Ground- truth monitoring 106 can involve one or more sleep experts reviewing or monitoring the sleep data and assigning a particular sleep state for each time for which sensor data is received.” As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 11 and 18 recite substantially similar limitations to those presented with respect to claim 1. As a result, claims 11 and 18 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-10, 12-17, and 19-20 recite a Mental Process because an ordinary person can reasonably find a relationship between data from radar sensors and non-radar sensors. As a result, claims 2-10, 12-17, and 19-20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “and the first dataset was gathered using a different type of sensor than the second dataset”. When considered in view of the claim as a whole, the step of “obtaining” does not integrate the abstract idea into a practical application because “obtaining” is an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Therefore, the claim is directed to an abstract idea.
As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 11 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A machine learning model system, comprising: one or more non-transitory processor-readable mediums” and claim 18 further recites “A non-transitory processor-readable medium, comprising processor- readable instructions”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 11 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-10, 12-17, and 19-20 do not include any additional elements beyond those recited by independent claims 1, 11, and 18. As a result, claims 2-10, 12-17, and 19-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “and the first dataset was gathered using a different type of sensor than the second dataset”. The step of “obtaining” does not amount to significantly more than the abstract idea because “obtaining” is well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 11 and 18 recite substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A machine learning model system, comprising: one or more non-transitory processor-readable mediums” and claim 18 further recites “A non-transitory processor-readable medium, comprising processor- readable instructions”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 11 and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-10, 12-17, and 19-20 do not include any additional elements beyond those recited by independent claims 1, 11, and 18. As a result, claims 2-10, 12-17, and 19-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Examiner’s Notes
The prior art references most closely resembling the Applicant’s claimed invention are Chang (US 20230186112 A1) and Amal Saadallah, Advanced Engineering Information, 2022 “Simulation and sensor data fusion for machine learning application” hereinafter Amal.
The system of Chang teaches “generating a plurality of pieces of augmented training data after a piece of training data with a missing item is determined; constituting a training dataset comprising pieces of training data without any missing item and the plurality of pieces of augmented training data; and building a prediction model based on the training dataset by a machine-learning algorithm. Similarly, a state prediction method includes: generating a plurality of pieces of estimation augmented data after a piece of source data with a missing item is determined; inputting the said plurality of pieces of estimation augmented data into a prediction model; and outputting a predicted state based on the output of the prediction model. Devices for performing the above methods are also disclosed” and Amal teaches “the present framework allows for the combination of real-world observations and simulation data at two levels, namely the data or the model level. At the data level, observations and simulation data are integrated to form an enriched data set for learning. At the model level, the models learned from observed and simulated data separately are combined using an ensemble technique. Based on the trade-off between model bias and variance, an automatic selection of the appropriate fusion level is proposed. Our framework is validated using two case studies of very different types. The first is an industry 4.0 use case consisting of monitoring a milling process in real-time. The second is an application in astroparticle physics for background suppression”
None of the cited documents by the Examiner, taken individually or in combination, discloses or suggests the combination and the details of features in the independent claims nor could a person skilled in the art easily conceive of such features even in the light of common technical knowledge at the time of filing. The pending claims 1-20 are therefore distinguished from the prior arts.
Conclusion
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-3734.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623