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 § 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 1-15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The judicial exception being an abstract idea. The claim(s) recite(s) organizing information and manipulating information through mathematical correlations, similar to Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014), which was found to be patent ineligible. This judicial exception is not integrated into a practical application because when the claims are considered as a whole, there is no element or combination of elements in the claims that are sufficient to ensure that the claims amount to significantly more that the abstract idea itself. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims fail to recite any improvements to another technology or technical field, improvements to the functioning of the processor itself, and/or meaningful limitation beyond generally link the use of an abstract idea to a particular environment (i.e. there is not structural relationship between the abstract idea of organizing and manipulating sensor data). The use of a physiological sensor is merely generic. Therefore, because there is no meaningful limitations in the claim to transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claim is rejected under 35 USC 101 as being directed to non-statutory subject matter.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goldsmith (WO 2020221993).
Regarding claim 1, Goldsmith discloses receiving subject data comprising physiological sensor data of a subject (page 16 lines 1-4, patient-related measurement” refers to data that is related to the patient. A patient-related measurement may be data relating directly to the patient, such as a “physiological measurement”, e.g. resting heart rate, systolic blood pressure at rest, blood glucose level, and biomarker concentration in blood); and adapting a trained machine learning model for monitoring a physiological status, wherein the trained machine learning model is configured to determine the physiological status of the subject based on the physiological sensor data (page 28 lines 22-27, The machine learning tasks include any combination of: training a model using data from a dataset stored in database 1 10 and/or using a trained model to classify an input such as data from a dataset stored in database), wherein the trained machine learning model has a plurality of fixed weights and a plurality of variable weights (page 32 lines 24-30, Data processing device 105 may be configured to apply a weighting factor to each of the patient-related measurements received in step 210 when generating the patient data. The weighting factor expresses a relative importance of a particular patient- related measurement relative to other patient-related measurements. Data processing device 105 may generate an individual weighting factor for each of the patient-related measurements. A given weighting factor may have the same value or a different value to another weighting factor); wherein adapting the trained machine learning model comprises: generating a first re-trained machine learning model by adapting the trained machine learning model by maintaining values of the plurality of fixed weights, and changing values of a first number of the plurality of variable weights (page 33 lines 8-13, a Bayesian prediction, can be used to predict the future condition of the patient using current and historical patient measurements as a function of each weighting factor. The set of weighting factors is selected based on the prediction. The set of weighting factors that minimizes the time it is expected for the patient to move from the patient position (i.e. their current state) to the desired patient endpoint is preferably selected. One or more weighting factors can be adjusted as necessary during the course of a treatment should the actual progress of the patient deviate significantly from the predicted progress of the patient); determining a level of agreement between the trained machine learning model and the first re-trained machine learning model (Page 32 lines 32-33, page 33 lines 1-4, The weighting factors may be defined by a clinician in conjunction with the patient. Preferably, data processing device 105 first receives a range for each weighting factor, and subsequently receives a value for each weighting factor that is within the respective range. The selection within the range can be based upon patient preferences, such as the desire for a particular benefit and/or the level of desire to avoid a particular side effect); in response to the level of agreement, generating a second re-trained machine learning model by adapting the trained machine learning model by maintaining the values of the plurality of fixed weights (page 32 lines 29, page 33 lines 11-19, A given weighting factor may have the same value to another weighting factor The set of weighting factors is selected based on the prediction. The set of weighting factors that minimizes the time it is expected for the patient to move from the patient position (i.e. their current state) to the desired patient endpoint is preferably selected. One or more weighting factors can be adjusted as necessary during the course of a treatment should the actual progress of the patient deviate significantly from the predicted progress of the patient. In step 215, data processing device 105 processes the dataset, the patient position and the desired patient endpoint to generate a co-therapy regimen. This step can comprise processing the dataset, patient position and desired patient endpoint using one or more rules, and/or using one or more machine learning algorithms), and changing values of a second number of the plurality of variable weights, wherein the second number is different from the first number (page 32 lines 29-30, page 33 lines 13-16, A given weighting factor may have a different value to another weighting factor. One or more weighting factors can be adjusted as necessary during the course of a treatment should the actual progress of the patient deviate significantly from the predicted progress of the patient).
Regarding the limitation, “second number of the plurality of variable weights, wherein the second number is different from the first number”, under the guidelines of Broadest Reasonable Interpretation, this is understood to mean “first number” is a first weight and the “second number” is a second weight – both in a single model – and the claim is requiring that both be adjusted to be different from each other.
Regarding claim 4, Goldsmith discloses the trained machine learning model is a trained neural network (page 20 lines 11-13, Examples of machine learning algorithms include but are not limited to a neural network).
Regarding claim 5, Goldsmith discloses the physiological sensor data indicates a physiological parameter indicative of a physiological status, and the subject data comprises reference physiological status data indicating a physiological status correlated with the physiological parameter (page 16 lines 2-4, 14-16, A patient-related measurement may be data relating directly to the patient, such as a “physiological measurement”, e.g. resting heart rate, systolic blood pressure at rest, blood glucose level, and biomarker concentration in blood. A patient-centered outcome may comprise an indication of whether the patient is receiving suitable relief from one or more symptoms of the disease or condition from which they are being treated).
Regarding claim 6, Goldsmith discloses the trained machine learning model is configured to provide sleep monitoring, the physiological sensor data comprises autonomic nervous system activity data, and the physiological status is a sleep stage, a sleep disordered breathing event or an arousal event (page 24 lines 15-33, The co-therapy of generated or provided by the methods of the present invention may be used to treat or prevent any disease or condition. This includes both acute and chronic diseases and conditions, such as those selected from the group consisting of sleep apnea; insomnia; narcolepsy; including REM and NREM parasomnias and nightmare disorders; sleep movement disorders).
Regarding claim 7, Goldsmith discloses the physiological sensor data comprises accelerometer data, cardiac data and/or respiratory data (Page 29 lines 22, an accelerometer).
Regarding claim 8, Goldsmith discloses the physiological sensor data comprises neurological activity data and/or central nervous system activity data (page 24 lines 15-33, neurodegeneration diseases, such as Mild Cognitive Impairment (MCI), Alzheimer's disease and Parkinson's disease).
Regarding claim 9, Goldsmith discloses the physiological sensor data comprises cardiac data and the physiological status is a cardiac-related condition (page 24 lines 15-33, cardiovascular disease, postural orthostatic tachycardia syndrome).
Regarding claim 10, Goldsmith discloses generating the trained machine learning model by: obtaining physiological sensor training data for a plurality of participants; obtaining reference training information, wherein the reference training information indicates a ground-truth associated with monitored physiological status; and inputting the physiological sensor training data and the reference training information into the machine learning model (page 32 lines 1-5, In the case where sensor data is provided, data processing device 105 preferably identifies the patient position by processing the patient-related information using one or more rules stored in database 110 and/or using a trained machine learning model stored in database).
Regarding claim 11, Goldsmith discloses obtaining a plurality of subject data sets, each data set corresponding to a respective time period; and determining the number of fixed weights based on the number of subject data sets (Page 33 lines 6-11, Preferably, each weighting factor is selected so as to minimize the time it is expected for the patient to move from the patient position to the desired patient endpoint. A probabilistic prediction of a patient condition, e.g. a Bayesian prediction, can be used to predict the future condition of the patient using current and historical patient measurements as a function of each weighting factor).
Regarding claim 14, Goldsmith discloses the system comprising: a processor configured to carry out the steps of claim 1 (Page 28 lines 20-22, Data processing device 105 comprises at least one processor and is configured to carry out any of the methods described in this specification, or one or more steps thereof).
Regarding claim 15, Goldsmith discloses computer program code means which, when executed on a computing device having a processor, cause the processor to perform all of the steps of the method according to claim 1 (Page 28 lines 17-22, Database is stored on a storage medium, e.g. a Cloud-based storage medium. Data processing device comprises at least one processor and is configured to carry out any of the methods described in this specification, or one or more steps thereof).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JON ERIC C MORALES whose telephone number is (571)272-3107. The examiner can normally be reached Monday-Friday 830AM-530PM CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Hamaoui can be reached at 571-270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JON ERIC C MORALES/Primary Examiner, Art Unit 3796
/J.C.M/Primary Examiner, Art Unit 3796