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 § 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 9-12 recites the limitation "the system of claim 9" in line 1 of each claim. There is insufficient antecedent basis for this limitation in the claim. Claim 9 is a dependent claim and recites no system. Examiner assumes the claim preambles are intended to recite “The device of claim 8,” as claim 8 is an independent claim, however claim 8 recites a device, not a system. Appropriate correction of claims 9-12 are required.
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.
(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 1-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C 102(a)(2) as being anticipated by Tran (U.S. PGPub No. 2021/0186329).
In regards to claim 1, Tran discloses a method comprising: receiving an electrocardiogram (ECG); determining a first ECG interpretation result of the ECG using an artificial intelligence (AI) model ([0005]: "The user activity sensor includes... an ECG detector", [0329]: "In one embodiment, a multi-functional bio-data acquisition provides programmable multiplexing of the same differential amplifiers for extracting EEG (electroencephalogram), ECG (electrocardiogram), or EMG (electromyogram) waves.", [0279]: "In one embodiment, feed forward artificial neural networks (NNs) are used to classify valve-related heart disorders."), determining a second ECG interpretation result of the ECG using a rule-based ECG analysis model ([0093]: " In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound, optical, acoustic, electromagnetic, or electrical parameters are encoded as distinct dimensions in a database. A predictive model, including time series models such as those employing autoregression analysis and other standard time series methods, dynamic Bayesian networks and Continuous Time Bayesian Networks, or temporal Bayesian-network representation and reasoning methodology, is built, and then the model, in conjunction with a specific query makes target inferences."), determining a third ECG interpretation result of the ECG based on the first ECG interpretation result and the second ECG interpretation result, and providing the third ECG interpretation result ([0331]: "The EMG signal can be rectified, integrated a specified interval of and subsequently forming a time series of the integrated values.").
In regards to claim 2, Tran discloses that the first ECG interpretation result, the second ECG interpretation result, and the third ECG interpretation result define strings of statement identifies representing a diagnostic statement from a library ([0325]: "In one implementation, the system uses PNL (probabilistic networks library) to detect unusual patient movement/ambulatory activities that will lead to a more extensive check for stroke occurrence. PNL supports dynamic Bayes nets, and factor graphs; influence diagrams. For inference, PNL supports exact inference using the junction tree algorithm, and approximate inference using loopy belief propagation or Gibbs sampling.").
In regards to claim 3, Tran discloses that the first ECG interpretation result and the second ECG interpretation result include a diagnosis of atrial-paced rhythm, ventricular-paced rhythm, atrial flutter, ectopic atrial tachycardia, sinus bradycardia, sinus tachycardia, junctional bradycardia, atrial fibrillation, left bundle branch block, or septal infarct ([0309]: "In addition to providing beat-to-beat timing information for other sensors to use, the patterns of the constituent waveform features determined by the HMM or neural networks, among other classifiers, can be used for detecting heart attacks or stroke attacks, among others...The EKG analyzer can analyze EKG/ECG patterns for Hypertrophy, Enlargement of the Heart, Atrial Enlargement, Ventricular Hypertrophy, Arrhythmias, Ectopic Supraventricular Arrhythmias, Ventricular Tachycardia (VT), Paroxysmal Supraventricular Tachycardia (PSVT), Conduction Blocks, AV Block, Bundle Branch Block, Hemiblocks, Bifascicular Block, Preexcitation Syndromes, Wolff-Parkinson-White Syndrome, Lown-Ganong-Levine Syndrome, Myocardial Ischemia, Infarction, Non-Q Wave Myocardial Infarction, Angina, Electrolyte Disturbances, Heart Attack, Stroke Attack, Hypothermia, Pulmonary Disorder, Central Nervous System Disease, or Athlete's Heart, for example.").
In regards to claim 4, Tran discloses determining features of the ECG using the rule-based ECG model, and wherein the features are used to determine the second ECG model, and wherein the features include an amplitude of a wave of the ECG or a duration of a wave of the ECG ([0285]: "The A2-P2 splitting interval (S1) is calculated by computing the cross-correlation function between A2 and P2 and measuring the time of occurrence of its maximum amplitude. SI is then normalized (NSI) for heart rate as follows: NSI=SI/cardiac cycle time. The duration of the cardiac cycle can be the average interval of QRS waves of the ECG").
In regards to claim 5, Tran discloses that the AI model is trained on training data, and wherein the AI model includes a set of variables that are tuned to different values with the application of the training data ([0117]: "The data used to build the analyzer or model in these sessions are typically referred to as training data. As data driven analyzers are developed by examining only training examples, the selection of the training data can significantly affect the accuracy and the learning speed of the data driven analyzer.").
In regards to claim 6, Tran discloses that the training data includes scores that facilitate the training process by providing a ground truth, and wherein the first ECG interpretation result is compared with the corresponding score and back-propagated through the AI model to adjust the set of variables ([0096]: "Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others. ").
In regards to claim 7, Tran discloses that the training data is clustered into groups based on identified similarities and patterns ([0096]: "In one embodiment, clustering operations are performed to detect patterns in the data. In another embodiment, a neural network is used to recognize each pattern as the neural network is quite robust at recognizing user habits or patterns. ").
In regards to claim 8, Tran discloses a device comprising a memory storing instructions ([0272]: " In one embodiment, the wearable appliance can store patient data in its data storage device such as flash memory. The data can include...Emergency Medical Instructions, which could include: administering of certain suggested drugs or physical treatments; calling emergency physician numbers listed; bringing the patient to a certain type of clinic or facility based on religious beliefs; and living will instructions in the case of seriously ill patients; Organ Donor instructions; Living Will instructions which could include: instructions for life support or termination of treatment..."), 'and one or more processors configured to execute the instructions to: receive an electrocardiogram (ECG), determine a first ECG interpretation result of the ECG using an artificial intelligence (AI) model ([0005]: "The user activity sensor includes... an ECG detector", [0329]: "In one embodiment, a multi-functional bio-data acquisition provides programmable multiplexing of the same differential amplifiers for extracting EEG (electroencephalogram), ECG (electrocardiogram), or EMG (electromyogram) waves.", [0279]: "In one embodiment, feed forward artificial neural networks (NNs) are used to classify valve-related heart disorders."), determine a second ECG interpretation result of the ECG using a rule-based ECG analysis model ([0093]: " In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound, optical, acoustic, electromagnetic, or electrical parameters are encoded as distinct dimensions in a database. A predictive model, including time series models such as those employing autoregression analysis and other standard time series methods, dynamic Bayesian networks and Continuous Time Bayesian Networks, or temporal Bayesian-network representation and reasoning methodology, is built, and then the model, in conjunction with a specific query makes target inferences."), determine a third ECG interpretation result of the ECG based on the first ECG interpretation result and the second ECG interpretation result, determine a third ECG interpretation result of the ECG based on the first ECG interpretation result and the second ECG interpretation result; and provide the third ECG interpretation result ([0331]: "The EMG signal can be rectified, integrated a specified interval of and subsequently forming a time series of the integrated values.").
In regards to claim 9, Tran discloses that the first ECG interpretation result, the second ECG interpretation result, and the third ECG interpretation result define strings of statement identifies representing a diagnostic statement from a library ([0325]: "In one implementation, the system uses PNL (probabilistic networks library) to detect unusual patient movement/ambulatory activities that will lead to a more extensive check for stroke occurrence. PNL supports dynamic Bayes nets, and factor graphs; influence diagrams. For inference, PNL supports exact inference using the junction tree algorithm, and approximate inference using loopy belief propagation or Gibbs sampling.").
In regards to claim 10, Tran discloses that the first ECG interpretation result and the second ECG interpretation result include a diagnosis of atrial-paced rhythm, ventricular-paced rhythm, atrial flutter, ectopic atrial tachycardia, sinus bradycardia, sinus tachycardia, junctional bradycardia, atrial fibrillation, left bundle branch block, or septal infarct ([0309]: "In addition to providing beat-to-beat timing information for other sensors to use, the patterns of the constituent waveform features determined by the HMM or neural networks, among other classifiers, can be used for detecting heart attacks or stroke attacks, among others...The EKG analyzer can analyze EKG/ECG patterns for Hypertrophy, Enlargement of the Heart, Atrial Enlargement, Ventricular Hypertrophy, Arrhythmias, Ectopic Supraventricular Arrhythmias, Ventricular Tachycardia (VT), Paroxysmal Supraventricular Tachycardia (PSVT), Conduction Blocks, AV Block, Bundle Branch Block, Hemiblocks, Bifascicular Block, Preexcitation Syndromes, Wolff-Parkinson-White Syndrome, Lown-Ganong-Levine Syndrome, Myocardial Ischemia, Infarction, Non-Q Wave Myocardial Infarction, Angina, Electrolyte Disturbances, Heart Attack, Stroke Attack, Hypothermia, Pulmonary Disorder, Central Nervous System Disease, or Athlete's Heart, for example.").
In regards to claim 11, Tran discloses the one or processors is also configured to execute instructions to determine features of the ECG using the rule-based ECG model, and wherein the features are used to determine the second ECG model, and wherein the features include an amplitude of a wave of the ECG or a duration of a wave of the ECG ([0093]: " In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound, optical, acoustic, electromagnetic, or electrical parameters are encoded as distinct dimensions in a database. A predictive model, including time series models such as those employing autoregression analysis and other standard time series methods, dynamic Bayesian networks and Continuous Time Bayesian Networks, or temporal Bayesian-network representation and reasoning methodology, is built, and then the model, in conjunction with a specific query makes target inferences.", [0285]: "The A2-P2 splitting interval (S1) is calculated by computing the cross-correlation function between A2 and P2 and measuring the time of occurrence of its maximum amplitude. SI is then normalized (NSI) for heart rate as follows: NSI=SI/cardiac cycle time. The duration of the cardiac cycle can be the average interval of QRS waves of the ECG").
In regards to claim 12, Tran discloses that the AI model is trained on training data, and wherein the AI model includes a set of variables that are tuned to different values with the application of the training data ([0117]: "The data used to build the analyzer or model in these sessions are typically referred to as training data. As data driven analyzers are developed by examining only training examples, the selection of the training data can significantly affect the accuracy and the learning speed of the data driven analyzer.").
In regards to claim 13, Tran discloses that the training data includes scores that facilitate the training process by providing a ground truth, and wherein the first ECG interpretation result is compared with the corresponding score and back-propagated through the AI model to adjust the set of variables ([0096]: "Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others.").
In regards to claim 14, Tran discloses that the training data is clustered into groups based on identified similarities and patterns ([0096]: "In one embodiment, clustering operations are performed to detect patterns in the data. In another embodiment, a neural network is used to recognize each pattern as the neural network is quite robust at recognizing user habits or patterns. ").
In regards to claim 15, Tran discloses a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: receive an electrocardiogram (ECG), determine a first ECG interpretation result of the ECG using an artificial intelligence (AI) model ([0005]: "The user activity sensor includes... an ECG detector", [0329]: "In one embodiment, a multi-functional bio-data acquisition provides programmable multiplexing of the same differential amplifiers for extracting EEG (electroencephalogram), ECG (electrocardiogram), or EMG (electromyogram) waves."), determine a second ECG interpretation result of the ECG using a rule-based ECG analysis model, determine a third ECG interpretation result of the ECG based on the first ECG interpretation result and the second ECG interpretation result ([0093]: " In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound, optical, acoustic, electromagnetic, or electrical parameters are encoded as distinct dimensions in a database. A predictive model, including time series models such as those employing autoregression analysis and other standard time series methods, dynamic Bayesian networks and Continuous Time Bayesian Networks, or temporal Bayesian-network representation and reasoning methodology, is built, and then the model, in conjunction with a specific query makes target inferences."), and provide the third ECG interpretation result ([0331]: "The EMG signal can be rectified, integrated a specified interval of and subsequently forming a time series of the integrated values. ").
In regards to claim 16, Tran discloses that the first ECG interpretation result, the second ECG interpretation result, and the third ECG interpretation result define strings of statement identifies representing a diagnostic statement from a library ([0325]: "In one implementation, the system uses PNL (probabilistic networks library) to detect unusual patient movement/ambulatory activities that will lead to a more extensive check for stroke occurrence. PNL supports dynamic Bayes nets, and factor graphs; influence diagrams. For inference, PNL supports exact inference using the junction tree algorithm, and approximate inference using loopy belief propagation or Gibbs sampling.").
In regards to claim 17, Tran discloses that the first ECG interpretation result and the second ECG interpretation result include a diagnosis of atrial-paced rhythm, ventricular-paced rhythm, atrial flutter, ectopic atrial tachycardia, sinus bradycardia, sinus tachycardia, junctional bradycardia, atrial fibrillation, left bundle branch block, or septal infarct ([0309]: "In addition to providing beat-to-beat timing information for other sensors to use, the patterns of the constituent waveform features determined by the HMM or neural networks, among other classifiers, can be used for detecting heart attacks or stroke attacks, among others...The EKG analyzer can analyze EKG/ECG patterns for Hypertrophy, Enlargement of the Heart, Atrial Enlargement, Ventricular Hypertrophy, Arrhythmias, Ectopic Supraventricular Arrhythmias, Ventricular Tachycardia (VT), Paroxysmal Supraventricular Tachycardia (PSVT), Conduction Blocks, AV Block, Bundle Branch Block, Hemiblocks, Bifascicular Block, Preexcitation Syndromes, Wolff-Parkinson-White Syndrome, Lown-Ganong-Levine Syndrome, Myocardial Ischemia, Infarction, Non-Q Wave Myocardial Infarction, Angina, Electrolyte Disturbances, Heart Attack, Stroke Attack, Hypothermia, Pulmonary Disorder, Central Nervous System Disease, or Athlete's Heart, for example.").
In regards to claim 18, Tran discloses determining features of the ECG using the rule-based ECG model, and wherein the features are used to determine the second ECG model, and wherein the features include an amplitude of a wave of the ECG or a duration of a wave of the ECG ([0093]: " In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound, optical, acoustic, electromagnetic, or electrical parameters are encoded as distinct dimensions in a database. A predictive model, including time series models such as those employing autoregression analysis and other standard time series methods, dynamic Bayesian networks and Continuous Time Bayesian Networks, or temporal Bayesian-network representation and reasoning methodology, is built, and then the model, in conjunction with a specific query makes target inferences.", [0285]: "The A2-P2 splitting interval (S1) is calculated by computing the cross-correlation function between A2 and P2 and measuring the time of occurrence of its maximum amplitude. SI is then normalized (NSI) for heart rate as follows: NSI=SI/cardiac cycle time. The duration of the cardiac cycle can be the average interval of QRS waves of the ECG").
In regards to claim 19, Tran discloses that the AI model is trained on training data, and wherein the AI model includes a set of variables that are tuned to different values with the application of the training data ([0117]: "The data used to build the analyzer or model in these sessions are typically referred to as training data. As data driven analyzers are developed by examining only training examples, the selection of the training data can significantly affect the accuracy and the learning speed of the data driven analyzer.").
In regards to claim 20, Tran discloses that the training data includes scores that facilitate the training process by providing a ground truth, and wherein the first ECG interpretation result is compared with the corresponding score and back-propagated through the AI model to adjust the set of variables ([0096]: "Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others.").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN M LEE whose telephone number is (703)756-1789. The examiner can normally be reached 9:00 am - 6:00 pm.
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/B.M.L./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796
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