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 .
Election/Restrictions
Applicant’s election of Group I in the reply filed on 11/10/2025 is acknowledged.
Election was made without traverse in the reply filed on 11/10/2025.
Status of Claims
This action is in reply to the application filed on 05/15/2024.
Claims 12 and 13 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention(s), there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 11/10/2025.
Claims 1-11 and 14-15 are currently pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” for performing the claimed function) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action: Claim 14 recites “means for applying a first machine learning algorithm and/or a rule-based algorithm”, “means for outputting a second data set”, “means for providing a third data set”, “means for applying a second machine learning algorithm” and “means for outputting a fourth data set”. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 § 112(b)
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 1-11 and 14-15 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 recites “providing a first data set comprising cardiac current curve data of a patient”. It is unclear what is meant by cardiac current curve data. Is it cardiac current related data that is arranged as a curve? For purposes of applying prior art, “cardiac current curve data” will be interpreted as any cardiac related data. Claims 14 and 15 recite similar limitations. Claims 1, 14 and 15 are therefore found to be indefinite, because the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-11, are rejected for at least the same reason.
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 1-11 and 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-11 and 14-15: Step 1
Claims 1-11 are drawn to a computer-implemented method for determining a heart failure status of a patient, which is within the four statutory categories (i.e. process). Claim 14 is drawn to a system for determining a heart failure status of a patient, which is within the four statutory categories (i.e. machine). Claim 15 is drawn to a computer program with program code to perform the method of claim 1 when the computer program is executed on a computer, which is within the four statutory categories (i.e. machine).
Claims 1-11 and 14-15: Step 2A Prong One
Claim 1 recites providing a first data set comprising cardiac current curve data of a patient, applying a rule-based algorithm to the cardiac current curve data for classification of a medical relevance of a parameter deviation from a norm of the cardiac current curve data, outputting a second data set comprising at least a first class representing a medically relevant parameter deviation or a second class representing a medically not relevant parameter deviation, in response to outputting the first class, triggering a patient information request, providing a third data set comprising the first data set and data provided in response to the patient information request, applying a second algorithm to the third data set for determining the heart failure status of the patient, outputting a fourth data set indicative of the heart failure status of a patient. Claims 14 and 15 recites similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user providing a first data set comprising cardiac current curve data of a patient, applying a rule-based algorithm to the cardiac current curve data for classification of a medical relevance of a parameter deviation from a norm of the cardiac current curve data, outputting a second data set comprising at least a first class representing a medically relevant parameter deviation or a second class representing a medically not relevant parameter deviation, in response to outputting the first class, triggering a patient information request, providing a third data set comprising the first data set and data provided in response to the patient information request, applying a second algorithm to the third data set for determining the heart failure status of the patient, and outputting a fourth data set indicative of the heart failure status of a patient. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claims 14 and 15 recite similar limitations.
Claims 2-11 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claim 2 further defines the fourth data set. Claim 3 further defines the patient information request. Claim 4 further defines the information provided. Claim 5 further defines sending notification. Claim 6 further defines action related to value threshold. Claim 7 further defines accessing notification or health status. Claim 8 further defines the second and fourth data sets. Claim 9 further defines the third data set. Claim 10 further defines the first data set. Claim 11 further defines acquiring cardiac curve data. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-11 and 14-15: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the computer-implemented method steps are performed (presumably by a computer). Claim 15 recites additional elements of computer program with program code to perform the method of claim 1 when the computer program is executed on a computer.
Claims 1-11 and 14-15, directly or indirectly, recite the following generic computer components: “computer-implemented method,” and “computer program with program code to perform the method of claim 1 when the computer program is executed on a computer” which are similar to adding the words “apply it” to the abstract idea. The written description does not appear to further describe the computer hardware or the computer program, but rather repeats the recitation from the claim limitations themselves. The written description discloses that the recited computer components encompass generic components including “The present invention relates to a computer-implemented method for determining a heart failure status of a patient “ (see at least Paragraph [0002]) and “Computer program with program code to perform the method of claim 1 when the computer program is executed on a computer” (see at least Claim 15) . Although the additional element “machine learning” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). 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.
Claims 1-11 and 14-15: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
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 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 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 of this title, if the differences between the claimed invention and the prior art axe 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-11 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Shusterman, U.S. Patent U.S. 7,801,591 B1 in view of Volosin et al., WIPO Application Publication WO 2021/022003 A1.
Claim 1:
Shusterman discloses the following limitations as shown below:
providing a first data set comprising cardiac current curve data of a patient acquired by an implantable medical device (Shusterman, see at least Col. 23, lines 35-49, process different types of serial information obtained from a patient with chronic congestive heart failure. Patients with chronic illnesses often have a number of chronically or intermittently abnormal indicators, whose dynamics are difficult to discern. A network of computers allows fast and accurate processing of the patient's information obtained using different diagnostic techniques (such as biochemical, electrocardiographic, nuclear magnetic resonance, stress-test, and other modalities; Col. 25, lines 25-29, A hypothetical patient with an implantable cardioverter-defibrillator has developed subtle instabilities of cardiac rhythm and slowly rising average heart rate. These changes are detected by the implantable device, which transmits this information wirelessly to a home health network computer);
applying a first machine learning algorithm and/or a rule-based algorithm to the cardiac current curve data for classification of a medical relevance of a parameter deviation from a norm of the cardiac current curve data (Shusterman – see at least Col. 23, lines 35-64, This theoretical example has been selected to show how the present invention could be implemented using a distributed network of computers with parallel processing and how it can be efficiently integrated with such methods of artificial intelligence as neural networks and expert systems to process different types of serial information obtained from a patient with chronic congestive heart failure. Patients with chronic illnesses often have a number of chronically or intermittently abnormal indicators, whose dynamics are difficult to discern. A network of computers allows fast and accurate processing of the patient's information obtained using different diagnostic techniques (such as biochemical, electrocardiographic, nuclear magnetic resonance, stress-test, and other modalities)). In a hypothetical patient B. with chronic congestive heart failure (Class II) and a three-year-old myocardial infarction, the above-described high-resolution analysis of serial ECG recordings could reveal a subtle decreasing trend in the amplitude of the ST-segment. This trend could be revealed because the serial ECG recordings were processed at the high-resolution level using a radial basis function (RBF) neural network, which was previously trained on patient's B electrocardiographic data. Because the neural network could learn the typical patient's B ECG pattern, it could detect subtle changes in this pattern. The magnitude of the changes may be so small and the changes so gradual, that they might escape detection by the standard ECG processing techniques, which are manually applied by the physicians or used by the current commercial ECG scanning software);
outputting a second data set comprising at least a first class representing a medically relevant parameter deviation or a second class representing a medically not relevant parameter deviation (Shusterman, see at least Col. 23, lines 50-59, In a hypothetical patient B. with chronic congestive heart failure (Class II) and a three-year-old myocardial infarction, the above-described high-resolution analysis of serial ECG recordings could reveal a subtle decreasing trend in the amplitude of the ST-segment. This trend could be revealed because the serial ECG recordings were processed at the high-resolution level using a radial basis function (RBF) neural network, which was previously trained on patient's B electrocardiographic data. Because the neural network could learn the typical patient's B ECG pattern, it could detect subtle changes in this pattern);
applying a second machine learning algorithm to the third data set for determining the heart failure status of the patient (Shusterman, see at least Col. 26, lines 1-5, The Scale II-III analysis confirms that the magnitude of the changes exceeded 3 standard deviations never been observed in this patient previously. The information is transferred to the integrated artificial intelligence system for further interpretation); and
outputting a fourth data set indicative of the heart failure status of a patient (Shusterman, see at least Col. 26, lines 5-7, The system classifies the changes as clinically significant and forwarded them to the medical personnel) .
Shusterman may not specifically disclose the following limitations, but Volosin as shown does:
in response to outputting the first class, triggering a patient information request (Volosin, see at least Paragraph 86, assess various parameters before, during, and after each rehabilitation session, as deemed appropriate by an HCP. These parameters can include heart rate assessment, blood pressure assessment, changes in patient weight, symptoms of exercise intolerance, symptoms of evidence of change in clinical status, changes in medication adherence, ECG monitoring, non-ECG physiological signal monitoring, and patient feedback; Paragraph 95, the patient device 305 can include a personal computer, a tablet computer, a smartphone, and other similar computing devices; Paragraph 189, 3. Patient symptom assessment (and comparing to predetermined criteria): a. Are you well enough to exercise? Yes or No; b. What’s your shortness of breath level? Scale 1-10 on the monitor c. What’s your chest pain level? Scale 1-10 on the monitor) ;
providing a third data set comprising the first data set and data provided in response to the patient information request (Volosin, see at least Paragraph 86, assess various parameters before, during, and after each rehabilitation session, as deemed appropriate by an HCP. These parameters can include heart rate assessment, blood pressure assessment, changes in patient weight, symptoms of exercise intolerance, symptoms of evidence of change in clinical status, changes in medication adherence, ECG monitoring, non-ECG physiological signal monitoring, and patient feedback; Paragraph 93, patient information as collected herein provides for a complete collection of historical data that has been continuously collected and can be further analyzed for any specific time period, e.g., the last 24 hours, last week, two weeks ago, and other similar predefined historical periods as described herein; Paragraph 94, For example, the ECG data can be recorded when a patient experiences and chooses to report a symptom such as fatigue, chest pain, tightness in the chest, a racing heartbeat, dizziness, and/or syncope. In implementations, the ECG data can be automatically recorded when a symptom is detected by a processor integrated into the wearable medical device (e.g., processor 118 as shown in FIG. 1) based on non-ECG physiological data, such as motion data from one or more sensors such as motion sensors; Paragraph 95, the patient device 305 can include a personal computer, a tablet computer, a smartphone, and other similar computing devices; Paragraph 189, 3. Patient symptom assessment (and comparing to predetermined criteria): a. Are you well enough to exercise? Yes or No; b. What’s your shortness of breath level? Scale 1-10 on the monitor c. What’s your chest pain level? Scale 1-10 on the monitor);
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the digital healthcare information management of Shusterman with the patient information of Volosin with the motivation that With the motivation or providing the benefit to “… perform a baseline assessment of the patient prior to patient performance of any given exercise session included in a cardiac rehabilitation plan and determine whether the patient should perform or skip/postpose the exercise session” (Volosin, see at least Paragraph 82).
Claims 14 and 15 recite substantially similar system and computer program with program code to perform the method when executed on a computer limitations to those of method claim 1 and, as such, are rejected for similar reasons as given above.
Claim 2:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein the fourth data set comprises at least a third class representing a normal heart failure status of the patient and a fourth class representing an abnormal heart failure status of the patient and/or wherein the fourth data set comprises a numerical value indicative of the heart failure status of the patient (Shusterman, see at least Col. 26, lines 5-7, The system classifies the changes as clinically significant and forwarded them to the medical personnel).
Claim 3:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman may not specifically disclose the following limitations, but Volosin as shown does:
wherein the patient information request is sent to a user communication device and/or smartphone, and wherein the patient is prompted to input information, in particular a body weight and/or symptoms, and/or information is imported from an app installed on the user communication device and/or the smartphone (see at least Paragraph 71, Wearable medical devices are continuously worn and so can continuously monitor ECG and other physiological information of ambulatory patients. Systems, devices, and techniques herein implement cardiac rehabilitation plans that drawn on such continuously available real time ECG and non-ECG physiological information in at least two ways. First, the device is configured to provide individualized plans that are dynamically adjusted based on the most current and/or real time ECG and non-ECG physiological information from the ambulatory patient. Second, the device is configured to measure the patient’s adherence to such plans based on the most current and/or real time ECG and non-ECG physiological information from the ambulatory patient. For example, the non-ECG physiological information can include patient motion information (such as step rate, patient position, and posture), respiration information, lung fluid level information, pulse information, blood oxygenation information (e.g., V02 metrics, VC02 metrics, etc.), blood pressure information, and other such information. In some implementations, the device is configured to process patient feedback received via a user interface (e.g., responses to questions, pre- and post-workout questions and surveys, indications of exertion, such as a rating of perceived exertion or RPE) when dynamically adjusting a plan or tracking patient adherence to the plan).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the digital healthcare information management of Shusterman with the patient information of Volosin for at least the same reasons given for claim 1.
Claim 4:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman may not specifically disclose the following limitations, but Volosin as shown does:
wherein the information provided by the patient and/or imported from the app installed on the user communication device and/or the smartphone is given by at least one numerical value associated with the body weight of the patient and/or an evaluation of symptoms, answers to multiple-choice questions, and/or text-based answers submitted in text fields (see at least Paragraph 71, In some implementations, the device is configured to process patient feedback received via a user interface (e.g., responses to questions, pre- and post-workout questions and surveys, indications of exertion, such as a rating of perceived exertion or RPE) when dynamically adjusting a plan or tracking patient adherence to the plan; Paragraph 78, the wearable medical device can transmit messages to the server that communicate the patient’s ECG and non-ECG physiological information and additional physical response data; Paragraph 83).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the digital healthcare information management of Shusterman with the patient information of Volosin for at least the same reasons given for claim 1.
Claim 5:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein in response to outputting the fourth class representing the abnormal heart failure status of the patient, a notification is sent to a communication device of a health care provider (Shusterman, see at least Col. 26, lines 5-7, The system classifies the changes as clinically significant and forwarded them to the medical personnel).
Claim 6:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein if the numerical value indicative of the heart failure status of the patient is outside a predetermined range, exceeds or falls below a predetermined threshold value, a notification is sent to a communication device of a health care provider (Shusterman, see at least Col. 11, lines 30-32, Medical decision support system for medical professionals implemented on a personal computer, a cell phone, a smart phone, or a personal digital assistant (PDA); Col. 26, lines 1-5, The Scale II-III analysis confirms that the magnitude of the changes exceeded 3 standard deviations never been observed in this patient previously. The information is transferred to the integrated artificial intelligence system for further interpretation; Col. 26, lines 5-7, The system classifies the changes as clinically significant and forwarded them to the medical personnel).
Claim 7:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein the notification and/or heart the failure status of the patient is accessible via a front-end application on/or the communication device, in particular a smart phone and/or a personal computer, of the health care provider (Shusterman, see at least Col. 26, lines 1-5, The Scale II-III analysis confirms that the magnitude of the changes exceeded 3 standard deviations never been observed in this patient previously. The information is transferred to the integrated artificial intelligence system for further interpretation; Col. 26, lines 5-7, The system classifies the changes as clinically significant and forwarded them to the medical personnel; Col. 11, lines 30-32, Medical decision support system for medical professionals implemented on a personal computer, a cell phone, a smart phone, or a personal digital assistant (PDA)).
Claim 8:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein the second data set outputted by the first machine learning algorithm and the fourth data set outputted by the second machine learning algorithm are stored on a central server and are accessible by the front-end application on/or the communication device of the health care provider (Shusterman, see at least Col. 11, lines 30-32, Medical decision support system for medical professionals implemented on a personal computer, a cell phone, a smart phone, or a personal digital assistant (PDA); Col. 23, line 64 - Col. 24, line 3, The computer server, where ECG recordings from this and other patients would be stored and analyzed, would be a part of a computer network that also includes servers for analysis of biochemical, stress-test, nuclear magnetic resonance, and other data. The servers would be organized into a hybrid artificial intelligence system, which combines a neural network and expert systems).
Claim 9:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein providing the third data set comprises providing the first data set stored on a central server (Col. 23, line 64 - Col. 24, line 3, The computer server, where ECG recordings from this and other patients would be stored and analyzed, would be a part of a computer network that also includes servers for analysis of biochemical, stress-test, nuclear magnetic resonance, and other data. The servers would be organized into a hybrid artificial intelligence system, which combines a neural network and expert systems)
Shusterman may not specifically disclose the following limitations, but Volosin as shown does:
and providing the data supplied in response to the patient information request via the user communication device and/or the smartphone of the patient (see at least Paragraph 71, In some implementations, the device is configured to process patient feedback received via a user interface (e.g., responses to questions, pre- and post-workout questions and surveys, indications of exertion, such as a rating of perceived exertion or RPE) when dynamically adjusting a plan or tracking patient adherence to the plan; Paragraph 78, the wearable medical device can transmit messages to the server that communicate the patient’s ECG and non-ECG physiological information and additional physical response data; Paragraph 83).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the digital healthcare information management of Shusterman with the patient information of Volosin for at least the same reasons given for claim 1.
Claim 10:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein the first data set further comprises arrhythmia data, a heart rate, a patient activity, a chest impedance, and/or readings from electrodes of the implantable medical device (Shusterman, see at least Col. 5, lines 25-27, A hypothetical patient with an implantable cardioverter-defibrillator has developed subtle instabilities of cardiac rhythm and slowly rising average heart rate. These changes are detected by the implantable device, which transmits this information wirelessly to a home health network computer; Col. 5, lines 56-62, A hypothetical patient with chronic congestive heart failure undergoing resynchronization pacing for 15 months has developed a gradual increase in the intrathoracic impedance, detected by Optivol, indicative of slowly progressing decompensation of cardiac function. These changes are detected by the implanted device, which used individually tailored monitoring thresholds at the Scale I analysis).
Claim 11:
The combination of Shusterman/Volosin discloses the limitations as shown in the rejections above. Shusterman further discloses the following limitations:
wherein the cardiac current curve data is acquired by the implantable medical device at predetermined intervals and/or on request, and wherein the cardiac current curve data is transmitted to a central server via a patient communication device or smartphone (Shusterman, see at least Col. 5, lines 25-27, A hypothetical patient with an implantable cardioverter-defibrillator has developed subtle instabilities of cardiac rhythm and slowly rising average heart rate; Col. 20, lines 36-46, FIG. 14 shows the changes in the PCA coefficients of these series in Scale III, dynamics of ECG in patient A in a space of the first, most significant PCA-coefficients. Y-axis represents the first PCA-coefficient that was obtained from T-wave amplitude. X-axis represents the first PCA-coefficient that was obtained from QT-interval. Each point corresponds to one-hour value. Values during 1-5 days are marked as pluses, values during 6-10 days are marked by stars, values during 11-16 days are marked by circles. Higher dispersion and change in the location of the points during 6-16 days compared to the first five days indicates instability of serial ECGs; Col. 23, line 64 - Col. 24, line 3, The computer server, where ECG recordings from this and other patients would be stored and analyzed, would be a part of a computer network that also includes servers for analysis of biochemical, stress-test, nuclear magnetic resonance, and other data. The servers would be organized into a hybrid artificial intelligence system, which combines a neural network and expert systems; Col. 28, lines 54-59, the subject recorded his ECG and sent it using a wireless (cell phone) transmission to a remote computer center, where the patient's historic data has been stored).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686