DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/332,045, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claim 15's subject matter is not included in the specification as originally filed on 4-18-2022 in application 63/332045 and thus will have priority of the non-provisional effective filing date on 4-18-2023. See MPEP 211.05(A).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 9 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 9 is dependent on claim 1 and recites a limitation in independent claim 1. Applicant may cancel the claim, amend the claim to place the claim in proper dependent form, rewrite the claim in independent form, or present a sufficient showing that the dependent claim complies with the statutory requirements.
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 an abstract idea without significantly more.
Step 1
Claims 1-20 recite subject matter within a statutory category as a process, machine, article of manufacture and/or composition of matter. However, it will be shown in the following steps, that claims 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 1 recites
A health monitoring system utilized for a user, comprising:
an enclosure configured to hold one or more components of the health monitoring system, the enclosure including: a wireless transceiver configured to communicate health data to a remote server;
a camera configured to capture an image associated with the user;
a temperature sensor configured to capture temperature data indicative of a body temperature indicative of the user;
a blood pressure sensor configured to capture blood pressure data indicative of a blood pressure of the user;
a heart rate sensor configured to capture heart rate data indicative of a heart rate of the user;
a glucose sensor configured to capture glucose data indicative of a glucose level of the user;
an electrocardiogram (ECG) configured to record an electrical signal associated with a heart of the user to capture ECG data;
a processor in communication with the wireless transceiver, camera, temperature sensor, blood pressure sensor, heart rate sensor, glucose sensor, and ECG, wherein the processor is further programmed to:
receive the images, the temperature data, the blood pressure data, the heart rate data,
send, via the wireless transceiver, to the remote server the images, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data;
utilizing a machine learning model and the image, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data, identify one or more health conditions associated with the user, the one or more health conditions includes one or more skin conditions, wherein the one or more skin conditions are identified based on the image;
and in response to the identifying the one or more health conditions exceeding a health threshold, output a health report indicative of one or more health assessments indicating a healthy condition or an illness associated with the user.
The broadest reasonable interpretation of these steps includes mental processes and/or organizing human activity because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms like “processor”, “blood pressure monitor”, “camera”, “temperature sensor”, “blood pressure sensor”, “heart rate sensor”, “glucose sensor”, “ECG”, “wireless transceiver”, “remote server”, and “machine learning model”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “wireless transceiver” language, “send, via the wireless transceiver, to the remote server the images, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data” in the context of this claim encompasses organizing a healthcare professional to share a patient’s pertinent medical information to another physician. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Organizing Human Activity” grouping of abstract ideas.
Accordingly,
receive the images, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data;
send, … the images, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data;
utilizing … the image, the temperature data, the blood pressure data, the heart rate data, the glucose data, and the ECG data, identify one or more health conditions associated with the user, the one or more health conditions includes one or more skin conditions, wherein the one or more skin conditions are identified based on the image;
and in response to the identifying the one or more health conditions exceeding a health threshold, output a health report indicative of one or more health assessments indicating a healthy condition or an illness associated with the user.
under the broadest reasonable interpretation, include managing personal behavior as an abstract idea.
Independent claims 11 and 17 cover similar steps of receiving patient information, sending patient information, and identifying a health condition associated with . These claims fall under the same category of an abstract idea and follows the same rationale as claim 1.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 6, reciting particular aspects of how “utilize[ing] a pattern recognition algorithm to identify abnormal health condition patterns or symptoms based on the received data” may be performed in the mind but for recitation of generic computer components).
Dependent claims 7 and 20 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 1' s system recites additional elements such as a “processor”, “blood pressure monitor”, “camera”, “temperature sensor”, “blood pressure sensor”, “heart rate sensor”, “glucose sensor”, “ECG”, “wireless transceiver”, “remote server”, and “machine learning model”. In addition to the generic components and additional elements listed above, independent claims 11 and 17' s system and method also includes a “switch” and a “graphical user interface”. The “processor”, “remote server”, “wireless transceiver”, “machine learning model”, “switch”, and “graphical user interface” will be treated as a generic computer component. The “blood pressure monitor”, “camera”, “temperature sensor”, “blood pressure sensor”, “heart rate sensor”, “glucose sensor”, and “ECG” will be treated as additional elements and analyzed for conventionality in the proceeding steps. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amounts to invoking computers as a tool to perform the abstract idea (such as recitation of “an enclosure configured to hold one or more components of the health monitoring system, the enclosure including: a wireless transceiver configured to communicate health data to a remote server”, “a camera configured to capture an image associated with the user”, “a processor in communication with the wireless transceiver, camera, temperature sensor, blood pressure sensor, heart rate sensor, glucose sensor, and ECG”, “via the wireless transceiver, to the remote server”, and “a machine learning model” which all introduce a computer component at a high level of generality to apply an exception.), see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “a temperature sensor configured to capture temperature data indicative of a body temperature indicative of the user”, “a blood pressure sensor configured to capture blood pressure data indicative of a blood pressure of the user”, “a heart rate sensor configured to capture heart rate data indicative of a heart rate of the user”, “a glucose sensor configured to capture glucose data indicative of a glucose level of the user”, “an electrocardiogram (ECG) configured to record an electrical signal associated with a heart of the user to capture ECG data”, amounts to mere data gathering, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For instance, dependent claims 7 and 20 add additional elements of “display” to their parent claims. Additionally, the limitations:
amount to mere instructions to apply an exception (such as recitation of [ 20 ] amounts to invoking computers as a tool to perform the abstract idea, see applicant' s specification [ 21 ], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of claim 2 “receive login information associated with the user from a human machine input interface; and associate one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, and the ECG data with the user utilizing the login information” amounts to mere data gathering, recitation of claim 3 “utilize the image and one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data to identify a skin condition associated with the user.” And claim 6’s “utilize a pattern recognition algorithm to identify abnormal health condition patterns or symptoms based on the received data.” amounts to selecting a particular data source or type of data to be manipulated, recitation of claim 3 “send, in response to exceeding the health threshold, the health report to one or more associated tags.” And claim 7’s “the display configured to output a graphical user interface associated with the health monitoring system.” And claim 15’s “initiate a driver in response to the input at the user interface, wherein the driver is configured to control and manage hardware of the one or more sensors in communication with the health monitoring system.” amounts to insignificant application, see MPEP 2106.05(g))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims (5, 8-10, 12-14, 16, 18, 19) do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a blood pressure monitor. Bailey (US4705047) demonstrates “FIG. 1 is a diagram of a prior art resistance bridge blood pressure meter for connection to a conventional blood pressure monitor” that blood pressure monitors were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of a camera. Whitley et. al (US3783763) demonstrates “The focusing screen 117 may be formed of any appropriate translucent material and is positioned as is well understood in the camera art to lie at exactly the same optical distance from the rear of the lens” that a camera was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of a temperature sensor. Yelderman et al. (US 5159936) demonstrates in paragraph [8] “Another widely appreciated advantage over conventional thermometers is that tympanic thermometry uses the ear, which is less likely to harbor pathogens” that that a temperature sensor was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of a heart rate sensor. Gallant et al (US4340225) demonstrates in paragraph [19] “Finally, the heart monitor 66 and elapsed time meter 72 are preferably any conventional heart rate monitor device and elapsed time meter, responsive to conventional digital data input” that a heart rate sensor was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of a glucose sensor. Karube et al. (US4976175) demonstrates in paragraph [41] “This is comparable to conventional glucose sensors” that a glucose sensor was conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
As previously noted, the claim recites an additional element of an ECG. Novak et al. (US3608545) demonstrates paragraph [1] “apparatus according to this invention for monitoring the heart rate of a patient 10 employs, as its two inputs, electrical signals from suitable transducers, for example, from the usual number of conventional ECG electrodes 11 suitably attached and located on the patient and from an arterial pulse pickup” that an ECG was conventional long before the priority data of the claimed invention.
As such, these additional elements, individually and in combination with the prior additional element, does not amount to significantly more. To elaborate:
“a temperature sensor configured to capture temperature data indicative of a body temperature indicative of the user”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
“a blood pressure sensor configured to capture blood pressure data indicative of a blood pressure of the user”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
“a heart rate sensor configured to capture heart rate data indicative of a heart rate of the user”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
“a glucose sensor configured to capture glucose data indicative of a glucose level of the user”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
“an electrocardiogram (ECG) configured to record an electrical signal associated with a heart of the user to capture ECG data”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. To elaborate:
To elaborate:
claim 2’s “receive login information associated with the user from a human machine input interface” is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 2’s “associate one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, and the ECG data with the user utilizing the login information” , is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
claim 3 “utilize the image and one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data to identify a skin condition associated with the user.” , is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
claim 6’s “utilize a pattern recognition algorithm to identify abnormal health condition patterns or symptoms based on the received data.”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
claim 4 “send, in response to exceeding the health threshold, the health report to one or more associated tags.” is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 7’s “the display configured to output a graphical user interface associated with the health monitoring system.”, is equivalently, selecting information, based on types of information and availability of information, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A;
claim 15’s “initiate a driver in response to the input at the user interface, wherein the driver is configured to control and manage hardware of the one or more sensors in communication with the health monitoring system.”
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
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, if the differences between the claimed invention and the prior art are 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Katragadda et al. (US 20210137468) in view of Maclellan et al. (US 20220051409)
Regarding claim 1, Katragadda teaches:
A health monitoring system (Figure 1, image of a health monitoring system) utilized for a user ([0020] “user may control the operation”) comprising: an enclosure (Figure 1, health monitoring system enclosure) configured to hold one or more components of the health monitoring system (holding), the enclosure including: a wireless transceiver ([0017] “3G/4G modem”, modem has transceiver) configured to communicate health data ([00127] “Such data… view[ed] by a healthcare provider” which communicates via the server) to a remote server ([00127] “server,”);
a camera ([0079] “camera”) configured to capture an image associated with a user; (video call is continuous imaging of the patient, i.e., user; [0079] “camera allow the user to initiate a video call with a clinician”,)
a temperature sensor configured to capture temperature data indicative of a body temperature indicative of the user; (temperature probe is a type of sensor for patient; [0014] “the device includes: one or more probes configured to measure or collect one or more of… temperature,”)
a blood pressure sensor configured to capture blood pressure data indicative of a blood pressure of the user; (blood pressure probe is a blood pressure sensor that collects patient data [0014] “the device includes: one or more probes configured to measure or collect one or more of: a blood pressure”)
a heart rate sensor configured to capture heart rate data indicative of a heart rate of the user; (an electrocardiogram is a type of heart rate sensor that collects heart rate data [0014] “In some embodiments, the device includes: one or more probes configured to measure or collect one or more of… an electrocardiogram”)
a glucose sensor configured to capture glucose data indicative of a glucose level of the user; (a strip sensing module for an analyte test strip includes a blood glucose test strip to sense and capture blood glucose data [0014] “the device includes: one or more probes configured to measure or collect one or more of: … a strip sensing module configured to receive a test strip therein for measuring an analyte” and includes [0007] “blood glucose level”)
an electrocardiogram (ECG) configured to record an electrical signal associated with a heart of a user to capture ECG data; ([0014] “the device includes: one or more probes configured to measure or collect one or more of… an electrocardiogram”)
a processor in communication with the wireless transceiver, camera, temperature sensor, blood pressure sensor, heart rate sensor, glucose sensor, and ECG, ([0014] “a processor communicatively coupled to the one or more probes”; [0014] “In some embodiments, the device includes: one or more probes configured to measure or collect one or more of: a blood pressure, a temperature, a blood oxygen saturation, an electrocardiogram, an image of an internal anatomical structure, or a combination thereof; a strip sensing module configured to receive a test strip therein for measuring an analyte” see above for analyte being glucose level) ([0079] “camera” where the camera is connected to the processor)
wherein the processor is further programmed to: receive the images, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data; ([0015] “In some embodiments, execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device”, biometric data may include the examples of health data listed above)
send, via the wireless transceiver, ([0017] “3G/4G modem”, modem has transceiver) to the remote server ([00127] may be transmitted to the cloud, server) the images, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data; ([0015] “transmitting, in real-time to a remote computing device, the one or more health parameters to the healthcare provider during the remote health appointment”; health parameters include various health data listed previously).
utilizing a machine learning model ([00127] “machine learning”) and the image, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data (See health parameters as health data reference above), identify one or more health conditions associated with the user; ([00127]” FIGS. 15-20 and/or any health parameter analysis may be performed using machine learning”; [0064] “detect anomalies in one or more sensed health parameters” where anomalies and health conditions are synonymous.)
Regarding claim 1, Katragadda does not explicitly teach, as taught by Maclellan:
the one or more health conditions includes one or more skin conditions, wherein the one or more skin conditions are identified based on the image ([0004] The systems and methods of this technical disclosure provide an artificial-intelligence (AI) based solution to analyze, diagnose, and provide treatment plans for various skin conditions. This technical solution can provide a cloud-based service for the analysis of skin images of a user or patient to determine the color, characteristics, and other information about the skin of the user.)
Regarding claim 1, Katragadda- Maclellan as a combination continues to teach.
and in response to the identifying the one or more health conditions (see health conditions reference above) exceeding a health threshold ([00107] “In some embodiments, the PDK further includes one or more limit alarms, for example to indicate when one or more parameters or measurements exceed the specifications, indicate a danger scenario, or other improper use” where limit alarms measure exceeding a threshold of the health data), output a health report ([Katragadda 0064] “prints reports”) indicative of one or more health assessments indicating a healthy condition or an illness associated with the user. ([Katragadda 0058] “The portable medical diagnostic device can instantaneously generate a health score based on a large sampling of biomarkers” where the health report comprising health assessment is a health score)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Katragadda with the teachings of Maclellan, with a reasonable expectation of success, by explicitly identifying skin conditions using artificial intelligence to process images of the patient. This would have more thoroughly identified skin conditions for a patient and therefore reducing a patients risk for misdiagnoses. Maclellan is adaptable to Katragadda as both claimed inventions utilize machine learning and various sensors, such as a camera, to diagnose a patient’s medical condition. Katragadda would have found Maclellan’s teaching while searching for the solution to the long felt need that in “certain areas there may be limited access to specialists that can provide consistent treatment to common skin ailments.”
Regarding claim 2, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the processor is further configured to receive login information associated with the user from a human machine input interface; ([0015] “some embodiments … optionally receiving a biometric of a patient as input into the portable device; optionally verifying the patient of the portable device based on the biometric” where login information is a patient’s biometric; [0073] “access his reports online anytime with secure login/password credentials” where access occurs through the device)
And associate one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data with the user utilizing the login information ([0015] “some embodiments … optionally receiving a biometric of a patient as input into the portable device; optionally verifying the patient of the portable device based on the biometric” where login information is a patient’s biometric and the biometrics comprise the various health data collected; [0073] “access his reports online anytime with secure login/password credentials” where access occurs through the device holding the user’s data).
Regarding claim 3, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the processor is configured to utilize the image and one or more of the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data to identify a skin condition associated with the user. ([00127]” FIGS. 15-20 and/or any health parameter analysis may be performed using machine learning”; where health parameter analysis is a diagnosis and [0064] “detect anomalies in one or more sensed health parameters”; see optionally [0009] “receiving an image of the patient with an image sensor of the portable device to perform facial recognition of the image of the patient” comprises determining a skin condition, i.e., redness or jaundice, associated with the user).
Regarding claim 4, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the processor is further programmed to send, in response to exceeding the health threshold, the health report (see reference to threshold and reference prior) to one or more associated tags. (where the tags are associated parameters, [0064] “The device may also be configured to perform one or more of the following: detect anomalies in one or more sensed health parameters (e.g., arrhythmia conditions in an ECG signal); display trends and/or efficacy plots for all the sensed health parameters to indicate health improvements or changes in health relative to past appointments; print reports instantly with an optionally attached thermal printer”, [0094] “Hardware board 140 performs analog signal conditioning and feature extraction for the measured parameters” where the tags are associated parameters).
Regarding claim 5, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the health report (see heath report above) outputs an index or a numerical value representing a health profile of the user. ([0080] “generating results … in the form of numeric values for each measured patient parameter” where the device is outputting these numerical values in the form of a health profile)
Regarding claim 6, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
The health monitoring system of claim 1, wherein the processor is further programmed to utilize pattern recognition algorithm ([0069] “machine learning algorithms that use pattern recognition”) to identify abnormal health condition patterns or symptoms based on the received data. ([0064] “The device may also be configured to perform one or more of the following: detect anomalies in one or more sensed health parameters (e.g., arrhythmia conditions in an ECG signal)”, where health condition patterns can be attributed to the pattern recognition)
Regarding claim 7, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the health monitoring system further includes a display ([0022] “display”) in communication with the processor ([0006] “a processor”), the display configured to output ([0083] “outputs appropriate messages and guiding inputs to the user on display”) a graphical user interface associated with the health monitoring system. ([0022] “a touch-enabled interactive configurable display.”, where this display is a graphical user display).
Regarding claim 8, Katragadda-Maclellan, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses “wherein the machine learning model is located at the remote server.”
Katragadda teaches all of the current invention as stated above, including “the machine learning module”.
Katragadda does not teach “the machine learning model is located at the remote server”.
Maclellan teaches “the machine learning model is located at the remote server” ([0063] “Referring to FIG. 1A, an implementation of a network environment is depicted. In brief overview, the network environment includes… one or more servers… or remote machines” and [0174] “The medical information accessor 392 can numerically encode the medical information such that it can be provided as an input to a machine learning model.” where processing is done with a machine learning model).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Katragadda with the teachings of Maclellan, with a reasonable chance of success, to access a remote machine learning model in lieu of a localized model. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Katragadda with the teachings of Maclellan, with a reasonable expectation of success, by adapting their machine learning to a remote server. This would have improved the timeliness of patient care by removing bottlenecks and latency issues by spreading out the processing requirements across multiple networks. Maclellan is adaptable to Katragadda as both claimed inventions utilize machine learning and various sensors, such as a camera, to diagnose a patient’s medical condition. Katragadda would have found Maclellan’s teaching of accessing a remote machine learning model for healthcare management after researching the latency issues that may arise with Katragadda’s telehealth appointments.
Claims 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Katragadda et al. (US 20210137468) in view of Maclellan et al. (US 20220051409) and further in view of Ghodrati et al. (WO 2022/198058)
Regarding claim 9, Katragadda-Maclellan, as shown above, discloses all of the limitations of claim 1. Katragadda’s claim 9 adds no further limitations and will follow the same rationale as claim 1.
Regarding claim 10, Katragadda, as shown above, discloses all of the limitations of claim 1. Katragadda also discloses:
wherein the processor (see processor reference above) is further programmed to, in response to utilizing a machine learning model (see machine learning reference above) and the image, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data, (See health parameters as health data reference above) output a health score ([0058] “generate a health score based on a large sampling of biomarkers”) indicative of one or more health assessments associated with the user. [0064] “The device may also be configured to perform one or more of the following: detect anomalies in one or more sensed health parameters (e.g., arrhythmia conditions in an ECG signal” where anomalies are based on biomarkers);
Regarding claim 11, Katragadda discloses:
A health monitoring system utilized for a user, ([0011] “user”) comprising: a wireless transceiver ([0017] “a 3G/4G modem” where a modem has a transceiver) configured to communicate ([0017] “the wireless communication module“) health data ([0011] “transmitting a remote user input, from the healthcare provider”) to a remote server; ([00127] “server”
one or more processors ([0006] “a processor”) in communication with the wireless transceiver (see modem above) wherein the one or more processors are collectively further programmed to: ([0025] “transmitting, in real-time to a remote computing device … and receiving, in real-time on the portable device” where the devices are processors)
upon receiving an input at a user interface of the health monitoring system, ([0015] “user input, on the display of the portable device” where the display may be a graphical interface) activate a switch to activate one or more sensors in communication with the health monitoring system ([0011] “initiate the acquisition of the one or more health parameters using the portable device”, where initiation uses the processor’s switch), wherein the one or more sensors are configured to collect one or more of an image of the user,([0009] “receiving an image of the patient”) temperature data, blood pressure data, heart rate data, glucose data, or echocardiogram data; [0007] “In some embodiments, the one or more health parameters are selected from the list consisting of: a blood pressure, a temperature, a blood oxygen saturation, an electrocardiogram, a blood glucose level”, where an electrocardiogram collects heart rate data and the list comprises inputs from the device’s sensors and these sensors are used to collect information)
send, via the wireless transceiver, to the remote server ([00127] “data … may be transmitted to the cloud, server”, where the transmitting occurs through said modem) the images, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data; (see [0007] and [0009] reference above).
utilizing a machine learning model ([00127] “machine learning”) and one or more of the image, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data, (see [0007] and [0009] reference above) identify one or more health conditions associated with the user; ([0127] “any health parameter analysis may be performed using machine learning” and [0064] “detect anomalies in one or more sensed health parameters (e.g., arrhythmia conditions in an ECG signal)”)
Regarding claim 11, Katragadda does not explicitly teach, as taught by Maclellan:
the one or more health conditions includes one or more skin conditions, wherein the one or more skin conditions are identified based on the image; ([0004] The systems and methods of this technical disclosure provide an artificial-intelligence (AI) based solution to analyze, diagnose, and provide treatment plans for various skin conditions. This technical solution can provide a cloud-based service for the analysis of skin images of a user or patient to determine the color, characteristics, and other information about the skin of the user.”)
Regarding claim 11, Katragadda-Maclellan as a combination continues to teach:
and in response to the identifying the one or more health conditions exceeding a health threshold, ([Katragadda 00107] “In some embodiments, the PDK further includes one or more limit alarms, for example to indicate when one or more parameters or measurements exceed the specifications, indicate a danger scenario, or other improper use” where limit alarms measure exceeding a threshold of the health data) output a health report ([Katragadda 0064] “prints report” where the report discusses health material) indicative of one or more health assessments indicating a healthy condition or an illness associated with the user. ([Katragadda 0058] “The portable medical diagnostic device can instantaneously generate a health score based on a large sampling of biomarkers” where the health report comprising health assessment is a health score).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Katragadda with the teachings of Maclellan, with a reasonable expectation of success, by explicitly identifying skin conditions using artificial intelligence to process images of the patient. This would have more thoroughly identified skin conditions for a patient and therefore reducing a patients risk for misdiagnoses. Maclellan is adaptable to Katragadda as both claimed inventions utilize machine learning and various sensors, such as a camera, to diagnose a patient’s medical condition. Katragadda would have found Maclellan’s teaching while searching for the solution to the long felt need that in “certain areas there may be limited access to specialists that can provide consistent treatment to common skin ailments.”
Regarding claim 12, Katragadda, as shown above, discloses all of the limitations of claim 11. Katragadda also discloses:
The system of claim 11, wherein the one or more processors (see processor above) is a single processor located remote from the health monitoring system. ([0018] “In some embodiments, the remote computing device comprises a healthcare provider computing device. In other embodiments, the remote computing device comprises a server or a cloud-based database.” Where the remote computing device is a processor)
Regarding claim 13, Katragadda, as shown above, discloses all of the limitations of claim 11. Katragadda also discloses:
wherein the one or more processors includes a single processer located at the health monitoring system and one or more remote processors located remote from the health monitoring system. (see remote computing device above and [0021] “a wireless scale communicatively coupled to the processor.” Where the processor is a part of the remote monitoring system and the scale is remote from the health monitoring system)
Regarding claim 14, Katragadda, as shown above, discloses all of the limitations of claim 11. Katragadda also discloses:
wherein the switch that is activated ([0006] “a processor”, where the processor contains a switch) is associated with the input at the user interface. ([0025] ” the processor … optionally receiv[es] a biometric of a patient as input into the portable device; optionally verif[ies] the patient of the portable device based on the biometric; receiv[es] a user input, on the display of the portable device, to initiate a remote health appointment with a healthcare provider” where the display is the user interface)
Regarding claim 15, Katragadda, as shown above, discloses all of the limitations of claim 11. Katragadda also discloses:
wherein the processor is further configured to initiate a driver ([0122] “Various probes and analytics may be calibrated” where the calibration is a driver to initiate the sensor’s integration into the system) in response to the input at the user interface, ([00122] “by user input” where input occurs at the user interface) wherein the driver is configured to control and manage hardware of the one or more sensors in communication with the health monitoring system. ([0080] “facilitates connecting to a BCA machine and weight machine or other hardware wirelessly. In some embodiments, it also connects to other devices like a digital stethoscope, a spirometer, etc. All these are grouped as external devices” where the external devices are various third-party sensors requiring configuration and made to connect wirelessly)
Regarding claim 16, Katragadda, as shown above, discloses all of the limitations of claim 11. Katragadda also discloses:
wherein the one or more sensors are in communication with the health monitoring system via a wireless communication protocol. ([0021] ”In some embodiments, the device further includes a wireless scale communicatively coupled to the processor” where the scale is a sensor and the processor is part of the health monitoring system)
Regarding claim 17 Katragadda discloses:
A method of monitoring a health of a user utilizing a personal health monitoring system, wherein the method includes: utilizing a camera, ([0079] “camera”) receiving an image associated with a user; ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device” where the biometric may be an image of the user”)
utilizing a temperature sensor, receiving temperature data indicative of a body temperature indicative of the user; ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device [0006] In some embodiments, the portable device includes one or more probes configured to measure … a temperature” where the probes collect biometric data)
utilizing a blood pressure monitor, receiving blood pressure data indicative of a blood pressure of the user utilizing a blood pressure monitoring; ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device [0006] In some embodiments, the portable device includes one or more probes configured to measure … a blood pressure ” where the probes collect biometric data)
utilizing a heart rate sensor, receiving heart rate data indicative of a heart rate of the user ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device [0006] In some embodiments, the portable device includes one or more probes configured to measure … an electrocardiogram” where the probes collect biometric data and the electrocardiogram has the heart rate)
utilizing a glucose monitor, receiving glucose data indicative of a glucose level of the user; ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device) ([0015] “acquiring one or more health parameters … wherein the one or more health parameters are … a blood glucose level” where the blood glucose level testing is collecting biometric data))
receiving, via one or more processor, the images, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data, ([0006] “execution of the instructions causes the processor to perform a method including: optionally receiving a biometric of a patient as input into the portable device” where a biometric comprises health parameters [0007] “In some embodiments, the one or more health parameters are selected from the list consisting of: a blood pressure, a temperature, a blood oxygen saturation, an electrocardiogram, a blood glucose level”))
sending, via the wireless transceiver, ([0017] “3G/4G modem”, modem has transceiver) to the remote server, ([00127] “server”) the images, the temperature data, the blood pressure data, the heart rate data, and the glucose data, the ECG data; ([0011] “transmitting a remote user input, from the healthcare provider, from the remote computing device to the portable device to initiate the acquisition of the one or more health parameters using the portable device.”; see health parameters listed above)
utilizing a machine learning model ([00127] “machine learning”) and the image, the temperature data, the blood pressure data, the heart rate data, and the gluc