DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the present action filed on 1/22/2026.
As stated in the Response to Restriction Requirement dated 1/22/2026, Claims 1-15 were elected without traverse, are currently pending, and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/06/2024 was filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 USC 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claim 1 is within one of the four statutory categories. Claim 1 is directed to a method, and dependent Claims 2-15 are further directed to a method and therefore are also within the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A method for integration of health information into a framework system, the method comprising:
formatting clinical data for a patient from a first source of medical healthcare into a first format for a patient data model of the framework system;
formatting patient-generated health data from the patient into a second format for the patient data model of the framework system;
integrating the clinical data and patient-generated health data as formatted into the patient data model for the patient, the integration storing both the clinical data and the patient-generated health data together as the patient data model;
providing an interface or interfaces to the patient data model, including access to the clinical data and the patient-generated health data, for both a patient and a physician;
generating, by a machine-learned model, a biomarker for the patient, the machine-learned model generating in response to input at least some of both of the clinical data and the patient-generated health data from the patient data model;
and outputting the biomarker.
The series of limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions – in this case formatting data into a format for a first and second format for a mode, integrating the data into the model, and generating a biomarker for the patient) e.g., see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 3, 5-8, and 10-15 include other limitations directed toward the abstract idea. For example, Claim 3 recites formatting the clinical data comprises formatting pursuant to a standard for medical data sharing, Claim 5 recites formatting comprises compressing data provided at a first frequency to a less frequent representation, Claim 6 recites the formatting comprises compressing the data to periodic event episodes based on event context of the data, Claim 7 recites formatting the health data comprises reformatting data from an application for gathering information input by the patient, Claim 8 recites formatting the patient-generated health data comprises compressing data, and formatting the clinical data comprises formatting the clinical data from the first source, Claim 10 recites displaying medical image from the clinical data, Claim 11 recites generating the biomarker comprises generating a health score for overall health of the patient, Claim 12 recites generating the biomarker comprises generating a disease specific biomarker, Claim 13 recites generating the biomarker comprises generating the biomarker as a change leading to an adverse event, Claim 14 recites generating the biomarker comprises generating the biomarker as a response to treatment and/or medication, Claim 15 recites providing comprises providing a summary of the patient-generated health data to the physician. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-15 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 3, 5-8, and 10-15 are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Step 2A Analysis – Prong Two:
Claim 1 is not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the framework system, interface, and machine-learned model of Claim 1) are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that in act 100, a computer populates a patient data model of the framework system. By accessing various sources using communications appropriate for those sources, health data is mined to populate the patient data model (see Applicant’s specification, ¶ 0031, Fig. 1). A processor is configured to generate a user interface for interacting with the framework by a patient including access to the clinical data. A display is configured to display part the user interface [0010]. The biomarker is generated by a machine-learned model implemented as a domain engine 408. The machine-learned model generates a value for the biomarker in response to input of PGHD and/or clinical data from the patient data model 240 and/or other sources. The machine-learned model may define the types of information to be input, such as inputting only some or relevant data of the patient data model 240 [0065]. Accordingly, these additional elements, whether considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claim 1 is directed to an abstract idea without practical application.
Dependent Claims 2, 4-6, and 8-10 also recite additional elements. Claim 2 recites new additional elements and specifies formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS) (new element), radiology information system (new), electronic health record (new), and/or laboratory information system (new) for a medical practice or facility. Claim 4 recites a new additional element of a wearable sensor and specifies formatting the health data comprises formatting where the patient-generated health data is from a wearable sensor, Claim 5 recites the previously recited wearable sensor and specifies compressing data from the wearable sensor, Claim 6 recites the previously recited wearable sensor and specifies compressing data from the wearable sensor to periodic event episodes based on event context of the data from the wearable sensor, Claim 8 recites a new additional element of a wearable sensor and specifies compressing data from a wearable sensor, and wherein formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS) or radiology information system (new elements), an electronic health record (new element), and laboratory information system (new element) for a medical practice or facility, Claim 9 recites the previously reciting interface and avatar and specifies providing the interface comprises displaying an avatar of the patient with a characteristic of the avatar representing the biomarker. Claim 10 recites the previously recited interface and avatar and specifies providing the interface comprises displaying the avatar and a medical image, and the image is selected based on selection of a landmark or anatomical region of the avatar. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered separately or as an ordered combination, do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the non-underlined limitations above – in this case the framework system, interface, and machine-learned model of Claim 1 amount to mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”) in step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). For these reasons, the independent Claim 1 is not patent eligible.
Dependent Claims 3, 7, and 11-15 do not recite any additional elements and only serve to further narrow the abstract idea. For example, Claim 3 recites formatting the clinical data comprises formatting pursuant to a standard for medical data sharing. Claim 7 recites formatting the health data comprises reformatting data from an application for gathering information input by the patient. Claim 11 recites generating the biomarker comprises generating a health score for overall health of the patient. Claim 12 recites generating the biomarker comprises generating a disease specific biomarker. Claim 13 recites generating the biomarker comprises generating the biomarker as a change leading to an adverse event. Claim 14 recites generating the biomarker comprises generating the biomarker as a response to treatment and/or medication, Claim 15 recites providing comprises providing a summary of the patient-generated health data to the physician.
Dependent Claims 5-6 and 9-10 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 5 recites the previously recited wearable sensor and specifies compressing data from the wearable sensor. Claim 6 recites the previously recited wearable sensor and specifies compressing data from the wearable sensor to periodic event episodes based on event context of the data from the wearable sensor. Claim 9 recites the previously reciting interface and avatar and specifies providing the interface comprises displaying an avatar of the patient with a characteristic of the avatar representing the biomarker. Claim 10 recites the previously recited interface and avatar and specifies providing the interface comprises displaying the avatar and a medical image, and the image is selected based on selection of a landmark or anatomical region of the avatar.
Dependent Claims 2, 4, and 8 recite new additional elements. Claim 2 recites new additional elements and specifies formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS) (new element), radiology information system (new), electronic health record (new), and/or laboratory information system (new) for a medical practice or facility. Claim 4 recites a new additional element of a wearable sensor and specifies formatting the health data comprises formatting where the patient-generated health data is from a wearable sensor. Claim 8 recites a new additional element of a wearable sensor and specifies compressing data from a wearable sensor, and wherein formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS) or radiology information system (new elements), an electronic health record (new), and laboratory information system (new) for a medical practice or facility.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-15 are rejected under 35 U.S.C. § 101 as being directed to a non-statutory subject matter.
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 (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 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.
Claims 1-3, 7, 11-12, and 15 are rejected under 35 USC § 103 as being unpatentable over Cha et al. (US 20200005900 A1) in view of Maier et al. (US 20160203263 A1).
Regarding Claim 1, Cha discloses the following:
A method for integration of health information into a framework system, the method comprising: (Cha discloses systems and methods that employ machine learning models to predict risk of patient outcomes, such as renal function decline, from electronic patient data [0002].)
…clinical data for a patient from a first source of medical healthcare…(Cha discloses the high dimensionality of data available from EHR systems provides a large array of potential features for use in modeling [0102]. The ML models offer strong predictive value in identifying both patients at risk …via the use of longitudinal patient data that is typically available in EHR systems [0224]. The Examiner interprets the patient records being the clinical data.)
formatting clinical data …from a first source of medical healthcare into a first format for a patient data model of the framework system; (Cha discloses at step 120, the system performs various preprocessing steps to clean, validate and/or normalize the initial data records into preprocessed data records. Such preprocessing may be required to create preprocessed data records comprising data tables having a standardized format or schema [0048]. The Examiner interprets the normalization of the data into a standardized format as formatting the data for use in the model.)
…patient-generated health data… (Cha discloses exemplary patient information stored by such data sources may comprise identification information,…and/or various information relating patient signs, symptoms and behaviors [0038]. The Examiner interprets patient symptoms as patient-generated health data. Such patient information may additionally or alternatively comprise…various patient-generated data (e.g., automated call responses, health risk assessment responses, patient surveys, etc.) [0038].)
formatting patient-generated health data from the patient into a second format for the patient data model of the framework system; (Cha discloses the system may identify and preprocess any patient information pertaining to signs, symptoms and behaviors stored in the initial data records. Preprocessing of such sub-disease and non-disease descriptors often requires custom normalizer data representations that combine multiple terminology standards [0059].)
integrating the clinical data and patient-generated health data as formatted into the patient data model for the patient, (Cha discloses as shown in FIG. 1, the system connects to the one or more data sources in order to ingest and store input data contained therein 110 [0054].)
the integration storing both the clinical data and the patient-generated health data together as the patient data model; (Cha discloses the ML engine 480 may comprise an internal or external memory (e.g., database 481) to store various information, such as patient records received from the server, determined risk information and/or updated patient records [0163].)
providing an interface or interfaces to the patient data model, including access to the clinical data and the patient-generated health data, for…a physician; (Cha discloses a patient information visualization application may display various user interface elements to allow administrative users and/or providers to view longitudinal patient information over time [0191].)
generating, by a machine-learned model, a biomarker for the patient, the machine-learned model generating in response to input at least some of both of the clinical data and the patient- generated health data from the patient data model; (Cha discloses the embodiments may employ machine learning models to determine predictive features for a given patient outcome from electronic patient data and/or to determine the importance of such features. The embodiments may evaluate such features for any number of patients to determine the likelihood that each patient will experience the outcome within one or more timeframes (e.g., via calculation of risk scores) [0025]. The Examiner interprets the risk score of the patient's health as the biomarker as it is a health score for overall health of the patient (see Applicant's definition of biomarker in ¶ 0009 of the specification).)
and outputting the biomarker. (Cha discloses calculating, by the computer, a risk score for the patient, based on the final values of the features, the risk score relating to a probability that the first patient will experience an outcome relating to a decline in renal function …and/or outputting the risk score [0014].)
Cha does not disclose providing an interface with access to the data to a patient device which is met by Maier:
providing an interface or interfaces… including access to the clinical data …for…a patient… (Maier teaches FIG. 1 shows a method for analyzing one or more medical images and creating a report, according to some embodiments. At least as shown, the computer-implemented method 100 comprises the computer-automated steps of: receiving at least a first medical image of a patient,…[0027]. The report format may include…an electronic report, a saved report, an emailed report, tablet device, smartphone, or wearable tech with a display interface such as an optical head-mounted display [0037]. As used herein, a “user” may be, for example, a patient, a patient's guardian… [0014].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate presenting the data to a patient device as taught by Maier. This modification would create a method capable of reporting important information to the people who require it and with the information tailored to the individual’s needs (see Maier, ¶ 0007).
Regarding Claim 2, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses the following:
formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS), radiology information system, electronic health record, and/or laboratory information system for a medical practice or facility. (Cha discloses at step 120, the system performs various preprocessing steps to clean, validate and/or normalize the initial data records into preprocessed data records. Such preprocessing may be required to create preprocessed data records comprising data tables having a standardized format or schema [0048]. Such data sources may include, but are not limited to: EHR systems (e.g., EPIC, CERNER, ALLSCRIPTS); health facility systems (e.g., systems associated with doctors' offices, laboratories, hospitals, pharmacies, etc.);…[0037].)
Regarding Claim 3, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses the following:
formatting the clinical data comprises formatting pursuant to a standard for medical data sharing. (Cha discloses the system performs various preprocessing steps to clean, validate and/or normalize the initial data records into preprocessed data records. Such preprocessing may be required to create preprocessed data records comprising data tables having a standardized format or schema [0048].)
Regarding Claim 7, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses the following:
formatting the patient-generated health data comprises reformatting data from an application for gathering information input by the patient. (Cha discloses the system may connect to any number of data sources that store patient information for one or more patients. Such data sources may include, but are not limited to: EHR systems … and/or various engagement systems (e.g., survey systems that store patient and/or provider survey responses) [0037]. The Examiner interprets the patient survey responses as patient generated information gathered by the patient. [T]he system may identify demographics information from patient…generated input data, such as… health risk assessment responses, patient surveys, socio-economic surveys and others. Upon identifying such information in an initial data record, the system may aggregate, encode and sort this information into a combined, unique preprocessed data record for each patient [0052].)
Regarding Claim 11, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses the following:
generating the biomarker comprises generating a health score for overall health of the patient. (Cha discloses the embodiments may employ machine learning models to determine predictive features for a given patient outcome from electronic patient data and/or to determine the importance of such features. The embodiments may evaluate such features for any number of patients to determine the likelihood that each patient will experience the outcome…[0025]. The Examiner interprets the risk score of the patient's health as the biomarker as it is a health score for overall health of the patient (see Applicant's definition of biomarker in ¶ 0009 of the specification).)
Regarding Claim 12, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha does not disclose the following limitations met by Maier:
generating the biomarker comprises generating a disease specific biomarker. (Maier teaches compare a patient's medical image(s)…to one or more comparison images by analyzing the patient's medical image(s) for the presence and/or extent/amount of an imaging biomarker. Such imaging biomarker be defined based on the comparison image(s) of the same or similar tissue regions from the other individual(s) for whom the corresponding health status…are known… The extent to which the biomarker is present or not present in the patient's image(s) may be translated into quantitative metrics related to the patient's current health status and/or their risks for future health outcomes [0020]. [T]he presence of greater than 10% relative volume of low density tissue in the upper lobes of the lung is an imaging biomarker for lung cancer,… [0021].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate generating a disease specific biomarker as taught by Maier. This modification would create a method capable of identifying specific information about a person’s current health status (see Maier, ¶ 0004).
Regarding Claim 15, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses:
providing comprises providing a summary of the patient-generated health data to the physician. (Cha discloses the system may identify and preprocess any patient information pertaining to signs, symptoms and behaviors stored in the initial data records [0059]. The system may transmit or display some or all information contained in one or more of the patient records (e.g., risk information and/or patient information) via one or more reports, notifications, alerts,… the patient records may be transmitted to, or otherwise accessed by, user devices associated with healthcare providers [0110].)
Claims 4-6 and 8 are rejected under 35 USC § 103 as being unpatentable over Cha et al. (US 20200005900 A1) and Maier et al. (US 20160203263 A1) in view of Miao et al. (Miao et al. A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone. Sensors (Basel, Switzerland) vol. 15,5 11465-84. 19 May. 2015. (Year: 2015)).
Regarding Claim 4, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses:
formatting the patient-generated health data comprises formatting… (Cha discloses the system may identify and preprocess any patient information pertaining to signs, symptoms and behaviors stored in the initial data records. Preprocessing of such sub-disease and non-disease descriptors often requires custom normalizer data representations that combine multiple terminology standards [0059].)
Cha and Maier do not teach the patient data coming from a wearable sensor which is met by Miao:
…where the patient-generated health data is from a wearable sensor. (Miao teaches we describe a wearable context-aware ECG monitoring system comprised of a self-designed integrated ECG sensor for continuous, long-term remote ECG monitoring and a smartphone for abnormal ECG patterns and physical activity recognition. (p. 3, ¶ 0001).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate data coming from a wearable sensor as taught by Miao. This modification would allow users to remain comfortable and mobile while wearing the sensor (see Miao, p. 2, ¶ 0002).
Regarding Claim 5, Cha, Maier, and Miao teach the limitations as seen in the rejection of Claim 4 above. Cha and Maier do not teach the following limitations met by Miao:
formatting comprises compressing data from the wearable sensor provided at a first frequency to a less frequent representation. (Miao teaches Table 11 gives the abnormal patterns detected before and after combined with context information and the comparison with the actual patterns from the clinician’s diagnosis (p. 15, ¶ 0003, see also Fig. 11). From Figure 11a we can see, the subject is with a heart rate of 56 while resting…after running, the subject rested 30 s with a heart rate of 91. When the subject was resting 300 s after running, heart rate of the subject recovered to the value before running. From the above report we can see, the context-aware system is necessary to evaluate the user’s real health condition. For example, a heart rate of 120 is usually deemed as tachycardia from professional experience (p. 15, ¶ 0002). The Examiner interprets reducing the data to only show data associated with an event or episode as compressing the data to a less frequent representation.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate compressing data from a first frequency to a less frequent representation as taught by Miao. This modification would create a method capable of reducing the power consumption of the ECG monitoring system while providing useful diagnosis information for the users is necessary (see Miao, p. 2, ¶ 0003).
Regarding Claim 6, Cha, Maier, and Miao teach the limitations as seen in the rejection of Claim 4 above. Cha and Maier do not teach the following limitations met by Miao:
formatting comprises compressing data from the wearable sensor to periodic event episodes based on event context of the data from the wearable sensor. (Miao teaches the proposed ECG monitoring system combined a wearable ECG acquisition sensor with a smartphone is described in Figure 1 (p. 3, ¶ 0003). A case for continuously monitoring one subject’s context-aware ECG with the proposed system is presented in Figure 11. From Figure 11a we can see, the subject is with a heart rate of 56 while resting. When the subject changed his activity status from walking to running, there was a sharp increase in heart rate of the subject from 67 to 120. Then, after running, the subject rested 30 s with a heart rate of 91. When the subject was resting 300 s after running, heart rate of the subject recovered to the value before running. From the above report we can see, the context-aware system is necessary to evaluate the user’s real health condition (p. 15, ¶ 0002).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate the data coming from the wearable device being compressed based on periodic event episodes as taught by Miao. This modification would create a method capable of reducing the power consumption of the ECG monitoring system while providing useful diagnosis information for the users is necessary (see Miao, p. 2, ¶ 0003).
Regarding Claim 8, Cha and Maier teach the limitations as seen in the rejection of Claim 7 above. Cha further discloses:
…and wherein formatting the clinical data comprises formatting the clinical data from the first source comprising a picture archiving and communications system (PACS) or radiology information system, an electronic health record, and laboratory information system for a medical practice or facility. (Cha discloses at step 120, the system performs various preprocessing steps to clean, validate and/or normalize the initial data records into preprocessed data records. Such preprocessing may be required to create preprocessed data records comprising data tables having a standardized format or schema [0048]. Such data sources may include, but are not limited to: EHR systems (e.g., EPIC, CERNER, ALLSCRIPTS); health facility systems (e.g., systems associated with doctors' offices, laboratories, hospitals, pharmacies, etc.);… [0037].)
Cha and Maier do not teach compressing data from a wearable sensor which is met by Miao:
formatting the patient-generated health data further comprises compressing data from a wearable sensor, (Miao teaches the proposed ECG monitoring system combined a wearable ECG acquisition sensor…described in Figure 1…In the ECG acquisition sensor, signal is amplified and filtered by a single chip of AFE module, then in MCU module the analog signal from AFE is converted to digital signal. After processed with compression algorithm, the digital signal is recorded in SD card or transmitted to smartphone for real-time display (p. 3, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate data coming from a wearable sensor as taught by Miao. This modification would allow users to remain comfortable and mobile while wearing the sensor (see Miao, p. 2, ¶ 0002).
Claims 9 and 10 are rejected under 35 USC § 103 as being unpatentable over Cha and Maier in view of de Ridder et al. (de Ridder et al., "Data processing and presentation for a personalised, image-driven medical graphical avatar," 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013.).
Regarding Claim 9, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further teaches:
…representing the biomarker (Cha discloses evaluate such features for any number of patients to determine the likelihood that each patient will experience the outcome within one or more timeframes (e.g., via calculation of risk scores) [0025]. The Examiner interprets the risk score of the patient's health as the biomarker as it is a health score for overall health of the patient.)
Cha and Maier do not teach the following limitations met by de Ridder:
providing the interface comprises displaying an avatar of the patient with a characteristic of the avatar representing the biomarker. (de Ridder teaches the results summarised in Table I, combined with the notion that clinical data can be reduced to five main types (image, video, text, signal and spatial), indicate that our MGA [medical graphical avatar] correctly classifies and appropriately displays all types of clinical data. That is, sub-types of these data types, e.g. PET is a sub-type of image, load with the correct viewers and are placed on or next to the correct body part of the avatar (p. 3, ¶ 0001). See Fig. 3 which shows the avatar and a tumor highlighted on the avatar which indicates the health status of the patient.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate the use of an avatar on the interface with a characteristic representing the biomarker as taught by de Ridder. This modification would create a method which can facilitate improved understanding and sharing of digital health data for patients (see de Ridder, p. 1, ¶ 0001).
Regarding Claim 10, Cha, Maier, and de Ridder teach the limitations as seen in the rejection of Claim 9 above. Cha and Maier do not teach the following limitations met by de Ridder:
providing the interface comprises displaying the avatar and a medical image from the clinical data, the medical image selected for the displaying based on selection of a landmark or anatomical region of the avatar. (de Ridder teaches the raw medical data is displayed ‘in situ’ over the avatar when a detailed view is selected (p. 2, ¶ 0001). Fig. 2 displays the avatar and a medical image of the esophagus over the avatar upon selection of the area.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate the use of an avatar on the interface with a medical image displayed upon selection of an anatomical region of the avatar as taught by de Ridder. This modification would create a method which can facilitate improved understanding and sharing of digital health data for patients (see de Ridder, p. 1, ¶ 0001).
Claims 13 and 14 are rejected under 35 USC § 103 as being unpatentable over Cha and Maier in view of Mansi et al. (US 20170235915 A1).
Regarding Claim 13, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses:
…the biomarker… (Cha discloses evaluate such features for any number of patients to determine the likelihood that each patient will experience the outcome within one or more timeframes (e.g., via calculation of risk scores) [0025]. The Examiner interprets the risk score of the patient's health as the biomarker as it is a health score for overall health of the patient.)
Cha and Maier do not teach the following limitations met by Mansi:
generating the biomarker comprises generating the biomarker as a change leading to an adverse event. (Mansi teaches his creation may be triggered by an adverse event, such as a health problem resulting in medical examination at a healthcare facility. The gathering of data for the personalization is performed at the healthcare facility, but may rely on data obtained from other sources outside the healthcare facility and/or from other times (e.g., after or prior to the adverse event) [0051].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate generating a biomarker as a change leading to an adverse event as taught by Mansi. This modification would create a method capable of mitigating or detecting an adverse event of the patient (see Mansi, ¶ 0001).
Regarding Claim 14, Cha and Maier teach the limitations as seen in the rejection of Claim 1 above. Cha further discloses:
…the biomarker… (Cha discloses evaluate such features for any number of patients to determine the likelihood that each patient will experience the outcome within one or more timeframes (e.g., via calculation of risk scores) [0025]. The Examiner interprets the risk score of the patient's health as the biomarker as it is a health score for overall health of the patient.)
Cha and Maier do not teach the following limitations met by Mansi:
generating the biomarker comprises generating the biomarker as a response to treatment and/or medication. (Mansi teaches the continuous monitoring of signals sensed from the patient may be used to infer models of organ growth and remodeling (e.g., reaction of the organ to treatment),… [0038].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method for formatting patient related data for a machine learning model and outputting a biomarker as taught by Cha to incorporate generating a biomarker as a response to a treatment as taught by Mansi. This modification would create a method capable of mitigating or detecting an adverse event of the patient (see Mansi, ¶ 0001).
Conclusion
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681