DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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 are within the four statutory categories. However, as will be shown below, claims 1-20
are nonetheless unpatentable under 35 U.S.C. 101.
Claims 1, 9, and 17 are representative of the inventive concept.
Claim 1 recites:
A mobile device comprising: processing circuitry; and memory including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations to:
receive, at a mobile device, compiled data generated by a sensor of a sensor device embedded in an orthopedic implant in a patient;
automatically modify, in response to receiving an indication that the patient is experiencing more pain than an average patient or a baseline, a sensor configuration parameter, the sensor configuration parameter indicating at least one of an increased data collection frequency or a changed type of sensor data collected;
obtain additional information from the sensor based on the sensor configuration parameter; determine, based on patient-specific information and the additional information, whether to use a local machine learning model operable at the mobile device, or a remote machine learning model operable at a remote device to output a prediction generated using the compiled data, wherein the local machine learning model is quicker and requires less processing, and wherein the remote machine learning model is more accurate, wherein the determination of which model to use is based on a clinical context including at least one of a time since an orthopedic procedure was completed or a nature of urgency, pain, or risk to the patient, and wherein the local machine learning model is used when a quicker and lower-complexity determination is sufficient and the remote machine learning model is used when greater accuracy is needed;
in accordance with a determination that the local machine learning model is to be used, predict, at the mobile device, an outcome for the patient by using the compiled data as an input to the local machine learning model;
and in accordance with a determination that the remote machine learning model is to be used, send, from the mobile device, the compiled data to a remote computing device to generate a predicted outcome at the remote computing device using the compiled data.
*Claims 9 and 17 recite similar limitations as claim 1, but for a machine-readable medium and a system, respectively.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes because the
highlighted components can practically be performed by the human mind (in this case, the process of
determining, and predicting) or using pen and paper. Other than reciting generic computer
components/functions such as “processing circuitry”, “mobile device”, “sensor of a sensor device”, “machine learning model”, and “device”, nothing in the claims precludes the highlighted portions from practically being performed in the mind. For example, in claim 1, but for the generic computer language, the claim encompasses the user collecting data and making a prediction based on the data. If a claim limitation, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components/functions, then it falls within “Mental Processes” grouping of abstract ideas. Additionally, the mere nominal recitation of a generic computer does not take the claim limitation out of the mental process grouping. Thus, the claim recites a mental process. The recitation of generic computer components/functions of obtaining also covers behavioral or interactions between people (i.e. a computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case a person is able to physically follow the steps to collect and analyze data), hence the claim falls under “Certain Methods of Organizing Human Activity”. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Dependent claims 2-8, 10-16, and 18-20 recite additional subject matter which further narrows or
defines the abstract idea embodied in the claims (such as claim 2, reciting what type of data that
the compiled data entails, but for recitation of generic computer components/functions).
Step 2A Prong Two
This judicial exception is no integrated into a practical application. In particular, the claims recite the
following additional limitations:
Claim 1 recites: “mobile device”, “sensor of a sensor device”, “machine learning model”, “remote device”, “device”, and “processing circuitry”, “in accordance with a determination that the remote machine learning model is to be used, send, from the mobile device, the compiled data to a remote computing device to generate a predicted outcome at the remote computing device using the compiled data.”, and “receive, at a mobile device, compiled data generated by a sensor of a sensor device embedded in an orthopedic implant in a patient”.
In particular, the additional elements do no integrate the abstract idea into a practical application, other
than the abstract idea per se, because the additional elements amount to no more limitations which:
Amount to mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of
are recited as being performed by a “mobile device”, “sensor of a sensor device”, “machine learning model”, “remote device”, “device”, and “processing circuitry”. A computer is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The machine learning models are used to generally apply the abstract idea without limiting how it functions.
Add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea such as the recitation of “in accordance with a determination that the remote machine learning model is to be used, send, from the mobile device, the compiled data to a remote computing device to generate a predicted outcome at the remote computing device using the compiled data.” (sending) and “receive, at a mobile device, compiled data generated by a sensor of a sensor device embedded in an orthopedic implant in a patient”, and “automatically modify, in response to receiving an indication that the patient is experiencing more pain than an average patient or a baseline, a sensor configuration parameter, the sensor configuration parameter indicating at least one of an increased data collection frequency or a changed type of sensor data collected.”
Dependent claims 2-8, 10-16, and 18-20 do not include any additional elements beyond those
already recited in independent claims 1, 9, and 17, and hence do not integrate the aforementioned abstract idea into a practical application. 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 any other technology. Their collective function merely provides conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
Claims 1, 9, and 17 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: A system in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, 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 demonstrated by the recitation of:
Sending which expressly means to transmit data or other information from one location or system to another (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Receiving data, which refers to acquiring or accepting information from another device, system, or course over a communication medium (TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)) in a manner that would be well-understood, routine, and conventional.
Modifying settings, which refers to adjusting technical parameters of a system (Para 0005, Sookyoung(US 20180164154 A1) discloses: “Accordingly, when the pixel arrangement of a conventional optical image sensor is simply modified, the sensitivity of an infrared ray image may be reduced.”) in a manner that would be well-understood, routine, and conventional.
Dependent claims 2-9 and 11-18 do not include any additional elements beyond those already recited in
independent claims 1 and 10. Therefore, they are not deemed to be significantly more than the abstract
idea because, as stated above, the limitations of the aforementioned dependent claims amount to no
more than generally linking the abstract idea to a particular technological environment or field of use,
and/or do not recite and additional elements not already recited in independent claims 1 and 10, hence
do not amount to “significantly more” than the abstract idea.
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 1-4, 7-12, 15-16, 17-18, and 20 are rejected under 35 U.S.C. 103 is being unpatentable over
Grantcharov(US20210076966A1) in view of Srivastava(US20190022397A1), and in further view of
Ma(US20190258904A1).
Claim 1
Grantcharov discloses:
A mobile device comprising: processing circuitry; and memory including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations to: receive, at a mobile device(Para 0318, Grantcharov discloses: “the encoder 22 may be a server, network appliance, embedded device, computer expansion unit, personal computer… mobile device[MOBILE DEVICE],… or any other computing device capable of being configured to carry out the methods…”), compiled data generated by a sensor of a sensor device(Para 0234, Grantcharov discloses: “The sensors 34 provide sensor data[DATA GENERATED BY A SENSOR/SENSOR DEVICE] as another example of medical or surgical data.”) embedded in an orthopedic implant in a patient(Para 0271, Grantcharov discloses: “retrieving one or more continuous data streams from sensors 34, audio devices 32, an anesthesia device, medical/surgical devices, implants[CAN BE AN ORTHOPEDIC IMPLANT IN A PATIENT]…”); determine, based on patient-specific information(Para 0234, Grantcharov discloses: “The sensors 34 provide sensor data as another example of medical or surgical data[SENSOR, MEDICAL, OR SURGICAL DATA CAN BE CONSIDERED PATIENT-SPECIFIC INFORMATION].”) and the additional information, whether to use a local(Para 0423, Grantcharov discloses: “the black box system leverages a medical centric perception engine 2000, which, as noted in FIGS. 10A and 10B, may reside in various locations and in various respects relative to the encoder 22 (e.g., as part of encoder 22[LOCAL], in the form of distributed computing resources in a server farm, on a remote device).”) machine learning model(Figure 17, Grantcharov discloses: machine learning model”) operable at the mobile device, or a remote(Para 0423, Grantcharov discloses: “the black box system leverages a medical centric perception engine 2000, which, as noted in FIGS. 10A and 10B, may reside in various locations and in various respects relative to the encoder 22 (e.g., as part of encoder 22, in the form of distributed computing resources in a server farm, on a remote device[REMOTE]).”) machine learning model operable at a remote device(Para 0423, Grantcharov discloses: “the black box system leverages a medical centric perception engine 2000, which, as noted in FIGS. 10A and 10B, may reside in various locations and in various respects relative to the encoder 22 (e.g., as part of encoder 22, in the form of distributed computing resources in a server farm, on a remote device[REMOTE DEVICE]).”) to output a prediction(Para 0568, Grantcharov discloses: “output prediction”) generated using the compiled data, wherein the local machine learning model is quicker and requires less processing, and wherein the remote machine learning model is more accurate, (Para 0130, Grantcharov discloses: “The prediction data objects can relate to… and expected post-operative outcome[PREDICTS OUTCOME FOR PATIENT]”) by using the compiled data as an input to the local machine learning model(Para 0124, Grantcharov discloses: “the appended biometric data or derivatives thereof may be utilized as an input, for example, into a machine learning model” [COMPILED DATA INPU INTO A MACHINE LEARNING MODEL]); and in accordance with a determination that the remote machine learning model is to be used, send, from the mobile device, the compiled data to a remote computing device(Para 0423, Grantcharov discloses: “the black box system leverages a medical centric perception engine 2000, which, as noted in FIGS. 10A and 10B, may reside in various locations and in various respects relative to the encoder 22 (e.g., as part of encoder 22, in the form of distributed computing resources in a server farm, on a remote device[REMOTE COMPUTING DEVICE]).”) to generate a predicted outcome at the remote computing device using the compiled data.
Grantcharov does not explicitly disclose:
automatically modify, in response to receiving an indication that the patient is experiencing more pain than an average patient or a baseline, a sensor configuration parameter, the sensor configuration parameter indicating at least one of an increased data collection frequency or a changed type of sensor data collected; obtain additional information from the sensor based on the sensor configuration parameter
wherein the determination of which model to use is based on a clinical context including at least one of a time since an orthopedic procedure was completed or a nature of urgency, pain, or risk to the patient, and wherein the local machine learning model is used when a quicker and lower-complexity determination is sufficient and the remote machine learning model is used when greater accuracy is needed
Srivastava discloses:
automatically modify, in response to receiving an indication that the patient is experiencing more pain than an average patient(Para 0008, Srivastava discloses sensors detecting and assessing pain) or a baseline(Para 0093, Srivastava discloses baseline pain-free condition), a sensor configuration parameter, the sensor configuration parameter indicating at least one of an increased data collection frequency or a changed type of sensor data collected(Figure 2, Para 0064, Srivastava discloses a pain analyzer circuit which adjusts(automatically modifies) sensor activation/deactivation(sensor configuration parameter) based in incoming data); obtain additional information from the sensor based on the sensor configuration parameter(Para 0064, Srivastava discloses pain analyzer circuit activating one of two sensors, which can provide additional information)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system which captures biometric data for event prediction of Grantcharov to add sensor parameter modifications and sensor capturing additional information, as taught by Srivastava. One of ordinary skill would have been so motivated to provide a means to evaluate pain in a patient in order to better treat the patient and improve patient outcomes, but in this case for sensor-based pain management(Para 0004, Srivastava discloses: “ Pain is one of the most common and among the most personally compelling reasons for seeking medical attention, and consumes considerable healthcare resources each year. ”).
Srivastava does not explicitly disclose:
wherein the determination of which model to use is based on a clinical context including at least one of a time since an orthopedic procedure was completed or a nature of urgency, pain, or risk to the patient, and wherein the local machine learning model is used when a quicker and lower-complexity determination is sufficient and the remote machine learning model is used when greater accuracy is needed
Ma discloses:
and wherein the remote machine learning model is more accurate, wherein the determination of which model to use is based on a clinical context including at least one of a time since an orthopedic procedure was completed or a nature of urgency, pain, or risk to the patient(Para 0173, Ma discloses sensor signals from medical devices which can be used to determine nature of urgency or pain), and wherein the local machine learning model is used when a quicker and lower-complexity determination is sufficient and the remote machine learning model is used when greater accuracy is needed(Figure 2B, Ma discloses selecting the best predictive model based on assessment results)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system which captures biometric data for event prediction of Grantcharov to add determination of which model to use is based on how accurate a prediction is needed based on clinical data, as taught by Ma. One of ordinary skill would have been so motivated to provide a means to evaluate several prediction models to determine patient health status, and to choose the most accurate model, which would improve patient diagnosis, treatment, and outcome, but in this case for an analytic system for machine learning prediction model selection(Para 0002, Ma discloses: “ Model assessment in healthcare is a critical step in the process of predictive model machine learning techniques. Because the input data used to train a predictive model may include events that are relatively rare, oversampling of the input data is commonly used to pre-process the input data.”).
Claim 2
Grantcharov discloses:
The mobile device of claim 1, wherein the compiled data includes data pre- processed (Para 0407, Grantcharov discloses: “the feeds already have features extracted and provided in the form of machine-readable and/or interpretable formats, in other embodiments, the feeds may first require processing or pre-processing[PRE-PROCESSING] to extract feature sets”) by the sensor device.
Claim 3
Grantcharov discloses:
The mobile device of claim 1, wherein to determine whether to use the local machine learning model or the remote machine learning model includes operations to determine a current time frame related to an orthopedic procedure performed on the patient, and comparing the current time frame to a threshold time frame(Para 0115, Grantcharov discloses: “The captured biometric data can be processed to generate derivative analytic values … which are then time-synchronized to timestamps of captured video or audio data streams[CAN INCLUDE CURRENT TIMEFRAME] to identify, abnormality-related durations of time during which the data values are greater or lower than a pre-defined threshold data value[CAN BE THRESHOLD TIMEFRAME].”)
Claim 4
Grantcharov discloses:
The mobile device of claim 1, wherein to determine whether to use the local machine learning model or the remote machine learning model includes operations to determine which model to use based on identifying an orthopedic procedure previously done on the patient(Para 0041, Grantcharov discloses: “ variables that may be used to determine the annotation and tagging dictionary may be: the type of medical procedure being performed[CAN BE ORTHOPEDIC PROCEDURE PREVIOUSLY DONE ON PATIENT], the aspect of the procedure that is being analyzed…”).
Claim 7
Grantcharov discloses:
The mobile device of claim 1, wherein to send the compiled data includes operations to sanitize the compiled data before being sent to remove personally identifying information from the compiled data(Para 0036, Grantcharov discloses: “the encoder implements identity anonymization and encryption[CAN BE DATA SANITIZATION] to the medical or surgical data.”).
Claim 8
Grantcharov discloses:
The mobile device of claim 1, wherein a prediction by the local machine learning model is obtained in less time(Para 0568, Grantcharov discloses: “ machine learning resource control engine 3212 operates to request additional computational resources allocated to the perception engine 2000 to increase a speed at which perception engine 2000 is able to output one or more predictions[MACHINE LEARNING PREDICTION OUTPUTS CAN BE ADJUSTED…”) than a prediction by the remote machine learning model.
Claim 9
Claim 9 discloses similar limitations as claim 1. See claim 1 analysis.
Claim 10
Claim 10 discloses similar limitations as claim 2. See claim 2 analysis.
Claim 11
Claim 11 discloses similar limitations as claim 3. See claim 3 analysis.
Claim 12
Claim 12 discloses similar limitations as claim 4. See claim 4 analysis.
Claim 15
Claim 15 discloses similar limitations as claim 7. See claim 7 analysis.
Claim 16
Claim 16 discloses similar limitations as claim 8. See claim 8 analysis.
Claim 17
Claim 17 discloses similar limitations as claim 1. See claim 1 analysis.
Claim 18
Claim 18 discloses similar limitations as claim 2. See claim 2 analysis.
Claim 20
Claim 20 discloses similar limitations as claim 7. See claim 7 analysis.
Claims 5-6, 13-13, and 19 are rejected under 35 U.S.C. 103 is being unpatentable over
Grantcharov(US20210076966A1) in view of Srivastava(US20190022397A1), view of
Ma(US20190258904A1) and in further view of Siewerdsen(US20220157459A).
Claim 5
Grantcharov discloses:
The mobile device of claim 1, wherein to determine whether to use the local machine learning model or the remote machine learning model includes operations to determine which model to use based on an input received at the mobile device of at least one of a pain level identified by the patient, a range of motion of the patient (Para 0269, Grantcharov discloses: “and/or individual data from the healthcare providers (e.g., heart rate, blood pressure, skin conductance, motion[CAN BE RANGE OF MOTION FOR A PATIENT] and eye tracking, etc.).”), or a patient exercise score.
Grantcharov, Srivastava, and Ma do not explicitly disclose: pain level identified by the patient, patient exercise score
Siewerdsen discloses: pain level identified by the patient, patient exercise score
pain level identified by the patient(Para 0015, Siewerdsen discloses: “These and/or other image-based features may be input to the data model and used to compute a predicted outcome (e.g., pain and function characteristics).”)
patient exercise score(Para 0029, Siewerdsen discloses: outcome scores (e.g., outcome scores relating to pain, function[FUNCTION SCORE CAN BE EXERCISE SCORE]), and/or the like.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system which captures biometric data for event prediction of Grantcharov to add pain level identified by the patient and patient exercise score, as taught by Siewerdsen. One of ordinary skill would have been so motivated to provide a means to evaluate patient symptoms post-procedure and use a scoring mechanism for activity level to determine a patient condition, but in this case for a data analytics platform for predictive modeling of surgical outcomes(Para 0002, Siewerdsen discloses: “ as the volume of data available to healthcare providers increases, clinicians cannot be reasonably expected to always have the ability to integrate all the available data into decision-making processes in an effective, reliable, and patient-specific manner. Such patient-specific decision-making and identification of suitable treatment pathways are important considering that multiple therapeutic modalities may be available and patients may have heterogeneous responses to different therapeutic modalities.”).
Claim 6
Grantcharov, Srivastava, and Ma do not explicitly disclose:
The mobile device of claim 1, wherein to determine whether to use the local machine learning model or the remote machine learning model includes operations to determine patient progress towards a goal(Para 0037, Siewerdsen discloses: “clinical decision support platform may use the intra-operative images and/or post-operative images to extract further image analytic features to evaluate the surgical procedure, the patient's recovery[PATIENT RECOVERY CAN BE PATIENT PROGRESS TOWARDS A GOAL], and/or the like.”) defined by the patient.
Siewerdsen discloses:
The mobile device of claim 1, wherein to determine whether to use the local machine learning model or the remote machine learning model includes operations to determine patient progress towards a goal(Para 0037, Siewerdsen discloses: “clinical decision support platform may use the intra-operative images and/or post-operative images to extract further image analytic features to evaluate the surgical procedure, the patient's recovery[PATIENT RECOVERY CAN BE PATIENT PROGRESS TOWARDS A GOAL], and/or the like.”) defined by the patient.
Before the effective filing date of the claimed invention, it would have been obvious to one of
ordinary skill in the art to have modified the system which captures biometric data for event prediction of Grantcharov to add determining patient progress towards a goal, as taught by Siewerdsen. One of ordinary skill would have been so motivated to provide a means to evaluate patient progress in recovery, for example, in comparison to an expected goal to determine effectiveness of a procedure, but in this case for a data analytics platform for predictive modeling of surgical outcomes(Para 0002, Siewerdsen discloses: “ as the volume of data available to healthcare providers increases, clinicians cannot be reasonably expected to always have the ability to integrate all the available data into decision-making processes in an effective, reliable, and patient-specific manner. Such patient-specific decision-making and identification of suitable treatment pathways are important considering that multiple therapeutic modalities may be available and patients may have heterogeneous responses to different therapeutic modalities.”).
Claim 13
Claim 13 discloses similar limitations as claim 5. See claim 5 analysis.
Claim 14
Claim 14 discloses similar limitations as claim 6. See claim 6 analysis.
Claim 19
Claim 19 discloses similar limitations as claim 5. See claim 5 analysis.
Response to Arguments
35 U.S.C. 101
(Page 9) Regarding the assertion that amended claim 1 does not recite managing human activity or a mental process.
Applicant's arguments filed have been fully considered but they are not persuasive. The amendments which include obtaining additional information would be considered human activity and the determining step amendment adds to the abstract concept of a mental process.
(Page 9) Regarding the assertion that the office action does not identify any recited abstract ideas.
Applicant's arguments filed have been fully considered but they are not persuasive. The abstract ideas are highlighted in bold in the Step 1, analysis above, and in the previous Office Actions.
(Pages 9-10) Regarding the assertion that the applicant’s claim are not directed to an abstract ideas because the claim as a whole integrates the exception into a practical application.
Applicant's arguments filed have been fully considered but they are not persuasive. Ex parte Desjardins referenced does not apply to the claims as written. The claim utilizes generic machine learning models and merely defines the input and outputs. The automatic modification of parameters in response to an indication is vague. There is no definition of what the indication is. The additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
(Pages 12) Regarding the assertion that the applicant’s claim recite a technological improvement.
Applicant's arguments filed have been fully considered but they are not persuasive. The additional elements do no integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more limitations which amount to mere instructions to apply an exception (MPEP 2106.05(f)) and add insignificant extra-solution activity (MPEP 2106.05(g)) to the abstract idea.
(Pages 13) Regarding the assertion that the applicant’s claim an inventive concept.
Applicant's arguments filed have been fully considered but they are not persuasive. Claims 1, 9, and 17 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: A system in claim 1; amount to no more than mere instructions to apply an exception to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity
35 U.S.C. 103
(Pages 14-17) Regarding the assertion that Grantcharov, Srivastava, and Ma do not teach the amended elements.
Applicant's arguments filed have been fully considered but they are not persuasive. Please refer to the above 103 analysis for amended claim 1. The claims have been drafted in a manner which leaves room for interpretation under BRI. Applying an application (defining inputs and outs for generic models/functions without explicitly providing technical detail on functionality) allows for broad interpretation and application of prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Peterson(US20190087544A1): Peterson discloses a surgery digital twin. Some disclosures of this invention are similar to that of this instant pending application. (Specifications, pages 3-6)
Casey(US20220047402A1): Casey discloses a system which links patient-specific medical devices with patient-specific data. Some disclosures of this invention are similar to that of this instant pending application. (Specifications, pages 3-6)
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/S.G.P./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685