DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 18,, 2025.
Claims 1-9 and 11-22 are pending.
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 .
Response to Amendment(s)
Claims 1-9 and 11-22 remain pending.
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 1-9 and 11-22 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1 and 14:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent Claim 1 falls within the statutory category of method
Independent Claim 14 falls within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1 and 14 (Claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in claims 1 and 14 (Claim 1 being representative):
A method for diagnosing a medical patient who has had an implant implanted into the patient body with pseudoarthrosis, comprising:
Generating a medical image
Sending the medical image
Generating connected implant data
Receiving medical image data associated with the patient and the medical image
receiving the connected implant data
Extracting, one or more features of interest from the medical imaging data and connected implant data;
and generating one or more reports based on the extracted features of interest the reports containing a clinical value which assists a physician to provide a proper diagnosis for the patient
wherein the one or more reports include bone regeneration indices overlaid on the medical image.
And based on the bone regeneration indices of the one or more reports, diagnosing the patient with pseudarthrosis if the bone regeneration indices include a non-fusion zone.
This abstract idea is “certain methods of organizing human activity” as it is following rules or instructions to generate a report to assist a physician in a diagnosis for a patient. See MPEP § 2106.04(a).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1 and 14 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
a computer
an imaging device
An implant device with one or more sensors which has been implanted in the patient
One or more artificial intelligence (AI) models
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a computer, is recited that is performing the abstract idea and is “apply-it” or an equivalent tool as it is merely recited as a general computer
The additional element, an imaging device, is recited as “apply-it” or an equivalent tool as to gather data
The additional element, An implant device with one or more sensors which has been implanted into the patient, is recited as merely a tool or equivalent as “apply-it” to acquire data
The additional element, One or more artificial intelligence (AI) models, is recited as merely generally linking the abstract idea to the technological field of artificial intelligence
Independent claim 14 recites the additional claim elements not already recited in the independent claim 1 below:
The additional element, A computer executing non-transitory computer-readable medium containing instructions is performing the abstract idea and is recited as merely a tool or equivalent as “apply-it” to generate a report.
Accordingly, independent claims 1 and 14 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements as analyzed above in step 2A prong 2, are merely applying the abstract idea or generally linking with general computer elements and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-9, 11-13 and 15-22:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-9 and 11-13 fall within the statutory category of method
The dependent claims 15-22 fall within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-9, 11-13, and 15-22 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claim 1 and 14 abstract idea by (1) further limiting the extraction and manipulation of data and (2) generating mathematical geometries and applying them. Therefore, the dependent claims inherit the same abstract idea which is “certain methods of organizing human activity” as it is following rules or instructions to generate a report to assist a physician in a diagnosis for a patient. See MPEP § 2106.04(a).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-9, 11-13, and 15-22 this judicial exception is not integrated into a practical application.
The dependent claims recite no additional claim elements not already recited in the independent claims; therefore purely considered the abstract idea
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements therefore, do not amount to significantly more. The claims are patent ineligible.
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, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 19, 20, 21, and 22 are rejected to under 35 U.S.C. 103 as being unpatentable over by Itu et. al (hereinafter Itu) (US20180247020A1) in view Jordan (US20210343017A1) and in further view of Reinke (hereinafter Reinke) (US11802873B2)
As per claim 1, Itu teaches:
A computer-assisted method for …[…]… diagnosing a medical patient …[…]…comprising: (abstract discloses, “A computer - implemented method for personalized assessment of a subject ' s bone health includes extracting a plurality of features of interest from non - invasive subject data , medical images of the subject , and subject - specific bone turnover marker values.” And see [ 0001 ] discloses, “The present invention relates generally to methods , systems , and apparatuses for personalized assessment of bone health.” And [007] discloses, “[ 0001 ] The present invention relates generally to methods , systems , and apparatuses for personalized assessment of bone health .” and see [0068] discloses, “The techniques described herein refer to the usage of machine learning ( ML ) algorithms for predicting measures of interest related to osteoporosis : existence / severity of osteoporosis / osteopenia ; fracture risk ( score , percentage ) ; biomechanical characteristic of interest , as extracted typically from finite element analyses : whole - bone strength , load - to - strength ratio , the type of fracture to be expected , local / average stress , local / average strain , local / global stiff ness ; effect of a drug - treatment , e . g . increase in bone strength in time ; disease evolution , e . g . decrease in bone strength in time . The prediction may be based on various features extracted from non - invasive patient data , medical imaging data ( DXA , CT , MRI , etc . ) , and bone turnover markers . One major advantage of the proposed ML - based workflows is that data from heterogeneous sources may be integrated to perform a comprehensive assessment . The techniques described herein may also use imaging data acquired previously for a different scope ( e . g . , CT scans : lung cancer screening , a cardiac CT for ruling out coronary artery disease , abdominal / pelvic region , etc . ) ; that is , they provide the opportunity for a secondary use of scans for the assessment of risk of fracture and osteoporosis diagnosis.”)
generating a medical image from an imaging device; sending the medical image to the computer; ([0070] discloses, “Non - invasive / invasive medical images of the patient are received at step 110 . These images may generally be received from any source including , for example , DXA , CT , MRI , and / or Ultrasound .”)
performing by the computer the following steps: receiving medical imaging data associated with the patient and the medical image; (abstract discloses, “A computer - implemented method for personalized assessment of a subject ' s bone health includes extracting a plurality of features of interest from non - invasive subject data , medical images of the subject , and subject - specific bone turnover marker values.” And see [0068] discloses, “The prediction may be based on various features extracted from non - invasive patient data , medical imaging data ( DXA , CT , MRI , etc . ) , and bone turnover markers” and see [0070] discloses, “Non - invasive / invasive medical images of the patient are received at step 110 . These images may generally be received from any source including , for example , DXA , CT , MRI , and / or Ultrasound .”)
extracting, via one or more artificial intelligence (Al) models, one or more features of interest from the medical imaging data and connected implant data; ([0037] discloses, “The prediction is based on various features extracted from non - invasive patient data , medical imaging ( invasive and non - invasive , perfusion ) , invasive measurements , blood biomarkers , etc . Furthermore , the disclosed techniques may also be applied to the prediction of risk of future events .” and see [0068] discloses, “The techniques described herein refer to the usage of machine learning ( ML ) algorithms for predicting measures of interest related to osteoporosis : existence / severity of osteoporosis / osteopenia ; fracture risk ( score , percentage ) ; biomechanical characteristic of interest , as extracted typically from finite element analyses : whole - bone strength , load - to - strength ratio , the type of fracture to be expected , local / average stress , local / average strain , local / global stiff ness ; effect of a drug - treatment , e . g . increase in bone strength in time ; disease evolution , e . g . decrease in bone strength in time . The prediction may be based on various features extracted from non - invasive patient data , medical imaging data ( DXA , CT , MRI , etc . ) , and bone turnover markers.” And see[0073] discloses, “At step 120 , features of interest are extracted from the patient data acquired at steps 105 - 115 , for example , using feature extraction techniques generally known in the art . The term “ extraction , ” as used herein refers to the act or process of retrieving data out of one or more data sources for further processing or storage” and see [0072] discloses, “Similarly , bone turnover biomarkers ( the same or different ) may have been acquired at different time points and used as features of the ML algorithm” and see [0074] discloses, “In general , any ML algorithm known in the art may be applied including , for example , algorithms based on artificial neural networks ( ANN ) , deep learning , or learning classifier / regression systems”)
and generating one or more reports based on the extracted features of interest, …[…]…([0121] discloses, “The advantage of ML algorithms is that the online prediction is extremely fast : it outputs results almost instantaneously in a matter of seconds ) . Hence , they can be run directly on the workstation on - site . However , there may be situations in which a hybrid on - site - off - site processing workflow is required . For example , off - site processing can provide more detailed information or additional information that would not be available on - site , which is enabled by the less strict requirement on the processing time . Examples of such scenarios include employing a different computational model available off - site ( e . g . FEM based ) but not on - site , providing different analyses or options as compared to the on - site processing ( e . g . therapy planning may only be available off - site ) ; or running multiple computational models off - site , and reporting their results in a combined way ;” and see [0136] discloses, “These processes may include receiving input data and / or parameters , performing opera tions on received input data and / or performing functions in response to received input parameters , and providing result ing output data and / or parameters .” and see [0062] discloses, “ FIG . 15 shows stress results obtained after per forming an organ level loading simulation for a human femur bone ;” / per instant application para. [0070] the report can be of any type including heat map visualization thus examiner cites the disclosed Fig. 15 which is heat map of bone stress)
…[…]…wherein the one or more reports include bone regeneration indices overlaid on the medical image; …[…]…([0103] discloses, “Computed results can be visualized on the scanner , or on another device , such as an imaging workstation . In case the measure of interest is a biomechanical characteristic any point on the image can be queried ( point & click ) for the associated value of the measure of interest , and the corresponding value is shown overlaid to the image . As an example , points of interest in the femur bone can be selected and the corresponding stress values shown in the image as demonstrated in FIG . 14 . Alternatively , the user can activate a “ no click ” mode , in which case the value of interest is displayed in correspondence of the cursor by just positioning the cursor on the position of interest.” And see Fig. 15 / examiner notes in instant application paragraph [0093] and [0070] bone regeneration indices overlaid on the medical image is defined as being mechanical behavior such as stress and heat map visualizations such as in cited prior art of Figs. 14 and 15 )
However, Itu does not explicitly teach the underlined portions:
A computer-assisted method for diagnosing a medical patient who has had an implant implanted into the patient body with pseudoarthrosis, the computer-assisted method, comprising:
implanting an implant device into a patient, the implant device having a sensor; generating connecting implant data based on the sensor;
receiving the connected implant data from the implant device via the sensor
generating one or more reports based on the extracted features of interest, the reports containing a clinical value which assists a physician to provide a proper diagnosis for the patient.
and based on the bone regeneration indices of the one or more reports, diagnosing the patient with pseudarthrosis if the bone regeneration indices include a non-fusion zone.
However, Jordan does teach the underlined portions:
A computer-assisted method for diagnosing a medical patient who has had an implant implanted into the patient body, with pseudoarthrosis, the computer-assisted method, comprising: ([0044] discloses, “This disclosure presents systems and methods for remote post - IMD monitoring for implantation site infections…[…]…This is done in an in - hospital , post - op setting where the patients can be continuously monitored”)
implanting an implant device into a patient, the implant device having a sensor; generating connecting implant data based on the sensor; ([0002] discloses, “This disclosure relates to medical devices , and in some specific examples , relates to computing devices ( e.g. , mobile devices ) configured to evaluate a patient's recovery from implantation of a medical device and / or to evaluate the performance of a medical device that is presently implanted in the patient.” And see [0237] discloses, “In an illustrative examples , an abnormality control procedure may include information that a HCP ( e.g. , a surgeon or implanting clinician ) used Medtronic's TYRXTM Absorbable Antibacterial Envelope , or another similar element , during implantation of the medical device . As will be understood , TYRXTM is a mesh envelope that holds an implantable cardiac device , implantable neurostimulator , or other IMD .” and see [0144] discloses, “In an illustrative example , computing device 2 may obtain temperature sensor data from IMD 6 to determine the likelihood of a device pocket infection in view of images obtained of an implantation site of IMD 6 because a temperature sensor increase at the IMD 6 that occurs prior to a temperature increase at other locations of patient 4 may indicate a device pocket infection.”)
receiving the connected implant data from the implant device via the sensor ([0076] discloses, “To illustrate , the second set of data items may include one or more of : interrogation data obtained from medical device ( s ) 17 ( e.g. , IMD 6 ) , one or more physiological parameters of patient 4 , and / or user - input data . In such examples , computing device 2 may determine , based at least in part on the first set of data items and the second set of data items , an abnormality corresponding to at least one of the patient or the IMD . In one example , the first set of data items and the second set of data items may indicate various abnormality states . The abnormality states may include device migration , a potential infection , abnormal healing , abnormal physiological parameters , abnormal device parameters , patient - input indicating a perceived abnormality , etc. In addition , the abnormality states may include healing granulation around edges of an implantation site , discharge from an implantation site , inflammation at the implantation site , tissue erosion at or around the implantation site , etc. Thus , the abnormality corresponding to patient 4 or IMD 6 may include an abnormality determination based on the various data items.” And see [0079] discloses, “In the illustrative and non - limiting example of FIG . 1 , medical device ( s ) 6 include at least one IMD . In such examples , the at least one IMD may be implanted outside of a thoracic cavity of patient 4 ( e.g. , subcutaneously in the pectoral location illustrated in FIG . 1 ) . In some examples , medical device ( s ) 6 may be positioned near the sternum near or just below the level of the heart of patient 4 , e.g. , at least partially within the cardiac silhouette . As used herein , an IMD may include , be , or be part of a variety of devices or integrated systems , such as , but not limited to , implantable cardiac monitors ( ICMs ) , implantable pacemakers , includ ing those that deliver cardiac resynchronization therapy ( CRT ) , implantable cardioverter - defibrillators ( ICDs ) , diag nostic devices , cardiac devices , etc. In some examples , the tools of this disclosure may be configured to monitor func tioning of or user adaptation to implants other than CIEDs , such as spinal cord stimulators , deep brain stimulators , gastrological stimulators , urological stimulators , other neurostimulators , orthopedic implants , respiratory monitoring implants , etc.” and see [0144] discloses, “In an illustrative example , computing device 2 may obtain temperature sensor data from IMD 6 to deter mine the likelihood of a device pocket infection in view of images obtained of an implantation site of IMD 6 because a temperature sensor increase at the IMD 6 that occurs prior to a temperature increase at other locations of patient 4 may indicate a device pocket infection . In some examples , computing device 2 may obtain a thermal image of the implantation site and / or areas adjacent the implantation site in order to compare the thermal image to the temperature data and determine the presence of an abnormality at an implantation site , such as where a temperature increase at the IMD 6 leads a temperature increase at other exterior locations of patient 4.”)
generating one or more reports based on the extracted features of interest, the reports containing a clinical value (“various measurements”) which assists a physician to provide a proper diagnosis for the patient. ([0081] discloses, “Medical device ( s ) 6 may transmit the diagnostic data or health status to computing device ( s ) 2 as interrogation data , such that computing device ( s ) 2 may correlate the interrogation data with image data to determine whether an abnormality present with a particular one of medical device ( s ) 6 ( e.g. , an IMD ) or patient 4 ( e.g. , infection at an implantation site ).” And see [ 0249 ] discloses, “In some examples , processing circuitry 20 may implement regional comparison zones for differential diag nostics ( e.g. , Dx ) . Processing circuitry 20 may perform a gradient analysis of implantation site 1704 and , for example , sternum areas for different skin tones , to determine a dif ferential diagnostic . Processing circuitry 20 may reference the differential diagnostic to determine whether a potential abnormality is present at implantation site 1704. AI engine ( s ) 28 and / or ML model ( s ) 30 also use various measurements in the user - provided picture to determine whether the implantation site is within a threshold of the “ normal ” state . In some instances , processing circuitry 20 may overlay a ruler or other augment or overlay on the image to assist the user with capturing an image useful for measurements . That is , processing circuitry 20 may provide an augment or another frame that the user can use to get the correct distance and / or perspective of the implantation site . In an illustrative example , processing circuitry 20 may guide a user to image the implantation site where camera 32 is a target distance from the implantation site and in some instances , from a second target distance from the implantation site . Processing circuitry 20 may further guide a user to image the implan tation site at a target angle relative to the implantation site or relative to a reference plane of camera 32 ( e.g. , a starting position of computing device ( s ) 2 ) . In such examples , pro cessing circuitry 20 may use overlays , augmented rulers , etc. , such as in an augmented reality implementation , in order to capture image ( s ) at particular angles ( e.g. , a par ticular view of the implantation site ) , with the implantation site at a particular relative size in a frame of image data , etc.” and see 0077 ] In one example , computing device 2 may deter mine , from physiological parameters obtained via a second subsession , an ECG change that indicates device migration and as such , increases the likelihood that a potential abnor mality is being detected from the image data . In such instances , computing device 2 may analyze the image data using a bias toward detecting an abnormality or may include , in a post - implant report , a heightened likelihood ( e.g. , probability , confidence interval ) that is based on the likeli hood of a potential abnormality determined from the first and second set data items . Computing device ( s ) 2 may output the post - implant report of the interactive session . In an illustrative example , the post - implant report may include an indication of the abnormality and / or indication of an amount of time that has transpired since the date of implantation of the IMD.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Itu’s teaching of predicted and known bone regeneration indications and simulating outcomes as previously cited with Jordan’s teachings of post operative implanted implant data where clinical values are determined to help with diagnosis and as previously cited, the motivation being that Itu discloses the use of the methods and applications in to strengthen diagnostic abilities which can give personalized assessment and predict future risk ([0037]) therefore, it would be obvious to improve and increase precision for treatment planning and prediction by including the post operative data and clinically measured values in a report to compare to pre-operative or future predications with ground truth post operative outcomes.
However, Jordan also does not teach:
…[…]…with pseudoarthrosis,..[…]….
…[…]…and based on the bone regeneration indices of the one or more reports, diagnosing the patient with pseudarthrosis if the bone regeneration indices include a non-fusion zone.
However, Reinke does teach:
…[…]…with pseudoarthrosis,..[…]….…[…]…and based on the bone regeneration indices of the one or more reports, diagnosing the patient with pseudarthrosis if the bone regeneration indices include a non-fusion zone. (Col. 6 lines 35-40 discloses, “In certain embodiments, the elevated probability of developing or having a non-fusion after spinal bone fusion surgery have been determined by a method according to the first aspect or any embodiment thereof.” And see Col. 6 lines 62-67 discloses, “FIG. 3 shows the results of flow cytometric analyses of the circulating immune cell subset CD45+CD3+CDS+ CD57+2S- T cells in the peripheral blood of patients with spinal non-fusion and normal fusion. Spinal non-fusion patients (healing class 2) showed a significantly higher frequency of CD45+CD3+CDS+CD57+2S- T cells” / examiner notes the instant specification notes the indices can be chemical properties see instant application para. [0093])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Itu’s teaching of predicted and known bone regeneration indications and simulating outcomes as previously cited and Jordan’s teachings of post operative implanted implant data where clinical values are determined to help with diagnosis and as previously cited with Reinke’s teachings of non-fusion areas, the motivation being that Itu discloses the use of the methods and applications in to strengthen diagnostic abilities which can give personalized assessment and predict future risk ([0037]) therefore, it would be obvious to improve and increase precision for treatment by documenting any failures of regeneration to improve overall patient outcomes and is choice data to look for as physicians dealing with bone regeneration diagnosing.
As per claim 2, Itu teaches:
The method of claim 1, further comprising: determining one or more matching similarities, wherein the determining comprises comparing the one or more extracted features of interest to one or more other features of interest from previous patient data associated with one or more additional patients, wherein the generating of the one or more reports is further based on the one or more matching similarities. ([0099] discloses, “Another possibility is to build a database with the patient - specific data of previous cases and to use this data base during the sequential ML approach , as shown in FIG . 12 . The shaded portion of FIG . 12 is substantially similar to the technique with respect to FIG . 11 . As described before , during the first step , the ML algorithm learned on synthetic data is used to generate a first prediction of the measure of interest . During the second step , the features extracted for the patient specific data are used to find similar cases in the patient database and a second machine learning algorithm is applied for predicting the final value of the measure of interest . In this case , data is retrieved from a database 1205 and used at step 1210 to train the ML algorithm for improv ing prediction of the biomechanical characteristic of interest . The data retrieved from database 1205 may include , for example , features ( geometric , patient data , etc . ) and / or data regarding the predicted biomechanical characteristic of interest.” And [0102] discloses, “Using the workflow 1300 , the outcome measures of interest predicted by the ML model may be : optimal treatment plan ( which drug , which quantity ) , improvement in bone strength after a certain period of time , decrease of fracture risk score , etc .”)
As per claim 3, Itu teaches:
The method of claim 1, further comprising: receiving invasive patient data associated with the patient, wherein the one or more features of interest are further extracted from the invasive patient data. ([0037] discloses, “The prediction is based on various features extracted from non - invasive patient data , medical imaging ( invasive and non - invasive , perfusion ) , invasive measurements , blood biomarkers , etc . Furthermore , the disclosed techniques may also be applied to the prediction of risk of future events .”)
As per claim 4, Itu teaches:
The method of claim 1, further comprising: receiving non-invasive patient data associated with the patient, wherein the one or more features of interest are further extracted from the non-invasive patient data. ([0037] discloses, “The prediction is based on various features extracted from non - invasive patient data , medical imaging ( invasive and non - invasive , perfusion ) , invasive measurements , blood biomarkers , etc . Furthermore , the disclosed techniques may also be applied to the prediction of risk of future events .”)
As per claim 5, Itu teaches:
The method of claim 1, further comprising: generating a set of medical prediction indices based on the plurality of extracted features, wherein the one or more reports comprise at least a subset of the medical prediction indices. (See Fig. 14 and 15 and see [0108] discloses, “This weight was derived from an organ level simulation of a femur ( see FIG . 15 ) : a femur ( modeled as an isotropic material was loaded with 90 kg using boundary conditions described herein . A slice was selected as visualized in the figure below ( in the femoral neck ) and the corresponding stress ' S ' was noted” and see [0109] discloses, “[ 0109 ] A finite element analysis was carried out on the cancellous model and the average ( normal ) strain was computed over all the finite elements.” And see [0075] discloses, “Continuing with reference to FIG . 1 , at step 130 , patient - specific predictions are visualized based on the out put of the ML algorithms . For example , in some embodiments , the data is presented in a numeric ( e . g . tabular ) form to the clinician . In other embodiments , the data is presented in a graphical way ( e . g . , overlaid on the medical images ) and presented to the clinician .”)
As per claim 6, Itu teaches:
The method of claim 1, further comprising: training the one or more artificial intelligence (AI) models to perform one or more tasks, wherein the one or more tasks comprise at least extracting the one or more features of interest. ([0077] discloses, “Once this database is established , at steps 210 and 215 , features are extracted from patient specific data and outcome measures of interest are extracted . Then , at step 220 , the extracted data is used to train a data - driven surrogate model for predicting the outcome measures of interest using ML algorithms”)
As per claim 7, Itu teaches:
The method of claim 6, wherein training the one or more Al models is performed using one or more transfer learning methods, wherein each transfer learning method has its own transfer learning dataset, and wherein the one or more transfer learning datasets are unrelated to the one or more tasks. ([0077] discloses, “The workflow displayed in FIG . 1 is used during the prediction phase which is performed online . To be able to use one or more machine learning algorithms these have to be trained a priori offline . FIG . 2 displays a generic workflow of the training phase , as it may be applied in some embodiments . The most important aspect for the training phase is the existence of a large database 205 comprising patient - specific information ( non - invasive data , medical images , bone turnover markers ) and patient - specific out come measures of interest for osteoporosis ( e . g . , fracture occurrence , fracture severity , quantity extracted from a biomechanical FEA , etc . ) . Once this database is established , at steps 210 and 215 , features are extracted from patient specific data and outcome measures of interest are extracted . Then , at step 220 , the extracted data is used to train a data - driven surrogate model for predicting the outcome measures of interest using ML algorithms.” / per instant application para. [0088] unrelated is disclosed as being unrelated to the task and therefore examiner interprets training with a large dataset offline unrelated to the actual prediction online and a transfer learning to the neural network)
As per claim 8, Itu teaches:
The method of claim 6, wherein training the one or more Al models is performed using one or more datasets based on synthetic data, and wherein the synthetic data is related to one or more synthetic models. (see Fig. 11, 1115 “USE FIRST MACHINE LEARNING ALGORITHM TRAINED ON SYNTHETIC DATA TO PREDICT BIOMECHANICAL CHARACTERISTIC OF INTEREST” and see [0039] discloses, “. In one embodiment , the measures of interest comprise one or more of stress and stress strain . In other another embodiment , at least a portion of the training data comprises synthetic data . This synthetic data may be generated , for example , by generating one or more baseline models and randomly or systematically perturbing the baseline models to obtain synthetic models comprising one or more of ( i ) synthetic bone anatomical models and ( ii ) synthetic in - vitro models . In one embodiment , these baseline models are subject - specific anatomical models . In one embodiment , the synthetic data comprises one or more of ( i ) synthetic bone anatomical models and ( ii ) synthetic in - vitro models generated according to a set of rules using one or more randomly or systematically perturbed parameter values .”)
As per claim 9, Itu teaches:
The method of claim 8, wherein training the one or more Al models further comprises: generating patient-specific synthetic geometries based on features extracted from the medical imaging data; ([0090] discloses, “Furthermore , the generation of synthetic data may comprise generating synthetic images , similar to those obtained from different imaging modalities ( DXA , CT , MRI , etc . )……The synthetic geometries may then be extracted from these synthetic images using the same techniques as in the case of patient - specific images.”)
generating one or more indices comprising physical or chemical properties of the generated synthetic geometries; generating one or more synthetic models based on the synthetic geometries and the indices; ([0091] discloses, “FIGS . 7A - 7C illustrates a three - step method for generating two - dimensional synthetic femur anatomical models . In the first step ( shown in FIG . 7A ) , a one - dimensional skeleton of the femur bone is generated , by specifying the shaft length , the neck length , the neck angle , and the shaft curvature . During the second step ( shown in FIG . 7B ) , various diameters ( condyle , head and large trochantic diam eters ) and the shaft width are prescribed . Once the second step is complete the outer contour of the femur bone can be generated by interpolating based on these parameters . Dur ing the third step , ( shown in FIG . 7C ) the inside properties of the bone are determined by specifying the width of the cortical bone and of the cancellous bone at each location.”)
extracting one or more measures from the one or more synthetic models, wherein the measures are similar or identical to those used to measure features of interest using the connected implant; ([0094] discloses, “Starting at step 905 , the synthetic bone anatomical models and associated input data required for FEM simulations is generated . At step 910 , input uncertainties ( geometry , cortical properties , cancellous properties , etc . ) are specified . These input uncertainties are used at step 925 to extract geometric features of the bone anatomical models and other features of interest…[…]…After the FEM simulation , the confidence interval of biomechanical measure of interest is determined at step 916.” And see [0097] discloses, “Continuing with reference to FIG . 10 , at step 1020 , additional patient - specific features are extracted from bio mechanical characteristics of interest . These additional features may include , for example , age , gender , BMI , fracture history , bone turnover markers , measurements from other medical / non - medical imaging modalities , etc”. / examiner notes that patient specific features used in the prediction are interpreted as similar to the extracted synthetic data which would be similar to the connected implant data previously cited as anatomical information on the bone)
and training the algorithm to output indices from the synthetic geometries and the measures. ([0094] discloses, “Next , at step 918 a data - drive surrogate model is trained to predict the confidence interval of biomechanical measure of interest using an ML method .”)
As per claim 11, Itu teaches:
The method of claim 1, further comprising: storing the one or more reports in one or more patient-specific medical records. ([0099] discloses, “The data retrieved from database 1205 may include , for example , features ( geometric , patient data , etc . ) and / or data regarding the predicted biomechanical characteristic of interest” / examiner notes in light of 112(a) the database is interpreted as a data repository for patient information as defined by instant application discloses in para. [0038] and [0045])
As per claim 12, Itu teaches:
The method of claim 1, wherein the one or more features of interest relate to bone regeneration, ([0097] discloses, “obtain the final prediction of a measure of interest related to osteoporosis ( e . g . , fracture risk ). And see [0068] discloses, “The techniques described herein refer to the usage of machine learning ( ML ) algorithms for predicting measures of interest related to osteoporosis : existence / severity of osteoporosis / osteopenia ; fracture risk ( score , percentage ) ; biomechanical characteristic of interest , as extracted typically from finite element analyses : whole - bone strength , load - to - strength ratio , the type of fracture to be expected , local / average stress , local / average strain , local / global stiff ness ; effect of a drug - treatment , e . g . increase in bone strength in time ; disease evolution , e . g . decrease in bone strength in time . The prediction may be based on various features extracted from non - invasive patient data , medical imaging data ( DXA , CT , MRI , etc . ) , and bone turnover markers . One major advantage of the proposed ML - based workflows is that data from heterogeneous sources may be integrated to perform a comprehensive assessment . The techniques described herein may also use imaging data acquired previously for a different scope ( e . g . , CT scans : lung cancer screening , a cardiac CT for ruling out coronary artery disease , abdominal / pelvic region , etc . ) ; that is , they provide the opportunity for a secondary use of scans for the assessment of risk of fracture and osteoporosis diagnosis.” And see [0120] discloses, “The techniques discussed herein may also have additional clinical applications other than those discussed above . For example , approaches similar to the ones described herein may be used to determine optimal treatment plans for patients with bone fractures which may or may not be related to osteoporosis : e . g . use metallic plates, rods, gypsum, etc. Additionally (or alternatively), approaches similar to the ones described herein may be used to assess patients with one of the following bone disorders : avascular necrosis or osteonecrosis, bone spur (osteophytes), fibrodysplasia ossificans progressive, fibrous dysplasi, Fong Disease or Nail - patella syndrome, giant cell tumor of bone, greenstick fracture, hypophosphatasia , hereditary multiple exostoses , Klippel - Feil syndrome , metabolic bone disease , multiple myeloma , osteoarthritis , osteitis deformans (or Paget ' s disease of bone), osteitis fibrosa cystica (or osteitis fibrosa, or Von Recklinghausen's disease of bone), osteitis pubis, condensing osteitis (or osteitis condensas), osteochondritis dissecans, osteochondroma (bone tumor), osteogenesis imperfecta, osteomalacia, osteo myelitis , osteopetrosis , porotic hyperostosis, primary hyperparathyroidism, renal osteodystrophy, Salter - Harris fractures.”) / per the instant applications para. [0035] bone regeneration refers to osteogenesis or fracture healing therefore under BRI all the techniques as disclosed in the cited prior art are disclosed as being used to assess indications of osteogenesis)
and wherein the one or more reports comprise a plurality of bone regeneration metrics. (See Fig. 14 and Fig. 15 / per the instant application para. [0071] and [0093] the bone regeneration indices or metrics can represent mechanical properties such as bone tissues under loads e.g. tensile, compression, etc. which make up stress calculations such as von mises)
As per claim 19, Itu teaches:
The non-transitory computer-readable medium of claim 14, further comprising: instructions for generating a set of medical prediction indices based on one or more matching similarities, wherein the one or more reports comprise at least a subset of the medical prediction indices. (See Fig. 14 and 15 and see [0108] discloses, “This weight was derived from an organ level simulation of a femur ( see FIG . 15 ) : a femur ( modeled as an isotropic material was loaded with 90 kg using boundary conditions described herein . A slice was selected as visualized in the figure below ( in the femoral neck ) and the corresponding stress ' S ' was noted” and see [0109] discloses, “[ 0109 ] A finite element analysis was carried out on the cancellous model and the average ( normal ) strain was computed over all the finite elements.” And see [0075] discloses, “Continuing with reference to FIG . 1 , at step 130 , patient - specific predictions are visualized based on the out put of the ML algorithms . For example , in some embodiments , the data is presented in a numeric ( e . g . tabular ) form to the clinician . In other embodiments , the data is presented in a graphical way ( e . g . , overlaid on the medical images ) and presented to the clinician .) and see 0124 ] The on - site assessment can be inconclusive or uncertain due to intrinsic uncertainty of the quantity of interest ( e . g . computed bone strength close to the cut - off value of a likely fracture ) . In this case , off - site processing can include consulting medical experts ( human or databases ) to find the best course of action for instance based on previous clinical cases with similar characteristics .”)
As per claims 14, 15, 16, 17, 20, 21, and 22 it is an article of manufacture claim which repeats the same limitations of claims 1, 2, 3, 4, 6, 12, and 13 the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Itu, Jordan, and Reinke as well as motivations to combine disclose the underlying process steps that constitute the method of claims 1, 2, 3, 4, 6, 12, and 13 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 14, 15, 16, 17, 20, 21, and 22 are rejected for the same reasons given above for claims 1, 2, 3, 4, 6, 12, and 13 .
Claim 13 is rejected to under 35 U.S.C. 103 as being unpatentable over Itu et. al (hereinafter Itu) (US20180247020A1) in view Jordan (US20210343017A1) in further view of Reinke (hereinafter Reinke) (US11802873B2) and in even further view of YANG et. al (hereinafter YANG) (CN111400953A)
As per claim 13, Itu further teaches:
The method of claim 12, further comprising: …[…]…predicting one or more bone regeneration indices based on …[…]… and the one or more bone regeneration metrics; and …[…]…based on the predicted bone regeneration indices and the one or more bone regeneration metrics. (See Fig. 14 and Fig. 15 / per the instant application para. [0071] and [0093] the bone regeneration indices or metrics can represent mechanical properties such as bone tissues under loads e.g. tensile, compression, etc. which make up stress calculations such as von mises)
However, Itu does not teach the underlined portions:
The method of claim 12, further comprising: initializing one or more distraction osteogenesis parameters; predicting one or more bone regeneration indices based on the distraction osteogenesis parameters and the one or more bone regeneration metrics; and generating optimized distraction osteogenesis parameters based on the predicted bone regeneration indices and the one or more bone regeneration metrics.
However, YANG does teach the underlined portion:
The method of claim 12, further comprising: initializing one or more distraction osteogenesis parameters; predicting one or more bone regeneration indices based on the distraction osteogenesis parameters and the one or more bone regeneration metrics; and generating optimized distraction osteogenesis parameters based on the predicted bone regeneration indices and the one or more bone regeneration metrics. (page 4 and page 5 disclose, “In the distraction osteogenesis simulation system provided by the present invention, the distraction osteogenesis simulation system applies different distraction conditions during the implementation of distraction osteogenesis, which will cause different osteogenesis effects in the callus area tissue; Perform simulation calculations on the bone regeneration process of distraction osteogenesis, and determine the best distraction loading mode according to the output osteogenesis results; in addition, the present invention provides personalized parameter settings for patients in the distraction parameter setting module including: distraction Phase stretch rate, stretch frequency, stretch duration, consolidation period applied traction-compression coupling stimulation period, traction-compression coupling load application rate, traction coupling load application frequency and distraction stiffness; users can Customized stretch parameters are convenient for doctors to find the best mechanical stimulation conditions for patients, which helps to shorten the entire treatment time and thereby reduce the incidence of complications.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Itu’s teaching of predicted and known bone regeneration indications and simulating outcomes as previously cited and Jordan’s teaching of post operative implant information as previously cited and Reinke’s teachings as previously cited with YANG’s teachings of optimized distraction osteogenesis parameters, the motivation being that Itu discloses the use of the methods and applications in relation to osteogenesis for treatment planning ([0120]) therefore, it would be obvious to improve and increase precision on that treatment planning and prediction by including the osteogenesis parameters with no unpredictable results.
Claim 18 is rejected to under 35 U.S.C. 103 as being unpatentable over Itu et. al (hereinafter Itu) (US20180247020A1) in view Jordan (US20210343017A1) in further view of Reinke (hereinafter Reinke) (US11802873B2) and in even further view of Boddington et. al (hereinafter Boddington) (US2024/0206990Al)
As per claim 18, Itu does not teach:
The non-transitory computer-readable medium of claim 14, further comprising: instructions for extracting, based on one or more matching similarities, one or more similar images, wherein the similar images have similar features to at least a subset of the one or more medical images of the patient, wherein the generated report comprises the one or more similar images.
However, Boddington does teach:
The non-transitory computer-readable medium of claim 14, further comprising: instructions for extracting, based on one or more matching similarities, one or more similar images, wherein the similar images have similar features to at least a subset of the one or more medical images of the patient, wherein the generated report comprises the one or more similar images. (“[0181] Now referring to FIG. 31 a grid data predictive map 301 is shown. The computing platform 100 identifies a best-matching nonoperative side image as compared to a current operative-side image using an image similarity metric; registering the best-matching non-operative side image to the current operative side image; and aligning the nonoperative- side image with the current operative side image to provide a guidance pose-guide image, wherein the guidance pose-guide image graphically illustrates the difference in the anatomical positioning of the non-operative and operative-side images as shown. A grid data predictive map 301 is a grid of points of interest in the image i.e., the coordinates position of landmarks.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Itu’s teaching of similarities between indication such as medical images of other patients as previously cited and Jordan’s teaching of post operative implant information and Reinke’s teachings as previously cited with Boddington’s explicit teachings of similarities between images being extracted and also reported of a patient, the motivation being that Itu discloses making sure similar cases and patient data are used for the final outcome (e.g. [0099]) therefore it would improve the precision and improve the accuracy of the prediction by including additional quality data.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on page 1-3 of the submitted remarks that the rejection of pending claims 1-9 and 10-22 under 35 U.S.C § 101 should be withdrawn for the following reasons: Regarding claims 1 and 14, the Examiner notes that the claims are directed to "certain methods of organizing human activity" as it is following rules or instructions to generate a report to assist a physician in a diagnosis for a patient. Therefore, according to the Examiner, the claims are directed to ineligible subject matter. However, this is incorrect. Claim 1 is amended. As claimed, the invention is a "computer-assisted method for diagnosing a medical patient who has had an implant implanted into the patient body with pseudoarthrosis." Thus, the claims are directed to the functioning of a computer, or an improvement to other technology or technical field (i.e., the computer-assisted method).
In the Action, the Examiner alleges that the claims are "directed to an abstract idea without significantly more." (Action, p. 3.) However, this is incorrect. The rejection should be withdrawn because, even if the claims are directed to a judicial exception, they adequately integrate the judicial exception into a practical application because the claims are directed to "[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field." (MPEP § 2106.04(d)(I).)
Applicant does not agree that claim 1 is directed to patent ineligible subject matter. However, even if it is, the claim, as a whole, is directed to a computer-assisted method for diagnosing a medical patient who has had an implant implanted into the patient body with pseudoarthrosis, and thus, includes ""[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field." Per the MPEP, "examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception in Step 2A Prong Two. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." (MPEP §2106.04(d).) Further, "A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not "directed to" the recited judicial exception." (MPEP § 2106.04(d)(1)).) Accordingly, because the claims, as a whole, "improve the functioning of a computer or improves another technology or technical field," any alleged judicial exception is adequately integrated into a practical application, and thus the claims are directed to patent eligible subject matter.
Because the claims are directed to patent eligible subject matter, the rejection of claim 1 should be withdrawn. The rejection of claim 14 should also be withdrawn, at least because it recites similar features. The rejection of claims 2-9, 11-13, and 15-22 should also be withdrawn, at least because they depend from claim 1 or 14.
Applicant's arguments have been fully considered but they are not persuasive.
The MPEP 2106.04(a)(2) states, “Examiners should determine whether a claim
recites an abstract idea by (1) identifying the specific limitation(s) in the claim under
examination that the examiner believes recites an abstract idea and (2) determining whether the
identified limitations(s) fall within at least one of the groupings of abstract ideas listed above.
Examiner has considered the claims and the claims are directed to the abstract idea within “certain methods of organizing human activity” as it is following rules or instructions to generate a report to assist a physician in a diagnosis for a patient. See MPEP § 2106.04(a).
Examiner notes that the abstract idea is not integrated into a practical application. The abstract idea cannot provide the improvement or practical application rather additional elements identified by the examiner are used to determine if a practical application or significantly more is present based on the recited claim language. The amended limitations while they may contain additional elements are claimed as “apply-it” and used as tools to gather data and output data for the report which a human makes an assessment and analysis on. There is no technological improvement within the confines of the general purpose computer environment or unconventional recitation to the additional elements alone or combined with the abstract idea within the confines of the recited general purpose computer environment. The claimed invention is using computers as a tool and generally linking to machine learning and any improvement present is an improvement to the abstract idea. Finally were applicants line of reasoning correct, the invention in Alice Corp. would have been subject matter eligible as it is an improvement to settlement risk mitigation.
Therefore, examiner maintains the rejection under 35 U.S.C 101.
Response to Arguments Regarding 35 U.S.C § 103 Rejections
Applicant argues on pages 3-4 of the remarks the amended claims 1-22 in regards to 35 U.S.C § 103 should be withdrawn.
Applicant’s arguments with respect to claims 1-9 and 10-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, Examiner maintains the § 103 rejection.
Prior Art not cited but made of record
Amiot et. al - US11723585B2
Example implants, systems and methods using sensors for orthopedic surgical assessment and/or planning are described herein. An example system can include a wearable sensor device for pre-operative use by a patient before an orthopedic surgery to generate pre-operative sensor data. The system can also include an implantable sensor device (e.g., a bone implant) to generate and aggregate postoperative sensor data associated with the patient after the surgery. The system can retrieve the pre-operative sensor data and the post-operative sensor data and predict, analyze or assess an outcome of the surgery.
Wiebe et. al – US11602361B2
Patient specific implant technology, in which an outline representation of a portion of an outer surface of a periphery of a bone volume is determined and the outline representation is used in operations related to implant matching. In addition, an instrument may be made to match a perimeter shape of a Patient Specific Knee Implant with features for locating holes in a distal femur such that posts or lugs in a femoral implant locate the femoral implant centered mediallaterally within an acceptable degree of precision to prevent overhang of either the side of the femoral implant over the perimeter of the distal femur bone resections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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