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 .
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/04/2026 has been entered.
Status of Claims
This action is in reply to the current action filed 06/04/2026.
Claims 1, 9, and 17 have been amended.
Claims 1-7 and 9-20 are currently pending and have been examined.
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 1-7 and 9-20 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1, 9, and 17 are within the four statutory categories. Claims 1, 9, and 17 are directed to a method, non-transitory computer-readable medium (i.e. a product of manufacture), a device (i.e. machine), and a device, respectively. Dependent Claims 2-7, 10-16, and 18-20 are further directed to a method, non-transitory computer-readable medium, and a device and therefore also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A method comprising: obtaining, by a device, a captured image of a knee, the captured image depicting (i) at least a portion of a femur and tibia after an initial anterior cruciate ligament (ACL) reconstruction procedure and (ii) hardware installed in the initial ACL reconstruction procedure;
analyzing, by the device, the captured image to perform a first segmentation of the captured image, the first segmentation including detection of respective tunnels of the femur and the tibia formed during the initial ACL reconstruction procedure;
further analyzing, by the device, the captured image, to perform a second segmentation of the captured image, the second segmentation including detection of the hardware installed in with the initial ACL reconstruction procedure;
and generating for display, by the device and using (i) the detection of the respective tunnels from the first segmentation and (ii) the detection of the hardware from the second segmentation, a three-dimensional (3D) model of the knee that includes 3D renderings of the femur, the tibia, the respective tunnels, and the hardware.
The limitations as shown in underline above, given the broadest reasonable interpretation, recite the abstract idea of mental processes and certain methods of organizing human activity because they recite a process that could practically be performed in the human mind (i.e., observations, evaluations, judgements, and/or opinions – in this case, the steps of obtaining an image, analyzing the image, detecting hardware in the image, and generating a display) or using a pen and paper, but for the recitation of generic computer components (i.e., the device) as a tool to perform the mental processes and managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions – in this case, identifying an image, analyzing the image, performing segmentation, and generating a 3D model) e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2-7, 10-16, and 18-20 include other limitations directed toward the abstract idea. For example, Claims 2, 10, and 18 recite performing the second segmentation according to a predetermined range of Hounsfield Units, Claims 3 and 11 recite the further analysis related to the second segmentation further comprises a thresholding operation, Claims 4, 12, and 19 recite providing the first segmentation and the second segmentation as input, Claims 5, 13, and 20 recite generating an operative plan for an ACL revision procedure based on the generated 3D model, Claims 6 and 14 recite the hardware corresponds to a set of screws used as part of the initial ACL reconstruction procedure, Claim 7 recites the image is at least one selected from a group comprising: a computed tomography (CT) image; and a magnetic resonance imaging (MRI) image, Claim 15 recites the image is a computed tomography (CT) image, and Claim 16 recites the image is a magnetic resonance imaging (MRI) image. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-7, 10-16, and 18-20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 2-7, 10-16, and 18-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 9, and 17.
Step 2A Analysis – Prong Two:
Claims 1, 9, and 17 are not integrated into a practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the device of Claim 1, the device and non-transitory computer-readable medium of Claim 9, and the device and processor of Claim 17) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that [a] client device may, for example, include a desktop computer or a portable device, such as a cellular telephone, a smart phone, a display pager, a radio frequency (RF) device,…a handheld computer, a tablet computer, a phablet, a laptop computer, a set top box, a wearable computer, smart watch, an integrated or distributed device combining various features, such as features of the forgoing devices, or the like (see Applicant’s specification, ¶ 0036). These computer program instructions can be provided to a processor of a general purpose computer to alter its function as detailed herein, a special purpose computer, ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus,…[0027]. For the purposes of this disclosure a non-transitory computer readable medium (or computer-readable storage medium/media) stores computer data, which data can include computer program code (or computer-executable instructions) that is executable by a computer, in machine readable form [0029]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1, 9, and 17 are directed to an abstract idea without practical application.
Dependent Claims 2, 4-5, 10, 12-14, and 18-20 recite additional elements. Claim 2, 10, and 18 recite the previously recited device and states the devices generates the second segmentation according to a predetermined range of Hounsfield Units (HU), the predetermined range corresponding to and enabling identification of a presence of a particular type of material from the image. Claims 4, 12, and 19 recite the previously recite devices as well as a new element of the MITK software application, and they recite the device provides the first segmentation and the second segmentation as input into a medical imaging interaction toolkit (MITK) software application, and executes the MITK software application, wherein the generation of the 3D model is based on the execution of the MITK software. Claims 5, 13, and 20 recite the previously recited device and specify the devices generates an operative plan for an ACL revision procedure based on the generated 3D model. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the device of Claim 1, the device and non-transitory computer-readable medium of Claim 9, and the device and processor of Claim 17 amount to no more than mere instruction to apply an exception using generic computer components. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”).
Dependent Claims 4, 12, and 19 recite new additional elements. Claims 4, 12, and 19 recite the previously recite devices as well as a new element of the MITK software application, and they recite the device provides the first segmentation and the second segmentation as input into a medical imaging interaction toolkit (MITK) software application, and executes the MITK software application, wherein the generation of the 3D model is based on the execution of the MITK software.
Dependent Claims 2, 5, 10, 13-14, 18, and 20 recite previously cited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 2, 10, and 18 recite the previously recited device and states the devices generates the second segmentation according to a predetermined range of Hounsfield Units (HU), the predetermined range corresponding to and enabling identification of a presence of a particular type of material from the image. Claims 5, 13, and 20 recite the previously recited device and specify the devices generates an operative plan for an ACL revision procedure based on the generated 3D model. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application. Hence, Claims 2-7, 10-16, and 18-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-7 and 9-20 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 6-7, 9, 11, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kiapour et al. (US 20240081728 A1) in view of Kitamura et al. (Kitamura et al. 3-Dimensional Printed Models May Be a Useful Tool When Planning Revision Anterior Cruciate Ligament Reconstruction. Arthroscopy, sports medicine, and rehabilitation vol. 1,1 e41-e46. 26 Sep. 2019), Risvas et al. (Risvas, K., Stanev, D., Benos, L. et al. Evaluation of anterior cruciate ligament surgical reconstruction through finite element analysis. Sci Rep 12, 8044 (2022)), and Miller et al. (US 20160317207 A1).
Regarding Claim 1, Kiapour discloses the following:
A method comprising: obtaining, by a device, a captured image of a knee, the captured image depicting…after an initial anterior cruciate ligament (ACL) reconstruction procedure; (Kiapour discloses a method of determining a condition of a tissue of a patient from analysis of a magnetic resonance image [0005]. In some cases, the MR image may be an MR image of a knee of a patient who has received ACL surgery, and the determination may be of the condition of the reconstructed ACL tissue [0024].)
analyzing, by the device, the captured image to perform a first segmentation of the captured image, the first segmentation…further analyzing, by the device, the captured image, to perform a second segmentation of the image, (Kiapour discloses image segmentation may be automated and may be executed using known methods in the art. As an example, an MR image of a knee may be received by the MR image analysis facility and the image analysis facility may first segment a ligament from the image of the joint before generating a projection of the image. The automated image segmentation may include an object detection, object identification, masking, classifying, and may involve detecting a global threshold and/or local thresholds associated with the regions to be segmented,…[0052].)
and generating for display, by the device, a three-dimensional (3D) model of the knee (Kiapour discloses the MR image stack 502 was acquired using a CISS sequence to image the knee of a patient following an ACL surgery. MR image stack depicts the ACL and surrounding tissue of the knee. The ACL portions of the image stack were manually segmented from the sagittal CISS image stacks to generate 3D segmented ACL 504 [0076, Fig. 5A].)
Kiapour does not disclose the following limitations met by Kitamura:
…an image depicting at least a portion of a femur and tibia (Kitamura teaches the purpose of this study was to determine whether using 3D-printed models in addition to CT scans to evaluate the primary femoral and tibial tunnels before revision ACL reconstruction leads to better agreement with the surgical approach than if CT alone is used (p. 2, ¶ 0003). Fig. 1 displays the distal femur and proximal tibia segmented.)
… detection of respective tunnels of the femur and the tibia formed during the initial ACL reconstruction procedure; (Kitamura teaches [i]n evaluating the femoral and tibial tunnels, the location, trajectory, and size of the index tunnels were considered. For each case, the participants responded to 2 questions: 1. Can you use the existing tibial tunnel for this patient? 2. Can you use the existing femoral tunnel for this patient? (p. 2, ¶ 0007-0009).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the image including the femur and tibia and segmentations being related to hardware and tunnels as taught by Kitamura. This modification would create a system and method capable of providing improved preoperative planning and recognition of nonatomic tunnels to reduce the risk of graft failure after revision (see Kitamura, p. 1, ¶ 0001).
Kiapour and Kitamura do not teach the display of the 3D image including the tunnels of the fibula and tibia which is met by Risvas:
(3D) model of the knee that includes 3D renderings of the femur, the tibia, and respective tunnels, and the hardware. (Risvas teaches Fig. 2 which shows an overview of ACLR surgery modeling workflow. Fig. 2(b) shows the ACL, tibia, femur, and hardware being drilled in to create tunnels.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the model being a 3D rendering of the femur, tibia, tunnels, and hardware as taught by Risvas. This modification would create a system and method capable of reproducing surgical scenarios that otherwise would require a significantly high number of patients and the arrangement of complex experimental setups (see Risvas, p. 3, ¶ 0001).
Kiapour, Kitamura, and Risvas do not teach the display of the installed hardware which is met by Miller:
and (ii) hardware installed in the initial ACL reconstruction procedure…including detection of hardware associated with the…ACL reconstruction procedure; (Miller teaches FIG. 17 provides CT images of a 18-year-old woman following ACL revision reconstruction utilizing bone-patellar tendon-bone graft. Referring to FIG. 17(A), the oblique axial CT image shows metal interference screw (asterisk) threads engaged with both the patellar bone graft (white arrows) and the allograft dowel (black arrows) filling the original tunnel from the first reconstruction. The dowel is integrated (black arrows) with the adjacent native bone and is intact except for mild reduction from the pathway of the revision tunnel and screw. Referring to FIG. 17(B), the oblique axial CT image shows excellent screw thread engagement (black arrows) with allograft dowel (arrowheads) [0062].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate displaying installed hardware as taught by Miller. This modification would create a system and method which can identify previous errors to prevent future failure regardless of previous tunnel placement (see Miller, ¶ 0071).
Regarding Claim 9, this claim recites limitations that are substantially similar to Claim 1 above; thus, the same rejection applies. Kiapour further discloses:
A non-transitory computer-readable storage medium (Kiapour discloses there is provided at least one non-transitory computer-readable storage medium storing executable instruction that, when executed by at least one processor, cause the at least one processor to perform the method [0007].)
Regarding Claim 17, this claim recites limitations that are substantially similar to Claim 1 above; thus, the same rejection applies. Kiapour further discloses:
A device comprising a processor (Kiapour discloses there is provided a computer system, comprising: at least one processor [0006]. Remote system 130 may be any suitable electronic device configured to receive information (e.g., from MRI system 110 and/or MRI system console 120) and to display generated MR images…[0045].)
Regarding Claim 3, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 1 above. Kiapour does not disclose the following limitations met by Kitamura:
the further analysis related to the second segmentation further comprises a thresholding operation. (Kitamura teaches bone segmentation was performed with a combination of automated thresholding and manual segmentation (p. 2, ¶ 0005).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the analysis of the segmentation including a thresholding operation as taught by Kitamura. This modification would create a system and method capable of providing improved preoperative planning and recognition of nonatomic tunnels to reduce the risk of graft failure after revision (see Kitamura, p. 1, ¶ 0001).
Regarding Claim 11, this claim recites limitations that are substantially similar to Claim 3 above; thus, the same rejection applies.
Regarding Claim 6, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 1 above. Kiapour further discloses:
… as part of the initial ACL reconstruction procedure. (Kiapour discloses the MR image stack 502 was acquired using a CISS sequence to image the knee of a patient following an ACL surgery. MR image stack depicts the ACL and surrounding tissue of the knee. The ACL portions of the image stack were manually segmented from the sagittal CISS image stacks to generate 3D segmented ACL 504. [0076, Fig. 5A]. The Examiner interprets this limitation as assessing the knee after the reconstruction procedure.)
Kiapour, Kitamura, and Risvas do not teach the following limitations met by Miller:
the hardware corresponds to a set of screws used … (Miller teaches FIG. 17 provides CT images of a 18-year-old woman following ACL revision reconstruction utilizing bone-patellar tendon-bone graft. Referring to FIG. 17(A), the oblique axial CT image shows metal interference screw (asterisk) threads engaged with both the patellar bone graft…The dowel is integrated (black arrows) with the adjacent native bone and is intact except for mild reduction from the pathway of the revision tunnel and screw. Referring to FIG. 17(B), the oblique axial CT image shows excellent screw thread engagement (black arrows) with allograft dowel (arrowheads) [0062].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the hardware being screws as taught by Miller. This modification would create a system and method which can identify previous errors to prevent future failure regardless of previous tunnel placement (see Miller, ¶ 0071).
Regarding Claim 14, this claim recites limitations that are substantially similar to Claim 6 above; thus, the same rejection applies.
Regarding Claim 7, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 1 above. Kiapour further discloses:
wherein the captured image is at least one selected from a group comprising:…a magnetic resonance imaging (MRI) image. (Kiapour discloses an image analysis facility for determining a condition of a tissue from MRI data. In some embodiments, such MRI data may have been captured using an MRI system for acquiring the MR images, where the system includes a magnetics system configured to produce one or more magnetic fields during MR imaging and at least one radio frequency coil configured to produce one or more radio frequency pulses during MR imaging [0034].)
Kiapour does not disclose the use of a CT image which is met by Kitamura:
…a computed tomography (CT) image… (Kitamura teaches 2-dimensional (2D) and 3D computed tomography (CT), magnetic resonance imaging (MRI),…have all been used to determine the correct tunnel drilling location (p. 1, ¶ 0002). During the first round, only the CT scans were presented using Horos software V2.4.0…In evaluating the femoral and tibial tunnels, the location, trajectory, and size of the index tunnels were considered (p. 2, ¶ 0007).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the use of CT scans as taught by Kitamura. This modification would create a system and method capable of providing improved preoperative planning to reduce the risk of graft failure after revision (see Kitamura, p. 1, ¶ 0001).
Regarding Claim 15, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 9 above. Kiapour does not disclose the following limitation met by Kitamura:
wherein the captured image is a computed tomography (CT) image (Kitamura teaches 2-dimensional (2D) and 3D computed tomography (CT), magnetic resonance imaging (MRI),…have all been used to determine the correct tunnel drilling location (p. 1, ¶ 0002). During the first round, only the CT scans were presented using Horos software V2.4.0…In evaluating the femoral and tibial tunnels, the location, trajectory, and size of the index tunnels were considered (p. 2, ¶ 0007).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the use of CT scans as taught by Kitamura. This modification would create a system and method capable of providing improved preoperative planning to reduce the risk of graft failure after revision (see Kitamura, p. 1, ¶ 0001).
Regarding Claim 16, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 9 above. Kiapour further discloses:
wherein the captured image is a magnetic resonance imaging (MRI) image. (Kiapour discloses an image analysis facility for determining a condition of a tissue from MRI data. In some embodiments, such MRI data may have been captured using an MRI system for acquiring the MR images, where the system includes a magnetics system configured to produce one or more magnetic fields during MR imaging and at least one radio frequency coil configured to produce one or more radio frequency pulses during MR imaging [0034].)
Claims 2, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kiapour, Kitamura, Risvas, and Miller in view of DaSilva et al. (US 20090087065 A1).
Regarding Claim 2, Kiapour, Kitamura, and Risvas teach the limitations as seen in the rejection of Claim 1 above. Kiapour further discloses:
performing, by the device, the second segmentation according to a predetermined range of Hounsfield Units (HU), the predetermined range corresponding to and enabling identification of a presence of a particular type of material from the captured image. (DaSilva teaches FIG. 3 shows another suitable segmentation approach in which the segmentation segments as foreign regions any region that is neither tissue nor bone, without distinguishing what foreign element the foreign region corresponds to. FIG. 4 plots estimated linear attenuation coefficient (LAC) for gamma rays at 140 keV as a function of CT image element value in Hounsfield units for bone, for an iodine-based contrast agent, and for a metal implants region [0018-19]. The identified material and the energy of the radiopharmaceutical can be input into a pre- programmed look-up table look-up table to retrieve the corresponding value or attenuation transform to generate the attenuation map… the look-up table can be based on foreign object type, listing for example general implant type such as hip implant, knee implant, screw implant, …[0042].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the segmenting based on Hounsfield Units to determine the presence of a material as taught by DaSilva. This modification would create a system which can provide improved contouring of segments (see DaSilva, ¶ 0043).
Regarding Claims 10 and 18, these claims recite limitations that are substantially similar to Claim 2 above; thus, the same rejection applies.
Claims 4, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kiapour, Kitamura, Risvas, and Miller in view of Tian et al. (J. Tian et al., "A Novel Software Platform for Medical Image Processing and Analyzing," in IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 800-812, Nov. 2008)).
Regarding Claim 4, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 1 above. Kiapour further discloses:
providing, by the device, the…segmentation and the…segmentation… (Kiapour discloses image segmentation may be automated and may be executed using known methods in the art. As an example, an MR image of a knee may be received by the MR image analysis facility and the image analysis facility may first segment a ligament from the image of the joint before generating a projection of the image. The automated image segmentation may include an object detection, object identification, masking, classifying, and may involve detecting a global threshold and/or local thresholds associated with the regions to be segmented,…[0052].)
Kiapour, Kitamura, Risvas, and Miller do not teach the following limitations met by Tian:
…as input into a medical imaging interaction toolkit (MITK) software application; and executing, by the device, the MITK software application, (Tian teaches a full platform solution for medical image processing and analyzing, including the Medical Imaging Toolkit (MITK) and the 3-Dimensional Medical Image Processing and Analyzing System (3DMed) (p. 2, ¶ 0002).)
wherein the generation of the 3D model is based on the execution of the MITK software. (Tian teaches 3) 3-D Interaction: The entire interaction framework of our platform is based on 3-D widgets [26]–[29]. In our platform, WidgetModels represent the 3-D widgets and act as the kernel elements in the interaction framework. According to some issues in designing 3-D widgets advanced in the works of Snibbe et al. [30], the design and implementation of WidgetModels should conform to the following rules (p. 5, ¶ 0004). Fig. 18 shows the application examples of the 3D widgets with (a) displaying a 3D model.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate the generation of the 3D model being executed by an MITK software as taught by Tian. This modification would create a system and method which is developed specially for medical image processing and analysis which is easy to use for average researchers and process the support to process out of core datasets (see Tian, p. 1, ¶ 0003-0004).
Regarding Claims 12 and 19, these claims recite limitations that are substantially similar to Claim 4 above; thus, the same rejection applies.
Claims 5, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kiapour, Kitamura, Risvas, and Miller in view of Mahfouz et al. (US 20160157751 A1).
Regarding Claim 5, Kiapour, Kitamura, Risvas, and Miller teach the limitations as seen in the rejection of Claim 1 above. Kiapour, Kitamura, Risvas, and Miller do not teach the following limitation which is met by Mahfouz:
generating, by the device, an operative plan for an ACL revision procedure based on the generated 3D model. (Mahfouz teaches using the patient-specific clavicle trauma plate dimensions, the software also receives anatomical data as to the position and location of the patient's soft tissue, vessels, and nerves within the area of the fractured clavicle to construct an incision plan. The incision plan is pre-operative and suggests a surgical approach to make one or more incisions that increases access to the fractured clavicle bone component parts,…[0415].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system and method for analyzing images following an ACL reconstructive surgery and generating a 3D model using segmentation of knee images as disclosed by Kiapour to incorporate using the model to determine an operative plan as taught by Mahfouz. This modification would create a system and method which optimizes surgery planning to minimize invasiveness and therefore recovery time of the procedure (see Mahfouz, ¶ 0416).
Regarding Claims 13 and 20, these claims recite limitations that are substantially similar to Claim 5 above; thus, the same rejection applies.
Relevant Art Not Currently Being Applied
The following references are not currently being applied but are considered pertinent to Applicant’s disclosure:
Fleming et al. (US 20200069257 A1) teaches a system for predicting the success of ACL surgical procedures using a segmentation of MRI images which include the femur and tibia.
Hampp et al. (US 20200205898 A1) teaches a surgical system which receives image data of an anatomy, generates bone model based on the image data, and plans the placement of an implant.
Benos et al. (Benos L, Stanev D, Spyrou L, Moustakas K and Tsaopoulos DE (2020) A Review on Finite Element Modeling and Simulation of the Anterior Cruciate Ligament Reconstruction. Front. Bioeng. Biotechnol. (Year: 2020)) teaches the use of finite element modeling of ACL reconstruction surgery which creates a three-dimensional model to simulate the surgical procedure on the knee.
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1-7 and 9-20, Application’s arguments have been considered, but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues the claims are not directed to the abstract idea of "certain methods of organizing human activity" for at least the reasons set forth in Pages 8-12 of the Response filed February 11, 2026, hereinafter "the Response"). Specifically, "not all methods of organizing human activity are abstract ideas" (MPEP § 2106.04(a)(2)II); Emphasis added). In other words, only certain, not all, explicitly listed methods of organizing human activity are considered to be abstract ideas, and the limitations of claim 1 are not analogous to any of the examples listed in MPEP § 2106.04(a)(2).II.C (see applicant’s Remarks. p. 8).
Regarding (a), Examiner respectfully disagrees. Examiner has responded to the arguments filed on 02/11/2026 in the office action filed on 04/03/2026. Furthermore, there is nothing in MPEP 2106.04(II)(C) that limits managing personal behavior or relationships or interactions between people to the listed examples. The examples are just that, examples.
Additionally, Examiner notes that the 101 rejection has been updated (see the rejection above) and the abstract idea groupings identified are now certain methods of organizing human activity as well as mental processes. The steps of receiving an image, analyzing the image to detect tunnels and hardware, and generating for display the detection of the hardware and tunnels are all identified as now reciting mental processes.
Applicant argues the Examiner asserts that certain activity between a person and a computer may fall within this category, citing the October 2019 Subject Matter Eligibility Update. The relevant portion of this Update states, in its entirety: The term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. First, not all methods of organizing human activity are abstract ideas (e.g., "a defined set of steps for combining particular ingredients to create a drug formulation" is not a "certain method of organizing human activity"). Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, managing personal behavior, and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in Section IHI(C) of the 2019 PEG. Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. The number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the subgroupings. This grouping is not to be expanded beyond these enumerated sub-groupings except in rare circumstances. Here, the additional examples provided are "a person following instructions or a person signing a contract online," "a commercial transaction," and "a method of anonymous loan shopping that a person conducts using a mobile phone." None of these examples are even remotely analogous to the steps performed by the device of claim 1, and nowhere does claim 1 recite or in any way require "activity between a person and a computer" that is analogous to the provided examples. This grouping still requires that the steps or activity in question, even if "between a person and a computer," corresponds to the explicitly enumerated sub-groupings, which is not true of the present claims. Examiner asserts that "the steps of analyzing an image, segmenting an image, and generating a 3D model are all limitations which can be carried out by a person following a set of instructions using a generic computer." Applicant disagrees and submits that the claim limitations do not recite a set of instructions. If this interpretation of the "organizing human activity" category were proper, then no claim could ever be considered anything but an abstract idea because every method or process could be carried out by a person following a set of instructions. Hence, this category is strictly and explicitly limited to the enumerated examples (see Applicant’s Remarks, p. 8-9).
Regarding (b), Examiner respectfully disagrees. Examiner notes that the 101 rejection has been updated (see the rejection above) and the abstract idea groupings identified are now certain methods of organizing human activity as well as mental processes. The steps of receiving an image, analyzing the image to detect tunnels and hardware, and generating for display the detection of the hardware and tunnels are all identified as now reciting mental processes in addition to certain methods of organizing human activity.
Applicant appears to be conflating the certain methods of organizing human activity subgroupings with the examples. Examiner notes that the “commercial transaction” example cited by Applicant is under the Commercial or Legal Interactions subgrouping, the “anonymous loan shopping” example is an example of mental processes, and “a person signing a contract online” is not an example of a subgrouping. Examiner notes that there is nothing in MPEP 2106.04(II)(C) that limits managing personal behavior or relationships or interactions between people to the listed examples. The examples are just that, examples. The claims recite certain methods of organizing human activity, specifically managing personal behavior or relationships or interactions between people, because they recite a series of steps recited at such a high level of generality such that a radiologist could follow to achieve the outcome of generating a three-dimensional model of a knee. This is comparable to a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982), which is listed in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in a claim.
Regarding rejections under 35 USC 103 to Claims 1-7 and 9-20, Applicant’s arguments have been considered and are persuasive in light of the amendments. However, upon further consideration, a new rejection has been made, rejecting the independent claims over Kiapour in view of Kitamura Risvas, and Miller.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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