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 Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/10/2026 has been entered. Claims 1-21 remain pending in the application.
Response to Amendment
The amendment filed 07/08/2026 has been entered. Claim 21 is added, and claims 1-21 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Final Office Action mailed 04/08/2026.
Response to Arguments
Applicant’s arguments filed 07/08/2026 with respect to claim(s) 1, 10, and 19 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.
Given the amendments to claims 1 and 10, reference to Casey is being relied upon to teach dependent claims 3-8 and 12-17 more-consistently with the instant claim language, as shown below.
Given the amendments to claims 1 and 10, reference to Chacon is being relied upon to teach dependent claims 2 and 11 more-consistently with the instant claim language, as shown below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
For claims 1, 10, and 19, the limitations “wherein detecting the location of the at least one internal marker comprises determining a first internal intensity variation at the location of the at least one internal marker and determining a second internal intensity variation at a separate location than the location of the at least one internal marker” and “extracting, using the first internal intensity variation and the second internal intensity variation, identification information encoded by the at least one internal marker, wherein extracting identification information comprises identifying spatial patterns of the at least one internal marker within the target object” is indefinite.
It is unclear why determining an internal intensity variation at a separate location than the location of the internal marker is necessary in order to extract spatial patterns of the internal marker within the target object, when extracting spatial patterns of the internal marker can be done by determining the internal intensity variation at the location of the internal marker. For the purpose of advancing prosecution, the examiner assumes extracting spatial patterns of the internal marker can be done by determining the internal intensity variation at the location of the internal marker.
Claims 2-9 and 21 are dependent of claim 1, claims 11-18 are dependent of claim 10, and claim 20 is dependent of claim 19, and therefore rejected under this 112(b) rejection as well.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, 10, 12-17, and 19-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Casey et al. (US 20230298728 A1, published September 21, 2023 with a priority date of August 11, 2020), hereinafter referred to as Casey.
Regarding claim 1, and similarly for claims 10 and 19, Casey teaches a method comprising:
performing, using an ultrasound scanner, ultrasound imaging to scan an interior of a target object (Fig. 8C; see para. 0120 – “For example, an imaging system 770 (e.g., a system for taking one or more of MRI, CAT scan, PET scan, X-Ray, ultrasound, etc.) can be positioned over the portion of the patient P [scanning interior of target object] including the implant 700. The imaging system 770 can obtain images of the implant 700, including images of the patient specific markings 725.”);
providing scanned images generated by the ultrasound imaging as input to a machine learning software system configured to host a convolutional neural network (CNN) model (Fig. 8C; see para. 0120 – “A computing module (e.g., the computing device 740) can analyze the image data obtained by the imaging system 770 to extract (e.g., via OCR, convoluted neural networks, etc.) information (e.g., the patient-specific marking 725, a retrieval feature such as a bar code, etc.) from the implant 700.”);
detecting, by the CNN model, a location of at least one internal marker inside the target object using the scanned images (see para. 0101 – “The constructed image can be compared to a virtual model [scanned images] showing the implant implanted in the patient at the target position to determine whether the patient-specific implant is at the target position [determine location of internal marker]. In some embodiments, an image analysis module, such as an artificial intelligence architecture, can be used to compare the constructed image to the virtual model. In some embodiments, the image analysis module is a convolutional neural network.”),
wherein the at least one internal marker comprises at least one code (Fig. 5; see para. 0089 – “In addition to or in lieu of the textual information, the patient-specific markings 525 can include machine-readable (MR) features (e.g., images, symbols, graphics, etc.), optically machine-readable features, one or more codes (e.g., a QR code, a bar code, and alpha-numeric code, etc.)…”),
wherein detecting the location of the at least one internal marker comprises
determining a first internal intensity variation at the location of the at least one internal marker (see para. 0104 – “The method 600 can further include determining an actual position of the implant in step 604. In some embodiments, the actual position of the implant can be determined by capturing image data of the implanted implant [first internal intensity variation at the location of the internal marker].”) and
determining a second internal intensity variation at a separate location than the location of the at least one internal marker (see para. 0014 – “A treatment planning module (e.g., treatment planning module 118 of FIG. 1 ) can analyze images of the patient to identify anatomical structures at or proximate to a target location suitable for engaging an implant [second internal intensity variation at a separate location than the location of the internal marker].”); and
extracting, using the first internal intensity variation and the second internal intensity variation, identification information encoded by the at least one internal marker, wherein extracting identification information comprises identifying spatial patterns of the at least one internal marker within the target object (Fig. 6; see para. 0105 – “The method 600 can further include, in step 606, comparing the actual position of the implant obtained in step 604 [first internal intensity variation at the location of the internal marker] to the target position of the implant obtained in step 602 [second internal intensity variation at a separate location than the location of the internal marker]. The comparison can be done manually, semi-automatically, or fully automatically. For example, in some embodiments, comparing the actual position to the target position includes using the image analysis module, as previously described.”; see para. 0089 – “In addition to or in lieu of the textual information, the patient-specific markings 525 can include machine-readable (MR) features (e.g., images, symbols, graphics, etc.), optically machine-readable features, one or more codes (e.g., a QR code, a bar code, and alpha-numeric code, etc.) [identifying spatial patterns of internal marker],…”).
Furthermore, regarding claims 3 and 12, Casey further teaches wherein the at least one code is an embedded bar code or an embedded quick response (QR) code (Fig. 5; see para. 0089 – “In addition to or in lieu of the textual information, the patient-specific markings 525 can include machine-readable (MR) features (e.g., images, symbols, graphics, etc.), optically machine-readable features, one or more codes (e.g., a QR code, a bar code, and alpha-numeric code, etc.),…”).
Furthermore, regarding claims 4 and 13, Casey further teaches wherein the at least one code is used to authenticate the target object (see para. 0096 – “The unique identifier retrieved from the data storage element 430 can thus be compared to the unique identifier stored with the patient's medical records to verify the implant 400 as authentic. In some embodiments, data can be transmitted back to the implant 400, indicating that the implant 400 was verified as authentic.”).
Furthermore, regarding claims 5 and 14, Casey further teaches wherein the target object comprises a three dimensional (3D) printed part (see para. 0063 – “For example, the manufacturing system 124 can be configured for additive manufacturing, such as three-dimensional (3D) printing…The manufacturing system 124 can manufacture one or more patient-specific medical devices based on fabrication instructions or data (e.g., CAD data, 3D data…).”).
Furthermore, regarding claims 6 and 15, Casey further teaches wherein the target object comprises one or more of a metallic material, a plastic material, or a composite material (see para. 0063 – “For example, the manufacturing system 124 can be configured for additive manufacturing, such as three-dimensional (3D) printing, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), selective heat sintering (SHM), electronic beam melting (EBM), laminated object manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photo resin printing, or like technologies, or combination thereof.” Target object made one or more of metallic material, a plastic material, or a composite material based on these additive manufacturing processes).
Furthermore, regarding claims 7 and 16, Casey further teaches wherein the CNN model utilizes explainable artificial intelligence (XAI) (see para. 0059 – “In some embodiments, the machine learning model (e.g., a neural network or a naïve Bayes classifier) may be trained on the training data set using a supervised learning method (e.g., gradient descent or stochastic gradient descent). The training dataset can include pairs of generated “input vectors” with the associated corresponding “answer vector” (commonly denoted as the target). The current model is run with the training data set and produces a result, which is then compared with the target, for each input vector in the training data set. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted.” Trained machine learning model as “explainable” AI).
Furthermore, regarding claims 8 and 17, Casey further teaches wherein the ultrasound scanner comprises a transducer element and the ultrasound imaging is conducted using the transducer element (Fig. 8C; see para. 0120 – “For example, an imaging system 770 (e.g., a system for taking one or more of MRI, CAT scan, PET scan, X-Ray, ultrasound, etc.) can be positioned over the portion of the patient P [scanning interior of target object] including the implant 700. The imaging system 770 can obtain images of the implant 700, including images of the patient specific markings 725.” Where ultrasound imaging via an ultrasound transducer is inherent and known in the art).
Furthermore, regarding claim 21, Casey further teaches wherein identifying spatial patterns of the at least one internal marker comprises identifying a depth, a size, a shape, a count, or an orientation of the at least one internal marker within the target object (see para. 0043 – “For example, the target position may be defined by one or more of the following parameters taken in relation to an anatomical landmark: an angle or degree of orientation, and angle or degree of translation, an insertion depth, an insertion angle, degree of contact between two surfaces, and the like…The target position can therefore include a three-dimensional position of the implant relative to patient anatomy (e.g., as defined by boundaries created by patient anatomy), and/or a target orientation of the implant relative to patient anatomy.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Casey in view of Chacon et al. (US 20200111213 A1, published April 9, 2020), hereinafter referred to as Chacon.
Regarding claims 2 and 11, Casey teaches all of the elements disclosed in claim 1 and 10 above.
Casey teaches an internal marker, but does not explicitly teach where the internal marker includes a material defect.
Whereas, Chacon, in an analogous field of endeavor, teaches wherein the at least one internal marker includes a material defect (see para. 0038 – “In some embodiments, an electronic system may receive information corresponding to one or more images, e.g., ultrasound images, of a patient. The information may then be analyzed by the electronic system, e.g., using mathematical models that approximate implants in a normal state and implants [internal marker] having one or more defects [material defect].”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an internal marker, as disclosed in Casey, by having an internal marker with a material defect, as disclosed in Chacon. One of ordinary skill in the art would have been motivated to make this modification in order to analyze and identify irregularities and abnormalities in real images of medical implants to help identify, diagnose, and/or resolve medical complications, as taught in Chacon (see para. 0009).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Casey in view of Mastrototaro et al. (US 20080161664 A1, published July 3, 2008), hereinafter referred to as Mastrototaro.
Regarding claims 9 and 18, Casey teaches all of the elements disclosed in claim 1 and 10 above, and
Casey further teaches wherein the ultrasound scanner comprises at least one sensor (Fig. 8C; see para. 0120 – “For example, an imaging system 770 (e.g., a system for taking one or more of MRI, CAT scan, PET scan, X-Ray, ultrasound, etc.) can be positioned over the portion of the patient P [scanning interior of target object] including the implant 700. The imaging system 770 can obtain images of the implant 700, including images of the patient specific markings 725.” Where ultrasound imaging via an ultrasound sensor is inherent and known in the art).
Casey teaches receiving ultrasound measurements from sensors (ultrasound imaging), but does not explicitly teach determining whether the at least one sensor is malfunctioning based on the ultrasound measurements.
Whereas, Mastrototaro, in an analogous field of endeavor, teaches determining whether the at least one sensor is malfunctioning by determining that a sequence of ultrasound imaging measurements from the at least one sensor fails at least one variance test by an error amount exceeding a threshold error (see para. 0063 "If the total error Et is greater than the threshold, the algorithm will indicate a sensor failure..."; see para. 0039 "In further alternative embodiments, the sensor, controller, and/or infusion communication systems may utilize a cable, a wire, fiber optic lines, RF, IR, or ultrasonic transmitters and receivers, or the like instead of the electrical traces.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified receiving ultrasound measurements from sensors (ultrasound imaging), as disclosed in Casey, by determining whether the at least one sensor is malfunctioning based on the ultrasound measurements, as disclosed in Mastrototaro. One of ordinary skill in the art would have been motivated to make this modification in order to improve the dependability and reliability of the sensor, as taught in Mastrototaro (see para. 0048)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Poltaretskyi et al. (US 20190380792 A1, published December 19, 2019) discloses the physical markers can be any type of marker that enables identification of a particular location relative to the real observed object. The physical markers may have bar codes (including one-dimensional or two-dimensional bars, such as QR codes), or the like) that aid in their identification by MR system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pascal Bui-Pho can be reached on 571-272-2714. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.C./Examiner, Art Unit 3798
