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 .
Response to Amendment
The amendment filed 08/17/2026 has been entered. Claims 1-17 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 05/15/2026.
Response to Arguments
Applicant’s arguments, see pg. 7-8, filed 08/17/2026, with respect to claims 12-13 have been fully considered and are persuasive. The 35 U.S.C. 112(a) rejection of claims 12-13 has been withdrawn.
Applicant’s arguments filed 08/17/2026 with respect to claim(s) 1, 16, and 17 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 claim 1, reference to Doerr is being relied upon to teach dependent claims 10-11 more-consistently with the instant claim language, as shown below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-7, 9-11, 14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Haynor et al. (EP 1126787 B1, published June 11, 2008), from IDS, in view of Doerr et al. (US 20110148400 A1, published June 23, 2011), hereinafter referred to as Haynor and Doerr, respectively.
Regarding claim 1, and similarly for claims 16 and 17, Haynor teaches a method for detecting magnetic noise by a magnetic marker localization system comprising a probe device configured for localizing a magnetic marker (Fig. 4; see para. 0026 – “For example, a plurality of magnetic sensors [probe] may be configured in a spherical arrangement around a patient's head to detect the location of the magnet 120 in the brain [localizing a magnetic marker].”), the method comprising:
receiving a set of magnetic field gradient values obtained using the probe device of the magnetic marker localization system (the probe detects the magnetic field of the marker; see para. 0069 "With respect to the flowchart of Figure 7A, the step 202 provides gradient values with respect to pairs of the magnetic sensors 108-114 [probe device].");
providing the set of magnetic field gradient values to a neural network, wherein the neural network (Fig. 5A, neural network 154) is trained to receive the set of magnetic field gradient values and predict a marker state of the magnetic marker (trained model receives magnetic field produced by marker/detected by probe as input to output marker location; Fig. 7A, step 204; see para. 0046 "In the operational mode, the 12 parameters from the magnetic sensors 108-114 are given to the neural network 154, which generates an initial estimate of the location and orientation of the magnet 120 [marker state of a magnetic marker].");
providing the predicted marker state from the neural network to a magnetic field gradient prediction model, wherein the magnetic field gradient prediction model is configured to receive the predicted marker state and generate a predicted set of magnetic field gradient values (input predicted marker location to a model to output a predicted magnetic field; Fig. 7A, "predict sensor values [predicted set of magnetic field gradient values] based on estimated position and orientation [predicted marker state]" step 210; see para. 0065 "In step210, the estimation processor 152 (see Figure 5A) calculates predicted sensor values."; see para. 0069 " …in step 210, calculates predicted sensor values using equation (2) [magnetic field gradient prediction model].");
comparing the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values (compare detected magnetic field by probe with predicted magnetic field; Fig. 7A; see para. 0065 "In step 212, the estimation processor 152 compares the predicted sensor values [predicted magnetic field gradient values] (i.e., Δij (predicted)) with the measured sensor values [set of magnetic field gradient values obtained using the probe device] (i.e., Δij(measured)).”); and
providing a magnetic noise depending on the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values, wherein the magnetic noise indicates an amount of magnetic noise corresponding to at least one object other than the magnetic marker (determine magnetic noise based on comparison, where the indicator indicates magnetic noise corresponding to something that’s not the marker; Fig. 7A-7B; see para. 0066 – “As a result of this process, the remaining residual values measured by the magnetic sensors 108-114 (see Figure 5A) are due to the effects of the Earth's magnetic field, including localized perturbations [magnetic noise].”).
Haynor teaches calculating residual error as magnetic noise, but does not explicitly teach providing a magnetic noise indicator (displaying an indicator) based on the residual error.
Whereas, Doerr, in an analogous field of endeavor, teaches providing a magnetic noise indicator, wherein the magnetic noise indicator indicates an amount of magnetic noise corresponding to at least one object other than the magnetic marker (see Claim 11 – “…emit electromagnetic pulses to signal that the implantable medical device is present in an electromagnetic field, and transmit information as well as an interference and display said interference on a screen [providing magnetic noise indicator] of a magnetic resonance imaging device…”).
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 calculating residual error as magnetic noise, as disclosed in Haynor, by also providing a magnetic noise indicator, as disclosed in Doerr. One of ordinary skill in the art would have been motivated to make this modification in order to allow administration of treatment and/or detection of electrical states of tissue only in time windows in which no electromagnetic interference fields are detected and/or carry out a reconstruction of a measurement for the regions in which the detection is not allowed on account of detected electromagnetic interference fields, as taught in Doerr (see Claim 11).
Furthermore, regarding claim 2, Haynor further teaches wherein comparing the set of magnetic field gradient values obtained using the probe device with the set of predicted magnetic field gradient values comprises calculating one or more similarity measures (Fig. 7A, step 216 "close match?" Similarity measure calculation is implicit; see para. 0065 "If the predicted sensor values and the measured sensor values are not a close match, the result of decision 216 is NO... When a close match between the predicted sensor values and the measured sensor values is achieved, the result of decision 216 is YES.").
Furthermore, regarding claim 5, Haynor further teaches wherein the one or more similarity measures comprise one or more magnitude differences, one or more angular differences, or any combination thereof (see para. 0038 "Equation (2), indicating the gradient G(s) is used by the estimation processor 152 (see Figure 5A) to determine the magnitude and a direction of error in the estimated location.").
Furthermore, regarding claim 6, Haynor further teaches providing one or more historical pose measurements of the probe device to the neural network (see para. 0044 "Neural-networks, by virtue of a learning process, are capable of receiving and processing large amounts of data in order to generate solutions to problems with many variables. The operation of a neural network is generally known in the art..."; see para. 0045 "This process is repeated a large number of times such that the neural network 154 "learns" to accurately estimate the location and orientation of the magnet 120 [probe] based on the 12 parameters. In the present case, the learning process described above (e.g., providing 12 parameters, estimating the location, and providing the actual location) was repeated 1,000 times [historical pose measurements of probe]." It is implicit that the neural network was trained using historical pose measurements).
Furthermore, regarding claim 7, Haynor further teaches wherein the one or more historical pose measurements comprise one or more position measurements, one or more orientation measurements, or a combination thereof (see para. 0044 "Neural-networks, by virtue of a learning process, are capable of receiving and processing large amounts of data in order to generate solutions to problems with many variables. The operation of a neural network is generally known in the art..."; see para. 0045 "This process is repeated a large number of times such that the neural network 154 "learns" to accurately estimate the location and orientation of the magnet 120 [probe] based on the 12 parameters. In the present case, the learning process described above (e.g., providing 12 parameters, estimating the location, and providing the actual location) was repeated 1,000 times [historical pose measurements of probe]." It is implicit that the neural network was trained using historical pose measurements).
Furthermore, regarding claim 9, Haynor further teaches wherein the magnetic field gradient prediction model comprises an equation (see para. 0069 "...in step 210, calculates predicted sensor values using equation (2) [magnetic field gradient prediction model].").
Furthermore, regarding claim 10, Doerr further teaches wherein the magnetic noise indicator comprises at least one of a visual indicator, an audible indicator, and a tactile indicator (see Claim 11 – “…emit electromagnetic pulses to signal that the implantable medical device is present in an electromagnetic field, and transmit information as well as an interference and display said interference on a screen [visual magnetic noise indicator] of a magnetic resonance imaging device…”).
Furthermore, regarding claim 11, Doerr further teaches changing a color or a shape for the visual indicator based on the comparison (see Claim 11 – “…emit electromagnetic pulses to signal that the implantable medical device is present in an electromagnetic field, and transmit information as well as an interference and display said interference on a screen [magnetic noise indicator] of a magnetic resonance imaging device…” where the presence/absence of displayed interference on a screen is “changing a shape for the visual indicator”).
Furthermore, regarding claim 14, Haynor further teaches wherein the comparison between the set of magnetic field gradient values obtained using the probe device and the set of predicted magnetic field gradient values is based on one or more predetermined criteria Fig. 5A; see para. 0065 "Indecision 216, the estimation processor 152 determines whether the predicted and measured sensor values match within a desired degree of tolerance [predetermined criteria]…”).
The motivation for claims 10-11 was shown previously in claim 1.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Haynor in view of Doerr, as applied to claim 2 above, and in further view of Milanfar et al. (US 20110311129A1, published December 22, 2011), hereinafter referred to as Milanfar.
Regarding claim 3, Haynor in view of Doerr teaches all of the elements disclosed in claim 2 above.
Haynor in view of Doerr teaches calculating one or more similarity measures between measured and predicted values, and it is inherent and known in the art that normalized error vector magnitude and cosine similarity are similarity measures, but does not explicitly teach where the similarly measures is a normalized error vector magnitude, a cosine similarity, or a combination thereof.
Whereas, Milanfar, in an analogous field of endeavor, teaches wherein the one or more similarity measures comprise a normalized error vector magnitude, a cosine similarity, or a combination thereof (Fig. 4; see para. 0066 "In the second stage, the feature matrices FT i and FQ [target and query] are compared using the Matrix Cosine Similarity measure.").
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 calculating similarity measures, as disclosed in Haynor in view of Doerr, by calculating cosine similarity. One of ordinary skill in the art would have been motivated to make this modification in order to take account of the strength and angle similarity of vectors at the same time, and to overcome the disadvantages of the conventional Euclidean distance which is sensitive to outliers, as taught in Milanfar (see para. 0165).
Furthermore, regarding claim 4, Haynor further teaches wherein the magnetic noise indicator is provided if the normalized error vector is greater than a first predetermined threshold or when the cosine similarity is greater than a second predetermined threshold (Fig. 7A, step216 "close match?" threshold is implicit; see para. 0065 "If cost function is too high, a close match may not be achieved in decision 216. Such conditions may occur, for example, in the presence of extraneous magnetic fields. In practice, it has been determined that close matches have a cost function in the range of 1-2 while the minimum cost function for an inaccurate local minimal are orders of magnitude greater. If a close match cannot be achieved (i.e., the cost function is too great), the detector system 100 can start the measurement process anew with a new estimated location or generate an error message indicating an unacceptably high cost function.").
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Haynor in view of Doerr, as applied to claim 1 above, and in further view of Shekhar et al. (US 20200197102 A1, published June 25, 2020), hereinafter referred to as Shekhar.
Furthermore, regarding claim 8, Haynor in view of Doerr teaches all of the elements disclosed in claim 1 above.
Haynor in view of Doerr teaches a magnetic field gradient prediction model, but does not explicitly teach where the model is a neural network.
Whereas, Shekhar, in an analogous field of endeavor, teaches wherein the magnetic field gradient prediction model comprises a neural network model (Fig. 8; see para. 0064 "Specifically, the machine learning-based framework can employ a machine learning algorithm, trained via supervised learning, including, among others, support vector machines, neural networks..."; see para. 0071 "For instance, in identifying a spatial location of a LUS [lung ultrasound] transducer, or virtual object, using a data input from EM tracking, the machine learning method 846 can predict the presence of a degrading feature, such as magnetic field distortion...").
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 the model, as disclosed in Haynor in view of Doerr, by having a neural network as a model, as disclosed in Shekhar. One of ordinary skill in the art would have been motivated to make this modification in order to further improve the accuracy and reliability of the results, as taught in Shekhar (see para. 0071).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Haynor in view of Doerr, as applied to claim 1 above, and in further view of Malkevich et al. (US 20190374278 A1, published December 12, 2019), hereinafter referred to as Malkevich.
Regarding claim 12, Haynor in view of Doerr teaches all of the elements disclosed in claim 1 above.
Haynor in view of Doerr teaches a neural network and a model, but does not explicitly teach selecting a model based on probe device type.
Whereas, Malkevich, in an analogous field of endeavor, teaches wherein the neural network or the magnetic field gradient prediction model is selected based on a probe device type associated with the probe device (Fig. 1; see para. 0043 the controller 165 will recognize the probe type and then select algorithms for operating the motor drive 105, RF source 225 and negative pressure source 220 as is needed for the particular probe." algorithms (models) selected based on probe device type).
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 a model, as disclosed in Haynor in view of Doerr, by selecting a model (algorithm) based on probe type, as disclosed Malkevich. One of ordinary skill in the art would have been motivated to make this modification in order to perform and automate many tasks to provide for system functionality, as taught in Malkevich (see para. 0043).
Furthermore, regarding claim 13, Malkevich further teaches wherein the neural network or the magnetic field gradient prediction model is selected based on a magnetic marker type associated with the magnetic marker (see para. 0045 "For example, a product portfolio may have from 2 to10 or more types of probes, such as depicted in FIGS. 1 and 4-7, and each such probe type can carry magnets 250 a, 250 b having a specific, different magnetic field strength. Then, the Hall sensor 240 and controller algorithms can be adapted to read the magnetic field strength of the particular magnet(s) in the probe which can be compared to a library of field strengths that correspond to particular probe types. Then, a Hall identification signal can be generated or otherwise provided to the controller 165 to select the controller algorithms for operating the identified probe..." algorithms (models) selected based on magnetic marker type, where magnetic marker type is based on magnetic field strength of the particular magnet(s) in the probe that correspond to particular probe types).
The motivation for claim 13 was shown previously in claim 12.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Haynor in view of Doerr, as applied to claim 1 above, and in further view of Yajima (JP 2017049136 A, published March 9, 2017), hereinafter referred to as Yajima.
Regarding claim 15, Haynor in view of Doerr teaches all of the elements disclosed in claim 1 above.
Haynor in view of Doerr teaches detecting magnetic noise, but does not explicitly teach updating the threshold of detecting magnetic noise.
Whereas, Yajima, in an analogous field of endeavor, teaches calculating a distance between the magnetic marker and the probe device; and updating the predetermined one or more criteria for detecting magnetic noise for the next iteration (see pg. 7, para. 3-4 "The threshold adjustment unit 14 7 has a function of adjusting the detection threshold. The function of the threshold adjustment unit 14 7 is effective when, for example, the targetship5 exists in a place farther in the horizontal direction than vertically below the aircraft 2 and the distance from the aircraft 2 to the ship 5 This is a case where L1 is equal to the distance L2 from the aircraft 2 to the magnetic source 6 that takes the local peak of the magnetic noise spectrum.").
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 detecting magnetic noise, as disclosed in Haynor in view of Doerr, by updating the threshold of detecting magnetic noise, as disclosed in Yajima. One of ordinary skill in the art would have been motivated to make this modification in order to limit the risk of raising a false detection rate, as taught in Yajima (see pg. 8, para. 4).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yatsuo et al. (US 20210116523 A1, published April 22, 2021) discloses specifying a noise generation site in the MRI apparatus, on the basis of a measurement time difference in the noise that is measured in the plurality of different sites.
Chiba et al. (US 20180035913 A1, published February 8, 2018) discloses The LUT storage unit stores a lookup table in which at least one of the position and the posture of the capsule endoscope , at least one of the position and the posture of the generation source of the interference magnetic field, as well as correction values for at least one of the position and the posture of the capsule endoscope are associated with one another.
Harmer et al. (US 20230277083 A1, published September 7, 2023) discloses the system is able to provide an indication to the user that a secondary source may be causing interference.
Martens et al. (US 20210270922 A1, published September 2, 2021) discloses with two sensors, the magnetic marker may be anywhere along a circle where the two spheres intersect. In practice, the presence of noise may cause the circular intersection to be a ring-like volume. With three sensors, the three spheres may intersect at a point (or approximately at a point)—in some applications, this may be sufficiently accurate enough for marker localization.
Sadjadi et al. (US 20160258782 A1, published September 8, 2016) discloses a probabilistic simultaneous localization and mapping (SLAM) algorithm that simultaneously estimates a pose of one or more instrument and updates a field distortion map, or parameters of a field distortion model.
Castleman et al. (US 20200400856 A1, published December 24, 2020) discloses detecting a presence of interference with a magnetic field generated by the EM tracking system.
Olson et al. (US 20200359932 A1, published November 19, 2020) discloses identifying and correct for magnetic field distortions, often associated with the intrusion of a metallic object into the magnetic field, within a magnetic field for localization of a medical device within a patient.
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 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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/N.C./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
