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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/31/2026 has been considered by the examiner.
Claim Objections
Although the courts have found that the use of the term “and/or” would not be indefinite, (Employers Mut. Liability Ins. Co. v. Tollefsen, 219 Wis. 434 (1935)), the board did note that the preferred way of writing the claim is through use of “at least one of A and B" in the future. Therefore, the Examiner object to the terms "and/or" in claim 10 such that it is written in accordance with the courts preferred way.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 12 recites “wherein the neural network or the magnetic field gradient prediction model is selected based on a probe device type associated with the probe device”. The specification and figures do not disclose how to select the model or neural network based on probe device type. The specification only recites the limitation (see para. 0008), and the system can use a different type of model to predict the marker state, such as a gradient descent optimization model (see para. 0035).
For claim 13, the limitation “wherein the neural network or the magnetic field gradient prediction model is selected based on a magnetic marker type associated with the magnetic marker”. The specification and figures do not disclose how to select the model or neural network based on magnetic marker type. The specification only recites the limitation (see para. 0008).
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.
Claim 1-2, 5-7, 9-11, 14, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haynor et al. (EP 1126787 B1, published June 11, 2008), from IDS, hereinafter referred to as Haynor.
Regarding claim 1, and similarly for claims 16 and 17, Haynor teaches a method (Fig. 7A) for detecting magnetic noise by a magnetic marker localization system (Fig. 5A-5B, system 100), the method comprising:
receiving a set of magnetic field gradient values obtained using a probe device of the magnetic marker localization system (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 a magnetic marker (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 (Fig. 7A, “predict sensor values [predicted set of magnetic field gradient values] based on estimated position and orientation” step 210; see para. 0065 – “In step 210, 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 (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 indicator 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 (Fig. 5A; see para. 0065 – “In decision 216, the estimation processor 152 determines whether the predicted and measured sensor values match within a desired degree of tolerance…If a close match cannot be achieved (i.e., the cost function is too great), the detector system 100 can… generate an error message [magnetic noise indicator] indicating an unacceptably high cost function.”).
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, Haynor further teaches wherein the magnetic noise indicator comprises a visual indicator, an audible indicator, and/or a tactile indicator (see para. 0065 – “If a close match cannot be achieved (i.e., the cost function is too great), the detector system 100 can… generate an error message [magnetic noise indicator] indicating an unacceptably high cost function.” Where a skilled person would consider an error message as a visual indicator).
Furthermore, regarding claim 11, Haynor further teaches changing a color or a shape for the visual indicator based on the comparison (see para. 0065 – “In decision 216, the estimation processor 152 determines whether the predicted and measured sensor values match within a desired degree of tolerance…If a close match cannot be achieved (i.e., the cost function is too great), the detector system 100 can… generate an error message [magnetic noise indicator] indicating an unacceptably high cost function.” Where a skilled person would consider an error message as a visual indicator, and the presence/absence of the error message 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 – “In decision 216, the estimation processor 152 determines whether the predicted and measured sensor values match within a desired degree of tolerance [predetermined criteria]…If a close match cannot be achieved (i.e., the cost function is too great), the detector system 100 can… generate an error message [magnetic noise indicator] indicating an unacceptably high cost function.”).
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 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Haynor in view of Milanfar et al. (US 20110311129 A1, published December 22, 2011), hereinafter referred to as Milanfar.
Regarding claim 3, Haynor teaches all of the elements disclosed in claim 2 above.
Haynor 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, 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, step 216 “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 Shekhar et al. (US 20200197102 A1, published June 25, 2020), hereinafter referred to as Shekhar.
Furthermore, regarding claim 8, Haynor teaches all of the elements disclosed in claim 1 above.
Haynor 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, 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 Malkevich et al. (US 20190374278 A1, published December 12, 2019), hereinafter referred to as Malkevich.
Regarding claim 12, Haynor teaches all of the elements disclosed in claim 1 above.
Haynor 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 base 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, 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 to 10 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 base 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 Yajima (JP 2017049136 A, published March 9, 2017), hereinafter referred to as Yajima.
Regarding claim 15, Haynor teaches all of the elements disclosed in claim 1 above.
Haynor 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 target ship 5 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, 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:
Banerjee et al. (US 20230109108 A1, published April 6, 2023) discloses measuring an error between the target multi-modal data and the predicted multi-modal data using a cosine similarity measure.
Aiken et al. (US 7242964 B1, published July 10, 2007) discloses selecting the equations to use is based on the type and amount of interference between the two EM fields to the two mobile terminals.
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
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