DETAILED ACTION
This is a response to the Amendment to Application # 18/354,204 filed on December 17, 2025 in which claims 1, 3, 8, 14, 16, 21, 27, and 30 were amended; claims 2, 15, and 28 were cancelled; and claims 31-33 were added/
Continued Examination Under 37 C.F.R. § 1.114
A request for continued examination under 37 C.F.R. § 1.114, including the fee set forth in 37 C.F.R. § 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 C.F.R. § 1.114, and the fee set forth in 37 C.F.R. § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 C.F.R. § 1.114. Applicant's submission filed on December 17, 2025 has been entered.
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 .
Status of Claims
Claims 1, 3-14, 16-27, and 29-33 are pending, of which claims 1, 4-6, 11, 12, 14, 17-19, 24, 25, and 27 are rejected under 35 U.S.C. § 102(a)(2) and claims 3, 7-10, 13, 16, 18-23, 26, and 38-33 are rejected under 35 U.S.C. § 103.
Interview Summary
On January 16, 2026, Applicant’s representative Siraj Husain contacted the examiner to discuss the Notice of Non-Complaint Amendment dated January 15, 2026. Agreement was reached that the Notice of Non-Compliant Amendment was sent in error.
Claim Objections
Independent claims 1, 14, and 27 have been amended to include the limitations “[a] method for inertial navigation, comprising: obtaining inertial measurement unit (IMU) data from a single IMU of a single device; generating, based on the IMU data obtained from the single IMU of the single device, a respective pose measurement vector …” or similar. The broadest reasonable interpretation of these limitations does not require that the generating be based exclusively on the IMU data obtained from the single IMU of the single device due to the use of the open ended “comprising” language. See MPEP § 2111. For example, a process that obtained IMU data from an IMU of a first device and an IMU of a second device and then fuses that data to generate the claimed plurality of pose measurements would meet this claim because it obtains data from a single IMU of a single device, and then receives more IMU data from a second device, and with data fusion, the resultant pose measurements are based on the data from the single IMU device as well as the data from the second device.
Should Applicant intend for the invention to be limited to using the data of the single IMU of the single device and no other data, the examiner recommends either replacing “comprising” with “consisting of” or adding a word such as “only” or “exclusively” to the “based on” clause of the generating step.
Claims 10 and 23 include the limitation “adjusting uncertainty parameters associated with a machine-learning model among the plurality of machine-learning models based on the one or more navigation context parameters, resulting in adjusted uncertainty parameters.” (Emphasis added). This appears to recite that the intended use of the adjusting is to have adjusted uncertainty parameters.
“An intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often . . . appear in the claim’s preamble,” In re Stencel, 828 F.2d 751, 754 (Fed. Cir. 1987), a statement of intended use or purpose can appear elsewhere in a claim. Id; Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1468 (Fed. Cir. 1990); see also Roberts v. Ryer, 91 U.S. 150, 157 (1875) (‘The inventor of a machine is entitled to the benefit of all the uses to which it can be put, no matter whether he had conceived the idea of the use or not.’). Thus, it is usually improper to construe non-functional claim terms in system claims in a way that makes infringement or validity turn on their function. Paragon Solutions, LLC v. Timex Corp., 566 F.3d 1075, 1091 (Fed. Cir. 2009).
Claim Rejections - 35 U.S.C. § 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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 11, 12, 14, 17-19, 24, 25, and 27 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Caruso et al., WO 2023/057780 (hereinafter Caruso).
Regarding claim 1, Caruso discloses a method for inertial navigation, comprising “obtaining inertial measurement unit (IMU) data from a single IMU of a single device” (Caruso ¶¶ 15, 61) by using only IMU data from a pedestrian hand-carried device (Caruso ¶ 15) and specifically discloses an embodiment where a “single” IMU is used. (Caruso ¶ 61). Additionally, Caruso discloses “generating, based on the IMU data obtained from the single IMU of the single device, a respective pose measurement vector according to each of a plurality of machine-learning models” (Caruso ¶ 15) by generating position and orientation (i.e., pose measurement vectors) for the IMU data using a neural network and an Extended Kalman Filter (i.e., a plurality of machine-learning models). See, also, Caruso ¶ 11, which indicates that “trained neural networks” (i.e., a plurality of neural networks) may be used. Further, Caruso discloses “resulting in a plurality of pose measurement vectors” (Caruso ¶ 15) by generating estimates for positions, orientation, velocity, and IMU biases. These are “pose measurement vectors” because each measure a component of a pose and are data points that can be input into a machine learning model. Moreover, Caruso discloses “wherein each of the plurality of pose measurement vectors is associated with a respective one of multiple motion classes” (Caruso ¶ 55) where the vectors are associated with motion classes such as “climbing staircases, sitting motions, and walking outdoors on uneven terrain.” Likewise, Caruso discloses “wherein each of the multiple motion classes corresponds to a different respective time interval value” (Caruso ¶ 55) where these motion classes are associated with time intervals of 3 to 7 minutes. Caruso also discloses “wherein each pose measurement vector indicates a change in at least one of position or orientation of the single device occurring over the respective time interval value corresponding to the motion class associated with that pose measurement vector” (Caruso ¶ 23) where the estimated state vector includes a rotation measurement, which indicates a pose change. Each of these vectors is associated with a time interval and motion class as discussed above. In addition, Caruso discloses “determining a device pose estimate for the single device based on the IMU data and the plurality of pose measurement vectors” (Caruso ¶ 11) where the process determines a pose of the device. Finally, Caruso discloses “the determining comprising: updating a state of a multi-measurement estimation filter bank based on the IMU data and each of the plurality of pose measurement vectors and determining the pose estimate based on the updated state of the multi-measurement estimation filter bank” (Caruso ¶ 24) by updating the filters during past estimations to correct for IMU biases based on the results and pose data.
Regarding claim 14, it merely recites an apparatus for performing the method of claim 1. The apparatus comprises computer hardware and software modules for performing the various functions. Caruso comprises computer software modules for performing the same functions. Thus, claim 14 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claim 27, it merely recites an apparatus for performing the method of claim 1. The apparatus comprises computer hardware and software modules for performing the various functions. Caruso comprises computer software modules for performing the same functions. Thus, claim 27 is rejected using the same rationale set forth in the above rejection for claim 1.
Regarding claims 4 and 17, Caruso discloses the limitations contained in parent claims 1 and 14 for the reasons discussed above. In addition, Caruso discloses “wherein each pose measurement vector of the plurality of pose measurement vectors comprises: one or more measurement parameters, including a respective measurement parameter for each of one or more dimensions” (Caruso ¶ 15) by including measurement parameters such as position, orientation, and velocity in a 3D environment. Further, Caruso discloses “one or more uncertainty parameters, including a respective uncertainty parameter for each of the one or more measurement parameters” (Caruso ¶ 21) where uncertainty measurements are generated for the positions.
Regarding claims 5 and 18, Caruso discloses the limitations contained in parent claims 4 and 17 for the reasons discussed above. In addition, Caruso discloses “for each of the one or more dimensions, the one or more measurement parameters include: a respective position measurement parameter; a respective orientation measurement parameter; or a combination of both” (Caruso ¶ 15) by including both position and orientation measurement parameters. Further, Caruso discloses “for each of the one or more dimensions, the one or more uncertainty parameters include: a respective position uncertainty parameter; a respective orientation uncertainty parameter; or a combination of both” (Caruso ¶ 21) where the uncertainty parameter includes at least a position uncertainty parameters.
Regarding claims 6 and 19, Caruso discloses the limitations contained in parent claims 4 and 17 for the reasons discussed above. In addition, Caruso discloses “wherein the device pose estimate comprises, for each of the one or more dimensions: a respective position estimate parameter; a respective orientation estimate parameter; or a combination of both” (Caruso ¶ 15) by including both position and orientation measurement parameters.
Regarding claims 11 and 24, Caruso discloses the limitations contained in parent claims 1 and 22 for the reasons discussed above. In addition, Caruso discloses “obtaining sensor information from one or more sensors” (Caruso ¶ 18) by accessing the sensor data. Further, Caruso discloses “determining the device pose estimate based on the IMU data, each of the plurality of pose measurement vectors, and the sensor information” (Caruso ¶ 11) by determining the pose using this data for the reasons discussed above.
Regarding claims 12 and 25, Caruso discloses the limitations contained in parent claims 11 and 24 for the reasons discussed above. In addition, Caruso discloses “wherein the one or more sensors include one or more of a camera, a radar sensor, a pressure sensor, an ultrasound sensor, and a global navigation satellite system (GNSS) receiver” (Caruso ¶¶ 23, 33) where the sensors include at least a camera and a pressure sensor.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims, the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 7, 9, 10, 20, 22, and 23 are rejected under 35 U.S.C. § 103 as being unpatentable over Caruso in view of Vesperman et al., US Publication 2022/0350991 (hereinafter Vesperman), as cited on the Information Disclosure Statement dated June 13, 2025.
Regarding claims 7 and 20, Caruso discloses the limitations contained in parent claims 6 and 19 for the reasons discussed above. In addition, Caruso does not appear to explicitly disclose “for each of the one or more dimensions: determining the respective position estimate parameter for that dimension by weighting respective position measurement parameters for that dimension among position measurement parameters of the plurality of pose measurement vectors according to respective position uncertainty parameters for that dimension among position uncertainty parameters of the plurality of pose measurement vectors; determining the respective orientation estimate parameter for that dimension by weighting respective orientation measurement parameters for that dimension among orientation measurement parameters of the plurality of pose measurement vectors according to respective orientation uncertainty parameters for that dimension among orientation uncertainty parameters of the plurality of pose measurement vectors; or a combination of the above.”
However, Vesperman discloses a navigation system using inertial measurement units and a vector of data, wherein “for each of the one or more dimensions: determining the respective position estimate parameter for that dimension by weighting respective position measurement parameters for that dimension among position measurement parameters of the plurality of pose measurement vectors according to respective position uncertainty parameters for that dimension among position uncertainty parameters of the plurality of pose measurement vectors; determining the respective orientation estimate parameter for that dimension by weighting respective orientation measurement parameters for that dimension among orientation measurement parameters of the plurality of pose measurement vectors according to respective orientation uncertainty parameters for that dimension among orientation uncertainty parameters of the plurality of pose measurement vectors; or a combination of the above” (Vesperman ¶¶ 57-58) where each edge is assigned a weight based on the accuracy (i.e., uncertainty) and the distance (i.e., position).
Caruso and Vesperman are analogous art because they are from the “same field of endeavor,” namely that of vision based vehicle navigation and measurement systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Caruso and Vesperman before him or her to modify the weights of Caruso to include the weights of Vesperman.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Caruso teaches the “base device” for navigating using measurements from an inertial measurement unit. Further, Vesperman teaches the “known technique” of weighting the data in the manner claimed that is applicable to the base device of Caruso. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Regarding claims 9 and 22, Caruso discloses the limitations contained in parent claims 1 and 14 for the reasons discussed above. In addition, Caruso does not appear to explicitly disclose “selecting at least one of the plurality of machine-learning models based on one or more navigation context parameters.”
However, Vesperman discloses a navigation system using inertial measurement units and a vector of data, wherein “selecting at least one of the plurality of machine-learning models based on one or more navigation context parameters” (Vesperman ¶¶ 75-76) by selecting an edge detection model at least partly based on navigational inputs by the user.
Caruso and Vesperman are analogous art because they are from the “same field of endeavor,” namely that of vision based vehicle navigation and measurement systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Caruso and Vesperman before him or her to modify the machine learning models of Caruso to include the selection of a machine learning model of Vesperman.
The motivation for doing so would have been that a person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that this would allow the system to use a more efficient and/or accurate model instead of relying on a “general purpose” model.
Regarding claims 10 and 23, the combination of Caruso and Vesperman discloses the limitations contained in parent claims 9 and 22 for the reasons discussed above. In addition, the combination of Caruso and Vesperman discloses “adjusting uncertainty parameters associated with a machine-learning model among the plurality of machine-learning models based on the one or more navigation context parameters, resulting in adjusted uncertainty parameters; and weighting a pose measurement vector associated with the machine-learning model according to the adjusted uncertainty parameters” (Vesperman ¶¶ 76, 94) by weighing the data by a lateral error, which is adjusted by an offset value, to weight the best fit data.
Claims 3, 8, 16, 21, and 29-33 are rejected under 35 U.S.C. § 103 as being unpatentable over Caruso in view of Levine, US Publication 2022/0350365 (hereinafter Levine).
Regarding claims 3, 16, and 29, Caruso discloses the limitations contained in parent claims 1, 14, and 27 for the reasons discussed above. In addition, Caruso does not appear to explicitly disclose “wherein the multi-measurement estimation filter bank comprises a state buffer, and wherein updating the state of the multi-measurement estimation filter bank based on the IMU data and each of the plurality of pose measurement vectors includes performing, for each of the plurality of pose measurement vectors, a respective update of the state buffer.”
However, Levine discloses a single wearable device with an inertial measurement unit that collects data and processes that data with machine learning models including “a state buffer, and wherein updating the state of the state buffer based on the IMU data and each of the plurality of pose measurement vectors includes performing, for each of the plurality of pose measurement vectors, a respective update of the state buffer” (Levine ¶ 37) by disclosing a buffer that includes current and prior pose estimates (i.e., states) and then updating those pose estimates.
A person of ordinary skill in the art prior to the effective filing date of the present invention would have recognized that when Levine was combined with Caruso, the state buffer of Levine would be part of the multi-measurement estimation filter bank of Caruso. Therefore, the combination of Caruso and Levine at least teaches and/or suggests the claimed limitation “wherein the multi-measurement estimation filter bank comprises a state buffer, and wherein updating the state of the multi-measurement estimation filter bank based on the IMU data and each of the plurality of pose measurement vectors includes performing, for each of the plurality of pose measurement vectors, a respective update of the state buffer,” rendering it obvious.
Caruso and Levine are analogous art because they are from the “same field of endeavor,” namely that of user carried devices with inertial measurement units for estimating motion.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Caruso and Levine before him or her to modify multi-measurement estimation filter bank of Caruso to include the state buffer of Levine.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Caruso teaches the “base device” for using a multi-measurement estimation filter bank to estimate a user’s position. Further, Levine teaches the “known technique” of using a state buffer that is applicable to the base device of Caruso. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Regarding claims 8, 21, and 30, Caruso discloses the limitations contained in parent claims 1, 14, and 27 for the reasons discussed above. In addition, Caruso does not appear to explicitly disclose “wherein the plurality of machine-learning models comprises at least three machine-learning models associated with at least three different time interval values.”
However, Levine discloses a single wearable device with an inertial measurement unit that collects data and processes that data with machine learning models “wherein the plurality of machine-learning models comprises at least three machine-learning models associated with at least three different time interval values” (Levine ¶ 42) where the first model is associated with the current time interval value, the second model is associated with the time interval value of the first third of the window, and the third model is associated with the time interval value of the second third of the window.
Caruso and Levine are analogous art because they are from the “same field of endeavor,” namely that of user carried devices with inertial measurement units for estimating motion.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Caruso and Levine before him or her to modify the three machine learning models of Caruso (Caruso ¶ 11) to include the associating each machine learning model with a time interval value of Levine.
The motivation/rationale for doing so would have been that of applying a known technique to a known device. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1396 (U.S. 2007) and MPEP § 2143(I)(D). Caruso teaches the “base device” for using three machine learning models to estimate a user’s position. Further, Levine teaches the “known technique” of associating each model with a different time interval that is applicable to the base device of Caruso. One of ordinary skill in the art would have recognized that applying the known technique would have yielded predictable results and resulted in an improved system.
Regarding claims 31, 32, and 33, the combination of Caruso and Levine discloses the limitations contained in parent claims 3, 16, and 29 for the reasons discussed above. In addition, the combination of Caruso and Levine discloses “wherein the respective update of the state buffer spans a number of buffer elements of the state buffer corresponding to the time interval value associated with that pose measurement vector” (Levine ¶ 39 and Fig. 2A) by showing that each buffer spans a number of elements corresponding to time intervals.
Claims 13 and 26 are rejected under 35 U.S.C. § 103 as being unpatentable over Caruso in view of Mohamad Yousuf Sait et al., US Publication 2023/0088884 (hereinafter Mohamad).
Regarding claims 13 and 26, Caruso discloses the limitations contained in parent claims 11 and 24 for the reasons discussed above. In addition, Caruso does not appear to explicitly disclose “selecting, based on uncertainty parameters of the plurality of pose measurement vectors, one or more types of supplemental measurements as inputs for determining the device pose estimate; and obtaining the sensor information from the one or more sensors based on the one or more selected types of supplemental measurements.”
However, Mohamad discloses an augmented reality device including an inertial measurement unit that estimates a user’s pose including “selecting, based on uncertainty parameters of the plurality of pose measurement vectors, one or more types of supplemental measurements as inputs for determining the device pose estimate; and obtaining the sensor information from the one or more sensors based on the one or more selected types of supplemental measurements” (Mohamad ¶ 49) by selecting a stable anchor (i.e., a supplemental measurement) based on the device being in a low accuracy state (i.e., an uncertainty parameter), which requires obtaining sensor information from different sensors, such as a GPS sensor or camera, depending on the type of anchor selected.
Caruso and Mohamad are analogous art because they are from the “same field of endeavor,” namely that of user devices for measuring a user’s pose from an inertial measurement unit.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Caruso and Mohamad before him or her to modify the uncertainty parameters of Caruso (Caruso ¶ 11) to include the specific steps based on the uncertainty measures of Mohamad.
The motivation for doing so would have been to improve the overall accuracy of the device over time. (Mohamad ¶ 52).
Response to Arguments
Applicant’s arguments filed December 17, 2025, with respect to the rejection of claims 27-30 under 35 U.S.C. § 112(b) (Remarks 11-12) have been fully considered and are persuasive. The rejection of claims 27-30 under 35 U.S.C. § 112(b) have been withdrawn.
Applicant’s arguments filed December 17, 2025, with respect to the rejections of claims 1-30 under 35 U.S.C. §§ 102 and 103 (Remarks 12-15) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Caruso.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW R DYER whose telephone number is (571)270-3790. The examiner can normally be reached Monday-Thursday 7:30-4:30.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662