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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/18/2026 has been entered.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo (WO-2024/085904-A1) in view of Guo et al (U.S. Pub. No. 2022/0075447, hereinafter “Guo2”) and Applicant Admitted Prior Art (AAPA).
Regarding Claim 1, Guo teaches a method (paragraph [0004] and Fig. 6) comprising: measuring, using a temperature sensor of a display device (Figs. 1 and 3, temperature sensor 340, paragraphs [0033]-[0034]), a temperature of an inertial measurement unit (IMU) (paragraph [0004], receiving a temperature of an IMU; Fig. 6, block 625) of a visual-inertial tracking system at a display device (Abstract, Fig. 4), the IMU comprising a gyroscope and an accelerometer configured to generate inertial sensor data (Figs. 1 and 3, IMU 300; gyroscope module 310, accelerometer module 320); identifying, from an IMU parametric model stored in a storage of the display device, an IMU intrinsic parameter estimate corresponding to the measured temperature, wherein the IMU parametric model comprises a lookup table that maps a plurality of IMU intrinsic parameter estimates to a corresponding plurality of temperatures (paragraph [0004], computing an estimated bias of the IMU for the temperature based on a model relating the estimated bias to the temperature, Figs. 1 and 6, memory 160, model 161, and thermal table 162 and 650); determining whether the IMU intrinsic parameter estimate corresponding to the measured temperature is valid by using the visual-inertial tracking system to detect an offset between the IMU intrinsic parameter estimate and motion data derived from an optical sensor of the display device (paragraph [0004], comparing the corrected IMU measurement to the camera measurement, which is equated to claimed detecting an offset, to determine that a model update criterion was satisfied; when the model update criterion is satisfied, which is equated to claimed validity determination, the lookup table is updated and the determined estimate bias is used in operation); responsive to determining that the IMU intrinsic parameter estimate is valid, operating, using the one or more processors of the display device, a mixed reality application and the visual-inertial tracking system with the IMU intrinsic parameter estimate as an initial IMU intrinsic parameter estimate (paragraph [0058], when model update criterion is satisfied, estimated bias is used in operation; method of Fig. 6 is performed online, i.e., during operation of the augmented reality system, which is equated to the mixed reality application and visual-inertial tracking system, see paragraphs [0055]-[0056]); determining an online IMU intrinsic parameter estimate by continuing to operate the visual-inertial tracking system (paragraph [0004], applying the estimated bias to the IMU measurement to generate a corrected IMU measurement; paragraph [0058], determining a change in bias for a temperature based on difference between CORR_IMU_MEAS and CAM_MEAS; bias change is equated to IMU intrinsic parameter estimate); storing, in the lookup table of the IMU parametric model, the online IMU intrinsic parameter estimate in association with the measured temperature (paragraph [0004], updating the estimated bias for the temperature; Fig. 6, block 630), thereby enabling reduced convergence time for a subsequent start of the visual-inertial tracking system by providing the online IMU intrinsic parameter estimate as an initial estimate for the subsequent start (the enabling is merely an intended result that is achieved by the storing step, which is taught by Guo; see also Guo paragraph [0005], more accurate subsequent motion tracking provided by the updated model; the unique updated model is stored in memory 160 and would be used at a next system start, see paragraphs [0016], [0036]); calibrating subsequent inertial sensor data from the IMU based on the stored online IMU intrinsic parameter estimate retrieved from the lookup table corresponding to a subsequently measured temperature (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660); and rendering virtual content on the display device at a position determined based on a pose of the display device calculated using the calibrated subsequent inertial sensor data (display 190; Figs. 5 and 6, online calibration 600).
Guo does not specifically teach responsive to determining that the IMU intrinsic parameter estimate is not valid, operating, using the one or more processors, the mixed reality application and the visual-inertial tracking system with a factory calibration parameter as the initial IMU intrinsic parameter estimate (emphasis added). However, Guo2 teaches performing strict calibration qualification (i.e., strict validation) for a VIO including an IMU such that original calibration parameters are only replaced by estimated calibration parameters if the estimated parameters are determined to be valid in paragraphs [0011], [0025], and [0030] (i.e., original calibration parameters are only replaced if new sets of VIO parameters are determined to be valid). Therefore, in Guo2, the VIO is operated with the newly determined parameters if the newly determined parameters are determined to be valid, and is operated with the original calibration parameters (equated to the factory calibration parameters) if the newly determined parameters are not determined to be valid. It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the strict calibration qualification, and operation of the system until convergence of parameters of Guo2 estimated bias for the temperature of Guo, in order to improve the overall quality of XR sessions and reduce the occurrence of failures (see Guo2, paragraph [0011]).
Guo does not specifically teach determining an online IMU intrinsic parameter estimate by continuing to operate the visual inertial tracking system until a difference between successive online IMU intrinsic parameter estimates falls below a convergence threshold (emphasis added). However, Guo does teach operating the VR system while the calibration operation is being performed, which may include convergence of the estimated bias change (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660). Further, Guo2 teaches that VIO calibration parameters (including bias, such as is taught in Guo) converge during operation (see Guo2, paragraphs [0039] and [0044]). Further, AAPA teaches, on page 11 of the Arguments filed on 2/18/2026, that the “concept of convergence inherently involves successive estimates approaching a stable value, i.e., the difference between successive estimates falling below a threshold”. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence of Guo2 during operation of the AR system of Guo, because convergence of calibration parameters may occur during operation (see Guo2, paragraphs [0039] and [0041]). Further, it would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence threshold of AAPA in the system of Guo and Guo2, because such a threshold is inherent to the concept of convergence.
Regarding Claim 2, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches storing, in the IMU parametric model, the online IMU intrinsic parameter estimate and the temperature (Fig. 6, blocks 630, 640, and 660; thermal table 650).
Regarding Claim 3, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches storing the IMU parametric model in the storage device of the visual-inertial tracking system (Fig. 4, paragraph [0039], memory; paragraph [0048], online calibrating may be performed using the AR glasses; Fig. 6, blocks 640 and 660).
Regarding Claim 4, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein updating and incorporating the IMU parametric model includes: updating the IMU intrinsic parameter estimate corresponding to the temperature with the online IMU intrinsic parameter estimate (paragraph [0058], updates of blocks 630 and 640 are based on bias change).
Regarding Claim 6, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein the IMU parametric model includes an IMU temperature model (Fig. 6, blocks 640 and 660).
Regarding Claim 7, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein the online IMU intrinsic parameter estimate includes an online IMU bias estimate (paragraph [0058], bias change).
Regarding Claim 8, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein measuring the temperature of the IMU includes measuring the temperature of the IMU of the visual-inertial tracking system during an operation of the visual-inertial tracking system (Fig. 6, block 625, paragraph [0058]).
Regarding Claim 9, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein measuring the temperature of the IMU includes periodically measuring the temperature of the IMU of the visual-inertial tracking system (Fig. 6, method 600, block 625; paragraph [0060, biases may be updated a number of times, therefore, method 600 is repeated).
Regarding Claim 10, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 1. Guo further teaches wherein the visual-inertial tracking system is part of an augmented reality display device (Fig. 4, AR glasses), and wherein a thresholding module dynamically adjusts predetermined thresholds based on environmental conditions detected by the visual-inertial tracking system (Fig. 6, temperature is an environmental condition).
Regarding Claim 11, Guo teaches a computing apparatus comprising: a processor (paragraph [0039], processor); an inertial measurement unit (IMU) comprising a gyroscope and an accelerometer configured to generate inertial sensor data (Figs. 1 and 3, IMU 300; gyroscope module 310, accelerometer module 320); a temperature sensor configured to measure a temperature of the IMU (Figs. 1 and 3, temperature sensor 340, paragraphs [0033]-[0034]); an optical sensor configured to capture image data (cameras 110 and 111); a storage device storing an IMU parametric model comprising a lookup table that maps a plurality of IMU intrinsic parameter estimates to a corresponding plurality of temperatures (Figs. 1 and 6, memory 160, model 161, and thermal table 162 and 650); and a memory storing instructions that, when executed by the processor, configure the apparatus (paragraph [0039], memory, e.g., non-transitory computer readable medium) to: identify, from the lookup table, an IMU intrinsic parameter estimate corresponding to a measured temperature from the temperature sensor (paragraph [0004], computing an estimated bias of the IMU for the temperature based on a model relating the estimated bias to the temperature, Figs. 1 and 6, memory 160 and thermal table 162 and 650); determine whether the IMU intrinsic parameter estimate corresponding to the measured temperature is valid by using the visual-inertial tracking system to detect an offset between the IMU intrinsic parameter estimate and motion data derived from an optical sensor of the display device (paragraph [0004], comparing the corrected IMU measurement to the camera measurement, which is equated to claimed detecting an offset, to determine that a model update criterion was satisfied; when the model update criterion is satisfied, which is equated to claimed validity determination, the lookup table is updated and the determined estimate bias is used in operation); responsive to determining that the IMU intrinsic parameter estimate is valid, operate a mixed reality application and a visual-inertial tracking system with the IMU intrinsic parameter estimate as an initial IMU intrinsic parameter estimate (paragraph [0058], when model update criterion is satisfied, estimated bias is used in operation; method of Fig. 6 is performed online, i.e., during operation of the augmented reality system, which is equated to the mixed reality application and visual-inertial tracking system, see paragraphs [0055]-[0056]); determine an online IMU intrinsic parameter estimate by continuing to operate the visual-inertial tracking system (paragraph [0004], applying the estimated bias to the IMU measurement to generate a corrected IMU measurement; paragraph [0058], determining a change in bias for a temperature based on difference between CORR_IMU_MEAS and CAM_MEAS; bias change is equated to IMU intrinsic parameter estimate); store, in the lookup table, the online IMU intrinsic parameter estimate in association with the measured temperature (paragraph [0004], updating the estimated bias for the temperature; Fig. 6, block 630), thereby enabling reduced convergence time for a subsequent start of the visual-inertial tracking system by providing the online IMU intrinsic parameter estimate as an initial estimate for the subsequent start (the enabling is merely an intended result that is achieved by the storing step, which is taught by Guo; see also Guo paragraph [0005], more accurate subsequent motion tracking provided by the updated model; the unique updated model is stored in memory 160 and would be used at a next system start, see paragraphs [0016], [0036]); calibrate subsequent inertial sensor data from the IMU based on the stored online IMU intrinsic parameter estimate retrieved from the lookup table corresponding to a subsequently measured temperature (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660); and render virtual content on a display at a position determined based on a pose of the apparatus calculated using the calibrated subsequent inertial sensor data (display 190; Figs. 5 and 6, online calibration 600).
Guo does not specifically teach responsive to determining that the IMU intrinsic parameter estimate is not valid, operate the mixed reality application and the visual-inertial tracking system with a factory calibration parameter as the initial IMU intrinsic parameter estimate (emphasis added). However, Guo2 teaches performing strict calibration qualification (i.e., strict validation) for a VIO including an IMU such that original calibration parameters are only replaced by estimated calibration parameters if the estimated parameters are determined to be valid in paragraphs [0011], [0025], and [0030] (i.e., original calibration parameters are only replaced if new sets of VIO parameters are determined to be valid). Therefore, in Guo2, the VIO is operated with the newly determined parameters if the newly determined parameters are determined to be valid, and is operated with the original calibration parameters (equated to the factory calibration parameters) if the newly determined parameters are not determined to be valid. It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the strict calibration qualification, and operation of the system until convergence of parameters of Guo2 estimated bias for the temperature of Guo, in order to improve the overall quality of XR sessions and reduce the occurrence of failures (see Guo2, paragraph [0011]).
Guo does not specifically teach determine an online IMU intrinsic parameter estimate by continuing to operate the visual inertial tracking system until a difference between successive online IMU intrinsic parameter estimates falls below a convergence threshold (emphasis added). However, Guo does teach operating the VR system while the calibration operation is being performed, which may include convergence of the estimated bias change (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660). Further, Guo2 teaches that VIO calibration parameters (including bias, such as is taught in Guo) converge during operation (see Guo2, paragraphs [0039] and [0044]). Further, AAPA teaches, on page 11 of the Arguments filed on 2/18/2026, that the “concept of convergence inherently involves successive estimates approaching a stable value, i.e., the difference between successive estimates falling below a threshold”. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence of Guo2 during operation of the AR system of Guo, because convergence of calibration parameters may occur during operation (see Guo2, paragraphs [0039] and [0041]). Further, it would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence threshold of AAPA in the system of Guo and Guo2, because such a threshold is inherent to the concept of convergence.
Regarding Claim 12, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein the instructions further configure the apparatus to: store, in the IMU parametric model, the online IMU intrinsic parameter estimate and the temperature (Fig. 6, blocks 630, 640, and 660; thermal table 650).
Regarding Claim 13, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein the instructions further configure the apparatus to: store the IMU parametric model in the storage device of the visual-inertial tracking system (Fig. 4, paragraph [0039], memory; paragraph [0048], online calibrating may be performed using the AR glasses; Fig. 6, blocks 640 and 660).
Regarding Claim 14, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein updating and incorporate the IMU parametric model includes: update the IMU intrinsic parameter estimate corresponding to the temperature with the online IMU intrinsic parameter estimate (paragraph [0058], updates of blocks 630 and 640 are based on bias change).
Regarding Claim 16, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein the IMU parametric model includes an IMU temperature model (Fig. 6, blocks 640 and 660).
Regarding Claim 17, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein the online IMU intrinsic parameter estimate includes an online IMU bias estimate (paragraph [0058], bias change).
Regarding Claim 18, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein measuring the temperature of the IMU includes measuring the temperature of the IMU of the visual-inertial tracking system during an operation of the visual-inertial tracking system (Fig. 6, block 625, paragraph [0058]).
Regarding Claim 19, Guo in view of Guo2 and AAPA teaches everything that is claimed above with respect to Claim 11. Guo further teaches wherein measuring the temperature of the IMU includes periodically measuring the temperature of the IMU of the visual-inertial tracking system (Fig. 6, method 600, block 625; paragraph [0060, biases may be updated a number of times, therefore, method 600 is repeated).
Regarding Claim 20, Guo teaches a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer (paragraph [0039], processor and memory, e.g., non-transitory computer readable medium) of a display device (Fig. 1, display 190; Fig. 4) having an inertial measurement unit (IMU) comprising a gyroscope and an accelerometer configured to generate inertial sensor data (Figs. 1 and 3, IMU 300; gyroscope module 310, accelerometer module 320), a temperature sensor (Figs. 1 and 3, temperature sensor 340, paragraphs [0033]-[0034]), and an optical sensor (cameras 110 and 111), cause the computer to: measure, using the temperature sensor (Figs. 1 and 3, temperature sensor 340, paragraphs [0033]-[0034]), a temperature of the IMU (paragraph [0004], receiving a temperature of an IMU; Fig. 6, block 625); identify, from an IMU parametric model stored in a storage of the display device, an IMU intrinsic parameter estimate corresponding to the measured temperature, wherein the IMU parametric model comprises a lookup table that maps a plurality of IMU intrinsic parameter estimates to a corresponding plurality of temperatures (paragraph [0004], computing an estimated bias of the IMU for the temperature based on a model relating the estimated bias to the temperature, Figs. 1 and 6, memory 160, model 161, and thermal table 162 and 650); determine whether the IMU intrinsic parameter estimate corresponding to the measured temperature is valid by using the visual-inertial tracking system to detect an offset between the IMU intrinsic parameter estimate and motion data derived from an optical sensor of the display device (paragraph [0004], comparing the corrected IMU measurement to the camera measurement, which is equated to claimed detecting an offset, to determine that a model update criterion was satisfied; when the model update criterion is satisfied, which is equated to claimed validity determination, the lookup table is updated and the determined estimate bias is used in operation); responsive to determining that the IMU intrinsic parameter estimate is valid, operate a mixed reality application and a visual-inertial tracking system with the IMU intrinsic parameter estimate as an initial IMU intrinsic parameter estimate (paragraph [0058], when model update criterion is satisfied, estimated bias is used in operation; method of Fig. 6 is performed online, i.e., during operation of the augmented reality system, which is equated to the mixed reality application and visual-inertial tracking system, see paragraphs [0055]-[0056]); determine an online IMU intrinsic parameter estimate by continuing to operate the visual-inertial tracking system (paragraph [0004], applying the estimated bias to the IMU measurement to generate a corrected IMU measurement; paragraph [0058], determining a change in bias for a temperature based on difference between CORR_IMU_MEAS and CAM_MEAS; bias change is equated to IMU intrinsic parameter estimate); store, in the lookup table of the IMU parametric model, the online IMU intrinsic parameter estimate in association with the measured temperature (paragraph [0004], updating the estimated bias for the temperature; Fig. 6, block 630), thereby enabling reduced convergence time for a subsequent start of the visual-inertial tracking system by providing the online IMU intrinsic parameter estimate as an initial estimate for the subsequent start (the enabling is merely an intended result that is achieved by the storing step, which is taught by Guo; see also Guo paragraph [0005], more accurate subsequent motion tracking provided by the updated model; the unique updated model is stored in memory 160 and would be used at a next start the system, see paragraphs [0016], [0036]); calibrate subsequent inertial sensor data from the IMU based on the stored online IMU intrinsic parameter estimate retrieved from the lookup table corresponding to a subsequently measured temperature (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660); and render virtual content on the display device at a position determined based on a pose of the display device calculated using the calibrated subsequent inertial sensor data (display 190; Figs. 5 and 6, online calibration 600).
Guo does not specifically teach responsive to determining that the IMU intrinsic parameter estimate is not valid, operating the mixed reality application and the visual-inertial tracking system with a factory calibration parameter as the initial IMU intrinsic parameter estimate (emphasis added). However, Guo2 teaches performing strict calibration qualification (i.e., strict validation) for a VIO including an IMU such that original calibration parameters are only replaced by estimated calibration parameters if the estimated parameters are determined to be valid in paragraphs [0011], [0025], and [0030] (i.e., original calibration parameters are only replaced if new sets of VIO parameters are determined to be valid). Therefore, in Guo2, the VIO is operated with the newly determined parameters if the newly determined parameters are determined to be valid, and is operated with the original calibration parameters (equated to the factory calibration parameters) if the newly determined parameters are not determined to be valid. It would have been obvious to one skilled in the art at the effective filing date of the invention to apply the strict calibration qualification, and operation of the system until convergence of parameters of Guo2 estimated bias for the temperature of Guo, in order to improve the overall quality of XR sessions and reduce the occurrence of failures (see Guo2, paragraph [0011]).
Guo does not specifically teach determine an online IMU intrinsic parameter estimate by continuing to operate the visual inertial tracking system until a difference between successive online IMU intrinsic parameter estimates falls below a convergence threshold (emphasis added). However, Guo does teach operating the VR system while the calibration operation is being performed, which may include convergence of the estimated bias change (method 600 of Fig. 6, paragraphs [0056]-[0058], online calibration process based on model 660). Further, Guo2 teaches that VIO calibration parameters (including bias, such as is taught in Guo) converge during operation (see Guo2, paragraphs [0039] and [0044]). Further, AAPA teaches, on page 11 of the Arguments filed on 2/18/2026, that the “concept of convergence inherently involves successive estimates approaching a stable value, i.e., the difference between successive estimates falling below a threshold”. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence of Guo2 during operation of the AR system of Guo, because convergence of calibration parameters may occur during operation (see Guo2, paragraphs [0039] and [0041]). Further, it would have been obvious to one skilled in the art before the effective filing date of the invention to include the convergence threshold of AAPA in the system of Guo and Guo2, because such a threshold is inherent to the concept of convergence.
Response to Arguments
Applicant’s arguments filed 2/18/2026 regarding the 101 rejections have been fully considered and are persuasive. Specifically, the Examiner agrees with Applicants arguments on pages 12-13, and deems the amended Claims to recite sufficient technical operations performed using specific hardware components (the validity determination based on the IMU and the optical sensor, operating the visual-inertial tracking system with different parameters based on the validity determination, and calibrating the IMU and rendering virtual content based on the calibration) such that the claims are not directed to an abstract idea.
Applicant's arguments filed 2/18/2026 regarding the 103 rejections have been fully considered but they are not persuasive.
Regarding the 103 rejections, Applicant argues on pages 19-20 that the cited references do not teach “determining whether the IMU intrinsic parameter estimate corresponding to the measured temperature is valid by using the visual-inertial tracking system to detect an offset between the IMU intrinsic parameter estimate and motion data derived from an optical sensor of the display device”. The Examiner disagrees. Guo teaches, in at least paragraph [0004], determining whether a model update criterion is satisfied by comparing a corrected IMU measurement to a camera measurement, which is equated to the claimed determination of validity based on detecting of an offset between the IMU intrinsic parameter estimate and motion data derived from an optical sensor of the display device (i.e., if the model update criterion is satisfied, the estimated bias is deemed to be valid).
Applicant further argues on pages 19-20 that Applicant’s invention includes performing the validity determination before using the stored parameter for system operation. It is noted that this is not actually required by Applicant’s claims, and it is unclear how the claimed offset would be determined without operating the system using the IMU intrinsic parameter estimate. Further, this does not appear to be taught by Applicant’s Specification as filed. The validity determination is only briefly mentioned in paragraphs [0041] and [0043] of the Specification, and the validity appears to be determined by actually operating the system using the IMU bias estimate to determine the offsets of paragraph [0041]. It is further noted that the Specification does not explicitly say how the offsets are determined.
Applicant goes on to argue on pages 20-21 that Guo in view of Guo2 does not teach the claimed conditional operation. The Examiner disagrees. The claimed offset detection, and operation with parameters that are determined to be valid, is taught by Guo, as noted above. Further, Guo2 explicitly teaches strict calibration qualification. Applicant argues on page 21 that observed VIO performance, such as is taught in Guo2, cannot be equated to the claimed offset detection; however, the Examiner disagrees, because a significant offset would necessarily affect VIO performance. Applicant further argues on page 21 that because in some embodiments, the original calibration of Guo2 may be previously estimated parameters, that Guo2 does not teach factory calibration of the Claims; the Examiner disagrees, because Guo2 explicitly teaches per-device factory calibration parameters in paragraph [0049].
On page 21, Applicant argues that neither Guo nor Guo2 teach the claimed convergence criterion. It is noted that Applicant’s specification as filed does not mention any convergence threshold; however, Applicant states on page 11 of the Remarks that convergence inherently involves such a threshold. Therefore, the teaching of convergence in Guo2 would also inherently involve the threshold, according the Applicant’s admitted prior art. Without this admission of inherency on Applicant’s part, the convergence threshold in the amended Claims would be new matter under 35 USC 112(a).
On page 22, Applicant argues that because Applicant’s claims are directed to pre-use validity determination, that they are not taught by Guo and Guo2. It is noted that pre-use validity determination is not actually required by Applicant’s claims, nor is it taught by Applicant’s Specification, as was discussed above. Further, it is unclear how the claimed offset would be determined without operating the system using the IMU intrinsic parameter estimate. Applicant’s actual claim language is taught by the cited references, which are properly combined to improve the quality of XR sessions by stopping use of invalid parameters. It is further noted that, in Guo, if the model update criterion is satisfied, then on a subsequent start, the updated parameter in the thermal table would be used (see paragraph [0004]), i.e., used in operation. It appears that, in Guo, if the model update criterion is not satisfied, the thermal table would not be updated and an original parameter in the thermal table would be used for a subsequent start of the system, which may, early in the lifecycle of the system, be a factory calibration parameter; this would also read on the conditional operation of the Claims. However, Guo2 is cited as explicitly teaching use of either updated or factory parameters based on validity determination.
In response to applicant's argument on page 22 that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant goes on to argue on pages 22-23 that Guo does not teach “thereby enabling reduced convergence time for a subsequent start of the visual-inertial tracking system by providing the online IMU intrinsic parameter estimate as an initial estimate for the subsequent start”. The Examiner disagrees, because the data stored in the thermal table of Guo is used for subsequent starts, and would enable reduced convergence time.
Conclusion
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/CYNTHIA L DAVIS/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
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