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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on December 20, 2023, September 23, 2024, and January 15, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 15, 2026 has been entered.
Response to Amendment
The Amendment filed June 15, 2026 has been entered. Claims 1-22 remain pending in the application. Claims 1, 8, 16, & 21 have been amended. Claim 22 is new. Applicant’s amendments to the Claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed October 30, 2025, hereafter referred to as the Non-Final Office Action.
Response to Arguments
Applicant’s arguments, see pp. 9-12 of applicant’s remarks, filed June 15, 2026, that the prior art reference with respect to the rejection(s) of amended independent claim(s) 1, 8, & 16 under U.S.C. § 103, Eom (US 2021/0041912A1), in view of Jin (US 2021/0247812A1), in view of Wolf (US 2008/0234935A1) have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, new grounds of rejections are made in view of Eom, in view of (US 2021/0247812 A1), in view of Wolf et al. (US 2008/0234935 A1), and in light of the amendments, further in view of Sohn (US 10274318 B1). Therefore, the rejections of amended independent claims 1, 8 & 16, and dependent claims 2-7, 9-15 & 17-22, which depend from and incorporate the limitations of amended independent claims 1, 8 & 16, are respectively maintained. Updated rejections based on amended features follow.
Applicant’s arguments, see pp. 8-11 of applicant’s remarks, filed June 15, 2026, that the prior art references with respect to the rejection(s) of amended dependent claim(s) 4, 11, 14, & 18, under U.S.C. § 103, Eom, in view of Liu (CN 109756630), in view of Jin, in view of Wolf, and further in view of DiFonzo (US 2017/0083071A1) have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection are made in view of Eom, in view of Jin, in view of Wolf, in view of Sohn, in view of Files (US 2019/0163432A1), and further in view of Clevorn (US 2019/0098374 A1). Therefore, the rejections of amended dependent claims 4, 11,14, & 18, which depend from amended independent claims 1, 8, & 16, which have been updated with new grounds of rejections, are respectively maintained. Updated rejections based on amended features follow.
Applicant’s arguments, see pg. 11 of applicant’s remarks, filed June 15, 2026, that the prior art references with respect to the rejection(s) of dependent claim(s) 6 & 13, under U.S.C. § 103, Eom, in view of Zhang (US 2022/0261093A1), have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Eom, in view of Jin, in view of Wolf, in view of Wolf, in view of Sohn, in view of Sang (US 2018/0088685 A1), and further in view of Hesketh (US 9973852 B1). Therefore, the rejections of dependent claims 6 & 13, which depend from amended independent claims 1 & 8, which have been updated with new grounds of rejection, are respectively maintained. Updated rejections based on amended features follow.
Applicant’s arguments, see pp.11-12 of applicant’s remarks, filed June 15, 2026, that the prior art references with respect to the rejection(s) of dependent claim(s) 7 & 15, under U.S.C. § 103, Eom, in view of Kim (US 12288492 B2), in view of Jin, and further in view of Wolf, have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Eom, in view Jin, in view of Wolf, in view of Sohn, and further in view of Tu (US 2016/0018900 A1). Therefore, the rejections of dependent claims 7 & 15, which depend from amended independent claims 1 & 8, which have been updated with new grounds of rejection, are respectively maintained. Updated rejections based on amended features follow.
Applicant’s arguments, see pg. 12 of applicant’s remarks, filed June 15, 2026, that the prior art references with respect to the rejection of dependent claim 21, under U.S.C. § 103, Eom, in view of Jin, in view of Wolf, and further in view of Luinge (US 2011/0028865 A1), have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Eom, in view Jin, in view of Wolf, in view of Sohn (US 10274318 B1), and further in view of Luinge. Therefore, the rejections of dependent claims 21, which depends from amended independent claim 1, which has been updated with new grounds of rejection, are respectively maintained. Updated rejections based on amended features follow.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 22 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 22 recites the limitation "and estimate the angle between the first component and the second component during an awake state of the device" in ll. 9-11, where ”an awake state of the device” was previously disclosed in ll. 6-7. The repeated recitation of “an awake state”, introduces indefiniteness, for the limitations in the claim. For examination purposes, examiner interprets “an awake state,” to refer to the same previously disclosed limitation in claim 22.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-3, 5, 8-10, 12, 16-17, & 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Eom et al. (US 2021/0041912 A1, Pub. Date Feb. 11, 2021, hereinafter, Eom), in view of Jin et al. (US 2021/0247812 A1, Pub. Date Aug. 12, 2021, hereinafter, Jin), in view of Wolf et al. (US 2008/0234935 A1, Pub. Date Sep. 25, 2008, hereinafter, Wolf), and further in view of Sohn (US 10274318 B1, Pat. Date Apr. 30, 2019, hereinafter, Sohn).
Regarding independent claim 1, Eom, teaches:
A device, comprising (Fig. 1; [Abstract]):
a first component including (Fig. 2A; [Abstract], [0056]-[0059], [0063]-[0067], & [0070]: first housing structure 210 interpreted as first component):
a second component coupled to the first component, the first and second components configured to rotate with respect to a hinge axis (Fig. 2A & 2B; [Abstract], [0005], [0056]-[0060], [0067]-[0069] & [0102]: second housing structure 220 interpreted as second component coupled to the first housing 210),
the third processor configured to: determine an angle between the first component and the second component ([0008], [0085]-[0090], [0095], [0102], [0114] & [0121]);
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Eom, in combination with Jin, are silent in regard to:
a first sensor unit including a first accelerometer, a first gyroscope, and a first processor, the first processor configured to determine a first orientation of the first component based on measurements by the first accelerometer and the first gyroscope;
the second component including: a second sensor unit including a second accelerometer, a second gyroscope, and a second processor, the second processor configured to determine a second orientation of the second component based on measurements by the second accelerometer and the second gyroscope; and
However, Eom, in combination with Wolf, further teach:
a first sensor unit including a first accelerometer, a first gyroscope, and a first processor (Eom: Figs. 1, 2A, & 4A; [Abstract], [0030], [0070]-[0071], & [0083]-[0089]: first motion sensor module 240/340 (first sensor) disposed in the first housing structure 210, sensor module is a combination of at least two an acceleration sensor, an angular velocity sensor (gyro sensor) or a geomagnetic sensor, the overall main processor 120/410; Wolf: Fig. 3; [0039] & [0054]: teaches a MSMPU (sensor unit) that integrates a local processor (320) with the accelerometer and gyroscope), the first processor configured to determine a first orientation of the first component based on measurements by the first accelerometer and the first gyroscope ( Eom: Figs. 1, 2A, & 4A; [Abstract], [0030], [0070]-[0071], & [0082]-[0089]; Wolf: Fig. 5; [0054] & [0065]-[0067]: teaches the local processor determining local orientation/motion integration using its specific sensors);
the second component including : a second sensor unit including a second accelerometer, a second gyroscope, and a second processor, the second processor configured to determine a second orientation of the second component based on measurements by the second accelerometer and the second gyroscope (Eom: Figs. 1, 2A, & 4A; [Abstract], [0030], [0071], [0085]-[0089], & [0103]: second motion sensor module 250/350 (second sensor unit) disposed in the second housing structure 220, sensor module is a combination of at least two acceleration sensor, an angular velocity sensor (gyro sensor) or a geomagnetic sensor, overall main processor 120/410; Wolf: Fig. 5; [0054] provides the distributed local processor architecture for localized sensor processing); and
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It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom by incorporating the local sensor processors taught by Wolf. Eom teaches a device with first and second components having motion sensors. Wolf teaches multi-sensor measurement processing units (smart sensors with local processors), including an accelerometer, a gyroscope, and a local processor configured to process orientation data measurements locally. Eom provides the foldable device structure, the hinge, the dual sensor modules in each component, and the central processor determining the hinge angle. Wolf teaches a smart sensor unit architecture (a local processor packaged directly with an accelerometer and gyroscope) to compute local orientations. The motivation to combine these teachings is to improve computational efficiency by decentralizing the processing load away from the main processor directly to the sensor units. Applying Wolf’s localize processing architecture to Eom’s dual-housing foldable device is a predictable variation and substitution to improve similar electronic devices. This modification reduces processing load on Eom’s central processor, improves power management, and ensures that the orientation from separate housing are processed efficiently, which Wolf identifies as an advantage. Furthermore, it would have been obvious to apply Wolf’s matrix rotation algorithms in Eom’s central processor to accurately combine the localized orientation vectors into an accurate relative hinge angle, according to known methods, and yield predictable results (KSR).
Eom, is silent in regard to:
a third processor coupled to the first sensor unit and the second sensor unit,
However, Eom, in combination with Jin, further teach:
a third processor coupled to the first sensor unit and the second sensor unit (Eom: Figs. 1, 2A, & 4A; [Abstract], [0008], [0030]-[0031], [0085]-[0090], [0102], & [Claim 1]: overall main processor 120/410 (third processor) coupled to the first and second motion sensor modules; Jin: [0098], [0108]-[[0109]: supports distributed multi-processor bus architecture across a hinge),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable electronic device of Eom to incorporate the distributed, multi-processor architecture taught by Jin. Jin discloses a multi-processor hierarchy featuring a first processor 431 acting as a dedicated sensor processor and a second processor 432 acting as an application processor. A POSITA would have been motivated to apply Jin’s distributed processor/sensor hub architecture to Eom’s angle-detecting device to reduce power consumption and decrease the continuous computational burden on the main application processor. Implementing this structure into the foldable device is a substitution of known processing control structures. By offloading the constant polling and baseline calculations of the IMU sensors to dedicated, localized processors (sensor hubs) as taught by Jin, Eom’s device could continuously monitor its folding state in a low-power background mode without draining the battery by keeping the main processor awake, according to known methods, and yielding predictable results (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
rotate, subsequent to the angle being determined, the second orientation of the second component such that the rotated second orientation of the second component is realigned with the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope, the second orientation of the second component being rotated by an amount determined based on (1) the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope and (2) the determined angle between the first component and the second component; and
estimate the angle between the first component and the second component based on the first orientation and the rotated second orientation.
However, Sohn, further teaches:
rotate, subsequent to the angle being determined, the second orientation of the second component such that the rotated second orientation of the second component is realigned with the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope, the second orientation of the second component being rotated by an amount determined based on (1) the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope and (2) the determined angle between the first component and the second component (Fig. 1; [Abstract], [Col. 1, ll. 42-59], [Col. 2, ll. [Col. 4, ll. 1-24 & 40-56], [Col. 6, ll. 19-28], [Col. 7, ll. 5-15 & 53-60], [Col. 9, ll. 3-11], [Col. 12, ll. 28-44], [Col. 17, ll. 50-61], [Col. 18, ll. 55-64], [Claim 1], [Claim 6], [Claim 9], [Claim 14], & [Claim 19]: provides the mathematical equations of rotating an orientation coordinate frame by a determined angle to realign it with a target orientation direction); and
estimate the angle between the first component and the second component based on the first orientation and the rotated second orientation ([Col. 2, ll. 45-59], [Col. 4, ll. 1-24 & 40-56], [Col. 5, ll. 59-67], [Col. 6, ll. 19-28], [Col. 8, ll. 34- [Col. 12, ll. 3-44], [Col. 16, ll. 28-47], [Col. 18, ll. 8-16 & 55-64], [Col. 19, ll. 20-35], [Claim 1], [Claim 2], [Claim 4], [Claim 6], [Claim 9], [Claim 10], [Claim 12], [Claim 14], [Claim 17], [Claim 18], & [Claim 19]: teaches estimating the final orientation state/angle by processing the first orientation with the second orientation, the posteriori state estimate).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device of Eom, Wolf, and Jin by incorporating the spatial frame realignment techniques taught by Sohn. Eom teaches determining a folded angle using dual motion sensors, Sohn proved specific mathematical limitations of rotating an orientation frame by a determined angle to realign it with a target direction for spatial estimation. The motivation to combine Sohn’s spatial alignment logic with the dual-housing hardware is to solve the problem of processing misaligned spatial data across a physical hinge. A foldable device’s second sensor frame is skewed relative to the first; applying Sohn’s known mathematical rotation to realign these frames ensures the device accurately computes a spatial orientation. Implementing this represents the application of a known technique to a known device, yielding predictable and improved accuracy in estimating the final hinge angle regardless of overall spatial position (KSR).
Regarding dependent claim 2, Eom, teaches:
The device of claim 1 ([Abstract]) wherein the third processor is configured to (Figs. 1 & 4A; [0030] & [0085]-[0090]):
Eom, is silent in regard to:
determine an orientation change of the second component due to an angle rotation with respect to an axis in Earth’s reference frame based on the angle between the first component and the second component; and
realign the second orientation with the first orientation based on the orientation change and the first orientation.
However, Eom, in combination with Sohn, further teach:
determine an orientation change of the second component due to an angle rotation with respect to an axis in Earth's reference frame based on the angle between the first component and the second component (Eom: [0085]-[0091], [0114], & [0120]-[0121]: determines the angle between the housings; Sohn: [Col. 3, ll. 26-37] & [Col. 12, ll. 28-45]: teaches calculating the rotation and orientation change of the sensor components with respect to the absolute Earth reference frame using the determined angles); and
realign the second orientation with the first orientation based on the orientation change and the first orientation (Eom: [0085]-[0091], [0102], [0139]-[0140], [0144]-[0145], [0148],[0152], & [0155]; Sohn: [Col. 12, ll. 28-45]: teaches the mathematical necessity of realigning one skewed sensor frame’s orientation with a target (measured) orientation frame based on the calculated orientation change and rotation parameters).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable electronic device of Eom by incorporating the spatial frame realignment and coordinate system synchronization techniques taught by Sohn. Eom teaches determining a physical folding angle between two hinged components. Sohn provides the mathematical limitations of determining an orientation change of a component due to an angle rotation with respect to an axis in Earth’s reference frame based on the determined angle, and realigning the second orientation with the first orientation based on the orientation change and the first orientation. The motivation to combine these teachings is to improve accuracy of the device’s spatial calculations ensuring that sensor data from distinct, physically rotating components remains synchronized within a coordinate system. Applying Sohn’s known technique of utilizing Earth’s reference frame and rotation quaternions to realign skewed sensor frames across a foldable hinge to the dual-housing device of Eom represents a predictable variation to improve similar devices. This substitution of standard kinematic algorithms ensures the multi-component device accurately tracks its position and orientation in physical space, improving overall tracking efficiency and reducing orientation drift (KSR).
Regarding dependent claim 3, Eom, teaches:
The device of claim 1 ([Abstract])
Eom, is silent in regard to:
wherein the third processor is configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer, gyroscope, magnetometer, or hall sensor.
However, Eom, in combination with Jin, further teach:
wherein the third processor is configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0085]-[0090], [0095], [0102]-[0103], & [0121]: discloses first and second motion sensor modules 240/250 contain an acceleration sensor (accelerometer) and an angular velocity sensor (gyro sensor); Jin: [0098], [0102]-[0103], & [0122]-[0124]: provides the multi-processor hierarchy (the third processor) that determines the angle and arrangement state utilizing the following sensors: accelerometer, a gyroscope, a magnetometer (geomagnetic sensor), or a hall sensor), gyroscope (Eom: [0070]-[0071], [0085]-[0087], [0103], & [0121]; Jin: [0098], [0102]-[0103], & [0122]-[0124]), magnetometer, or hall sensor (Eom: [0008], [0070]-[0071], [0091]-[0093], & [0103]-[0104]: magnetic sensor module 252 detects a magnetic force, the motion sensor can include a geomagnetic sensor (magnetometer) and uses a magnetic force sensor and a magnetic body to determine the angle, the magnetic force sensor module detecting a magnet corresponds to a Hall sensor and detects a magnetic force to estimate the angle; Jin: [0098], [0102]-[0103], & [0122]-[0124]).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable electronic device of Eom by incorporating the processor and sensor array teachings of Jin. Eom teaches determining a folding angle between two hinged components using motion and magnetic sensors. Jin provides a third processor (first processor 431 and second processor 432) configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer, gyroscope, magnetometer, or hall sensor. The motivation to combine Jin’s dedicated multi-processor hierarchy and specific sensor array with Eom’s device is to optimize power management by offloading continuous angle and arrangement state determinations to a dedicated sensor processor. Implementing this arrangement to process the specific sensor measurements represents a substitution of known processing control structures, that would predictably yield a responsive device capable of accurately determining the device’s folded angle while maintaining maximum power efficiency (KSR).
Regarding dependent claim 5, Eom, teaches:
The device of claim 1 ([Abstract])
Eom, is silent in regard to:
wherein the third processor is configured to:
However, Eom, in combination with Jin, further teach:
wherein the third processor is configured to (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], & [0085]-[0090]: discloses a main processor coupled to the motion sensors; Jin: [0098]: provides the multi-processor hierarchy, establishing the third processor (e.g., the dedicated sensor processor 431) that manages the incoming sensor data):
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable electronic device of Eom by incorporating the processor architecture and continuous sensor sampling teachings of Jin. Eom teaches determining a folding angle between two components using distinct motion sensors. Jin provides a multi-processor hierarchy configured to update the arrangement state and angle by continuously obtaining first and second inertial sensor data at preset intervals. The motivation to combine Jin’s multi-processor structure and continuous sampling logic with Eom’s device is to allow for real-time updating of the device’s folded angle while maintaining optimal power efficiency. Implementing this continuous data-gathering and processing arrangement represents a substitution of known control structures and expected predictable results yielding a device capable of capturing live movements from the accelerometers and gyroscopes (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
update the angle between the first component and the second component based on measurements by the first accelerometer, the first gyroscope, the second accelerometer, and the second gyroscope.
However, Jin, in combination with Sohn, further teach:
update the angle between the first component and the second component based on measurements by the first accelerometer, the first gyroscope, the second accelerometer, and the second gyroscope (Jin: [0119] & [0123]: teaches that the processor continuously obtains data from the first and second inertial sensors (include the accelerometers and gyroscopes) to monitor the angle over time; Sohn: Fig. 1; [Col. 2, ll. 14-27 & 45-59], [Col. 4, ll. 7-24 & 57-67], [Col. 17, ll. 50-61], [Col. 18, ll. 8-16], [Claim 1], [Claim 6], [Claim 8], [Claim 9], [Claim 14], [Claim 16], & [Claim 19]: teaches the updating of this orientation angle based on the combination of both the accelerometer and gyroscope measurements to refine the spatial estimate).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combination of Eom and Jin by incorporating the orientation updating techniques taught by Sohn. Eom and Jin teach continuously gathering data to determine an angle utilizing multiple motion sensors. Sohn provides mathematical equations that can dynamically update a calculate orientation angle based on the combination of accelerometer and gyroscope measurements over time. The motivation to integrate Sohn’s updating logic into the dual-sensor system of Eom and Jin is to improve the accuracy of the angle tracking by correcting gyroscope drift using the accelerometer data. Applying Sohn’s sensor fusion and orientation updating algorithms to the foldable device represents the application of a known technique to improve similar devices. This combination ensures the processor continuously and accurately updates the hinge angle, resolving spatial calculation errors that accumulate over time, and yield expected predictable results (KSR).
Regarding independent claim 8, Eom, teaches:
A method, comprising ([Abstract]):
Eom, in combination with Jin, are silent in regard to:
determining, by a first sensor unit, a first orientation of a first component of a device, the first component including the first sensor unit, the first sensor unit including a first accelerometer and a first gyroscope, the first sensor unit determining the first orientation based on measurements by the first accelerometer and the first gyroscope;
determining, by a second sensor unit, a second orientation of a second component of the device, the first and second components configured to rotate with respect to a hinge axis, the second component including the second sensor unit, the second sensor unit including a second accelerometer and a second gyroscope, the second sensor unit determining the second orientation based on measurements by the second accelerometer and the second gyroscope;
However, Eom, in combination with Wolf, further teach:
determining, by a first sensor unit, a first orientation of a first component of a device, the first component including the first sensor unit, the first sensor unit including a first accelerometer and a first gyroscope, the first sensor unit determining the first orientation based on measurements by the first accelerometer and the first gyroscope (Eom: Figs. 1, 2A, & 4A; [0030], [0055]-[0060], [0070]-[0071] & [0082]-[0089]: first motion sensor module 240/340 (accelerometer & gyroscope) in first housing structure 210 detects pose/orientation and motion of that first component based on the inertial measurements; Wolf: Fig. 3; [0039], [0054]-[0055], & [0065]-[0067]);
determining, by a second sensor unit, a second orientation of a second component of the device, the first and second components configured to rotate with respect to a hinge axis, the second component including the second sensor unit, the second sensor unit including a second accelerometer and a second gyroscope, the second sensor unit determining the second orientation based on measurements by the second accelerometer and the second gyroscope (Eom: Figs: 1, 2A, & 4A; [Abstract], [0005], ][0030], [0055]-[0060], [0068], [0070]-[0071], [0082]-[0089], & [0102]-[0103]: second motion sensor module 250/350 (accelerometer & gyroscope) in second housing structure 220 detects pose/orientation, hinge structure connects housings; Wolf: Fig. 5;[0054]-[0055]: smart sensor methodology makes it obvious that the second sensor unit determines the second orientation locally using its own accelerometer and gyroscope);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom by incorporating the local sensor processors taught by Wolf. Eom teaches a device with first and second components having motion sensors. Wolf teaches multi-sensor measurement processing units (smart sensors with local processors), including an accelerometer, a gyroscope, and a local processor configured to process orientation data measurements locally. Eom provides the foldable device structure, the hinge, the dual sensor modules in each component, and the central processor determining the hinge angle. Wolf teaches a smart sensor unit architecture (a local processor packaged directly with an accelerometer and gyroscope) to compute local orientations. The motivation to combine these teachings is to improve computational efficiency by decentralizing the processing load away from the main processor directly to the sensor units. Applying Wolf’s localize processing architecture to Eom’s dual-housing foldable device is a predictable variation and substitution to improve similar electronic devices. This modification reduces processing load on Eom’s central processor, improves power management, and ensures that the orientation from separate housing are processed efficiently, which Wolf identifies as an advantage. Furthermore, it would have been obvious to apply Wolf’s matrix rotation algorithms in Eom’s central processor to accurately combine the localized orientation vectors into an accurate relative hinge angle, according to known methods, and yield predictable results (KSR).
Eom, is silent in regard to:
determining, by a third processor, an angle between the first component and the second component; and
However, Eom, in combination with Jin, further teach:
determining, by a third processor, an angle between the first component and the second component (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0085]-[0090], [0095], [0102], [0104] & [0121]: teaches the central processor step of determining the angle, processor calculates “first angle” (folding angle) from motion sensor data of both housings; Jin: [0042]-[0043], [0072]-[0075], & [0108]-[0109]: teaches the multi-processor methodology, where a separate, third application processor calculates the final states based on data from the local sensor processors); and
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of Eom by incorporating the multi-processor architecture taught by Jin. Eom teaches a method of utilizing a processor to determine a folding angle based on measurements from first and second sensor modules. Jin provides delegating state and sensor management to an auxiliary third processor 123, which operates independently from a main processor to control sensor modules. The motivation to combine Jin’s multi-processor hierarchy with Eom’s angle-determination method is to optimize power consumption by allowing a dedicated low-power auxiliary processor to handle continuous sensor polling and calculations without continuously waking the main application processor. Implementing this auxiliary third processor to calculate the hinge angle represents a substitution of known computer architectures to improve similar foldable electronic devices. This predictable variation ensures continuous angle detection while maintaining maximum power efficiency, yielding expected predictable results (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
rotating, by the third processor and subsequent to the angle being determined, the second orientation of the second component such that the rotated second orientation of the second component is realigned with the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope, the second orientation of the second component being rotated by an amount determined based on (1) the first orientation of the first component as determined based on measurements by the first accelerometer and the first gyroscope and (2) the determined angle between the first component and the second component; and
estimating, by the third processor the angle between the first component and the second component based on the first orientation and the rotated second orientation.
However, Sohn, further teaches:
rotating, by the third processor and subsequent to the angle being determined, the second orientation of the second component such that the rotated second orientation of the second component is realigned with the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope, the second orientation of the second component being rotated by an amount determined based on (1) the first orientation of the first component as determined based on measurements by the first accelerometer and the first gyroscope and (2) the determined angle between the first component and the second component (Fig. 1; [Abstract], [Col. 1, ll. 42-59], [Col. 2, ll. [Col. 4, ll. 1-24 & 40-56], [Col. 6, ll. 19-28], [Col. 7, ll. 5-15 & 53-60], [Col. 9, ll. 3-11], [Col. 12, ll. 28-44], [Col. 17, ll. 50-61], [Col. 18, ll. 55-64], [Claim 1], [Claim 6], [Claim 9], [Claim 14], & [Claim 19]: teaches mathematically rotating a second orientation frame to realign it with a first reference orientation frame, where the rotation amount is calculated based on the initial orientation state and the angular difference between the two frames); and
estimating, by the third processor the angle between the first component and the second component based on the first orientation and the rotated second orientation ([Col. 2, ll. 45-59], [Col. 4, ll. 1-24 & 40-56], [Col. 5, ll. 59-67], [Col. 6, ll. 19-28], [Col. 8, ll. 34- [Col. 12, ll. 3-44], [Col. 16, ll. 28-47], [Col. 18, ll. 8-16 & 55-64], [Col. 19, ll. 20-35], [Claim 1], [Claim 2], [Claim 4], [Claim 6], [Claim 9], [Claim 10], [Claim 12], [Claim 14], [Claim 17], [Claim 18], & [Claim 19]: discloses estimating the final orientation state/angle by processing the first orientation with the rotated and realigned second orientation, the posteriori state estimate).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of Eom, Wolf, and Jin by incorporating the mathematical orientation rotation and estimation techniques taught by Sohn. Eom and Jin teach calculating a hinge angle using multiple inertial sensors, Eom specifically teaches determining a folded angle using dual motion sensors. Sohn teaches specific mathematical limitations of rotating an orientation frame by a determined angle to realign it with a target direction for spatial estimation and subsequently estimated the angle based on the first orientation and the rotated second orientation. The motivation to combine Sohn’s mathematical realignment and estimation logic with the multi-sensor foldable device method of Eom and Jin is to improve the accuracy of the angle estimation by correcting for spatial drift and coordinate misalignment between two distinct inertial sensor units. Applying Sohn’s known mathematical sensor fusion and spatial rotation algorithms to the device’s angle determination method represents a predictable variation to improve similar multi-sensor devices. This combination yields an accurate, drift-compensated folding angle estimate regardless of the device’s overall orientation in space, yielding expected predictable results (KSR).
Regarding dependent claim 9, Eom, teaches:
The method of claim 8, further comprising ([Abstract]):
Eom, is silent in regard to:
determining, by the third processor,
However, Eom, in combination with Jin, further teach:
determining, by the third processor (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0085]-[0090], [0095]-[0096], [0099]-[0100], & [0108]-[0109]: teaches determining angles and states with a processor coupled to the motions sensors; Jin: [0030]-[0031], [0074]-[0075], & [0098]: provides the multi-processor hierarchy utilizing a dedicated auxiliary processor acting as the third processor to execute sensor management methods independently from the main CPU, actively manages device states),
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the foldable device method of Eom by incorporating the multi-processor architecture taught by Jin. Eom teaches a method utilizing a processor to determine a folding angle based on measurements from first and second sensor modules. Jin provides delegating state and sensor management an auxiliary third processor, which operates independently from a main processor to control sensor modules. The motivation to combine Jin’s muti-processor hierarchy with Eom’s angle-determination method is to optimize power consumption by allowing a dedicated low-power auxiliary processor to handle continuous sensor polling and orientation calculations without constantly waking the main application processor. Implementing this auxiliary third processor to execute sensor management methods represents a substitution of known computing architectures to improve similar foldable electronic devices. This predictable variation ensures continuous angle detection while maintaining maximum power efficiency, yielding expected predictable results (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
an orientation change of the second component due to an angle rotation with respect to an axis in Earth's reference frame based on the angle between the first component and the second component; and
realigning, by the third processor, the second orientation with the first orientation based on the orientation change and the first orientation.
However, Eom, in combination with Sohn, further teach:
an orientation change of the second component due to an angle rotation with respect to an axis in Earth's reference frame based on the angle between the first component and the second component (Eom: [0085]-[0091], [0114], & [0120]-[0121]: determines the angle between the housings; Sohn: [Col. 3, ll. 26-37], [Col. 8, ll. 34-45], & [Col. 12, ll. 21-45]: teaches determining an orientation change (frame rotation) utilizing an angle θa around an axis vector, calculated relative to an Earth reference frame, matching the spatial coordinate logic); and
realigning, by the third processor, the second orientation with the first orientation based on the orientation change and the first orientation (Eom: [0085]-[0091], [0102], [0139]-[0140], [0144]-[0145], [0148] ,[0152], & [0155]; Sohn: [Col. 12, ll. 21-45]: teaches taking a predicted orientation and realigning it with a measured/reference orientation by applying the calculated angle/orientation change (the error gap frame rotation) to the initial orientation frames).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the method of Eom and Jin with the mathematical orientation tracking and realignment techniques taught by Sohn. Eom and Jin teach calculating a hinge angle using multiple processors and sensors, Eom specifically teaches determining a physical folding angle between two hinged components. Sohn provides the method of determining an orientation change of a component due to an angle rotation with respect to an axis in Earth’s reference frame, and realigning the second orientation with the first orientation based on the orientation of the first orientation. The motivation to combine Sohn’s Earth reference frame tracking and mathematical realignment logic with the multi-sensor foldable device method of Eom and Jin is to improve accuracy of the device’s spatial calculations by correcting for drift and coordinate misalignment between distinct inertial sensor units, ensuring that sensor data from distinct, physically rotating components remains synchronized within a coordinate system. Applying Sohn’s known mathematical sensor fusion and spatial realignment algorithms to the device’s angle determination method represents a predictable variation to improve similar multi-sensor devices. This substitution of standard kinematic algorithms ensures the multi-component device accurately tracks its position and orientation in physical space, improving overall tracking efficiency and reducing orientation drift, regardless of the device’s overall orientation in the Earth’s reference frame, yielding expected predictable results (KSR).
Regarding dependent claim 10, Eom, teaches:
The method of claim 8, further comprising ([Abstract]):
the angle between the first component and the second component (Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0085]-[0090], [0095], [0102], & [0121]: teaches determining the angle between a first housing (first component) and a second housing (second component)) based on measurements generated by at least one accelerometer ([0070]-[0071], [0085]-[0087], [0103] & [0121]: first and second motion sensor modules 240/250 contain an acceleration sensor (accelerometer) and an angular velocity sensor (gyro sensor)), gyroscope ([0070]-[0071], [0085]-[0087], [0103] & [0121]), magnetometer, or hall sensor ([0008], [0070]-[0071], [0091]-[0093], [0095], & [0103]-[0104]: magnetic sensor module 252 detects a magnetic force, the motion sensor can include a geomagnetic sensor (magnetometer) and uses a magnetic force sensor and a magnetic body to determine the angle, the magnetic force sensor module detecting a magnet corresponds to a Hall sensor and detects a magnetic force to estimate the angle).
Eom, is silent in regard to:
determining, by the third processor,
However, Eom, in combination with Jin, further teach:
determining, by the third processor (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0085]-[0090], [0095]-[0096], [0099]-[0100], & [0108]-[0109]: teaches determining angles and states with a processor coupled to the motions sensors; Jin: [0030]-[0031], [0074]-[0075], & [0098]: provides the multi-processor hierarchy utilizing a dedicated auxiliary processor acting as the third processor to execute sensor management methods independently from the main CPU, actively manages device states),
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the foldable device method of Eom by incorporating the multi-processor architecture taught by Jin. Eom teaches a method utilizing a processor to determine a folding angle based on measurements from first and second sensor modules. Jin provides delegating state and sensor management an auxiliary third processor, which operates independently from a main processor to control sensor modules. The motivation to combine Jin’s muti-processor hierarchy with Eom’s angle-determination method is to optimize power consumption by allowing a dedicated low-power auxiliary processor to handle continuous sensor polling and orientation calculations without constantly waking the main application processor. Implementing this auxiliary third processor to execute sensor management methods represents a substitution of known computing architectures to improve similar foldable electronic devices. This predictable variation ensures continuous angle detection while maintaining maximum power efficiency, yielding expected predictable results (KSR).
Regarding dependent claim 12, Eom, teaches:
The method of claim 8, further comprising ([Abstract]):
based on measurements by the first accelerometer, the first gyroscope, the second accelerometer, and the second gyroscope ([0008], [0070]-[0071], [0085]-[0093], [0095], [0114], [0121]-[0122], & [0153]-[0155]: teaches the angle is continuously updated and calculated based on acceleration and angular velocity data (i.e., measurements from accelerometers and gyroscopes) located in both the first and second housing structures, components such as motion sensor modules to determine the angle).
Eom, is silent in regard to:
updating, by the third processor, the angle between the first component and the second component
However, Eom, in combination with Jin, further teach:
updating, by the third processor, the angle between the first component and the second component (Eom: Figs. 1, 2A, 4A, & 5; [0008], [0030]-[0031], [0085]-[0090], [0095]-[0096], [0099]-[0100], [0108]-[0109], [0114], [0118], [0121]-[0122], & [0153]-[0155]: teaches periodically detecting (updating) the angle over time; Jin: [0030]-[0031], [0074]-[0075], & [0098]: provides the multi-processor hierarchy utilizing a dedicated auxiliary processor acting as the third processor to independently manage sensor data an continuously update and actively manages device states)
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the foldable device method of Eom by incorporating the multi-processor architecture taught by Jin. Eom teaches a method of periodically updating the angle between a first component and a second component based on measurements by a first accelerometer, a first gyroscope, a second accelerometer, and a second gyroscope, utilizing a processor to determine a folding angle based on measurements from first and second sensor modules. Jin provides delegating state and sensor management an auxiliary third processor, that operates independently from a main processor to control sensor modules. The motivation to combine Jin’s auxiliary processor hierarchy with Eom’s angle-updating method is to improve overall computational efficiency and maintain power consumption by delegating continuous, power-intensive sensor polling to a low-power auxiliary processor and sensor hub, preventing the main application processor from continuously waking up. Implementing this auxiliary third processor to execute the periodic sensor measurements and angle updates represents a substitution of known computing architectures to improve similar foldable electronic devices. This predictable variation ensures that the device can continuously update its folded state utilizing inertial sensors while preserving battery life, yielding expected predictable results (KSR).
Regarding independent claim 16, Eom, teaches:
A device, comprising ([Abstract] & [0008]):
a first sensor unit ([0008], [0057], [0070]-[0071], & [0082]-[0085]: first motion sensor module 240/340 interpreted as first sensor unit);
a first housing including the first sensor unit ([0057 & [0070]-[0071]: teaches the first housing and the first sensor unit (motion sensor module)),
a second sensor unit ([0008], [0056], [0070]-[0071] & [0082]-[0085]: second motion sensor module 250/350 interpreted as second sensor unit);
a second housing coupled to the first housing, the first and second housings configured to rotate with respect to a hinge axis (Fig. 2A; [0008], [0030], [0055]-[0060] & [0070]-[0071]: second housing structure 220 interpreted as second component coupled to the first housing 210, hinge structure connects housings),
a processor configured to determine an angle between the first housing and the second housing (Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0085]-[0091], [0095], [0102], & [0114]: overall main processor 120/410 coupled to the first and second motion sensor modules), and
Eom, in combination with Jin, are silent in regard to:
the first sensor unit configured to determine a first orientation of the first housing based on measurements generated by the first sensor unit;
the second housing including the second sensor unit, the second sensor unit configured to determine a second orientation of the second housing based on measurements generated by the second sensor unit; and
However, Eom, in combination with Wolf, further teach:
the first sensor unit configured to determine a first orientation of the first housing based on measurements generated by the first sensor unit (Eom: Figs. 1, 2A, & 4A; [0008], [0030], [0055]-[0060], [0070]-[0071], & [0082]-[0089]: first housing structure 210 interpreted as first component, first motion sensor module 240/340 (first sensor) disposed in the first housing structure 210, sensor module is a combination of at least two an acceleration sensor, an angular velocity sensor (gyro sensor) or a geomagnetic sensor, the overall main processor 120/410; Wolf: [0065]-[0067]:uses a smart sensor unit MSMPU capable of determining its own local orientation (motion integration) based on its own generated measurements);
the second housing including the second sensor unit, the second sensor unit configured to determine a second orientation of the second housing based on measurements generated by the second sensor unit (Eom: Figs: 1, 2A, & 4A; [0030], [0055]-[0060], [0070]-[0071], [0083]-[0089], & [0103]: second motion sensor module 250/350 (accelerometer & gyroscope) in second housing structure 220 detects/determines pose/orientation; Wolf: [0054]-[0055]: combining Eom’s physical layout with Wolf’s smart sensor units); and
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom to incorporate the smart sensor units (MSMPUs) and coordinate rotation matrix mathematics taught by Wolf. The motivation to do so would be to reduce central processing bandwidth/power, reducing the continuous computational burden and data-routing overhead on the central processor and kinematic accuracy. To improve the accuracy of the joint angle calculation, a POSITA would be motivated to apply Wolf’s known mathematical rotation matrix techniques to Eom’s central processor to rotate and realign the second component’s coordinate frame with the first component’s coordinate frame, ensuring an accurate calculation of the final folding angle. This combination represents the substitution of a known element (a standard IMU) for another known element a smart IMU with local processing), and the application of a known mathematical technique (coordinate rotation) to a known device ready for improvement to yield predictable results (KSR), that would allow the combined elements to perform their standard, known functions to yield a more efficient and accurate angle-detecting device.
Eom, in combination with Jin, and Wolf, are silent in regard to:
rotate, subsequent to the angle being determined, the second orientation of the second housing such that the rotated second orientation of the second housing is realigned with the first orientation of the first housing as determined based on the measurements generated by the first sensor unit, the second orientation of the second housing being rotated by an amount determined based on (1) the first orientation of the first housing as determined based on the measurements by the first sensor unit and (2) the determined angle between the first housing and the second housing.
However, Sohn, further teaches:
rotate, subsequent to the angle being determined, the second orientation of the second housing such that the rotated second orientation of the second housing is realigned with the first orientation of the first housing as determined based on the measurements generated by the first sensor unit (Fig. 1; [Abstract], [Col. 1, ll. 42-59], [Col. 2, ll. [Col. 4, ll. 1-24 & 40-56], [Col. 6, ll. 19-28], [Col. 7, ll. 5-15 & 53-60], [Col. 9, ll. 3-11], [Col. 12, ll. 21-45], [Col. 17, ll. 50-61], [Col. 18, ll. 55-64], [Claim 1], [Claim 6], [Claim 9], [Claim 14], & [Claim 19]: teaches taking a second orientation frame and mathematically rotating it to realign it with a first measured reference orientation frame by applying the calculated angular frame rotation), the second orientation of the second housing being rotated by an amount determined based on (1) the first orientation of the first housing as determined based on the measurements by the first sensor unit and (2) the determined angle between the first housing and the second housing (Fig. 1; [Abstract], [Col. 1, ll. 42-59], [Col. 2, ll. [Col. 4, ll. 1-24 & 40-56], [Col. 6, ll. 19-28], [Col. 7, ll. 5-15 & 53-60], [Col. 9, ll. 3-11], [Col. 12, ll. 21-45], [Col. 17, ll. 50-61], [Col. 18, ll. 55-64], [Claim 1], [Claim 6], [Claim 9], [Claim 14], & [Claim 19]: teaches calculating the amount of rotation necessary to correct the orientation based directly on the initial reference orientation vectors and the angular difference (error gap) determined between the two frames).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the foldable device of Eom by incorporating the mathematical orientation rotation, tracking and realignment techniques taught by Sohn. Eom discloses a processor determining a hinge angle between first and second housings utilizing independent multiple processors and sensor units. Sohn teaches rotating the second orientation such that it is realigned with the first orientation, with the rotation amount determined based on the first orientation and the determined angle. The motivation to combine Sohn’s mathematical realignment logic with the multi-sensor foldable device of Eom is to improve accuracy of the device’s spatial calculations by correcting for spatial drift and coordinate misalignment between the two distinct inertial sensor units, ensuring that sensor data from distinct, physically rotating components remains synchronized within a coordinate system. Applying Sohn’s known mathematical sensor fusion and spatial rotation and realignment algorithms to Eom’s angle determination method represents a predictable variation to improve similar multi-sensor devices. This combination yields accurate, drift-compensated folding angle estimate to ensure the device interface operates correctly regardless of its overall orientation in space, improving overall tracking efficiency and reducing orientation drift, yielding expected predictable results (KSR).
Regarding dependent claim 17, Eom, teaches:
The device of claim 16 ([Abstract], [0071], [0083], & [0102]) wherein the processor is configured to (Figs. 1 & 4A; [0008], [0030], [0085]-[0090], & [0102]):
Eom, in combination with Jin, and Wolf, are silent in regard to:
determine an orientation change of the second housing due to an angle rotation with respect to an axis in Earth's reference frame based on the angle between the first housing and the second housing; and
realign the second orientation with the first orientation based on the orientation change and the first orientation.
However, Eom, in combination with Sohn, further teach:
determine an orientation change of the second housing due to an angle rotation with respect to an axis in Earth's reference frame based on the angle between the first housing and the second housing (Eom: [0085]-[0091], [0114], & [0120]-[0121]: determines the angle between the housings; Sohn: [Col. 3, ll. 26-37], [Col. 8, ll. 34-45], & [Col. 12, ll. 21-45]: teaches determining an orientation change (frame rotation) utilizing an angle θa around an axis vector, calculated relative to an Earth reference frame, matching the spatial coordinate logic to determine orientation changes due to an angle rotation); and
realign the second orientation with the first orientation based on the orientation change and the first orientation (Eom: [0085]-[0091], [0102], [0139]-[0140], [0144]-[0145], [0148] ,[0152], & [0155]; Sohn: [Col. 12, ll. 21-45]: teaches taking a predicted orientation and mathematically realigning it with a measured reference orientation by applying the calculated angle/orientation change (the error gap frame rotation) to the initial orientation frames).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the foldable device of Eom by incorporating the mathematical orientation tracking and realignment techniques taught by Sohn. Eom teaches a device utilizing a processor to calculate a folding angle using multiple processors and sensors, between a first housing and a second housing. Sohn provides determining an orientation change of a component due to an angle rotation with respect to an axis in Earth’s reference frame, and realigning the second orientation with the first orientation based on the orientation change of the first orientation. The motivation to combine Sohn’s Earth reference frame tracking and mathematical realignment logic with the multi-sensor foldable device method of Eom is to improve accuracy of the device’s spatial calculations by mathematically correcting for drift and coordinate misalignment between two distinct inertial sensor units located in the separate housings, ensuring that sensor data from distinct, physically rotating components remains synchronized within a coordinate system. Applying Sohn’s known mathematical sensor fusion and spatial realignment algorithms to Eom’s angle determination configuration represents a predictable variation to improve similar multi-sensor devices. This substitution of yields an accurate, drift-compensated folding angle estimate, the multi-component device accurately tracks its position and orientation in physical space, improving overall tracking efficiency and reducing orientation drift, regardless of the device’s overall orientation in the Earth’s reference frame, yielding expected predictable results (KSR).
Regarding dependent claim 19, Eom, teaches:
The device of claim 16 ([Abstract], [0056]-[0057], [0071], [0075], [0082]-[0083], & [0102]) wherein the processor is configured to (Figs. 1 & 4A; [Abstract], [0030], [0085]-[0090], & [0102]) determine the angle between the first housing and the second housing (Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0085]-[0090], [0095], [0102], & [0121]: teaches a processor configured to calculate the folding angle between the first housing and the second housing) based on measurements generated by at least one accelerometer ([0070]-[0071], [0085]-[0087], [0103], & [0121]) gyroscope ([0070]-[0071], [0085]-[0087], [0103], & [0121]), magnetometer, or hall sensor [0008], [0070]-[0071], [0091]-[0093], [0103]-[0104], & [0121]: teaches that the processor determines the angle based on measurements (acceleration data, angular velocity data, magnetic force) generated by motion sensors containing accelerometers, gyroscopes, geomagnetic sensors (magnetometers) and magnetic force sensors (Hall sensors)).
Regarding dependent claim 20, Eom, teaches:
The device of claim 16 ([Abstract], [0008], [0029]-[0030], [0056]-[0057], [0071], [0075], [0082]-[0085], & [0102]) wherein the processor is configured to (Figs. 1 & 4A; [0008], [0030]-[0031], [0085]-[0090], & [0102]: discloses an electronic device comprising at least one processor configured to perform angular determinations and spatial calculations):
update the angle between the first housing and the second housing based on measurements by the first sensor unit and measurements by the second sensor unit ([0008], [0070]-[0071], [0085]-[0093], [0095]-[0100], [0112], [0114], [0116]-[0117]-[0118], [0121]-[0122], [0128]-[0135], & [0153]-[0155]: teaches that the processor is configured to periodically detect (update) the angle between the first and second housings utilizing the acceleration and angular velocity data (measurements) continually obtained from the first and second sensor units).
Eom, in combination with Jin, and Wolf, are silent in regard to:
estimate the angle between the first housing and the second housing based the first orientation and the rotated second orientation.
However, Sohn, further teaches:
estimate the angle between the first housing and the second housing based the first orientation and the rotated second orientation ([Col. 2, ll. 45-59], [Col. 4, ll. 1-24 & 40-56], [Col. 5, ll. 59-67], [Col. 6, ll. 19-28], [Col. 8, ll. 34- [Col. 12, ll. 3-44], [Col. 16, ll. 28-47], [Col. 18, ll. 8-16 & 55-64], [Col. 19, ll. 20-35], [Claim 1], [Claim 2], [Claim 4], [Claim 6], [Claim 9], [Claim 10], [Claim 12], [Claim 14], [Claim 17], [Claim 18], & [Claim 19]: teaches taking a predicted orientation frame, mathematically rotating it to realign with a measured reference orientation, and estimating the final angular orientation state (a posteriori state estimate) based on processing the first orientation with the rotated second orientation).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom by incorporating the mathematical orientation rotation and estimation techniques taught by Sohn. Eom teaches a device configured to periodically update the angle between a first housing and a second housing based on measurements by a first sensor unit and a second sensor unit. Sohn teaches estimating an angular orientation state based on a first reference orientation and a mathematically rotated second orientation. The motivation to combine Sohn’s mathematical realignment and estimation logic with the multi-sensor foldable device of Eom is to improve the accuracy of the device’s spatial calculations by mathematically correcting for spatial drift and coordinate misalignment between two distinct inertial sensor units over time. Applying Sohn’s known mathematical sensor fusion and spatial rotation algorithms to Eom’s angle updating configuration represents a predictable variation to improve similar multi-sensor devices. This combination yields an accurate, drift-compensated folding angle estimate to ensure the device interface operates correctly regardless of its continuous motion in space, and represents the application of a known technique to a known device, yielding predictable and improved accuracy in estimating the final hinge angle regardless of overall spatial position (KSR).
Claims 4, 11, 14, & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Eom, in view of Jin, in view of Wolf, in view of Sohn, in view of Files et al. (US 2019/0163432 A1, Pub. Date May 30, 2019, hereinafter, Files), in view of Kabasawa et al. (US 2010/0033424 A1, Pub. Date Feb. 11, 2010, hereinafter, Kabasawa), and further in view of Clevorn (US 2019/0098374 A1, Pub. Date Mar. 28, 2019, hereinafter, Clevorn).
Regarding dependent claim 4, Eom, teaches:
The device of claim 1 ([Abstract], [0008], [0030]-[0031], [0081], [0085]-[0090])
Eom, is silent in regard to:
wherein the third processor is configured to:
detect a screen off event in which the device is set to a low-powered or off state,
However, Eom, in combination with Jin, further teach:
wherein the third processor is configured to (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], & [0085]-[0090], & [0108]-[0109]: discloses a main processor coupled to the motions sensors; Jin: [0098]: provides the multi-processor hierarchy, the third processor that actively manages device states):
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom by incorporating the hierarchical processor architecture taught by Jin. Eom teaches determining a folding angle between a first and second component utilizing motion and magnetic sensors. Jin provides a third processor, comprising a first processor 431 and a second processor 432, configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer, gyroscope, magnetometer, or hall sensor. The motivation to combine Jin’s multi-processor structure with Eom’s device is to optimize power management by offloading continuous angle and arrangement state determinations from a main application processor directly to a dedicated sensor hub. Implementing this hierarchical processor arrangement represents a substitution of known processing control structures to improve similar electronic devices. This predictable variation yields a responsive foldable device capable of continuously processing sensor data while maintaining maximum power efficiency, yielding expected predictable results (KSR).
However, Jin, in combination with Files, and Kabasawa, further teach:
detect a screen off event in which the device is set to a low-powered or off state (Jin: [0110]: teaches the processor detecting inactivity to trigger a “sleep state” (a low-powered state) where the display is turned off; Files: [0004], [0021], & [0067]: reinforces by teaching turning off a screen to conserve energy; Kabasawa: [0392]: teaches transitioning the device into a low-power mode where primary sensors are “stopped” to save power),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the display and sensor power management techniques taught by Files and Kabasawa. Jin teaches entering a sleep state where the display is turned off to save power, Files teaches turning off a specific display screen to conserve energy, and Kabasawa teaches stopping power to sensors during the inactive states. The motivation to combine these teachings is to improve overall power efficiency and extend battery life by disabling displays and high-power orientation sensors when they are not actively being utilized. Implementing this power management system into the foldable device represents a substitution of known power-saving techniques to improve similar electronic devices. This predictable variation ensures the device conserves maximum power during low-powered or screen-off events, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, Files, and Kabasawa, are silent in regard to:
the second orientation being rotated in response to the screen off event being detected.
However, Clevorn, further teaches:
the second orientation being rotated in response to the screen off event being detected (Fig. 1; [0032] & [0046]: teaches that the device entering a low-powered idle or sleep state acts as the direct trigger to wake the rotation control logic and rotate the sensor’s orientation).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the device of Eom, Jin, Files, and Kabasawa with the active sensor reorientation logic taught by Clevorn. Clevorn teaches triggering a sensor system’s rotation in response to the device entering a low-powered idle or sleep state. The motivation to combine Clevorn’s idle-triggered rotation logic with the screen-off sleep state of the foldable device is to optimize sensor orientation and prepare the device’s coordinate frame for background tracking without disrupting the user’s active viewing experience. Implementing this automated, state-triggered sensor rotation represents a predictable variation of known hardware optimization techniques to improve similar devices. This combination ensures the device reorients its internal sensors immediately upon detecting a low-power screen-off event to maximize background efficiency, yielding expected predictable results (KSR).
Regarding dependent claim 11, Eom, teaches:
The method of claim 8, further comprising ([Abstract], [0008], [0030]-[0031], [0081], & [0086]-[0090]):
Eom, is silent in regard to:
detecting a screen off event in which the device is set to a low-powered or off state,
However, Jin, in combination with Files, and Kabasawa, further teach:
detecting a screen off event in which the device is set to a low-powered or off state (Jin: [0110]: teaches the processor detecting inactivity to trigger a “sleep state” (a low-powered state) where the display is turned off; Files: [0004], [0021], & [0067]: reinforces by teaching turning off a screen to conserve energy; Kabasawa: [0392]: teaches transitioning the device into a low-power mode where primary sensors are “stopped” to save power),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the display and sensor power management techniques taught by Files and Kabasawa. Eom teaches a method for determining a folded angle using motion sensors. Jin teaches entering a sleep state where the display is turned off to save power, Files teaches turning off a specific display screen to conserve energy, and Kabasawa teaches stopping power to sensors during the inactive states. The motivation to combine these teachings is to improve overall power efficiency and extend battery life by disabling displays and high-power orientation sensors when they are not actively being utilized. Implementing this power management system into the foldable device represents a substitution of known power-saving techniques to improve similar electronic devices. This predictable variation ensures the device conserves maximum power during low-powered or screen-off events, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, Files, and Kabasawa, are silent in regard to:
the second orientation being rotated in response to the screen off event being detected.
However, Clevorn, further teaches:
the second orientation being rotated in response to the screen off event being detected (Fig. 1; [0032] & [0046]: teaches that the device entering a low-powered idle or sleep state acts as the direct trigger to wake the rotation control logic and rotate the sensor’s orientation).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the method of Eom, Jin, Files, and Kabasawa with the active sensor reorientation logic taught by Clevorn. Clevorn teaches triggering a sensor system’s rotation in response to the device entering a low-powered idle or sleep state. The motivation to combine Clevorn’s idle-triggered rotation logic with the screen-off sleep state of the foldable device is to optimize sensor orientation and prepare the device’s coordinate frame for background tracking without disrupting the user’s active viewing experience. Implementing this automated, state-triggered sensor rotation represents a predictable variation of known hardware optimization techniques to improve similar devices. This combination ensures the device reorients its internal sensors immediately upon detecting a low-power screen-off event to maximize background efficiency, yielding expected predictable results (KSR).
Regarding dependent claim 14, Eom, teaches:
The method of claim 8, further comprising ([Abstract]):
Eom, is silent in regard to:
detecting a screen off event in which a screen of the device is set to a low-powered or off state; and
setting the device to a sleep state in which the third processor is set to a low-powered or off state,
However, Jin, in combination with Files, and Kabasawa, further teach:
detecting a screen off event in which a screen of the device is set to a low-powered or off state (Jin: [0110]: teaches entering a state resulting in a screen off event where the display is turned off or set to a low-powered Always-On Display (AOD) mode; Files: [0004], [0021], & [0067]: reinforces by teaching turning off a screen to conserve energy; Kabasawa: [0392]: teaches transitioning the device into a low-power mode where primary sensors are “stopped” to save power),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the display and sensor power management techniques taught by Files and Kabasawa. Eom teaches a method for determining a folded angle using motion sensors. Jin teaches entering a sleep state where the display is turned off to save power, Files teaches turning off a specific display screen to conserve energy, and Kabasawa teaches stopping power to sensors during the inactive states. The motivation to combine these teachings is to improve overall power efficiency and extend battery life by disabling displays and high-power orientation sensors when they are not actively being utilized. Implementing this power management system into the foldable device represents a substitution of known power-saving techniques to improve similar electronic devices. This predictable variation ensures the device conserves maximum power during low-powered or screen-off events, yielding expected predictable results (KSR).
However, Jin, further teaches:
setting the device to a sleep state in which the third processor is set to a low-powered or off state ([0109]-[0110] & [0170]: teaches setting the device to a sleep state where the dedicated processor (third processor) is sent into a low-powered, idle state to minimize consumption),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of Eom by incorporating the sleep state and display power management steps taught by Jin. Eom teaches a method for determining a folded angle using motion sensors. Jin teaches detecting a screen off event and setting the device to a sleep state in which the processor is sent to a low-powered state. The motivation to combine Jin’s low-powered sleep state teachings with Eom’s method is to improve overall power efficiency and extend battery life during periods of user inactivity by selectively disabling the displays and lowering processor functions. Implementing this power-saving state into the foldable device represents a substitution of known power-management techniques to improve similar electronic devices. This predictable variation ensures the device conserves maximum power when the screen is turned off without permanently losing baseline processor functionality, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, Files, and Kabasawa, are silent in regard to:
the first orientation and the second orientation being determined in response to the device being set to the sleep state.
However, Clevorn, further teaches:
the first orientation and the second orientation being determined in response to the device being set to the sleep state ([0028], [0032], & [0046]: teaches that the device entering a low-powered idle or sleep state acts as the direct trigger to evaluate, determined, and adjust the orientation of the sensor portions).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of Eom and Jin with the active sensor orientation taught by Clevorn. Clevorn teaches the first orientation and the second orientation being determined in response to the device being set to the sleep state, teaching that sensor orientations are determined and optimized triggered by the device entering an idle or sleep state. The motivation to combine Clevorn’s idle-triggered orientation determination logic with the sleep state of the foldable device is to optimize sensor alignment for background tracking while the user is not actively interacting with the display. Implementing this automatic, state-triggered orientation determination represents a predictable variation of known hardware optimization techniques to improve similar mobile devices. This combination ensures the device evaluates and updates its internal sensor orientations immediately upon detecting a low-power sleep event to maximize background efficiency without disrupting the user, yielding expected predictable results (KSR).
Regarding dependent claim 18, Eom, teaches:
The device of claim 16 ([Abstract], [0049], [0071], [0073], [0083], & [0102])
Eom, is silent in regard to:
wherein the third processor is configured to:
detect a screen off event in which the device is set to a low-powered or off state,
However, Eom, in combination with Jin, further teach:
wherein the third processor is configured to (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], & [0085]-[0090], & [0108]-[0109]: discloses a main processor coupled to the motions sensors; Jin: [0098]: provides the multi-processor hierarchy, the third processor that actively manages device states):
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom by incorporating the hierarchical processor architecture taught by Jin. Eom teaches determining a folding angle between a first and second component utilizing motion and magnetic sensors. Jin provides a third processor, comprising a first processor 431 and a second processor 432, configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer, gyroscope, magnetometer, or hall sensor. The motivation to combine Jin’s multi-processor structure with Eom’s device is to optimize power management by offloading continuous angle and arrangement state determinations from a main application processor directly to a dedicated sensor hub. Implementing this hierarchical processor arrangement represents a substitution of known processing control structures to improve similar electronic devices. This predictable variation yields a responsive foldable device capable of continuously processing sensor data while maintaining maximum power efficiency, yielding expected predictable results (KSR).
However, Jin, in combination with Files, and Kabasawa, further teach:
detect a screen off event in which the device is set to a low-powered or off state (Jin: [0110]: teaches the processor detecting inactivity to trigger a “sleep state” (a low-powered state) where the display is turned off; Files: [0004], [0021], & [0067]: reinforces by teaching turning off a screen to conserve energy; Kabasawa: [0392]: teaches transitioning the device into a low-power mode where primary sensors are “stopped” to save power),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the display and sensor power management techniques taught by Files and Kabasawa. Jin teaches entering a sleep state where the display is turned off to save power, Files teaches turning off a specific display screen to conserve energy, and Kabasawa teaches stopping power to sensors during the inactive states. The motivation to combine these teachings is to improve overall power efficiency and extend battery life by disabling displays and high-power orientation sensors when they are not actively being utilized. Implementing this power management system into the foldable device represents a substitution of known power-saving techniques to improve similar electronic devices. This predictable variation ensures the device conserves maximum power during low-powered or screen-off events, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, Files, and Kabasawa, are silent in regard to:
the second orientation being rotated in response to the screen off event being detected.
However, Clevorn, further teaches:
the second orientation being rotated in response to the screen off event being detected (Fig. 1; [0032] & [0046]: teaches that the device entering a low-powered idle or sleep state acts as the direct trigger to wake the rotation control logic and rotate the sensor’s orientation).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the device of Eom, Jin, Files, and Kabasawa with the active sensor reorientation logic taught by Clevorn. Clevorn teaches triggering a sensor system’s rotation in response to the device entering a low-powered idle or sleep state. The motivation to combine Clevorn’s idle-triggered rotation logic with the screen-off sleep state of the foldable device is to optimize sensor orientation and prepare the device’s coordinate frame for background tracking without disrupting the user’s active viewing experience. Implementing this automated, state-triggered sensor rotation represents a predictable variation of known hardware optimization techniques to improve similar devices. This combination ensures the device reorients its internal sensors immediately upon detecting a low-power screen-off event to maximize background efficiency, yielding expected predictable results (KSR).
Claims 6 & 13 are rejected under 35 U.S.C. 103 as being unpatentable over Eom, in view of Jin, in view of Wolf, in view of Sohn, in view of Sang et al. (US 2018/0088685 A1, Pub. Date Mar. 29, 2018, hereinafter, Sang), and further in view of Hesketh et al. (US 9973852 B1, Pat. Date May 15, 2018, hereinafter, Hesketh).
Regarding dependent claim 6, Eom, teaches:
The device of claim 5 ([Abstract])
Eom, is silent in regard to:
wherein the third processor is configured to:
detect a screen off event in which the device is set to a low-powered or off state,
However, Eom, in combination with Jin, further teach:
wherein the third processor is configured to (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], & [0085]-[0090], & [0108]-[0109]: discloses a main processor coupled to the motions sensors; Jin: [0098]: provides the multi-processor hierarchy, the third processor that actively manages device states):
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom by incorporating the hierarchical processor architecture taught by Jin. Eom teaches determining a folding angle between a first and second component utilizing motion and magnetic sensors. Jin provides a third processor, comprising a first processor 431 and a second processor 432, configured to determine the angle between the first component and the second component based on measurements generated by at least one accelerometer, gyroscope, magnetometer, or hall sensor. The motivation to combine Jin’s multi-processor structure with Eom’s device is to optimize power management by offloading continuous angle and arrangement state determinations from a main application processor directly to a dedicated sensor hub. Implementing this hierarchical processor arrangement represents a substitution of known processing control structures to improve similar electronic devices. This predictable variation yields a responsive foldable device capable of continuously processing sensor data while maintaining maximum power efficiency, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, and Sohn, are silent in regard to:
convert the first orientation and the second orientation from a first format to a second format different from the first format;
However, Sang, further teaches:
convert the first orientation and the second orientation from a first format to a second format different from the first format ([Abstract[, [0004], [0014]-[0015], &[0018]-[0019]: teaches transforming or converting orientation data from a first format (e.g., 3D quaternions or Euler angles) into a second, different format (e.g., 2D representation) for processing);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the mathematical orientation conversion techniques taught by Sang. Jin teaches a third processor managing sensor data, Sang add the mathematical step of converting an orientation from a first format, such as 3D quaternions, to a second format different from the first format, such as a 2D representation. The motivation to combine Sang’s coordinate format conversion logic with the foldable device is to improve the computational efficiency of the device’s spatial calculations by streamlining complex 3D rotational matrices into manageable data representations. Applying Sang’s know mathematical conversion technique to the dual-sensor system represents a predictable variation and substitution of standard algorithms to improve similar electronic devices. This combination ensures the processor efficiently standardizes data formats for faster downstream processing, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, and Sang, are silent in regard to:
determine a distance between the converted first orientation and the second orientation; and
remap the distance to the estimated angle.
However, Hesketh, further teaches:
determine a distance between the converted first orientation and the second orientation ([Col. 7, ll. 7-32]: teaches analyzing the orientation information to calculate individual angular distances and subsequently determining the cumulative angular change (i.e., the mathematical distance) between the two components); and
remap the distance to the estimated angle ([Col. 7, ll. 7-32]: teaches taking the calculated distance (cumulative angular change) and mapping or translating it to the final estimated hinge angle by combining it with a reference baseline).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combination of Eom, Jin, and Sang by incorporating the angular calculation mechanisms taught by Hesketh. Hesketh provides determining a distance between the orientations by calculating a cumulative angular change from individual angular distances, and subsequently remapping that distance to the estimated angle by combining it with a reference angle to determine the final hinge angle. The motivation to combine Hesketh’s angular distance and combination logic with the foldable device is to optimize the calculation of the hinge angle by leveraging angular displacements from a known baseline rather than relying on absolute spatial positioning. Implementing this logic to process the converted orientation formats represents a substitution of known spatial calculation techniques to improve similar devices. This predictable variation yields improved tracking efficiency and reduces the computational load required to determine the device’s folded state, yielding expected predictable results (KSR).
Regarding dependent claim 13, Eom, teaches:
The method of claim 12, further comprising ([Abstract]):
Eom, is silent in regard to:
converting, by the third processor, the first orientation and the second orientation from a first format to a second format different from the first format;
However, Eom, in combination with Jin, and Sang, further teach:
converting, by the third processor, the first orientation and the second orientation from a first format to a second format different from the first format (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], & [0085]-[0090], & [0108]-[0109]: discloses a main processor coupled to the motions sensors; Jin: [0098]: established the method steps executed by the third processor that actively manages the sensor data; Sang: []: teaches the method step of transforming or converting orientation data from a first format (e.g., 3D quaternions or Euler angles) into a second, different format (e.g., 2D representation) for processing);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the dual-housing foldable device of Eom and Jin by incorporating the mathematical orientation conversion techniques taught by Sang. Jin teaches a method utilizing a third processor to manage sensor data, Sang adds the mathematical method step of converting an orientation from a first format, such as 3D quaternions, to a second format different from the first format, such as a 2D representation. The motivation to combine Sang’s coordinate format conversion logic with the foldable device method is to improve the computational efficiency of the device’s spatial calculations by streamlining complex 3D rotational matrices into manageable data representations. Applying Sang’s know mathematical conversion technique to the dual-sensor method represents a predictable variation and substitution of standard algorithms to improve similar electronic devices. This combination ensures the processor efficiently standardizes data formats for faster downstream processing, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, and Sang, are silent in regard to:
determining, by the third processor, a distance between the converted first orientation and the second orientation; and
remapping, by the third processor, the distance to the estimated lid angle.
However, Hesketh, further teaches:
determine a distance between the converted first orientation and the second orientation ([Col. 7, ll. 7-32]: teaches analyzing the orientation information to calculate individual angular distances and subsequently determining the cumulative angular change (i.e., the mathematical distance) between the two components); and
remapping, by the third processor, the distance to the estimated lid angle ([Col. 7, ll. 7-32]: teaches taking the calculated distance (cumulative angular change) and mapping or translating it to the final estimated hinge angle (equivalent to the lid angle of the upper foldable component) by combining it with a reference baseline).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combination of Eom, Jin, and Sang by incorporating the angular calculation mechanisms taught by Hesketh. Hesketh provides the missing method of determining a distance between the orientations by calculating a cumulative angular change from individual angular distances, and subsequently remapping that distance to the estimated lid angle by combining it with a reference angle to determine the final hinge angle. The motivation to combine Hesketh’s angular distance and combination logic with the foldable device method is to optimize the calculation of the hinge angle by leveraging angular displacements from a known baseline rather than relying on absolute spatial positioning. Implementing this logic to process the converted orientation formats represents a substitution of known spatial calculation techniques to improve similar devices. This predictable variation yields improved tracking efficiency and reduces the computational load required to determine the device’s folded state, yielding expected predictable results (KSR).
Claims 7 & 15 are rejected under 35 U.S.C. 103 as being unpatentable over Eom, in view of Jin, in view of Wolf, in view of Sohn, and further in view of Tu et al. (US 2016/0018900 A1, Pub. Date Jan. 21, 2016, hereinafter, Tu).
Regarding dependent claim 7, Eom, teaches:
The device of claim 1 ([Abstract])
Eom, in combination with Jin, are silent in regard to:
wherein the first processor determines the first orientation and the second processor determines the second orientation in a case where the device is in a sleep state, and
However, Eom, in combination with Wolf, and Tu further teach:
wherein the first processor determines the first orientation and the second processor determines the second orientation (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0083]-[0089], & [0157]-[0168]: first motion sensor module 240/340 and overall main processor 120/410; second motion sensor module 250/350 and overall main processor 120/410; Wolf: [0070] & [0079]: teaches using multiple, distributed local processors (first and second processors) to independently evaluate local sensor orientations in low-power states; Tu: [0010]-[0012], [0038], [0041], [0044]-[0046], [0056], [0181], [0185]-[0186], [0188], [0193]-[0194], [0196], [0198], [0208]-[0209], [0211], [0213]-[0214], [0216]-[0217], [0221]-[0222], [0224]-[0225], [0228], [0230]-[0231], [0233], [0237]-[0239], [0241]-[0242],& [0245]: teaches utilizing a secondary processor to evaluate spatial orientations while a main processor remains in a sleep state, and mentions that orientations can be processed by one or more processors as well), and
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combination of Eom and Tu by applying the distributed sensor processing architecture of Wolf to the foldable device’s dual housing structures. Eom in view of Tu teaches utilizing a secondary processor during a sleep state to monitor orientation while a main processor sleeps. Wolf teaches a multi-sensor measurement processing unit (MSMPU 1000) equipped with a local processor (520) that communicates with another separate local processor (MSMPU 1084) to independently collect and determine sensor orientations in low-power modes before waking an external processor (1082). This combination distributes the orientation workload, whereby a first processor and a second processor independently determine the first and second orientations in a sleep state, leaving the third external processor to determine the final angle in the awake state. The rationale for this modification is the use of a known technique to improve similar devices, yielding the predictable variation of reduced power consumption and lower processing latency by localizing the orientation calculations at the distinct sensor locations before waking the primary system, yielding expected predictable results (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
the third processor determines the angle in a case where the device is in an awake state.
However, Eom, in combination with Tu, further teach:
the third processor determines the angle in a case where the device is in an awake state (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0083]-[0089], & [0157]-[0168]: discloses determining the folding angle using a main processor; Tu: [0010]-[0012], [0038], [0041], [0044]-[0046], [0056], [0181], [0185]-[0186], [0188], [0193]-[0194], [0196], [0198], [0208]-[0209], [0211], [0213]-[0214], [0216]-[0217], [0221]-[0222], [0224]-[0225], [0228], [0230]-[0231], [0233], [0237]-[0239], [0241]-[0242],& [0245]: teaches that the main applications processor handles functions when operating in an awake/wake state, corresponds to a third, primary processor determining the final angle while awake).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combination of Eom to incorporate the sleep and wake state processor architecture of Tu Eom discloses a foldable device that utilizes a main processor and an auxiliary processor to determine orientations and folding angles based on motion sensors. Tu teaches a mobile device incorporating a coprocessor 208 that continuously determines spatial orientations based on sensor data while an applications processor 204 resides in a low-power sleep state. This combination allows a lower-power processor to determine the first orientation during a sleep state, while the main processor calculates the overall device angle during an awake state. The motivation to combine these references is the substitution of known multi-processor power management techniques to improve similar devices. Solving the problem of excessive battery drain by preventing the main processor from waking up to determine individual housing orientations, yielding expected predictable results (KSR).
Regarding dependent claim 15, Eom, teaches:
The method of claim 8, further comprising ([Abstract]):
Eom, in combination with Jin, Wolf, and Sohn, are silent in regard to:
detecting a screen on event in which a screen of the device is set to an on state; and
setting the device to an awake state in which the third processor is set to an on state, the angle being determined in response to the device being set to the awake state.
However, Tu, further teaches:
detecting a screen on event in which a screen of the device is set to an on state ([0059], [0130], [0179], [0185], [0195]-[0196], [0210]-[0211], [0213], [0215]-[0216], [0223]-[0224], [0229]-[0231], [0233]-[0234], [0237], [0240]-[0241], [0244], [Claim 1], [Claim 4], [Claim 8], [Claim 14], [Claim 20], & [Claim 21]: discloses detecting events that trigger powering a display screen from an inactive state to an active or on state); and
setting the device to an awake state in which the third processor is set to an on state ([0059], [0130], [0179], [0185], [0195]-[0196], [0210]-[0211], [0213], [0215]-[0216], [0223]-[0224], [0229]-[0231], [0233]-[0234], [0237], [0240]-[0241], [0244], [Claim 1], [Claim 4], [Claim 8], [Claim 14], [Claim 20], & [Claim 21]: discloses setting the device to a wake (awake) state by transitioning the main applications processor (corresponds to the third processor) from a sleep state to an active, on state),
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify Eom to incorporate Tu’s power management and wake state transitions, to detect a screen on event wherein the screen is set to an on state, and setting the device to an wake state in which the third processor is set to an on state, with the angle being determined in response to the device being set to the awake state. Eom discloses determining a folding angle of a device using processors. Tu teaches power management methods comprising detecting events to power a display to an on state, and setting the device to an awake state where a main application processer (204) transitions from a sleep state to resume active execution. The motivation to combine these references is the substitution of known multi-processor power management and wake-state triggering techniques to improve similar devices. This combination achieves the predictable variation of improved efficiency and maintained power by ensuring the primary processor only executes angle determination tasks when the device screen is actively turned on and the system is woken, yielding expected predictable results (KSR).
However, Eom, in combination with Tu, further teach:
the angle being determined in response to the device being set to the awake state (Eom: [0008], [0030]-[0031], [0070]-[0071], [0083]-[0089], & [0157]-[0168]: discloses determining the folding angle using a processor; Tu: [0010]-[0012], [0038], [0041], [0044]-[0046], [0056], [0181], [0185]-[0186], [0188], [0193]-[0194], [0196], [0198], [0208]-[0209], [0211], [0213]-[0214], [0216]-[0217], [0221]-[0222], [0224]-[0225], [0228], [0230]-[0231], [0233], [0237]-[0239], [0241]-[0242],& [0245]: teaches that the main processor resumes its execution and primary functions upon entering the awake state, the determination of the angle is a task executed in response to the device waking up).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify Eom to incorporate Tu’s screen activation and processor wake-state transitions of Tu, with the angle being determined in response to the device being set to the awake state. Eom discloses determining a foldable electronic device determining a folding angle using processors. Tu teaches a power management system that detects events to power a user interface display from a sleep state to a wake state to resume active execution. This combination corresponds to transitioning the display screen to an on state during a screen on event and setting the device to an awake state where the third processor is turned on, allowing the third processor to determine the angle only in response to entering this awake state. The rational to combine these references is the substitution of known processor wake-state and screen activation techniques to improve similar devices. Applying Tu’s teachings to Eom achieves the predictable variation of improved power efficiency and extended battery life by ensuring that the primary processor executes angle determination tasks only when the device screen is actively turned on and the system is awoken, yielding expected predictable results (KSR).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Eom, in view of Jin, in view of Wolf, in view of Sohn, and further in view of Luinge et al. (US 2011/0028865 A1, Pub. Date Feb. 3, 2011, hereinafter, Luinge).
Regarding dependent claim 21, Eom, teaches:
The device of claim 1 ([Abstract] & [0008])
Eom, in combination with Jin, and Wolf, are silent in regard to:
wherein the rotation of the second orientation of the second component includes:
a first rotation of the second orientation of the second component with respect to an axis in Earth's reference frame by a first amount determined based on the angle between the first component and the second component; and
However, Sohn, in combination with Luinge, further teach:
wherein the rotation of the second orientation of the second component includes (Sohn: [Col. 8, ll. 34-65]; Luinge: [0025]-[0028] & [0085]-[0088]):
a first rotation of the second orientation of the second component with respect to an axis in Earth's reference frame by a first amount determined based on the angle between the first component and the second component (Sohn: [Col. 8, ll. 34-65]: establishes the baseline rotation of a sensor frame relative to an Earth reference frame; Luinge: [0025]-[0028] & [0085]-[0088]: builds on by teaching that the orientations of multiple connected segments must first be expressed in the Global (Earth) coordinate frame prior to resolving the joint constraints. The mathematical rotation matrix of the second segment relies on the relative orientation/angle established at the connecting joint); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the single-device Earth reference frame rotation taught by Sohn by incorporating the multi-segment mathematical kinematic coupling transformations/algorithms for hinged segments taught by Luinge. Sohn teaches performing a first rotation of a sensor rotation with respect to an axis in an Earth reference frame to correct orientation errors. Luinge teaches performing a subsequent second rotation of a second component’s orientation with respect to a hinge axis, determined based on translating the first orientation from the global coordinate frame to the hinge joint axis. A two-step rotation process where a first rotation of a second segment’s orientation is performed with respect to a global (Earth) coordinate frame, followed by a second rotation with respect to a hinge joint axis based on the first segment’s known orientation and the global transformation. Specifically, Luinge further teaches that acceleration measured by an IMU on segment A is expressed in the global coordinate frame and translated to the joint to determine the relative orientation of segment B. The motivation to combine Luinge’s two-step hinge-based kinematic rotation translation logic with Sohn’s Earth reference frame tracking is to improve the accuracy of the device’s 3D spatial calculations for hinged devices by mathematically accounting for the physical movement constraints of the mechanical hinge joint connecting the two distinct inertial sensor units, by utilizing standard rigid-body kinematic algorithms that reference a global coordinate system (Earth’s gravity/magnetic frame) and the known spatial orientation of the primary housing. Applying Luinge’s known multi-segment kinematic coupling technique to Sohn’s orientation determination method represents a predictable variation and substitution of standard spatial calculation algorithms, where utilizing a sequence of coordinate frame rotations (e.g., rotation matrices or quaternions) to track bodies is a standard, predictable engineering practice to improve similar multi-sensor devices,. This combination yields an accurate, drift-compensated orientation estimate between two hinged components regardless of their overall orientation in the global reference frame, that would yield predictable results (KSR).
Eom, in combination with Jin, Wolf, and Sohn, are silent in regard to:
a second rotation, subsequent to the first rotation, of the second orientation of the second component with respect to the hinge axis by a second amount determined based on (1) the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope and (2) the first rotation of the second orientation.
However, Luinge, further teaches:
a second rotation, subsequent to the first rotation, of the second orientation of the second component with respect to the hinge axis by a second amount determined based on (1) the first orientation of the first component as determined based on the measurements by the first accelerometer and the first gyroscope and (2) the first rotation of the second orientation ([0006]-[0007], [0025], [0039], [0041], [0044], [0050], [0083]-[0089], & [0100]: teaches the second rotation step, where the orientation of the second segment (segment B) is resolved by translating the global coordinate measurements to the physical hinge joint connecting the segments. This second rotation around the hinge axis is determined based on the known first orientation of segment A and the global coordinate frame (first) rotation based on the known orientation of the first segment and their shared kinematic data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the multi-processor foldable device of Eom, Jin, and Sohn, by incorporating the mathematical kinematic coupling transformations/algorithms for hinged segments taught by Luinge. Sohn teaches rotating a predicted sensor frame relative to an Earth reference frame. Luinge teaches a two-step rotation process where a first rotation of a second segment’s orientation is performed with respect to a global (Earth) coordinate frame, followed by a second rotation with respect to a hinge joint axis based on the first segment’s known orientation and the global transformation. Specifically, Luinge further teaches that acceleration measured by an IMU on segment A is expressed in the global coordinate frame and translated to the joint to determine the relative orientation of segment B. The motivation to combine Luinge’s two-step hinge-based kinematic rotation logic with the multi-sensor foldable device is to improve the accuracy of the device’s 3D spatial calculations by mathematically correcting for the physical constraints of the mechanical hinge joint connecting the two distinct inertial sensor units, by utilizing standard rigid-body kinematic algorithms that reference a global coordinate system (Earth’s gravity/magnetic frame) and the known spatial orientation of the primary housing. Applying Luinge’s known multi-segment kinematic coupling technique to the device’s angle determination method represents a predictable variation and substitution of standard algorithms, where utilizing a sequence of coordinate frame rotations (e.g., rotation matrices or quaternions) to track bodies is a standard, predictable engineering practice to improve similar multi-sensor devices, that would yield predictable results (KSR).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Eom, in view of Jin, in view of Wolf, in view of Sohn, in view of Tu, and further in view of Lin et al. (US 2018/0275724 A1, Pub. Date Sep. 27, 2018, hereinafter, Lin).
Regarding dependent claim 22, Eom, teaches:
The device of claim 1 ([Abstract] & [0008])
Eom, in combination with Jin, Wolf, and Sohn, are silent in regard to:
wherein in response to the device being set to a sleep state,
the first sensor unit and the second sensor unit remain operational and the third processor enters a low-powered state, and
in response to the device being set to an awake state, the third processor determines the angle between the first component and the second component,
However, Tu, further teaches:
wherein in response to the device being set to a sleep state ([0010]-[0012], [0041], [0044]-[0050], [0056], [0181], [0185]-[0186], [0188], [0193]-[0194], [0196], [0198], [0208]-[0209], [0211], [0213]-[0214], [0216]-[0217], [0221]-[0222], [0224]-[0225], [0228], [0231], [0233], [0237]-[0239], [0241]-[0242], & [0245]),
the first sensor unit and the second sensor unit remain operational and the third processor enters a low-powered state ([0010]-[0012], [0041], [0044]-[0050], [0056], [0181], [0185]-[0186], [0188], [0193]-[0194], [0196], [0198], [0208]-[0209], [0211], [0213]-[0214], [0216]-[0217], [0221]-[0222], [0224]-[0225], [0228], [0231], [0233], [0237]-[0239], [0241]-[0242], & [0245]: teaches a device architecture where a primary applications processor (third processor equivalent) is set to a low-powered sleep state, while the motion sensors and auxiliary processors remain continuously operational to monitor spatial movements), and
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom by incorporating the power-state processor delegation architecture taught by Tu. Eom teaches determining a folding angle based on sensor modules. Tu provides setting the device to a sleep state where motion sensors remain operational while a primary applications processor enters a low-powered state, and subsequently setting the device to an wake state where the primary applications processor awakens to execute higher-level determinations. The motivation to combine Tu’s sleep-state sensor evaluation logic with Eom’s foldable device is to maximize battery efficiency by delegating continuous orientation monitoring to lower-level components during a sleep mode, reserving the power-intensive primary processor for complex angle determinations only when the device is actively awake. Applying Tu’s known state-based power management technique to a dual-sensor system represents a predictable variation and substitution of standard operational logic to improve similar electronic devices. This combination ensures that the device continuously tracks its physical orientation state without prematurely draining the battery prior to the third processor awakening to perform calculations, yielding expected predictable results (KSR).
However, Eom, in combination with Tu, further teach:
in response to the device being set to an awake state, the third processor determines the angle between the first component and the second component (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0083]-[0089], & [0157]-[0168]: discloses determining the folding angle using a processor; Tu: [0059], [0130], [0179], [0185], [0195]-[0196], [0198], [0210]-[0211], [0213], [0215]-[0216], [0223]-[0224], [0229]-[0231], [0233]-[0234], [0237], [0240]-[0241], [0244], [Claim 1], [Claim 4], [Claim 8], [Claim 14], [Claim 20], & [Claim 21]: teaches transitioning the main applications processor to a fully operational wake state to execute higher-level device functions. In combination with Eom, this awakened third processor then handles the overall folding angle determination between the components),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the foldable device of Eom by incorporating the power-state processor taught by Tu. Eom teaches determining a folding angle based on dual sensor modules. Tu places a primary applications processor in a low-power sleep state while lower-level processors and motion sensors remain operational to continuously track orientation. The motivation to combine Tu’s sleep-state sensor evaluation logic with Eom’s foldable device is to maximize battery efficiency by delegating continuous low-level orientation monitoring to secondary processors during sleep mode, reserving the primary processing power for complex angle determinations only when the device is fully awake. Applying Tu’s known state-based power management technique to the dual-sensor system represents a predictable variation and substitution of standard operational logic to improve similar electronic devices, yielding expected predictable results (KSR).
Eom, in combination with Jin, Wolf, Sohn, and Tu, are silent in regard to:
the first processor determiners the first orientation of the first component and the second processor determines the second orientation of the second component,
However, Eom, in combination with Lin, further teach:
the first processor determiners the first orientation of the first component and the second processor determines the second orientation of the second component (Eom: Figs. 1, 2A, & 4A; [0008], [0030]-[0031], [0070]-[0071], [0083]-[0089], & [0157]-[0168]: first motion sensor module 240/340 and overall main processor 120/410; second motion sensor module 250/350 and overall main processor 120/410; Lin: [0019], [0044], [0066]-[0067], [Claim 9], [Claim 10], & [Claim 20]: teaches a distributed processing architecture when both the first device portion and the second device portion contain their own dedicated processing devices to operate and process local data. Applying to Eom, the first local processor determines the first orientation for the first component’s sensor and the second local processor determines the second orientation for the second component),
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combination of Eom and Tu with the distributed processing architecture taught by Lin. The combination of Eom and Tu teaches low-power processors monitoring sensors while a main processor sleeps. Lin provides a first device portion each comprising at least one dedicated processing device configured to operate and process data independently for their respective portions. The motivation to combine Lin’s distributed processing units with the power-managed foldable device is to distribute the computational load and allow each physical portion of the device to locally pre-process its own raw inertial sensor data into orientations, reducing the data transmission bandwidth required across the device’s central hinge. Implementing localized processors for each sensor component represents a substitution of known computing architectures to improve similar multi-part devices, yielding expected predictable results (KSR).
Eom, in combination with Jin, and Wolf, are silent in regard to:
rotates the second orientation of the second component, and estimates the angle between the first component and the second component during an awake state of the device.
However, Sohn, further teaches:
rotates the second orientation of the second component, and estimates the angle between the first component and the second component during an awake state of the device (Fig. 1; [Abstract], [Col. 1, ll. 42-59], [Col. 2, ll. 45-59], [Col. 4, ll. 1-24 & 40-56], [Col. 5, ll. 59-67], [Col. 6, ll. 19-28], [Col. 7, ll. 5-15 & 53-60], [Col. 8, ll. 34-[0045], [Col. 9, ll. 3-11], [Col. 12, ll. 3-44], [Col. 16, ll. 28-37], [Col. 17, ll. 50-61], [Col. 18, ll. 8-16 & 55-64], [Col. 19, ll. 20-35], [Claim 1], [Claim 2], [Claim 4], [Claim 6], [Claim 9], [Claim 10], [Claim 12], [Claim 14], [Claim 17], [Claim 18], & [Claim 19]: teaches the awake-state algorithms executed by a main processor; mathematically rotating a predicted orientation to realign with a reference orientation, and estimating the final angle based on the combination of the first orientation and the rotated second orientation).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the combination of Eom, Tu, and Lin with the orientation and estimation techniques taught by Sohn. The combined primary art teaches waking a primary third processor to aggregate locally-determined orientations. Sohn teaches mathematically rotating the second orientation to realign with the first orientation and estimating the angle based on the rotated second orientation. The motivation to combine Sohn’s mathematical realignment and estimation logic with the distributed-processor foldable device is to improve the accuracy of the overall angle estimation by mathematically correcting for spatial drift and coordinate misalignment between the two independently processed sensor frames once the main processor wakes up. Applying Sohn’s known sensor fusion and spatial rotation techniques to the device represents a predictable variation to improve similar multi-sensor devices, yielding an accurate folding angle estimate regardless of the device’s continuous motion in space, and yielding expected predictable results (KSR).
Conclusion
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/HUGO NAVARRO/ Examiner, Art Unit 2858 July 27, 2026
/A.A/ Primary Examiner, Art Unit 2858
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