DETAILED ACTION
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.
Claims 5-6, 12-13 and 18-19 are 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.
The term “low power” in claims 5-6, 12-13 and 18-19 is a relative term which renders the claim indefinite. The term “low power” is not defined by the claim and the specification does not provide a standard for ascertaining the requisite degree.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without being integrated into a practical application and do not include additional elements that amount to significantly more than the judicial exception.
Utilizing the two-step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline, Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 USC 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong one), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then proceeding to the second part of Step 2A (Prong two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination, provide “inventive concept” that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 USC 101.
Looking at the claims, the claims satisfy the first part of the test 1A, namely the claims are directed to one of the four statutory class, apparatus and method. In Step 2A Prong one, we next identify any judicial exceptions in the claims. In Claim 1 (as a representative example), we recognize that the limitations “collecting N samples of acceleration data after detecting a free-fall event, calculating a number of crossing events from the N samples of acceleration data, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determining that the fall is on the hard surface in response to the number of crossing events being greater than a second threshold; and determining that the fall is on the soft surface in response to the number of crossing
events being less than the second threshold,” are abstract ideas, as they involve a mental process. Similar rejections are made for other independent and dependent claims. Additionally, the limitation “high-pass filter” and “norm” are abstract ideas, as they involve mathematical concept. With the identification of abstract ideas, we proceed to Step 2A, Prong two, where with additional elements and taken as a whole, we evaluate whether the identified abstract idea is being integrated into a practical application.
In Step 2A, Prong two, the claims additionally recite limitations in regard to accelerometer, processor and memory storage, but said limitations, recited at high level of generality, are merely directed to insignificant data collection activity and recitation of general-purpose computer for implementing the abstract idea. The claims also recite
finite state machine circuit, and both the state machine circuit and the machine learning core both being always ON, and in low power, etc., but said limitations, recited at high level of generality, are merely directed to generic mathematical modeling of finite state machine and machine learning implemented as a general circuit to process the collected data, and do not cure in integrating the abstract idea into a practical application, since 1) there is no evidence that the functioning of the circuits themselves are improved, and 2) lack of improvement in technology due to the absence of any detail as to how their generic inclusion necessarily improves detecting fall event. At most, the claims are directed to the abstract idea of improvement in the detecting the fall event. However, improved or new abstract ideas are still abstract ideas, and not eligible under the 101. As such, the abstract idea is not integrated into a practical application. Consequently, with the identified abstract idea not being integrated into a practical application, we proceed to Step 2B and evaluate whether the additional elements provide “inventive concept” that would amount to significantly more than the abstract idea.
In Step 2B, the claims additionally recite limitations in regard to accelerometer, processor and memory storage, but said limitations, recited at high level of generality, are merely directed to insignificant data collection activity and recitation of general-purpose computer for implementing the abstract idea, that are well-understood, routine and conventional. The claims also recite limitations in regard to finite state machine circuit, and both the state machine circuit and the machine learning core both being always ON, and in low power, etc., but said limitations, recited at high level of generality, are merely directed to conventional generic mathematical modeling of finite state machine and machine learning implemented as a general circuit to process the collected data, that are well-understood, routine and conventional. As such, the claims do not provide additional elements that would amount to significantly more than the abstract idea.
In Summary, the claims recite abstract idea without being integrated into a practical application, and do not provide additional elements that would amount to significantly more. As such, taken as a whole, the claims are ineligible under the 35 USC 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 (hereinafter Goel) in view of Barfield, US-PGPUB 2013/0054180 (hereinafter Barfield)
Regarding Claim 1. Goel discloses determining whether a fall of an electronic device is on a hard surface or a soft surface (Abstract, electronic device being dropped on surfaces; Paragraph [0036], surface type, hard surface, soft surface), comprising:
collecting N samples of acceleration data after detecting a free-fall event (Paragraph [0031], acceleration data at a sampling rate; Paragraph [0037], free-fall event; Fig. 5);
an acceleration norm calculation for each sample of the N samples exceeds a first threshold (Fig. 5, 502, YES; Paragraph [0037], norm), and determining that the fall is on hard surface and soft surface (Paragraph [0056], classifying as hard, soft surface, using the classifier)
Goel does not disclose calculating a number of crossing events from the N samples of acceleration data, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold; determining that the fall is on the hard surface in response to the number of crossing events being greater than a second threshold; and determining that the fall is on the soft surface in response to the number of crossing events being less than the second threshold.
Barfield discloses detecting the fall of an object on soft and hard grounds (Paragraph [0066], based on the thresholds and magnitude of impacts, followed by referring back to 400 for further refining), which includes calculating a number of crossing events from the N samples of acceleration data, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determining that the fall is on the hard surface in response to the number of crossing events being greater than a second threshold, and determining that the fall is on the soft surface in response to the number of crossing events being less than the second threshold (Figs. 3-4; description of figures explained in Paragraphs [0049]-[0063], in two stages, using counters and predetermined number (Paragraph [0049], and index (i) (Paragraph [0051]), which categorizes soft and hard surface falls from the magnitude of the impacts).
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Barfield in Goel and calculate a number of crossing events from the N samples of acceleration data, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determine that the fall is on the hard surface in response to the number of crossing events being greater than a second threshold, and determine that the fall is on the soft surface in response to the number of crossing events being less than the second threshold, and thereby accurately determine the fall event.
Regarding Claim 7. Barfield discloses the first threshold and the second threshold are stored in a memory storage of the electronic device, the first threshold and the second threshold being configurable threshold values (Paragraph [0004], loading thresholds from the memory)
Claims 2 and 3 rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in view of Barfield, US-PGPUB 2013/0054180 as applied to Claim 1 above, and further in view of STMICROELECTRONICS, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) (cited by the Applicant) (hereinafter STM)
Regarding Claim 2. The modified Goel does not disclose detecting the free-fall event by a finite state machine circuit of a sensor of the electronic device.
STM discloses detecting the free-fall event by a finite state machine circuit of a sensor of the electronic device (as an overall summary, pages 1-2, Features: “Finite State Machine, free-fall).
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of STM in the modified Goel and detect the free-fall event by a finite state machine circuit of a sensor of the electronic device, and accurately and reliably detect free-fall.
Regarding Claim 3. Goel discloses determining that the fall is on the hard surface or the soft surface comprises determining by a machine learning core of the sensor, the method further comprising communicating surface type of the fall to a processor of the electronic device (Paragraph [0044]; Figs. 1-2, classifier module 102a, surface classification module 206)
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in views of Barfield, US-PGPUB 2013/0054180 and STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 3 above, and further in view of Wehba et al., US-PGPUB 2022/0176201 (hereinafter Wehba)
Regarding Claim 4. Goel discloses the machine learning core determines whether the fall is on the hard surface or the soft surface (Paragraph [0044])
STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
STM teaches that the Finite State Machine are always ON. With Wehba teaching using the finite state machine and machine learning together to analyze motion in real-time, it’s obvious that the machine learning core is also always ON in conjunction with the Finite State Machine to process motion data in a timely basis in real-time. As such, at the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba in the modified Goel and have the finite state machine circuit and the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface, accurately and in timely manner in real-time.
11. Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in views of Barfield, US-PGPUB 2013/0054180 and STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 3 above, and further in view of Wehba, US-PGPUB 2022/0176201 and Lee et al., US-PGPUB 2014/0344194 (hereinafter Lee)
Regarding Claim 5. STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
Regarding Claim 6. STM discloses the sensor can change device configurations independently from the processor, wherein the finite state machine circuit is always ON (pages 1-2, always ON; section 2.3, Finite State Machine, independently executed),
Finite state machine circuit detecting the free-fall event (pages 1-2, free fall)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
12. Claims 8 is rejected under 35 U.S.C. 103 as being unpatentable over Goel, US-PGPUB 2023/0029857 in views of Jin et al., US-PGPUB 2011/0144542 (hereinafter Jin), Barfield, US-PGPUB 2013/0054180 and Narashimhan et al., US-PGPUB 2013/0120147 (hereinafter Narashimhan).
Regarding Claim 8. A method for determining whether a fall of an electronic device is on a hard surface or a soft surface (Abstract, electronic device being dropped on surfaces; Paragraph [0036], surface type, hard surface, soft surface), comprising:
collecting N samples of acceleration data after detecting a free-fall event (Paragraph [0031], acceleration data at a sampling rate; Paragraph [0037], free-fall event; Fig. 5);
calculating a variance from the N samples of acceleration data (Paragraph [0041], variance);
determining that the fall is on the hard surface in response to the variance from the N
samples of acceleration data on the N samples of acceleration data, and determining that the fall is on the soft surface in response to the variance from the N samples of acceleration data on the N samples of acceleration data (Paragraph [0070], variance and inputting into the classifier to determine soft or hard surface; Fig. 5, Paragraph [0073]-[0075])
Goel does not disclose applying a high-pass filter on the N samples of acceleration data, and determining that the fall is on the hard surface in response to variance being greater than a threshold, and determining that the fall is on the soft surface in response to variance being lesser than a threshold.
Narashimhan discloses applying a high-pass filter on the N samples of acceleration data (Paragraph [0032]).
Jin discloses determining that the sensor module has suffered an impact if a variance of the measurements from the accelerometer exceeds a predetermine threshold (Paragraph [0013])
Barfield discloses determining the soft or hard ground depending on the magnitudes of the impacts of the object falling (Paragraph [0067])
Jin teaches using threshold to distinguish different magnitudes of impact associated with variance. Although Barfield does not explicitly disclose “threshold” to distinguish soft from the hard impacts, it would have been obvious to do so, based on the teachings of Jin. As such, at the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Narasimhan in Goel, and calculate a variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data, determine that the fall is on the hard surface in response to the variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data being greater than a threshold; and determine that the fall is on the soft surface in response to the variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data being less than the threshold, so as to accurately determine the free-fall event with reduced noise.
13. Claims 9-10 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in views of Jin, US-PGPUB 2011/0144542, Barfield, US-PGPUB 2013/0054180 and Narashimhan, US-PGPUB 2013/0120147as applied to Claim 1 above, and further in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages)
Regarding Claim 9. The modified Goel does not disclose detecting the free-fall event by a finite state machine circuit of a sensor of the electronic device.
STM discloses detecting the free-fall event by a finite state machine circuit of a sensor of the electronic device (as an overall summary, pages 1-2, Features: “Finite State Machine, free-fall).
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of STM in the modified Goel and detect the free-fall event by a finite state machine circuit of a sensor of the electronic device, and accurately and reliably detect free-fall.
Regarding Claim 10. Goel discloses determining that the fall is on the hard surface or the soft surface comprises determining by a machine learning core of the sensor, the method further comprising communicating surface type of the fall to a processor of the electronic device (Paragraph [0044]; Figs. 1-2, classifier module 102a, surface classification module 206)
Regarding Claim 14. Barfield discloses the threshold is stored in a memory storage of the electronic device, the threshold being configurable (Paragraph [0004], loading thresholds from the memory)
14. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in views of Jin, US-PGPUB 2011/0144542, Barfield, US-PGPUB 2013/0054180 and Narashimhan, US-PGPUB 2013/0120147 as applied to Claim 10 above, and further in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 10, and further in view of Wehba, US-PGPUB 2022/0176201.
Regarding Claim 11. Goel discloses the machine learning core determines whether the fall is on the hard surface or the soft surface (Paragraph [0044])
STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
STM teaches that the Finite State Machine are always ON. With Wehba teaching using the finite state machine and machine learning together to analyze motion in real-time, it’s obvious that the machine learning core is also always ON in conjunction with the Finite State Machine to process motion data in a timely basis in real-time. As such, at the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba in the modified Goel and have the finite state machine circuit and the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface, accurately and in timely manner in real-time.
15. Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in views of Jin, US-PGPUB 2011/0144542, Barfield, US-PGPUB 2013/0054180 and Narashimhan, US-PGPUB 2013/0120147 as applied to Claim 10 above, and further in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 10, and further in views of Wehba, US-PGPUB 2022/0176201 and Lee, US-PGPUB 2014/0344194.
Regarding Claim 12. STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
Regarding Claim 13. STM discloses the sensor can change device configurations independently from the processor, wherein the finite state machine circuit is always ON (pages 1-2, always ON; section 2.3, Finite State Machine, independently executed), finite state machine circuit detecting the free-fall event (pages 1-2, free fall)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
16. Claims 15-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in view of Barfield, US-PGPUB 2013/0054180 in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages)
Regarding Claim 15. Goel discloses a sensor of an electronic device (Figs. 1-2), the sensor comprising: an accelerometer configured to collect acceleration data (Paragraph [0028], accelerometer);
N samples of acceleration data collected after the free-fall event (Paragraph [0031], acceleration data at a sampling rate; Paragraph [0037], free-fall event; Fig. 5);
and determining that the fall is on hard surface and soft surface using machine learning (Paragraphs [0044], [0056], classifying as hard, soft surface, using the classifier)
Goel does not disclose a finite state machine circuit configured to receive the acceleration data from the accelerometer and detect a free-fall event, and
a machine learning core, in response to detecting the free-fall event, configured to:
calculate a number of crossing events from N samples of acceleration data collected
after the free-fall event, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determine that a fall of the electronic device is on a hard surface in response to the number of crossing events being greater than a second threshold, and
determine that the fall is on a soft surface in response to the number of crossing events being less than the second threshold.
STM discloses detecting the free-fall event by a finite state machine circuit of a sensor of the electronic device (as an overall summary, pages 1-2, Features: “Finite State Machine, free-fall).
Barfield discloses detecting the fall of an object on soft and hard grounds (Paragraph [0066], based on the thresholds and magnitude of impacts, followed by referring back to 400 for further refining), which includes calculating a number of crossing events from the N samples of acceleration data, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determining that the fall is on the hard surface in response to the number of crossing events being greater than a second threshold, and determining that the fall is on the soft surface in response to the number of crossing events being less than the second threshold (Figs. 3-4; description of figures explained in Paragraphs [0049]-[0063], in two stages, using counters and predetermined number (Paragraph [0049], and index (i) (Paragraph [0051]), which categorizes soft and hard surface falls from the magnitude of the impacts).
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of STM and Barfield in Goel and have the a finite state machine circuit configured to receive the acceleration data from the accelerometer and detect a free-fall event, and a machine learning core, in response to detecting the free-fall event, configured to: calculate a number of crossing events from N samples of acceleration data collected after the free-fall event, the number of crossing events corresponding to a number of times an acceleration norm calculation for each sample of the N samples exceeds a first threshold, determine that a fall of the electronic device is on a hard surface in response to the number of crossing events being greater than a second threshold, and determine that the fall is on a soft surface in response to the number of crossing events being less than the second threshold with accuracy and reliability.
Regarding Claim 16. Goel discloses the machine learning core is further configured to communicate surface type of the fall to a processor of the electronic device (Paragraph [0044]; Figs. 1-2, classifier module 102a, surface classification module 206)
Regarding Claim 20. Goel discloses the first threshold and the second threshold are stored in a memory storage of the electronic device, the first threshold and the second threshold being configurable threshold values (Paragraph [0004], loading thresholds from the memory)
17. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in view of Barfield, US-PGPUB 2013/0054180 in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 16, and further in view of Wehba, US-PGPUB 2022/0176201 (hereinafter Wehba)
Regarding Claim 17. Goel discloses the machine learning core determines whether the fall is on the hard surface or the soft surface (Paragraph [0044])
STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
STM teaches that the Finite State Machine are always ON. With Wehba teaching using the finite state machine and machine learning together to analyze motion in real-time, it’s obvious that the machine learning core is also always ON in conjunction with the Finite State Machine to process motion data in a timely basis in real-time. As such, at the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba in the modified Goel and have the finite state machine circuit and the machine learning core are always ON, wherein the finite state machine circuit communicates the free-fall event detection to the machine learning core, and, in response, the machine learning core determines whether the fall is on the hard surface or the soft surface, accurately and in timely manner in real-time.
18. Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Goel et al., US-PGPUB 2023/0029857 in view of Barfield, US-PGPUB 2013/0054180 as applied to Claim 10 above, and further in view of STM, “iNEMO inertial module: always on 3D accelerometer and 3D gyroscope,” LSM6DSO Datasheet, DS 12140 (2019) (172 pages) as applied to Claim 16, and further in views of Wehba, US-PGPUB 2022/0176201 and Lee, US-PGPUB 2014/0344194.
Regarding Claim 18. STM discloses the finite state machine circuit is always ON, detecting free-fall event (title; pages 1-2 and Section 2 on Finite State Machine with accelerometer-gyroscope that is always ON)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein the finite state machine circuit communicates the free-fall event detection to the processor, and, in response, the electronic device enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
Regarding Claim 19. STM discloses the sensor can change device configurations independently from the processor, wherein the finite state machine circuit is always ON (pages 1-2, always ON; section 2.3, Finite State Machine, independently executed),
Finite state machine circuit detecting the free-fall event (pages 1-2, free fall)
The modified Goel does not disclose the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface.
Wehba discloses using finite state machine and machine learning together to calculate motion (Paragraphs [0034]-[0038]; [0075]-[0078]; Fig. 5 and 7) in real-time (Paragraph [0029])
Lee discloses the machine learning is at relatively low power (Paragraph [0024])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Wehba and Lee in the modified Goel and have the machine learning core is initially in low-power mode, wherein, in response to the finite state machine circuit detecting the free-fall event, the finite state machine circuit enables the machine learning core and the machine learning core determines whether the fall is on the hard surface or the soft surface, with accuracy, while optimizing the usage of power.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rivolta et al., US-PGPUB 2020/0026365
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/HYUN D PARK/Primary Examiner, Art Unit 2857