DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 17 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 17 recites the limitation "the second one gait parameter" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the second one gait parameter” will be interpreted as “the second gait parameter”.
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.
Claims 1-4 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Barth (US 20190150793) (cited by Applicant) in view of Khandelwal et al: "Novel methodology for estimating Initial Contact events from accelerometers positioned at different body locations", Gait and Posture, 2018, Elsevier B.V., vol.59, pgs. 278 – 285 (cited by Applicant).
Regarding claim 1, Barth teaches an analysis system for assessing gait quality and/or gait-related health status of a human (Paragraph [0001]: method and system for analyzing gait of an individual human being), the analysis system comprising: a first sensor device arranged at a region of a first leg of the human, and a second sensor device arranged at a region of a second leg of the human, wherein the first and the second sensor devices each comprises at least one 3-axis accelerometer and at least one 3-axis gyroscope (Paragraph [0035]: data representing the 3D-movement of a foot means any type of data that contains information about the movement of a foot, and also the corresponding leg, of a subject; sensor systems comprising a 3D-accelerometer and/or a 3D-gyroscope are employed; Paragraph [0128]: 3D-movement of both feet of a subject are analyzed), wherein the first and the second sensor devices are configured to provide gait data and a computing unit configured to receive the gait data from the first and the second sensor devices, analyze the received gait data for determining at least one gait parameter related to stride characteristics of the human, and analyze the at least one gait parameter to assess gait quality and/or gait-related health status of the human (Paragraph [0022] system for analyzing gait of a subject comprising an input for data representing the 3D-movement of a foot/feet, wherein said 3D-movement comprises a plurality of strides of said foot, a processing unit adapted for identifying first data segments, representing at least one stride, and determining one or more stride features; Paragraph [0030] which may allow for a more accurate analysis of gait impairments for which said stride features are representative).
However, Barth does not teach “wherein the at least one gait parameter comprises information of at least one computed energy density spectrum”.
Khandelwal (2018), in a related field of endeavor, teaches wherein the at least one gait parameter comprises information of at least one computed energy density spectrum. (See Description of Fig. 1).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth to provide “wherein the at least one gait parameter comprises information of at least one computed energy density spectrum” as taught by Khandelwal (2018). Doing so provides a system capable of estimating initial contact events from different body locations based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during normal gait. (Page 278, Abstract, Introduction).
Regarding claim 2, Barth does not teach “wherein the at least one computed energy density spectrum comprises at least one energy density spectrum computed from both or either one of sets of acceleration signals”.
Khandelwal (2018) teaches wherein the at least one computed energy density spectrum comprises at least one energy density spectrum computed from both or either one of sets of acceleration signals. (See Description of Fig. 1).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth to provide “wherein the at least one computed energy density spectrum comprises at least one energy density spectrum computed from both or either one of sets of acceleration signals” as taught by Khandelwal (2018). Doing so achieves accuracy and robustness for estimating initial contact events from different accelerometers. (Page 278, Abstract; Page 284, col. 1).
Regarding claim 3, Barth does not teach “wherein the computing unit is further configured to analyze the at least one energy density spectrum by: measuring a variability by comparing each energy density spectrum to itself over a predetermined time period”.
Regarding claim 3, Khandelwal (2018) teaches wherein the computing unit is further configured to analyze the at least one energy density spectrum by: measuring a variability by comparing each energy density spectrum to itself over a predetermined time period (Page 281-282, Step 2, consistent tracking of the spectral energy scale via a running window).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth to provide “wherein the computing unit is further configured to analyze the at least one energy density spectrum by: measuring a variability by comparing each energy density spectrum to itself over a predetermined time period” as taught by Khandelwal (2018). Doing so achieves accuracy and robustness for estimating initial contact events from different accelerometers. (Page 278, Abstract; Page 284, col. 1).
Regarding claim 4, Barth does not teach “wherein the computing unit is configured to compute accelerometer energy density spectrums by: receiving the sets of acceleration signals; for each set of received acceleration signals, computing a resultant acceleration signal; based on the computed resultant acceleration signals, determining if the human is performing a gait related activity or is inactive; and if it is determined that the human is performing a gait related activity, computing an accelerometer energy density spectrum for each resultant acceleration signal, wherein each accelerometer energy density spectrum corresponds to one leg of the human”.
Khandelwal (2018) teaches wherein the computing unit is configured to compute accelerometer energy density spectrums by: receiving the sets of acceleration signals (Abstract, Fig. 2, Step 1); for each set of received acceleration signals, computing a resultant acceleration signal (Page 279, column 1, Equation 1; Fig. 2, Step 1); based on the computed resultant acceleration signals, determining if the human is performing a gait related activity or is inactive (Fig. 4b, Page 279, column 2, where a subject walks with no arm swing and then changes to swinging the arms, leading to varying peak amplitudes in Es(wrist)); and if it is determined that the human is performing a gait related activity, computing an accelerometer energy density spectrum for each resultant acceleration signal, (Fig. 2, Step 2.2, compute energy density spectrum), wherein each accelerometer energy density spectrum corresponds to one leg of the human (Fig. 1, third column, energy density spectrum of ankle, thigh).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth to provide “wherein the computing unit is configured to compute accelerometer energy density spectrums by: receiving the sets of acceleration signals; for each set of received acceleration signals, computing a resultant acceleration signal; based on the computed resultant acceleration signals, determining if the human is performing a gait related activity or is inactive; and if it is determined that the human is performing a gait related activity, computing an accelerometer energy density spectrum for each resultant acceleration signal, wherein each accelerometer energy density spectrum corresponds to one leg of the human” as taught by Khandelwal (2018). Doing so achieves accuracy and robustness for estimating initial contact events from different accelerometers. (Page 278, Abstract; Page 284, col. 1).
Regarding claim 17, Barth teaches wherein the computing unit is configured to analyze the received gait data for determining at least two gait parameters, wherein the second gait parameter comprises one or more of: information relating to stride details of the human (Paragraph [0019] determining one or more stride features for each of said first data segments; Paragraph [0049] stride time, stride length, swing time, stance time, angle course, angle heel-strike, angle toe-off, clearance course, maximum toe clearance, minimum toe clearance, cadence, stride velocity, ground turning angle, medio-lateral sway, double support time and heel clearance course).
Regarding claim 18, Barth teaches wherein the assessed gait quality and/or gait-related health status is used to detect at least one of: one or more improvements in health status of the human, no or at least one change in the gait quality of the human, and/or an increase in risk of one or more injuries and/or diseases of the human. (Paragraph [0121] determining a score which combines at least two generic or biomechanical stride features of the strides/data segments of one cluster or artificial gait sequence defined based on one cluster may represent an indication if a person suffers from a certain motion impairing disease and/or movement impairment.).
Regarding claim 19, Barth teaches a method for assessing gait quality and/or gait-related health status of a human (Paragraph [0001]), being equipped with a first sensor device at a region of a first leg of the human and a second sensor device at a region of a second leg of the human, wherein the first and the second sensor devices each comprises at least one 3-axis accelerometer and at least one 3-axis gyroscope, (Paragraphs [0035], [0128]), and wherein the first and the second sensor devices are configured to provide gait data, the method comprising: receiving the gait data from the first and the second sensor devices; analyzing the received gait data for determining at least one gait parameter related to stride characteristics of the human, and analyzing the at least one gait parameter to assess gait quality and/or gait-related health status of the human. (Paragraphs [0022], [0030]).
However, Barth does not teach “wherein the at least one gait parameter comprises information of at least one computed energy density spectrum”.
Khandelwal (2018) teaches wherein the at least one gait parameter comprises information of at least one computed energy density spectrum. (See Description of Fig. 1).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth to provide “wherein the at least one gait parameter comprises information of at least one computed energy density spectrum” as taught by Khandelwal (2018). Doing so provides a system capable of estimating initial contact events from different body locations based on utilizing domain knowledge about the fundamental spectral relationships present between the movement of different body parts during normal gait. (Page 278, Abstract, Introduction).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Barth in view of Khandewal (2018) in view of Khandelwal et al: "Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, USA, vol.24, Dec 2016, pg.1363 – 1372 (cited by Applicant).
Regarding claim 5, Barth as modified does not teach “wherein determining if the human is performing a gait related activity further involves: computing a moving standard deviation signal of the resultant acceleration signals; generating a filtered acceleration signal by performing filtering of the computed moving standard deviation signal; and determining if a total number of elements of the filtered acceleration signal having a value greater than or equal to a value of a corresponding element of a predetermined walking threshold”.
Khandelwal (2016) teaches wherein determining if the human is performing a gait related activity further involves: computing a moving standard deviation signal of the resultant acceleration signals (Page 1364, col. 2, “Time-Frequency Analysis”); generating a filtered acceleration signal by performing filtering of the computed moving standard deviation signal (Fig. 3(b) Description); and determining if a total number of elements of the filtered acceleration signal having a value greater than or equal to a value of a corresponding element of a predetermined walking threshold (Fig. 3(a) Description; Fig 3(b)).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to provide “wherein determining if the human is performing a gait related activity further involves: computing a moving standard deviation signal of the resultant acceleration signals; generating a filtered acceleration signal by performing filtering of the computed moving standard deviation signal; and determining if a total number of elements of the filtered acceleration signal having a value greater than or equal to a value of a corresponding element of a predetermined walking threshold” as taught by Khandelwal (2016). Doing so removes high frequency noise and window edge effects. (Page 1366, col. 2, para. 1).
Regarding claim 6, Barth as modified by Khandewal (2018) does not explicitly teach “to compute gyroscope energy density spectrums by: receiving the sets of gyroscope signals; for each set of received gyroscope signals, computing a resultant gyroscope signal; and for each resultant gyroscope signal, computing a gyroscope energy density spectrum, wherein each gyroscope energy density spectrum corresponds to one leg of the human”.
Khandelwal (2016) teaches the use of inertial sensors such as gyroscopes for gait assessment.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to replace the accelerometers of Barth as modified by Khandewal (2018) with the gyroscopes of Khandewal (2016) “to compute gyroscope energy density spectrums by: receiving the sets of gyroscope signals; for each set of received gyroscope signals, computing a resultant gyroscope signal; and for each resultant gyroscope signal, computing a gyroscope energy density spectrum, wherein each gyroscope energy density spectrum corresponds to one leg of the human”, because one of ordinary skill in the art would have been able to carry out such a simple substitution of parts, wherein the results were reasonably predictable.
Claim 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Barth in view of Khandewal (2018) in view of Kim et al: "Gait event detection algorithm based on smart insoles", ETRI Journal, Vol. 42, Issue 1, pp. 46-53, John Wiley and Sons Inc. (cited by Applicant).
Regarding claim 7, Barth as modified does not teach “wherein the computing unit is configured to combine the accelerometer energy density spectrum and a gyroscope energy density spectrum for assessing gait quality and/or gait-related health status of the human”.
Kim, in a related field of endeavor, teaches wherein the computing unit is configured to combine the accelerometer energy density spectrum and a gyroscope energy density spectrum for assessing gait quality and/or gait-related health status of the human. (Fig. 7 illustrates the energy density spectra obtained from data measured from triaxial accelerometers and gyroscope sensors; See also “Conclusion” gait parameter detection algorithm is based on the triaxial acceleration and gyroscopic signals).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the computing unit is configured to combine the accelerometer energy density spectrum and a gyroscope energy density spectrum for assessing gait quality and/or gait-related health status of the human” as taught by Kim. Doing so provides a wider range of detection. (See 2.1 “Gait Data”).
Regarding claim 8, Barth as modified does not teach “wherein the accelerometer energy density spectrum and/or a gyroscope energy density spectrum is used to measure fluctuations in gait over time”.
Kim teaches wherein the accelerometer energy density spectrum and/or a gyroscope energy density spectrum is used to measure fluctuations in gait over time. (Fig. 7 illustrates the energy density spectra obtained from data measured from triaxial accelerometers and gyroscope sensors; See also “Conclusion” gait parameter detection algorithm is based on the triaxial acceleration and gyroscopic signals).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the accelerometer energy density spectrum and/or a gyroscope energy density spectrum is used to measure fluctuations in gait over time” as taught by Kim. Doing so provides a wider range of detection when measuring fluctuations in gait over time. (See 2.1 “Gait Data”).
Claims 9-11, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Barth in view of Khandewal (2018), further in view of Thompson (US 10610131) (cited by Applicant).
Regarding claim 9, Barth as modified does not teach “wherein the computing unit is further configured to: receive at least one metadata associated with the human, analyze the at least one gait parameter and the at least one metadata to assess gait quality and/or gait-related health status of the human.”
Thompson, in a related field of endeavor, teaches a limb motion sensor system wherein the computing unit is further configured to: receive at least one metadata associated with the human, analyze the at least one gait parameter and the at least one metadata to assess gait quality and/or gait-related health status of the human. (Col. 5, lines 1-3, device may be applied to humans; Col. 8, lines 11-56).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the computing unit is further configured to: receive at least one metadata associated with the human, analyze the at least one gait parameter and the at least one metadata to assess gait quality and/or gait-related health status of the human” as taught by Thompson. Doing so provides additional data that can be sent to a practitioner for follow-up evaluation in addition to the gait analysis. (Col. 8, lines 24-26).
Regarding claim 10, Barth as modified does not teach “wherein the at least one metadata comprises one or more of: information of subject data of the human, information of person data of persons connected to the human, information of accessory data related to accessories of the human, and information of training data of the human.”
Thompson teaches wherein the at least one metadata comprises one or more of: information of subject data of the human, information of person data of persons connected to the human, information of accessory data related to accessories of the human, and information of training data of the human. (Col. 5, lines 1-3, device may be applied to humans; Col. 8, lines 11-56).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the at least one metadata comprises one or more of: information of subject data of the human, information of person data of persons connected to the human, information of accessory data related to accessories of the human, and information of training data of the human” as taught by Thompson. Doing so provides additional data that can be sent to a practitioner for follow-up evaluation in addition to the gait analysis. (Col. 8, lines 24-26).
Regarding claim 11, Barth as modified does not teach “wherein the at least one metadata is based on data received from at least one additional sensor and/or based on data being inputted to the system by a user”.
Thompson teaches wherein the at least one metadata is based on data being inputted to the system by a user. (Col. 8, lines 21-26 capture photographs (e.g., healing limb wounds or the like) and video recordings of the subject in motion as run-specific metadata 514 that can be sent to a veterinarian for follow-up evaluation in addition to the gait analysis results).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the at least one metadata is based on data received from at least one additional sensor and/or based on data being inputted to the system by a user” as taught by Thompson. Doing so provides additional data that can be sent to a practitioner for follow-up evaluation in addition to the gait analysis. (Col. 8, lines 24-26).
Regarding claim 13, Barth as modified does not teach “wherein the at least one gait parameter
Thompson teaches wherein the at least one gait parameter
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the at least one gait parameter
Regarding claim 15, Barth as modified does not teach “wherein the computing unit is further configured to store the assessed gait quality and/or gait-related health status and/or to communicate the gait quality and/or gait-related health status to an external device having a display, wherein the external device is configured to present the assessed gait quality and/or gait-related health status to a user”.
Thompson teaches wherein the computing unit is further configured to store the assessed gait quality and/or gait-related health status (Abstract, Col. 8, lines 31-36), and/or to communicate the gait quality and/or gait-related health status to an external device having a display, wherein the external device is configured to present the assessed gait quality and/or gait-related health status to a user. (Col. 10, lines 29-30; Col. 11, lines 32-38).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the computing unit is further configured to store the assessed gait quality and/or gait-related health status and/or to communicate the gait quality and/or gait-related health status to an external device having a display, wherein the external device is configured to present the assessed gait quality and/or gait-related health status to a user” as taught by Thompson. Doing so provides the ability to assess uniformity across multiple motion parameters for a subject and assists with detection of the onset of subtle lameness or the effectiveness of a therapy as early as possible. (Col. 11, lines 35-38).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Barth in view of Khandewal (2018) and Thompson, further in view of Wang (US 20170273601) (cited by Applicant).
Regarding claim 12, Barth as modified does not teach “wherein the at least one additional sensor is one or more of: a GPS-sensor, a temperature sensor, a weather sensor, and a pulse sensor”.
Wang teaches wherein the at least one additional sensor is one or more of: a GPS-sensor, a temperature sensor, a weather sensor, and a pulse sensor. (Paragraphs [0026]-[0027]).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the at least one additional sensor is one or more of: a GPS-sensor, a temperature sensor, a weather sensor, and a pulse sensor” as taught by Wang. Doing so provides additional measurement data which may include data on geolocation, distance covered, elevation changes, as well as suitable biological functions. (Paragraphs [0026]-[0027]).
Claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Barth in view of Khandewal (2018) and Thompson, further in view of Zaphrir (WO 2021161314) (cited by Applicant).
Regarding claim 14, Barth as modified does not teach “wherein the computing unit is further configured to compute statistical data and/or historical data of the at least one metadata and the at least one gait parameter.”
Zaphrir, in a related field of endeavor, teaches a motion determining system wherein the computing unit is further configured to compute statistical data and/or historical data of the at least one metadata (i.e., users over 90 years old using a walker) and the at least one gait parameter (i.e., range). (Paragraph [060]).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the computing unit is further configured to compute statistical data and/or historical data of the at least one metadata and the at least one gait parameter” as taught by Zaphrir. Doing so provides a baseline measurement to which to compare newly measured data. (Paragraph [060]).
Regarding claim 16, Barth as modified does not teach “wherein the computing unit is further configured to generate and transmit a deviating signal to the external device if the at least one metadata and/or the at least one gait parameter exceeds a predetermined deviating threshold value.”
Zaphrir teaches wherein the computing unit is further configured to generate and transmit a deviating signal to the external device if the at least one gait parameter exceeds a predetermined deviating threshold value. (Paragraph [064]).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Barth as modified to teach “wherein the computing unit is further configured to generate and transmit a deviating signal to the external device if the at least one metadata and/or the at least one gait parameter exceeds a predetermined deviating threshold value” as taught by Zaphrir. Doing so provides an alert may to the user and/or to a caregiver to assess preventive/rehab treatment and mitigate the observed growing risk. (Paragraph [064]).
Conclusion
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/OM PATEL/Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791