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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/06/2026 has been entered.
Claim Objections
Claims 1, 27 and 33 are objected to because of the following informalities: “a gain smoothness model” should be “a gait smoothness model”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
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.
Claims 1, 3-7, and 17-38 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.
Claim 1 contains the term “balanced gait double support time asymmetry”. It is unclear if this is the “double support time” defined in the specification, or a different mobility metric. Figure 1A of the provided drawings shows “balanced gait DST asymmetry” as a feature, but fails to define how it differs from DST. For purposes of examination the term is being interpreted as “gait double support time”. The same issue is present in claims 1,17, and 33.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite.
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, 27, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 20170344919 A1 - previously cited) in view of Barth (US 20190150793 A1) in view of Kyoko (JP 3569188 B2 – cited as 数藤 恭子) in view of Fukui (US 20220246302 A1- previously cited) in view of Pathak (US 20170213145 A1- previously cited) in view of Plumbley (US 20210117869 A1- previously cited).
In regards to claim 1, Chang teaches a method comprising:
obtaining, with at least one processor of a mobile device ([0025] It would be obvious to put the computer readable instructions on the same mobile phone recording sensor data), one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device ([0029] [0032] Position data is from GPS in mobile phone, other data comes from inertial measurement system 110);
evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods ([0020] In some cases the task is a discrete event or window of time) to derive one or more longitudinal features indicative of variability of the user's gait ([0070]);
and generating, with the at least one processor, at least one walking steadiness indicator for the user based on a gait compensatory model ([0119] a gait ergonomic model) and the one or more longitudinal features ([0119] “if a factory worker exhibits erratic walking gait patterns such as significant asymmetry in left and right strides, cadence variability and significant pelvic rotations, the system may detect an injury or a high risk state”).
Chang fails to teach a method wherein the longitudinal features include balanced gait double support time asymmetry; and generating, with the at least one processor, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode. Barth teaches generating, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode ([0019] “c) determining one or more stride features for each of said first data segments; and” [0020] “d) defining one or more clusters on the basis of at least one stride feature of said one or more stride features, wherein each cluster represents a class of strides”, [0030] “This assigns the stride type/class to a stride related to certain movements (e.g. straight walking, running, walking on stairways etc.), which may allow for a more accurate analysis of gait impairments for which said stride features are representative” Clustering is the walking steadiness indicator [0049] features are double support time). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang to include the model of Barth in order to determine gait impairment’s the user may have.
Chang/Barth fails to teach generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model. Kyoko teaches generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model ([0024-0026] “Further, the dynamic rationality evaluation parameter generation unit 131 obtains a time differential value of foot pressure from the time-series feature vector sequence X = (X .sub.1 , X .sub.2 ,..., X .sub.T ), and walks this time differential value. by comparing the smoothness of the mechanical rationality criteria (thresholds), calculates the mechanical rationality evaluation parameters a .sub.2 showing the scores for the smoothness of gait. In this case, as the foot pressure changes smoothly, as the energy efficiency of the walk is a good healthy walking scores of mechanical rationality evaluation parameters a .sub.2 is increased”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth to include the model of Kyoko in order to determine smoothness of the user’s gait which can indicate heathy or unhealthy walking.
Chang/Barth/Kyoko fails to teach inferring, with the at least one processor, a fall risk score for the user based on the at least three walking steadiness indicators and a fused inference model that includes the trained ensemble of machine learning steadiness models and providing, with the at least one processor, the determined fall risk score to a fitness or health monitoring application to alert the user of a fall risk. Fukui teaches using an fused inference model (ensemble model 300) that integrates determination results of an ensemble of three models to output a degree of healthy walking ([0031]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth/Kyoko to include the ensemble model and included models of Fukui and use the walking steadiness indicators as inputs into the models of Fukui. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of detecting a probability of healthy walking.
Chang/Barth/Kyoko/Fukui fails to teach inferring, with the at least one processor, a fall risk score, and providing, with the at least one processor, the determined fall risk to a fitness or health monitoring application to alert the user of a fall risk. Chang teaches displaying an output on a health monitoring application ([0038]). Pathak teaches using a machine learning model to determine a fall risk score. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of C Chang/Barth/Kyoko/Fukui to include a model that determines a fall risk score like the method of Pathak using the Fukui’s probability of healthy walking (that is based on the three walking steadiness indicators and the fused inference model) as an input for the fall risk model, and to display this fall risk score on the health monitoring application of Chang. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of indicating to the user or caretaker if the user is at risk of falling.
Chang/Barth/Kyoko/Fukui/Pathak teaches an ensemble of machine learning steadiness models (Fukui [0031]) but, fails to teach how the models are trained. Plumbley teaches training an ensemble of machine learning models using labeled data sets ([0032]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth/Kyoko/Fukui/Pathak to include a step of using the processor to train the models of the fusion model using labeled data like Plumbley. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of providing a way to train the models.
In regards to claim 27, Chang teaches a system comprising:
at least one processor ([0025]);
memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform steps comprising ([0025]):
obtaining, with at least one processor of a mobile device ([0025] It would be obvious to put the computer readable instructions on the same mobile phone recording sensor data), one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device ([0029] [0032] Position data is from GPS in mobile phone, other data comes from inertial measurement system 110);
evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods ([0020] In some cases the task is a discrete event or window of time) to derive one or more longitudinal features indicative of variability of the user's gait ([0070]);
and generating, with the at least one processor, at least one walking steadiness indicator for the user based on a gait compensatory model ([0119] a gait ergonomic model) and the one or more longitudinal features ([0119] “if a factory worker exhibits erratic walking gait patterns such as significant asymmetry in left and right strides, cadence variability and significant pelvic rotations, the system may detect an injury or a high risk state”).
Chang fails to teach a system wherein the longitudinal features include balanced gait double support time asymmetry; and generating, with the at least one processor, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode. Barth teaches generating, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode ([0019] “c) determining one or more stride features for each of said first data segments; and” [0020] “d) defining one or more clusters on the basis of at least one stride feature of said one or more stride features, wherein each cluster represents a class of strides”, [0030] “This assigns the stride type/class to a stride related to certain movements (e.g. straight walking, running, walking on stairways etc.), which may allow for a more accurate analysis of gait impairments for which said stride features are representative” Clustering is the walking steadiness indicator [0049] features are double support time). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chang to include the model of Barth in order to determine gait impairment’s the user may have.
Chang/Barth fails to teach generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model. Kyoko teaches generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model ([0024-0026] “Further, the dynamic rationality evaluation parameter generation unit 131 obtains a time differential value of foot pressure from the time-series feature vector sequence X = (X .sub.1 , X .sub.2 ,..., X .sub.T ), and walks this time differential value. by comparing the smoothness of the mechanical rationality criteria (thresholds), calculates the mechanical rationality evaluation parameters a .sub.2 showing the scores for the smoothness of gait. In this case, as the foot pressure changes smoothly, as the energy efficiency of the walk is a good healthy walking scores of mechanical rationality evaluation parameters a .sub.2 is increased”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chang/Barth to include the model of Kyoko in order to determine smoothness of the user’s gait which can indicate heathy or unhealthy walking.
Chang/Barth/Kyoko fails to teach inferring, with the at least one processor, a fall risk score for the user based on the at least three walking steadiness indicators and a fused inference model that includes the trained ensemble of machine learning steadiness models and providing, with the at least one processor, the determined fall risk score to a fitness or health monitoring application to alert the user of a fall risk. Fukui teaches using an fused inference model (ensemble model 300) that integrates determination results of an ensemble of three models to output a degree of healthy walking ([0031]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chang/Barth/Kyoko to include the ensemble model and included models of Fukui and use the walking steadiness indicators as inputs into the models of Fukui. Doing so would merely be combining prior art elements according to known systems to yield the predictable result of detecting a probability of healthy walking.
Chang/Barth/Kyoko/Fukui fails to teach inferring, with the at least one processor, a fall risk score, and providing, with the at least one processor, the determined fall risk to a fitness or health monitoring application to alert the user of a fall risk. Chang teaches displaying an output on a health monitoring application ([0038]). Pathak teaches using a machine learning model to determine a fall risk score. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of C Chang/Barth/Kyoko/Fukui to include a model that determines a fall risk score like the system of Pathak using the Fukui’s probability of healthy walking (that is based on the three walking steadiness indicators and the fused inference model) as an input for the fall risk model, and to display this fall risk score on the health monitoring application of Chang. Doing so would merely be combining prior art elements according to known systems to yield the predictable result of indicating to the user or caretaker if the user is at risk of falling.
Chang/Barth/Kyoko/Fukui/Pathak teaches an ensemble of machine learning steadiness models (Fukui [0031]) but, fails to teach how the models are trained. Plumbley teaches training an ensemble of machine learning models using labeled data sets ([0032]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chang/Barth/Kyoko/Fukui/Pathak to include a step of using the processor to train the models of the fusion model using labeled data like Plumbley. Doing so would merely be combining prior art elements according to known systems to yield the predictable result of providing a way to train the models.
In regards to claim 33, Chang teaches a non-transitory, computer-readable storage medium having stored thereon instructions, that when executed by at least one processor, cause the at least one processor to perform operations comprising ([0025] Memory is a non-transitory, computer-readable storage medium):
obtaining, with at least one processor of a mobile device ([0025] It would be obvious to put the computer readable instructions on the same mobile phone recording sensor data), one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device ([0029] [0032] Position data is from GPS in mobile phone, other data comes from inertial measurement system 110);
evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods ([0020] In some cases the task is a discrete event or window of time) to derive one or more longitudinal features indicative of variability of the user's gait ([0070]);
and generating, with the at least one processor, at least one walking steadiness indicator for the user based on a gait compensatory model ([0119] a gait ergonomic model) and the one or more longitudinal features ([0119] “if a factory worker exhibits erratic walking gait patterns such as significant asymmetry in left and right strides, cadence variability and significant pelvic rotations, the system may detect an injury or a high risk state”).
Chang fails to teach a method wherein the longitudinal features include balanced gait double support time asymmetry; and generating, with the at least one processor, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode. Barth teaches generating, a walking steadiness indicator for the user based on an anomalous gait model wherein the balanced gait double support time asymmetry is input into the anomalous gait mode ([0019] “c) determining one or more stride features for each of said first data segments; and” [0020] “d) defining one or more clusters on the basis of at least one stride feature of said one or more stride features, wherein each cluster represents a class of strides”, [0030] “This assigns the stride type/class to a stride related to certain movements (e.g. straight walking, running, walking on stairways etc.), which may allow for a more accurate analysis of gait impairments for which said stride features are representative” Clustering is the walking steadiness indicator [0049] features are double support time). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang to include the model of Barth in order to determine gait impairment’s the user may have.
Chang/Barth fails to teach generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model. Kyoko teaches generating, with the at least one processor, a walking steadiness indicator for the user based on a gait smoothness model ([0024-0026] “Further, the dynamic rationality evaluation parameter generation unit 131 obtains a time differential value of foot pressure from the time-series feature vector sequence X = (X .sub.1 , X .sub.2 ,..., X .sub.T ), and walks this time differential value. by comparing the smoothness of the mechanical rationality criteria (thresholds), calculates the mechanical rationality evaluation parameters a .sub.2 showing the scores for the smoothness of gait. In this case, as the foot pressure changes smoothly, as the energy efficiency of the walk is a good healthy walking scores of mechanical rationality evaluation parameters a .sub.2 is increased”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth to include the model of Kyoko in order to determine smoothness of the user’s gait which can indicate heathy or unhealthy walking.
Chang/Barth/Kyoko fails to teach inferring, with the at least one processor, a fall risk score for the user based on the at least three walking steadiness indicators and a fused inference model that includes the trained ensemble of machine learning steadiness models and providing, with the at least one processor, the determined fall risk score to a fitness or health monitoring application to alert the user of a fall risk. Fukui teaches using an fused inference model (ensemble model 300) that integrates determination results of an ensemble of three models to output a degree of healthy walking ([0031]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth/Kyoko to include the ensemble model and included models of Fukui and use the walking steadiness indicators as inputs into the models of Fukui. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of detecting a probability of healthy walking.
Chang/Barth/Kyoko/Fukui fails to teach inferring, with the at least one processor, a fall risk score, and providing, with the at least one processor, the determined fall risk to a fitness or health monitoring application to alert the user of a fall risk. Chang teaches displaying an output on a health monitoring application ([0038]). Pathak teaches using a machine learning model to determine a fall risk score. It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of C Chang/Barth/Kyoko/Fukui to include a model that determines a fall risk score like the method of Pathak using the Fukui’s probability of healthy walking (that is based on the three walking steadiness indicators and the fused inference model) as an input for the fall risk model, and to display this fall risk score on the health monitoring application of Chang. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of indicating to the user or caretaker if the user is at risk of falling.
Chang/Barth/Kyoko/Fukui/Pathak teaches an ensemble of machine learning steadiness models (Fukui [0031]) but, fails to teach how the models are trained. Plumbley teaches training an ensemble of machine learning models using labeled data sets ([0032]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chang/Barth/Kyoko/Fukui/Pathak to include a step of using the processor to train the models of the fusion model using labeled data like Plumbley. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of providing a way to train the models.
Claims 3-4, 28-29, and 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 20170344919 A1 - previously cited) in view of Barth (US 20190150793 A1) in view of Kyoko (JP 3569188 B2 – cited as 数藤 恭子) in view of Fukui (US 20220246302 A1- previously cited) in view of Pathak (US 20170213145 A1- previously cited) in view of Plumbley (US 20210117869 A1- previously cited) as applied to claims 1, 27, and 33, in view of Golińska (Poincaré Plots in Analysis of Selected Biomedical Signals – previously cited).
In regards to claim 3, modified Chang teaches the method of claim 1 wherein the at least one walking steadiness indicator is a degree of dispersion computed over the one or more specified time periods (Chang [0106] Variability and degree of dispersion are the same thing), and determining with the at least one processor, metrics for step length, walking speed and cadence of the user based on the sensor data (Chang [0084] [0029] accelerometer collects speed changes so it would be obvious for “other suitable health related biomechanical properties” to include walking speed). Modified Chang fails to explicitly teach determining, with the at least one processor, correlations between the metrics over the one or more specified time periods; and determining, with the at least one processor, the degree of dispersion based on the determined correlations.
Golińska teaches a method wherein an at least one walking steadiness indicator is a degree of dispersion computed over one or more specified time periods, the dispersion value computed by:
determining, with the at least one processor, correlations between gait metrics over the one or more specified time periods (Pages 122 and 123 show inputting gait metrics into a Poincaré plot to determine correlations);
and determining, with the at least one processor, the degree of dispersion based on the determined correlations (Page 118 short term and long term variability).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to use an ellipse fitted Poincaré plot as taught by Golińska in order to determine the variability in the subject’s gait and use that as a walking steadiness indicator. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining a degree of dispersion in the form of gait variability for use in determining a fall risk.
In regards to claim 4, modified Chang teaches the method of claim 3, wherein the degree of dispersion is determined based on parameters of an ellipse-fitted Poincaré plot that indicate at least one of short-term or long-term variability in the user's gait (Golińska pages 118, 122, 123).
In regards to claim 28, modified Chang teaches the system of claim 27 wherein the at least one walking steadiness indicator is a degree of dispersion computed over the one or more specified time periods [(0106] Variability and degree of dispersion are the same thing), and determining with the at least one processor, metrics for step length, walking speed and cadence of the user based on the sensor data ([0084] [0029] accelerometer collects speed changes so it would be obvious for “other suitable health related biomechanical properties” to include walking speed). Modified Chang fails to explicitly teach determining, with the at least one processor, correlations between the metrics over the one or more specified time periods; and determining, with the at least one processor, the degree of dispersion based on the determined correlations.
Golińska teaches a method wherein an at least one walking steadiness indicator is a degree of dispersion computed over one or more specified time periods, the dispersion value computed by:
determining, with the at least one processor, correlations between gait metrics over the one or more specified time periods (Pages 122 and 123 show inputting gait metrics into a Poincaré plot to determine correlations);
and determining, with the at least one processor, the degree of dispersion based on the determined correlations (Page 118 short term and long term variability).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to use an ellipse fitted Poincaré plot as taught by Golińska in order to determine the variability in the subject’s gait and use that as a walking steadiness indicator. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining a degree of dispersion in the form of gait variability for use in determining a fall risk.
In regards to claim 29, modified Chang teaches the system of claim 28, wherein the degree of dispersion is determined based on parameters of an ellipse-fitted Poincaré plot that indicate at least one of short-term or long-term variability in the user's gait (Golińska pages 118, 122, 123).
In regards to claim 34. modified Chang teaches the non-transitory, computer-readable storage medium of claim 33, wherein the at least one walking steadiness indicator is a degree of dispersion computed over the one or more specified time periods [(0106] Variability and degree of dispersion are the same thing), and determining with the at least one processor, metrics for step length, walking speed and cadence of the user based on the sensor data ([0084] [0029] accelerometer collects speed changes so it would be obvious for “other suitable health related biomechanical properties” to include walking speed). Modified Chang fails to explicitly teach determining, with the at least one processor, correlations between the metrics over the one or more specified time periods; and determining, with the at least one processor, the degree of dispersion based on the determined correlations.
Golińska teaches a method wherein an at least one walking steadiness indicator is a degree of dispersion computed over one or more specified time periods, the dispersion value computed by:
determining, with the at least one processor, correlations between gait metrics over the one or more specified time periods (Pages 122 and 123 show inputting gait metrics into a Poincaré plot to determine correlations);
and determining, with the at least one processor, the degree of dispersion based on the determined correlations (Page 118 short term and long term variability).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to use an ellipse fitted Poincaré plot as taught by Golińska in order to determine the variability in the subject’s gait and use that as a walking steadiness indicator. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of determining a degree of dispersion in the form of gait variability for use in determining a fall risk.
In regards to claim 35, modified Chang teaches the non-transitory, computer-readable storage medium of claim 34, wherein the degree of dispersion is determined based on parameters of an ellipse-fitted Poincaré plot that indicate at least one of short-term or long-term variability in the user's gait (Golińska pages 118, 122, 123).
Claims 5, 30, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 20170344919 A1 - previously cited) in view of Barth (US 20190150793 A1) in view of Kyoko (JP 3569188 B2 – cited as 数藤 恭子) in view of Fukui (US 20220246302 A1- previously cited) in view of Pathak (US 20170213145 A1- previously cited) in view of Plumbley (US 20210117869 A1- previously cited) as applied to claims 1, 27, and 33, in view of Deng (US 20220054030 A1 – previously cited).
In regards to claim 5, modified Chang teaches the method of claim 1, including continuously monitoring movement (Chang [0061]). Modified Chang fails to teach determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time. Deng teaches determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time in order to prevent inaccurate measurements (Deng [0039 and 0041]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to determine the sensor data covers a duration that satisfies a minimum threshold time like method of Deng in order to prevent inaccurate measurements.
In regards to claim 30, modified Chang teaches the system of claim 27, including continuously monitoring movement ([0061]). Modified Chang fails to teach determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time. Deng teaches determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time in order to prevent inaccurate measurements (Deng [0039 and 0041]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to determine the sensor data covers a duration that satisfies a minimum threshold time like method of Deng in order to prevent inaccurate measurements.
In regards to claim 36, modified Chang teaches the non-transitory, computer-readable storage medium of claim 33, including continuously monitoring movement ([0061]). Modified Chang fails to teach determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time. Deng teaches determining, with the at least one processor and prior to the evaluating, that the sensor data covers a duration that satisfies a minimum threshold time in order to prevent inaccurate measurements (Deng [0039 and 0041]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to determine the sensor data covers a duration that satisfies a minimum threshold time like method of Deng in order to prevent inaccurate measurements.
Claims 6-7, 31-32, and 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Chang (US 20170344919 A1 - previously cited) in view of Barth (US 20190150793 A1) in view of Kyoko (JP 3569188 B2 – cited as 数藤 恭子) in view of Fukui (US 20220246302 A1- previously cited) in view of Pathak (US 20170213145 A1- previously cited) in view of Plumbley (US 20210117869 A1- previously cited) as applied to claims 1, 27, and 33, in view of Naveh (US 11751813 B2 – previously cited).
In regards to claim 6, modified Chang teaches the method of claim 1 comprising: generating, with the at least one processor, the at least three walking steadiness indicators based on the gait compensatory model, anomalous gait model, gait smoothness model ([0119]). Modified Chang fails to teach generating the walking steadiness indicator based population norms. Naveh teaches comparing just sensed results with population norms in order to compute risk levels (Naveh Col 12 lines 30-36). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to compare the gait patterns of the subject to population norms like the method of Naveh in order to determine if the subject’s gait is abnormal and the subject in a high risk state. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of comparing a subject’s measurements to a population norm to see if the subject has abnormal gait.
In regards to claim 7, modified Chang teaches the method of claim 6, wherein the population norms indicate normative behavior for different user populations based on demographics (Naveh Col 12 lines 30-36, population norms inherently are based on normative behavior for different user populations based on demographics).
In regards to claim 31, modified Chang teaches the system of claim 27 comprising: generating, with the at least one processor, the at least one walking steadiness indicator based on the gait compensatory model ([0119] a gait ergonomic model determines if the user is in a high risk state) and the one or more longitudinal features ([0119]). Modified Chang fails to teach generating the walking steadiness indicator based population norms. Naveh teaches comparing just sensed results with population norms in order to compute risk levels (Naveh Col 12 lines 30-36). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to compare the gait patterns of the subject to population norms like the method of Naveh in order to determine if the subject’s gait is abnormal and the subject in a high risk state. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of comparing a subject’s measurements to a population norm to see if the subject has abnormal gait.
In regards to claim 32, modified Chang teaches the system of claim 31, wherein the population norms indicate normative behavior for different user populations based on demographics (Naveh Col 12 lines 30-36, population norms inherently are based on normative behavior for different user populations based on demographics).
In regards to claim 37, modified Chang teaches the non-transitory, computer-readable storage medium of claim 33 comprising: generating, with the at least one processor, the at least one walking steadiness indicator based on the gait compensatory model ([0119] a gait ergonomic model determines if the user is in a high risk state) and the one or more longitudinal features ([0119]). Modified Chang fails to teach generating the walking steadiness indicator based population norms. Naveh teaches comparing just sensed results with population norms in order to compute risk levels (Naveh Col 12 lines 30-36). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor of modified Chang to compare the gait patterns of the subject to population norms like the method of Naveh in order to determine if the subject’s gait is abnormal and the subject in a high risk state. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of comparing a subject’s measurements to a population norm to see if the subject has abnormal gait.
In regards to claim 38, modified Chang teaches the non-transitory, computer-readable storage medium of claim 37, wherein the population norms indicate normative behavior for different user populations based on demographics (Naveh Col 12 lines 30-36, population norms inherently are based on normative behavior for different user populations based on demographics).
Response to Arguments
Applicant’s arguments, see remarks, filed 02/06/2026, with respect to 35 U.S.C. 112(b) rejections of claims 1, 3-7, and 27-38 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejections of claims 1, 3-7, and 27-38 have been withdrawn.
Applicant’s arguments, see remarks, filed 02/06/2026, with respect to the rejection(s) of claim(s) 1, 27, and 33, and dependent claims under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Chang/Barth/Kyoko/Fukui/Pathak/Plumbley.
Conclusion
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791