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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Objections
Claims 2 and 12 are objected to because of the following informalities: the last line of the claim currently recites the limitation “held by the wearer and the body height and body weight of the user”, but these parameters should be separated by a comma in order to be more grammatically correct. This limitation could be written as “held by the wearer, the body height, and the body weight of the user”.
Claims 3 and 13 are objected to because of the following informalities: the last line of the claim currently recites the limitation “during the work time approaches of a specified threshold limit value”, but this is not grammatically correct. This limitation could be written as “during the work time approaches a specified threshold limit value”.
Appropriate correction is required.
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 1-20 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.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, Claims 1 and 11 recites the broad recitation “based on a result of the forecast of the wearer’s risk” recited in the fourth to last line of the claim, and the claim also recites “in response to the forecasted risk exceeding a threshold” recited in the second to last line of the claim which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims. It is being interpreted that the limitation “based on a result” language recited in the fourth to last line of the claim should be deleted from the claim.
Regarding Claims 3 and 13, the limitation “lower back pain symptom is high when a maximum value of the intervertebral disk pressure force during the work time approaches a specified threshold limit value” recited in lines 5-7 of Claim 3 and lines 4-6 of Claim 13 is indefinite. More specifically, it is unclear what qualifies as the metes and bounds for the limitation term “approaches” when determining a “lower back pain symptom is high”. The term “approaches” is broad and does not provide a set value. Furthermore, the claims fail to provide definition as to what type of parameters and/or situations would be considered meeting this type of limitation. For example, if “the specified threshold limit” is on a scale of 1 to 10 and a person experiences a “maximum value” of 6, is that value close enough to be considered “high risk”? Furthermore, if a person experiences a “maximum value” of 2 during one type of lifting process but then experiences a “maximum value” of 3 during another lifting process, would that increase from 2 to 3 be considered “approaching a threshold” and therefore be considered “high risk”?
Claims not explicitly rejected above are rejected due to their dependence on the above claims.
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, 3, 6, 8, 11, 13, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zelik et. al.’352 (U.S. Patent Application 20230270352 – previously cited) in view of Sung Lee’102 (WO Patent Application 2016105102 – previously cited) as evidenced by Daynard et. al.'2001 (Applied Ergonomics: Biomechanical analysis of peak and cumulative spinal loads during simulated patient-handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers – previously cited).
Regarding Claim 1, Zelik et. al.’352 discloses a lower back part load evaluation apparatus comprising a wearable motion assistance device worn by securing either a wearer's both femoral parts or the wearer's abdominal part and shoulder parts, or both of them, to assist motions of a lower back part of the wearer (see Paragraphs [0028-0038]; Paragraph [0064] - in one embodiment, the at least one motion/orientation sensor comprises a first IMU operably attached to the trunk or pelvis of the user, and two second IMUs operably attached to the left and right thighs or shanks of the user; Figure 2), the lower back part load evaluation apparatus comprising:
a trunk angle measuring processor that is provided in the wearable motion assistance device and continuously measures an inclination angle of the lower back part of the wearer as a trunk angle of the wearer (see Paragraphs [0028-0038]; Paragraph [0063] - inertial measurement unit (IMU) operably attached to the trunk; Paragraph [0085] - Sagittal trunk angle and vertical GRFs are the most important signals for estimating lumbar moments, consistent with the findings from idealized wearable sensor analysis in FIG. 3. Signal importances here are from the real wearable sensor algorithm for estimating lumbar extension moments. R=right; L=left), and
an intervertebral disk pressure force estimation processor that continuously estimates an intervertebral disk pressure force applied to the wearer's intervertebral disks in a vertebral-bodies-connecting direction on the basis of a weight of an object held by the wearer and a body height and body weight of the wearer which are externally input (see Paragraphs [0028-0038]; Paragraph [0038] - data from a single IMU (or other motion/orientation sensor) that monitors trunk orientation of the wearer can be combined with object weight data transmitted from the warehouse management system to estimate lumbar loading without requiring a pressure-sensing insole sensor; Paragraph [0050] - the user inputs comprise height, weight…and/or other personal health or demographic data; Paragraph [0013] - …multiple wearable sensors at distributed locations on the body has the potential to provide better estimates of low back loading by capturing and integrating additional dynamics data (e.g., body segment motions or orientations, forces or moments, muscle activity)), and the trunk angle measured by the trunk angle measuring processor (see Paragraphs [0028-0038]; Paragraph [0063] - inertial measurement unit (IMU) operably attached to the trunk; Paragraph [0085] - sagittal trunk angle and vertical GRFs are the most important signals for estimating lumbar moments), and
a section elapsed time measuring processor that divides the intervertebral disk pressure force estimated by the intervertebral disk pressure force estimation processor into sections according to a load level and, at the same time, measures elapsed time for each of the sections in chronological order (Paragraph [0026] - using signals from the trunk IMU and the pressure insoles together with a gradient boosted decision tree algorithm provides a practical, accurate, and automated way to monitor time series lumbar moments across the range of material handling tasks; Paragraph [0088] - gray areas are approximately when the participant was holding the 10 kg box, white areas are when the participant had no object in their hands…trunk IMU tends to perform worse when the box is being held or lifted, whereas the trunk IMU plus pressure insoles, and distributed sensors, are able to better track key lumbar loading trends (gray areas); Paragraph [0089] - peak lumbar moment of squat tasks when increasing box masses are lifted (10 kg, 15 kg, 23 kg are shown)…captures the trend of increasing lumbar moment with heavier object mass), wherein measuring elapsed time for each of the sections in chronological order comprises tracking a duration of time spent in each load level section sequentially as the intervertebral disk pressure force changes over time during a work time of a muscular work (Paragraph [0193] - estimate the time series lumbar extension moment (as opposed to just peak moments) because this enables us to identify bending/lifting frequency, to partition out individual movement cycles, and to better understand and distinguish cyclic lifts vs. prolonged bending. Time series data enables the assessment of loading and cumulative risk across all tasks, as well as the ability to perform task-specific load and risk assessment; Paragraph [0223] - In contrast, these elevated back loads from the handheld mass are captured by solutions that use sensors at multiple locations that include pressure insoles along with at least one IMU, as shown in FIG. 8. The time-series plots show a representative lifting task, while the scatter plots and tables presented in the Results provide comprehensive results from all the participants and across all the manual material handling tasks collected; Figure 8) and
a lower back pain occurrence risk forecasting processor that forecasts the wearer's risk of developing a lower back pain symptom in real-time during the muscular work (Paragraph [0154] - In some embodiments, the estimates of the musculoskeletal loading and/or damage and/or injury risk are computed via real-time or near-real-time estimation algorithms; Paragraph [0155] - In some embodiments, the estimated musculoskeletal loading and/or damage and/or injury risk is communicated to the user and/or a party of interest via one or more wireless or wired communication interfaces, either in real-time, near-real-time or at a later time) on the basis of a measurement history of the section elapsed time measuring processor during the work time of the muscular work which is defined as work involving lifting objects weighing more than a predetermined weight to handle the object (Paragraphs [0028-0038]; Paragraph [0038] - A look-up table or regression equation can be developed to quantitatively relate trunk orientation to the estimated horizontal distance between the spine and object. Data from the synchronized warehouse management system (or other kind of workplace or inventory management system) would provide information on the weight of each object. These two streams of data are sufficient to estimate the peak load moment (a musculoskeletal loading metric for the back) by multiplying the weight of the object by the peak horizontal distance from the spine to object. The peak load moment for each lift could then be input to LiFFT, an existing ergonomics assessment tool to compute cumulative damage and injury risk to the low back), wherein
the wearable motion assistance device includes a drive mechanism that drives a lower-back frame by generating a driving torque to relatively rotationally drive each of the lower-back frame (Paragraph [0056] - In one embodiment, the musculoskeletal loading is used for control or evaluation of the exoskeleton, exosuit, smart clothing or other wearable assistance device. One example of how to use musculoskeletal loading for control is a powered exoskeleton controller designed to provide an assistance magnitude proportional to the amount of back loading (e.g., lumbar moment)), and wherein
based on a result of the forecast of the wearer's risk, the wearable motion assistance device automatically and immediately changes a configuration of the drive mechanism in response to the forecasted risk exceeding a threshold to control an output torque of the drive mechanism (Paragraph [0056] - Another example would be an exoskeleton controller that was designed to only assist the user once they surpassed a threshold of musculoskeletal loading, or cumulative damage, or injury risk. An example of how to use musculoskeletal loading for evaluation is that the exoskeleton moment contribution about the back can be subtracted out of the lumbar moment estimate to assess how much back relief the exoskeleton is providing, or how much exoskeleton assistance reduces musculoskeletal tissue damage or injury risk; Paragraph [0152] - The device moment contribution would either come directly from the assistance device itself (e.g., in the case of robotic devices that contain sensors and compute assistance levels on board); Paragraph [0170] - the control of the exoskeleton, exosuit, smart clothing or other wearable assistive device is optimized from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories, using a reinforcement learning algorithm).
Zelik et. al.’352 fails to disclose wherein the section elapsed time measuring processor divides the intervertebral disk pressure forces into five load level sections. Sung Lee’102 teaches determining – as a form of dividing – risk levels associated with weight loads held at different postures during lifting procedures through using a well-known NIOSH calculation method (Page 9 Paragraphs 2-4 - the score of the overall load index to determine the 5 levels of decision-making… the risk level may be classified into five levels corresponding to the final score. Specifically, if the final score corresponds to 1, the risk level can be ignored as 0 level, if the final score is between 2 and 3, the risk level is low to 1 level, and the final score is 4 to 7 If the final score is between 8 and 10, the risk level is as high as 3 levels, and if the final score is between 11 and 15, the risk level is rated as 4 levels). It would have been obvious to one of ordinary skill in the art to have modified the evaluation apparatus of Zelik et. al.’352 to include a calculation similar to NIOSH that is well known in the art that uses discrete level ranges and other parameters to categorize lifting actions into 5 sections to recommend corrective actions for the postures and categorized weight loads as seen in Sung Lee’102. Additionally, as evidenced by Daynard et. al.’2001, the standard NIOSH index identifies common loads such as 2744N, 3000N, and 3400N as common limits (Page - spinal compression exceeds approximately 3400 N some workers will be at increased risk of low-back injury. They call this ‘limit’ the ‘Action Limit’ (AL). Interestingly, Norman et al. (1998) showed that the average spinal compression of workers who had reported low-back pain (cases) in a large auto-assembly plant was 3402 N. For the referent group, workers who had not reported low-back pain (controls), the spinal compression was statistically significantly lower at 2744 N…observed damage to tissues in the lumbar spines of cadavers subjected to shear loading at about 3000N). Additionally, without specifics within the description, the specified level ranges divided into separate sections would have been an obvious design choice of range as seen in In re Bergen, 120 F.2d 329, 332, 49 USPQ 749, 751-52 (CCPA 1941) wherein the court found that the overlapping endpoint of the prior art and claimed range was sufficient to support an obviousness rejection, particularly when there was no showing of criticality of the claimed range.
Regarding Claim 3, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation apparatus comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses wherein the lower back pain occurrence risk forecasting processor determines, on the basis of an estimation history of the intervertebral disk pressure force estimation processor, that the wearer's risk of developing the lower back pain symptom is high when a maximum value of the intervertebral disk pressure force during the work time approaches a specified threshold limit value (Paragraph [0045] - the processing unit is further configured to alert the user, via audio or vibrotactile feedback, when the musculoskeletal loading or microdamage accumulation or injury risk is greater than a threshold that is predetermined or a threshold that is calibrated for a specific user).
Regarding Claim 6, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation apparatus comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses wherein the lower back pain occurrence risk forecasting processor forecasts the wearer's risk of developing the lower back pain symptom according to a relation between a maximum value of the trunk angle of the wearer and a weight of the object during the work tine on the basis of the measurement history of the trunk angle measuring processor (Paragraph [0038] - the trunk orientation (e.g., sagittal bending angle) estimate could be used as a surrogate for (or correlate of) the horizontal distance between a person's spine and object being lifted…a person generally has to bend further forward to pick up or set down objects that are further away…quantitatively relate trunk orientation to the estimated horizontal distance between the spine and object…data from the synchronized warehouse management system (or other kind of workplace or inventory management system) would provide information on the weight of each object…sufficient to estimate the peak load moment (a musculoskeletal loading metric for the back) by multiplying the weight of the object by the peak horizontal distance from the spine to object…the peak load moment for each lift could then be input to LiFFT, an existing ergonomics assessment tool to compute cumulative damage and injury risk to the low back).
Regarding Claim 8, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation apparatus comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses further comprising: a workload analysis processor that analyzes a work load for reducing load on the lower back part and an endoskeleton system of the wearer relating to the heavy muscular work on the basis of a forecasted result of the lower back pain occurrence risk forecasting processor (Paragraph [0056] - the musculoskeletal loading is used for control or evaluation of the exoskeleton, exosuit, smart clothing or other wearable assistance device…an exoskeleton controller that was designed to only assist the user once they surpassed a threshold of musculoskeletal loading, or cumulative damage, or injury risk…use musculoskeletal loading for evaluation is that the exoskeleton moment contribution about the back can be subtracted out of the lumbar moment estimate to assess how much back relief the exoskeleton is providing, or how much exoskeleton assistance reduces musculoskeletal tissue damage or injury risk), and
an optimization control data generation processor that generates control data in order to optimize motion assistance control performance by the wearable motion assistance device on the basis of an analysis result of the workload analysis processor (Paragraph [0170] - method further comprises controlling or evaluating the exoskeleton, exosuit, smart clothing or other wearable assistance device using the musculoskeletal loading…the control of the exoskeleton, exosuit, smart clothing or other wearable assistive device is optimized from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories, using a reinforcement learning algorithm; Paragraph [0147] - a reinforcement learning algorithm incrementally learns the optimal control of an assistive device from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories).
Regarding Claim 11, Zelik et. al.’352 discloses a computer-implemented lower back part load evaluation method using a wearable motion assistance device worn by securing either a wearer's both femoral parts or the wearer's abdominal part and shoulder parts, or both of them, to assist motions of a lower back part of the wearer (see Paragraphs [0028-0038]; Paragraph [0064] - the at least one motion/orientation sensor comprises a first IMU operably attached to the trunk or pelvis of the user, and two second IMUs operably attached to the left and right thighs or shanks of the user; Paragraph [0107] - These elements may be implemented using electronic hardware, computer software, or any combination thereof; Figure 2), the lower back part load evaluation method comprising:
a first step of continuously measuring an inclination angle of the lower back part of the wearer wearing the wearable motion assistance device as a trunk angle of the wearer (see Paragraphs [0028-0038]; Paragraph [0063] - inertial measurement unit (IMU) operably attached to the trunk; Paragraph [0085] - Sagittal trunk angle and vertical GRFs are the most important signals for estimating lumbar moments, consistent with the findings from idealized wearable sensor analysis in FIG. 3. Signal importances here are from the real wearable sensor algorithm for estimating lumbar extension moments. R=right; L=left), and
a second step of continuously estimating an intervertebral disk pressure force applied to the wearer's intervertebral disks in a vertebral-bodies-connecting direction on a basis of a weight of an object held by the wearer and a body height and body weight of the wearer which are externally input, and the trunk angle measured in the first step (see Paragraphs [0028-0038]; Paragraph [0038] - data from a single IMU (or other motion/orientation sensor) that monitors trunk orientation of the wearer can be combined with object weight data transmitted from the warehouse management system to estimate lumbar loading without requiring a pressure-sensing insole sensor; Paragraph [0050] - the user inputs comprise height, weight…and/or other personal health or demographic data; Paragraph [0013] - …multiple wearable sensors at distributed locations on the body has the potential to provide better estimates of low back loading by capturing and integrating additional dynamics data (e.g., body segment motions or orientations, forces or moments, muscle activity)), and
a third step of dividing the intervertebral disk pressure forces estimated in the second step into sections according to a load level and, at a same time, measuring elapsed time for each of the sections in chronological order (Paragraph [0026] - using signals from the trunk IMU and the pressure insoles together with a gradient boosted decision tree algorithm provides a practical, accurate, and automated way to monitor time series lumbar moments across the range of material handling tasks; Paragraph [0088] - gray areas are approximately when the participant was holding the 10 kg box, white areas are when the participant had no object in their hands…trunk IMU tends to perform worse when the box is being held or lifted, whereas the trunk IMU plus pressure insoles, and distributed sensors, are able to better track key lumbar loading trends (gray areas); Paragraph [0089] - peak lumbar moment of squat tasks when increasing box masses are lifted (10 kg, 15 kg, 23 kg are shown)…captures the trend of increasing lumbar moment with heavier object mass), wherein measuring elapsed time for each of the sections in chronological order comprises tracking a duration of time spent in each load level section sequentially as the intervertebral disk pressure force changes over time during a work time of the muscular work (Paragraph [0193] - estimate the time series lumbar extension moment (as opposed to just peak moments) because this enables us to identify bending/lifting frequency, to partition out individual movement cycles, and to better understand and distinguish cyclic lifts vs. prolonged bending. Time series data enables the assessment of loading and cumulative risk across all tasks, as well as the ability to perform task-specific load and risk assessment; Paragraph [0223] - In contrast, these elevated back loads from the handheld mass are captured by solutions that use sensors at multiple locations that include pressure insoles along with at least one IMU, as shown in FIG. 8. The time-series plots show a representative lifting task, while the scatter plots and tables presented in the Results provide comprehensive results from all the participants and across all the manual material handling tasks collected; Figure 8), and
a fourth step of forecasting the wearer's risk of developing a lower back pain symptom in real-time during a muscular work (Paragraph [0154] - In some embodiments, the estimates of the musculoskeletal loading and/or damage and/or injury risk are computed via real-time or near-real-time estimation algorithms; Paragraph [0155] - In some embodiments, the estimated musculoskeletal loading and/or damage and/or injury risk is communicated to the user and/or a party of interest via one or more wireless or wired communication interfaces, either in real-time, near-real-time or at a later time) on the basis of a measurement history in the third step during the work time of the muscular work which is defined as work involving lifting object weighing more than a predetermined weight to handle the object (see Paragraphs [0028-0038]; Paragraph [0221] - the pressure insoles provide unique and highly valuable force data, as shown in FIGS. 2 and 3, and Table 3, that can help distinguish when someone is lifting a heavy object vs. simply bending forward, and that can greatly improve capabilities for monitoring trends in low back loading (particularly at higher magnitudes)), wherein
the wearable motion assistance device includes a drive mechanism comprising an actuator that drives belts or a lower-back frame by generating a driving torque to relatively rotationally drive each of the lower-back frame (Paragraph [0056] - In one embodiment, the musculoskeletal loading is used for control or evaluation of the exoskeleton, exosuit, smart clothing or other wearable assistance device. One example of how to use musculoskeletal loading for control is a powered exoskeleton controller designed to provide an assistance magnitude proportional to the amount of back loading (e.g., lumbar moment)), and wherein
based on a result of the forecast of the wearer's risk, the wearable motion assistance device automatically and immediately changes a configuration of the drive mechanism in response to the forecasted risk exceeding a threshold to control an output torque of the drive mechanism (Paragraph [0056] - Another example would be an exoskeleton controller that was designed to only assist the user once they surpassed a threshold of musculoskeletal loading, or cumulative damage, or injury risk. An example of how to use musculoskeletal loading for evaluation is that the exoskeleton moment contribution about the back can be subtracted out of the lumbar moment estimate to assess how much back relief the exoskeleton is providing, or how much exoskeleton assistance reduces musculoskeletal tissue damage or injury risk; Paragraph [0152] - The device moment contribution would either come directly from the assistance device itself (e.g., in the case of robotic devices that contain sensors and compute assistance levels on board); Paragraph [0170] - the control of the exoskeleton, exosuit, smart clothing or other wearable assistive device is optimized from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories, using a reinforcement learning algorithm).
Zelik et. al.’352 fails to disclose dividing the intervertebral disk pressure forces into five load level sections. Sung Lee’102 teaches determining – as a form of dividing – risk levels associated with weight loads held at different postures during lifting procedures through using a well-known NIOSH calculation method (Page 316 - the score of the overall load index to determine the 5 levels of decision-making… the risk level may be classified into five levels corresponding to the final score. Specifically, if the final score corresponds to 1, the risk level can be ignored as 0 level, if the final score is between 2 and 3, the risk level is low to 1 level, and the final score is 4 to 7 If the final score is between 8 and 10, the risk level is as high as 3 levels, and if the final score is between 11 and 15, the risk level is rated as 4 levels). It would have been obvious to one of ordinary skill in the art to have modified the evaluation apparatus of Zelik et. al.’352 to include a calculation similar to NIOSH that is well known in the art that uses discrete level ranges and other parameters to categorize lifting actions into 5 sections to recommend corrective actions for the postures and categorized weight loads as seen in Sung Lee’102. Additionally, as evidenced by Daynard et. al.’2001, the standard NIOSH index identifies common loads such as 2744N, 3000N, and 3400N as common limits (Page - spinal compression exceeds approximately 3400 N some workers will be at increased risk of low-back injury. They call this ‘limit’ the ‘Action Limit’ (AL). Interestingly, Norman et al. (1998) showed that the average spinal compression of workers who had reported low-back pain (cases) in a large auto-assembly plant was 3402 N. For the referent group, workers who had not reported low-back pain (controls), the spinal compression was statistically significantly lower at 2744 N…observed damage to tissues in the lumbar spines of cadavers subjected to shear loading at about 3000N). Additionally, without specifics within the description, the specified level ranges divided into separate sections would have been an obvious design choice of range as seen in In re Bergen, 120 F.2d 329, 332, 49 USPQ 749, 751-52 (CCPA 1941) wherein the court found that the overlapping endpoint of the prior art and claimed range was sufficient to support an obviousness rejection, particularly when there was no showing of criticality of the claimed range.
Regarding Claim 13, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation method according to Claim 11. Zelik et. al.’352 further discloses comprising the wearable motion assistance device wherein in the fourth step, it is determined, on the basis of an estimation history of the second step, that the wearer's risk of developing the lower back pain symptom is high when a maximum value of the intervertebral disk pressure force during the work time approaches a specified threshold limit vale (Paragraph [0045] - the processing unit is further configured to alert the user, via audio or vibrotactile feedback, when the musculoskeletal loading or microdamage accumulation or injury risk is greater than a threshold that is predetermined or a threshold that is calibrated for a specific user).
Regarding Claim 16, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation method comprising the wearable motion assistance device according to Claim 11. Zelik et. al.’352 further discloses wherein in the fourth step, as an additional forecasting approach, the wearer's risk of developing the lower back pain symptom according to a relation between a maximum value of the trunk angle of the wearer and a weight of the object during the work time is forecasted on the basis of the measurement history of the first step (Paragraph [0038] - the trunk orientation (e.g., sagittal bending angle) estimate could be used as a surrogate for (or correlate of) the horizontal distance between a person's spine and object being lifted…a person generally has to bend further forward to pick up or set down objects that are further away…quantitatively relate trunk orientation to the estimated horizontal distance between the spine and object…data from the synchronized warehouse management system (or other kind of workplace or inventory management system) would provide information on the weight of each object…sufficient to estimate the peak load moment (a musculoskeletal loading metric for the back) by multiplying the weight of the object by the peak horizontal distance from the spine to object…the peak load moment for each lift could then be input to LiFFT, an existing ergonomics assessment tool to compute cumulative damage and injury risk to the low back).
Regarding Claim 18, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation method comprising the wearable motion assistance device according to Claim 11. Zelik et. al.’352 further discloses comprising: a fifth step of analyzing a work load for reducing load on the lower back part and an endoskeleton system of the wearer relating to the muscular work on the basis of a forecasted result of the fourth step (Paragraph [0056] - the musculoskeletal loading is used for control or evaluation of the exoskeleton, exosuit, smart clothing or other wearable assistance device…an exoskeleton controller that was designed to only assist the user once they surpassed a threshold of musculoskeletal loading, or cumulative damage, or injury risk…use musculoskeletal loading for evaluation is that the exoskeleton moment contribution about the back can be subtracted out of the lumbar moment estimate to assess how much back relief the exoskeleton is providing, or how much exoskeleton assistance reduces musculoskeletal tissue damage or injury risk), and
a sixth step of generating control data in order to optimize motion assistance control performance by the wearable motion assistance device on the basis of an analysis result of the fifth step (Paragraph [0170] - method further comprises controlling or evaluating the exoskeleton, exosuit, smart clothing or other wearable assistance device using the musculoskeletal loading…the control of the exoskeleton, exosuit, smart clothing or other wearable assistive device is optimized from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories, using a reinforcement learning algorithm; Paragraph [0147] - a reinforcement learning algorithm incrementally learns the optimal control of an assistive device from wearable sensor inputs based on real-time feedback from the user and previously observed motion trajectories).
Claims 2, 9-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zelik et. al.’352 (U.S. Patent Application 20230270352 – previously cited) in view of Sung Lee’102 (WO Patent Application 2016105102 – previously cited) as evidenced by Daynard et. al.'2001 (Applied Ergonomics: Biomechanical analysis of peak and cumulative spinal loads during simulated patient-handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers – previously cited) as applied to Claims 1 and 11 above, further in view of Tokuyoshi et. al.’991 (JP Patent Application 2007282991 – previously cited).
Regarding Claims 2 and 12, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus and method comprising the wearable motion assistance device as applied to Claims 1 and 11, but does not disclose an apparatus or method further comprising a hip joint angle measuring processor that measures motion angles upon bending and stretching of right and left hip joints of the wearer. Tokuyoshi et. al.’991 teaches a hip joint angle measuring processor that measures motion angles upon bending and stretching of right and left hip joints of the wearer, respectively, as right and left hip joint angles of the wearer (Paragraph [0006] - …the wearable movement assist device, when the device is controlled, the hip joint [is] detected from the rotation angle between the frame worn on the waist and the frame worn on the thigh. The thigh drive motor is controlled from the angle and the surface myoelectric potential of the iliopsoas and gluteal muscles). The iliopsoas are muscles of the hip.
Further, Zelik et. al.’352 does not disclose an estimation processor that continuously estimates the intervertebral disk pressure force applied to the wearer's intervertebral disks on the basis of a combination of the trunk angle of the wearer and the right and left hip joint angles of the wearer. Tokuyoshi et. al.’991 teaches an estimation processor that estimates the intervertebral disk pressure force applied to the wearer's intervertebral disks on the basis of a combination of the trunk angle of the wearer and the right and left hip joint angles of the wearer (Paragraph [0018] - Corresponding to each joint of the load transmission device so that the load of the load can be calculated and transmitted to each thigh wearing device via the upper body wearing device and the load transmitting device. By having the function of controlling the force applied by the actuator, the load transmission device can be used when the loader lifts and lowers the load while holding the required load with the upper body bent. Thus, it is possible to automatically control the load transmitted from the upper body wearing tool to the left and right thigh wearing tools; Paragraph [0020] - a part of the load on the upper body of the wearer 7 is transmitted from the upper body wearer 2 to the left and right thigh wearers 3 via the left and right load transmission devices 4 to the left and right of the wearer 7; Paragraph [0005] - The left and right thigh drive motors assist the rotational force between the hip and thigh centered on the hip joint).
Therefore, it would be have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 by applying a sensor (Paragraph [0013] - using multiple wearable sensors at distributed location on the body) at the location of the hips to detect joint angles as denoted by Tokuyoshi et. al.’991 above. Analyzing an angle created by the hips of the wearer allows more force to be reduced across the span of the body as more data is given for where torque needs to be applied as seen in Tokuyoshi et. al.’991 (Paragraph [0006] - when the device is controlled, the hip joint [is] detected from the rotation angle between the frame worn on the waist and the frame worn on the thigh; Paragraph [0018] - when the upper body is bent, a part of the load acting on the waist from the upper body is transferred to the thigh…the torque which acts on a waist | hip | lumbar part by the load of a wearer's upper body can be decreased, and the burden of this waist | hip | lumbar part can be reduced).
Regarding Claim 9, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses the lower-back frame which is configured to be mounted on the lower back of the wearer and can be connected to right and left sides of the lower-back frame (Paragraph [0056] - a powered exoskeleton controller designed to provide an assistance magnitude proportional to the amount of back loading (e.g., lumbar moment)), and the belts are configured to be secured to right and left femoral parts of the wearer and can be connected to an outside of each of the femoral parts (Paragraph [0029] - two second IMUs operably attached to the left and right thighs or shanks of the user).
Zelik et. al.’352 does not disclose a hip joint angle measuring processor that is provided in the drive mechanism and detects rotation angles between the lower-back frame and the belts respectively as right and left hip joint angles of the wearer or a control processor that judges a motion and posture of the wearer on the basis of the right and left hip joint angles of the wearer by the hip joint angle measuring processor and causes the drive mechanism to generate a driving torque according to a result of the judgment based on the biosignals’ output from the biosignal detection processor and the right and left hip joint angles. Tokuyoshi et. al.’991 teaches a hip joint angle measuring unit that is provided in the drive mechanism and detects rotation angles between the lower-back frame and the femoral part securing units respectively as right and left hip joint angles of the wearer (Paragraph [0029] - detecting the relative rotation angles of the frames 8 and 10, the frames 10 and 11, and the frames 11 and 9, it can be output as a detection signal for the bending angle in the front-rear direction at the joints 5 a, 5 b, and 5 c; Paragraph [0006] - …the wearable movement assist device, when the device is controlled, the hip joint detected from the rotation angle between the frame worn on the waist and the frame worn on the thigh) and a control unit that judges a motion and posture of the wearer on the basis of the right and left hip joint angles of the wearer by the hip joint angle measuring unit and causes the drive mechanism to generate a driving torque according to a result of the judgment (Paragraph [0018] - Each joint part of the load transmitting device is provided with an angle sensor for detecting a bending angle at each joint part, and the upper body wearing tool is attached to the upper body and each thigh wearing tool is attached to the left and right thighs. A device for detecting the force generated at the waist of the wearer, and a signal input from the angle sensor and a device for detecting the force generated at the waist of the wearer. Based on the input signal, it is configured to include a control device for controlling the force applied by the corresponding actuator to each joint portion of the load transmission device, and more specifically, the control device includes based on the input from the angle sensor, the posture of the upper body of the wearer is detected, the torque required for the waist to support the weight of the upper body when the detected posture is taken).
Therefore, it would be prima facie obvious to apply a sensor from Zelik et. al.’352 (Paragraph [0013] - using multiple wearable sensors at distributed location on the body) at the location of the hips to detect joint angles as denoted by Tokuyoshi et. al.’991 above. Analyzing an angle created by the hips of the wearer allows more force to be reduced across the span of the body as more data is given for where there needs to be torque applied as seen in Tokuyoshi et. al.’991 (Paragraph [0006] - when the device is controlled, the hip joint [is] detected from the rotation angle between the frame worn on the waist and the frame worn on the thigh; Paragraph [0018] - when the upper body is bent, a part of the load acting on the waist from the upper body is transferred to the thigh…the torque which acts on a waist | hip | lumbar part by the load of a wearer's upper body can be decreased, and the burden of this waist | hip | lumbar part can be reduced).
Regarding Claim 10, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 does not disclose a biosignal detection processor that detects a biosignal of movements of the wearer's left thigh and right thigh and a biosignal of the wearer's back or wherein the control unit judges the wearer's motion and posture on the basis of the biosignals output from the biosignal detection and the right and left hip joint angles of the wearer and causes the drive mechanism to generate a driving torque according to a result of the judgment based on the biosignals’ output from the biosignal detection processor and the right and left hip joint angles. Tokuyoshi et. al.’991 teaches a biosignal detection processor that detects a biosignal of muscles associated with movements of the wearer's left thigh and right thigh and a biosignal of a muscle of the wearer's back (Paragraph [0030] - …the myoelectric potential detection electrode 22 is affixed to a position slightly deviated from the spine in the waist of the wearer 7, and input from the myoelectric potential detection electrode 22 through the preamplifier 23 and the biological amplifier 24. The electropneumatic regulator 15 is controlled by controlling the electropneumatic regulator 15 based on the myoelectric potential signal of the waist of the wearer 7 and the signals input from the angle sensors 18 of the joints 5a, 5b, and 5c of the load transmitting devices 4). Tokuyoshi et. al.’991 further teaches a control processor that judges the wearer's motion and posture on the basis of the biosignals output from the biosignal detection and the right and left hip joint angles of the wearer and causes the drive mechanism to generate a driving torque according to a result of the judgment (Paragraph [0033] - …when a person takes a posture in which the upper body is bent at a certain angle, the load applied to the waist from the upper body, such as the weight of the head, left and right arms, and upper body, can be calculated from the dynamic model of the human body. The torque required for the waist to support the load can be calculated. Furthermore, information on the degree of force generated at the waist can be obtained from the myoelectric potential signal at the waist of the human body).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 by using a sensor that they recited (Paragraph [0013] - using multiple wearable sensors at distributed location on the body) at the location of the rectus femoris muscle, gluteus maximus muscle, latissimus dorsi muscle, and the hips to detect joint angles and movements as denoted by Tokuyoshi et. al.’991 above. By applying more sensors to obtain more data points, provides a more accurate intervertebral force estimation and allows more area of the wearer to be observed. Tokuyoshi et. al.’991 teaches how multiple locations with known data reduces burden on the wearer at different positions (Paragraph [0018] - the torque which acts on a waist | hip | lumbar part by the load of a wearer's upper body can be decreased, and the burden of this waist | hip | lumbar part can be reduced; Paragraph [0043] - the burden on the lower back when the wearer 7 has a heavy load is further reduced, or even when the wearer 7 has no heavy load, it becomes possible to support the bending and stretching operation).
Claims 4-5, 7, 14-15, 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zelik et. al.’352 (U.S. Patent Application 20230270352 – previously cited) in view of Sung Lee’102 (WO Patent Application 2016105102 – previously cited) as evidenced by Daynard et. al.'2001 (Applied Ergonomics: Biomechanical analysis of peak and cumulative spinal loads during simulated patient-handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers – previously cited) as applied to Claims 1 and 11 above, further in view of Haytham et. al.’806 (U.S. Patent Application 20170245806 – previously cited) as evidenced by Hoesl Xavier’871 (DE Patent Application 102010003871 – previously cited).
Regarding Claims 4 and 14, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus and method, according to Claims 1 and 11. Zelik et. al.’352 further discloses where the lower back pain occurrence risk forecasting processor determines, on the basis of a measurement history of the trunk angle measuring processor (Paragraph [0063] - inertial measurement unit (IMU) operably attached to the trunk; Paragraph [0085] - Sagittal trunk angle and vertical GRFs are the most important signals for estimating lumbar moments, consistent with the findings from idealized wearable sensor analysis in FIG. 3. Signal importances here are from the real wearable sensor algorithm for estimating lumbar extension moments. R=right; L=left), but does not disclose the trunk angle of the wearer continuing to be within a range for a specified amount of time results in a higher risk of lower back pain.
Haytham et. al.’806 teaches the risk of developing lower back pain is high when at a certain trunk angle while lifting for a specified amount of time (Paragraph [0016] - calculates measurements of the wearer for the time period during the lifting activity from the first signal segment and the second signal segment and calculates a risk metric from a risk model based on the measurements of the wearer for the time period during the lifting activity, the risk metric being indicative of high risk lifting activity; Paragraph [0028] - The method may further determine a conclusion time for the lifting activity). As evidenced by Hoesl Xavier’871, a correct posture when lifting has been identified to be around 42 degrees (Page 1 Paragraph 3 - In the case of a correct posture with an inclination α (main extension direction of the spinal column with respect to the perpendicular direction) of approximately 42°). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 to include a time period for how long an individual is performing different lifting tasks in order to gather more cumulative, specific, time-sensitive data regarding developing lower back pain risk while completing different lifting/bending tasks as taught by Haytham et. al. (Paragraph [0028] - the signal segment is identified by identifying an initiation time for the lifting activity and excerpting the signal segment corresponding to a time period after the initiation time. The method may further determine a conclusion time for the lifting activity and repeat the method to identify a plurality of lifting activities over an evaluation period). Additionally, without specifics within the description, this range would have been an obvious design choice of range as seen in In re Bergen, 120 F.2d 329, 332, 49 USPQ 749, 751-52 (CCPA 1941) wherein the court found that the overlapping endpoint of the prior art and claimed range was sufficient to support an obviousness rejection, particularly when there was no showing of criticality of the claimed range.
Regarding Claims 5 and 15, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus and method, according to Claims 1 and 11. Zelik et. al.’352 further discloses where a lower back pain occurrence risk forecasting processor calculates a load imposed on the lower back part of the wearer during the work time to determine that the wearer's risk of developing the lower back pain symptom is high when the value becomes equal to or larger than a specified threshold value (Paragraph [0141] - the musculoskeletal loading or cumulative damage or injury risk is greater than a threshold that is predetermined or a threshold that is calibrated for a specific user). Zelik et. al.’352 does not disclose using an average load value. Haytham et. al.’806 teaches comparing an average load, determining high risk if above a certain threshold, and how to reduce that risk (Paragraph [0179] - …determine that the value has changed by checking each value underlying the metric for each lift against the average value of the corresponding measurement; Paragraph [0180] - if the risk as described by the model is above a threshold, the individual components of the risk models may be analyzed to determine the cause of the underlying risk…recommendations may be provided based on reducing the frequency rates of lifts or having more people perform the job so as to reduce the load on each individual worker). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 to take an average of the loads so that the data was more representative of the entire work applied to the wearer of the apparatus and would be able to get rid of outlier data/signals received as seen in Haytham et. al. (Paragraph [0080] - include methods such as low pass filtering, Kalman filters, Gaussian moving averages etc., all of which combine to reduce the noise in the signal and remove unwanted drift of signals).
Regarding Claims 7 and 17, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses a lower back part load evaluation apparatus and method, according to Claims 1 and 11. Zelik et. al.’352 further discloses wherein the lower back pain occurrence risk forecasting processor determines that the wearer's risk of developing the lower back pain symptom is high when time in which the load level of the intervertebral disk pressure force during the work time belongs to a section equal to or higher than a specified level is equal to or longer than a specified period of time (Paragraph [0193] - …estimate the time series lumbar extension moment (as opposed to just peak moments) because this enables us to identify bending/lifting frequency, to partition out individual movement cycles, and to better understand and distinguish cyclic lifts vs. prolonged bending. Time series data enables the assessment of loading and cumulative risk across all tasks, as well as the ability to perform task-specific load and risk assessment), but does not disclose an “accumulated time”. However, Haytham et. al.’806 teaches an accumulated time spent applying a load is used to calculate a high risk value (Paragraph [0016] - calculates measurements of the wearer for the time period during the lifting activity from the first signal segment and the second signal segment and calculates a risk metric from a risk model based on the measurements of the wearer for the time period during the lifting activity, the risk metric being indicative of high risk lifting activity). In Haytham et. al.’806, “time period” is the entirety of time spent on the lifting action. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 to include a time period for how long an individual is performing different lifting tasks in order to gather more cumulative, specific, time-sensitive data regarding the risks of developing lower back pain while completing different lifting/bending tasks throughout an accumulation of time as taught by Haytham et. al.’806 (Paragraph [0120] - the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift).
Regarding Claim 19, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation method comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses the lower back pain occurrence risk forecasting processor determines that the wearer's risk of developing the lower back pain symptom is high based on a combination of: (i) a maximum value of the intervertebral disk pressure force during the work time approaching a specified threshold limit value (Paragraph [0045] - the processing unit is further configured to alert the user, via audio or vibrotactile feedback, when the musculoskeletal loading or microdamage accumulation or injury risk is greater than a threshold that is predetermined or a threshold that is calibrated for a specific user), wherein the forecasting processor evaluates instant loads and cumulative loads to forecast a risk (Paragraph [0193] - Time series data enables the assessment of loading and cumulative risk across all tasks, as well as the ability to perform task-specific load and risk assessment).
Zelik et. al.’352 fails to disclose (ii) accumulated time in which the load level of the intervertebral disk pressure force during the work time belongs to a section equal to or higher than load level 3. Haytham et. al.’806 teaches the risk of developing lower back pain is high when at a certain trunk angle while lifting for a specified amount of time (Paragraph [0016] - calculates measurements of the wearer for the time period during the lifting activity from the first signal segment and the second signal segment and calculates a risk metric from a risk model based on the measurements of the wearer for the time period during the lifting activity, the risk metric being indicative of high risk lifting activity; Paragraph [0028] - The method may further determine a conclusion time for the lifting activity). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified Zelik et. al.’352 to include a time period for how long an individual is performing different lifting tasks in order to gather more cumulative, specific, time-sensitive data regarding developing lower back pain risk while completing different lifting/bending tasks as taught by Haytham et. al. (Paragraph [0028] - the signal segment is identified by identifying an initiation time for the lifting activity and excerpting the signal segment corresponding to a time period after the initiation time. The method may further determine a conclusion time for the lifting activity and repeat the method to identify a plurality of lifting activities over an evaluation period). Additionally, without specifics within the description, this load level 3 would have been an obvious design choice of load level as seen in In re Bergen, 120 F.2d 329, 332, 49 USPQ 749, 751-52 (CCPA 1941) wherein the court found that the overlapping endpoint of the prior art and claimed range was sufficient to support an obviousness rejection, particularly when there was no showing of criticality of the claimed range.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Zelik et. al.’352 (U.S. Patent Application 20230270352 – previously cited) in view of Sung Lee’102 (WO Patent Application 2016105102 – previously cited) as evidenced by Daynard et. al.'2001 (Applied Ergonomics: Biomechanical analysis of peak and cumulative spinal loads during simulated patient-handling activities: a substudy of a randomized controlled trial to prevent lift and transfer injury of health care workers – previously cited) as applied to Claim 1 above, further in view of Yoshimi et. al.’092 (U.S. Publication Number 20200189092).
Regarding Claim 20, Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 discloses the lower back part load evaluation method comprising the wearable motion assistance device according to Claim 1. Zelik et. al.’352 further discloses the drive mechanism adjusts the output torque proportionally based on which of the five load level sections a currently estimated intervertebral disk pressure force belongs to (Paragraph [0152] - One example of how to use musculoskeletal loading for control is a powered exoskeleton controller designed to provide an assistance magnitude proportional to the amount of back loading (e.g., lumbar moment)) as well as a workload analysis processor analyzes the work load based on the forecasted result to optimize a motion assistance control performance (Paragraph [0196] - this algorithm estimates the target load metric by building an ensemble of decision trees in a stage-wise fashion, where in each stage the new tree tries to estimate (and thus, remove) the residual error after combining the predictions of the previous trees), but fails to disclose that the output torque increases progressively as the load level section increases from load level 1 to load level 5. Yoshimi et. al.’092 teaches progressively increasing levels of assistance provided by a device (Paragraph [0064] - The assist device 1 further has a manipulation unit R1 (so-called remote controller) that is used by the person being assisted to adjust a motion mode (lowering assistance, lifting assistance, etc.), a gain in an assisting torque, and a speed with which the amount of assisting torque is increased, or to check the adjusted state etc., and a housing part R1S that houses the manipulation unit R1; Paragraph [0117] - an example in which the speed number has six numbers from −1 to 4, but the speed number is not limited to six numbers). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the system of Zelik et. al.’352 in view of Sung Lee’102 as evidenced by Daynard et. al.'2001 to include progressively increasing levels of assistance provided by a device in order to control an assistive device in moderation and control levels of assistance as seen in Yoshimi et. al.’092.
Response to Arguments
Applicant's arguments filed 12 December 2025 have been fully considered and they are not entirely persuasive.
Applicant’s amendments to the claims have overcome the previous objections of claims, but additional objections have been addressed in Paragraphs 3-4 above.
Applicant’s amendments to the claims have overcome the previous rejections of claims under 35 U.S.C. 112(a) and 112(b). However, additional 112(b) rejections have been addressed in Paragraph 5 above.
Applicant’s amendments to the above claims are rejected under 35 U.S.C. 103, as seen in Paragraph 6-9 above, as necessitated by amendment.
Examiner has considered the applicant’s arguments regarding Zelik et. al.’352 failing to disclose “automatically and immediately changes the configuration of the drive mechanism”, but these reasonings were found to not be persuasive. The examiner has cited additional paragraphs from Zelik et. al.’352 that discloses a moment contribution and assistance levels being computed on-board (Paragraph [0152]) as well as real-time feedback controlling the exoskeleton (Paragraph [0170]). The examiner notes that these two recitations provide clarity that the drive mechanism of Zelik et. al.’352 is able to be automatically and immediately controlled/changed.
Additionally, the examiner has considered the applicant’s arguments regarding Zelik et. al.’352 failing to disclose “tracking time spent in each load level”, but these reasonings were found to not be persuasive given the additional citations from Zelik et. al.’352 cited in Paragraph 6 above with a focus on figures from Zelik et. al.’352 such as Figure 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Matijevich et. al.’020 (U.S. Publication Number 20210236020) could be modified by Chang et. al.’919 (U.S. Publication Number 20170344919) in order to track potential risks a user encounters while performing actions such as lifting loads of different weight levels for various durations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAH ANN WESTFALL whose telephone number is (571) 272-3845. The examiner can normally be reached Monday-Friday 7:30am-4:30pm EST.
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/SARAH ANN WESTFALL/Examiner, Art Unit 3791
/ETSUB D BERHANU/Primary Examiner, Art Unit 3791