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 .
Response to Arguments
Applicant’s arguments, see Remarks, filed 06/16/2026, with respect to the rejections of claims 12, 16, 18-20, and 28-30 under 35 USC 101 have been fully considered and are persuasive. The rejections of claims 12, 16, 18-20, and 28-30 under 35 USC 101 have been withdrawn.
Applicant’s arguments, see Remarks, filed 06/16/2026, with respect to the rejections of claims 27 and 28 under 35 USC 112(a) have been fully considered and are persuasive. The rejections of claims 27 and 28 under 35 USC 112(a) have been withdrawn.
Applicant’s arguments, see pg. 12-15, filed 06/16/2026, with respect to claims 1, 4-8, and 33 rejected under 35 USC 102 have been fully considered and are persuasive. The rejections of 35 USC 102 have been withdrawn. New rejections, as necessitated by amendments to the claims, have been made (see Response to Amendment section below).
Applicant's arguments, see pgs. 16-24, filed 06/16/2026 have been fully considered but they are not persuasive regarding the rejections of claims 2, 9-20, and 27-32 under 35 USC 103. The Examiner contends that the references relied upon correct deficiencies in Ramos. In particular, the Examiner disagrees with the Applicant’s assertion that Davoodi does not teach the deficiencies of Ramos in the independent claims (see details in 103 rejection below). The Examiner contends that the claimed subject matter is obvious in view of the references detailed in the rejections below (see Response to Amendment section).
Response to Amendment
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.
Claim 1, 2, 4-8, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”) in view of Davoodi (US 20070016265 A1, “Davoodi”).
Regarding claim 1, Ramos teaches a device (Fig. 1: Generator system 100) comprising: one or more motion sensors (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), the sensors configured to generate one or more respective motion signals indicative of movement of a first body part of a human (para. [0084]: "Suitable connections between the motion sensors and the computing system 109 may also exist for the computer 109 to receive motion signals generated by the motion sensors."; Fig. 1 shows sensors 101 disposed on the forearm of the subject); a muscle stimulator (para. [0106]: "the rehabilitation system may comprise another type of body actuator, such as e.g. a neuromuscular stimulation system or a combination thereof. Examples of neuromuscular stimulation systems are e.g. a Functional electrical stimulation (FES) system..."), wherein the muscle stimulator is configured to generate a predetermined sequence of stimulation signals (para. [0024] shows the patient performs predefined exercises: " to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb." The predefined exercises are based on inputs from neuromuscular signals of the paretic arm (Fig. 4, 401). The sequence of signals are predetermined based on the predefined exercise mapped to the neuromuscular signal.) to cause one or more muscles to displace a second body part to perform a selected intentional action by the human (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals." The paretic limb is considered to be the second body part.); and a processor connected with the one or more motion sensors and the muscle stimulator (para. [0085]: "[0085] The computing system 109 may comprise a memory and a processor. The memory may store a computer program comprising instructions that are executable by the processor for causing the performance of a generator method for generating a neuromuscular-to-motion decoder from a healthy limb." The decoder uses data obtained from the motion signals (see para. [0013]).), the processor including data storage (para. [0085]; The memory included in the computing system is a form of data storage), the data storage including a pre-trained expected trajectory (para. [0023]: "The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data"; the trajectory data from the predefined exercise data is considered pre-trained since the data comes from previous motions of the healthy limb) associated by the human with an intention of the human to perform intentional actions (Fig. 4, step 402 shows that the signal data is input into a neuromuscular decoder, which associates intended motion from the signals of the paretic arm in 401.) wherein the selected intentional action is associated with a selected pre-trained trajectory (Fig. 4, step 402; The intentional actions, from the signals in step 401, are associated with a trajectory during the decoding process; Further, they are associated with pre-trained trajectories since they are decoded using the predefined exercise data of the healthy limb), and wherein the processor: receives the one or more signals from the one or more motion sensors (para. [0012]: "The controller system (of the generator system) is further configured to receive motion signals obtained by the motion sensors associated to predefined positions of the healthy limb."); calculates an actual trajectory of the first body part (Abstract: "to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb"); and actuates the muscle stimulator (Fig. 4; step 407) to apply the pre-determined sequence of stimulation signals to displace the second body part to perform the at least one selected intentional action (para. [0024]: "The controller system (of the rehabilitation system) is still additionally configured to determine final motion commands depending on the first motion commands and the second motion commands, and to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb."; The predefined exercise is being performed with the paretic limb, meaning it is selected based on the signals from the paretic arm, making it intentional. Further, since the actions are mapped (decoded), and the patient is stimulated according to the mapping, the stimulation signals can be considered to be a predetermined sequence that is based on the signal associated with the stimulation. However, Ramos does not explicitly teach a plurality of pretrained expected trajectories, where the movements are in two or three dimensions, or a comparison between the actual trajectory with the plurality of pre-trained expected trajectories and, based on the comparison identifying the actual trajectory with the selected pre-trained trajectory.
It would have been obvious for one of ordinary skill in the art to modify the processor and memory of Ramos to include a plurality of pretrained expected trajectories and a plurality of intentional actions. One of ordinary skill would recognize that the processor of Ramos would be able to store a plurality of exercise trajectories, not just a single exercise trajectory. They would also recognize that the processor and memory of Ramos would be able to store a plurality of intentional exercise trajectories associated with the expected trajectories. Referencing MPEP (VI)(B): “…the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.” In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).” Since Ramos discloses a pretrained expected trajectory associated by the human with an intention of the human to perform a respective intentional action, it would have been obvious to duplicate the expected trajectories to capabilities for a plurality of exercises and trajectories and their associated actions. Further, para. [0015] of the instant specification discloses at least one expected trajectory and para. [0016] discloses a plurality of expected trajectories. Therefore, the instant specification does not express criticality for a plurality of trajectories, and using a plurality of trajectories instead of a singular trajectory is a matter of choice.
Although Ramos does not explicitly disclose movement of a first body part of a human in two or three dimensions, or that the plurality of trajectories are in two or three dimensions, Ramos discloses generating a neuromuscular to motion decoder for each of all or some degrees of freedom or permitted motions (para. [0119] – [0120]: “At block 303, a neuromuscular-to-motion decoder may be generated for each of all or some of the degrees of freedom (in exoskeleton-based examples) or permitted motion directions (in examples based on a stimulation system) under consideration… Given a particular degree of freedom (or permitted motion direction), a neuromuscular-to-motion decoder may be generated by mapping the received neuromuscular signals and motion signals corresponding to said degree of freedom (or permitted motion direction). It is also possible to generate a single neuromuscular-to-motion decoder with different outputs corresponding to different degrees of freedom.”). It would have been obvious for one of ordinary skill to record the movement signals in two or three dimensions and for the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses comparing an actual trajectory (para. [0016]: "sensed voluntary movement signals") with a plurality of pre-trained expected trajectories (para. [0016]: "known movement patterns 42"); and, based on the comparison (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patterns 42."), identifies the actual trajectory with the selected pre-trained trajectory (para. [0016]: "Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to include the technique of comparing trajectories for selecting limb movement of Davoodi. One of ordinary skill would recognize that the technique of Davoodi is used to select limb movement actuation. One would recognize that the comparison technique for predicting and selecting limb movement could be implemented into the device of Ramos to yield the predictable result of correctly identifying the trajectory of the limb based on the sensed signal and known patterns.
Regarding claim 2, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). However, Ramos does not expressly teach wherein the processor computes a difference between the actual trajectory and the plurality of expected trajectories and identifies the selected trajectory based on the difference.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses wherein the processor computes a difference between the actual trajectory (para. [0016]: “sensed voluntary movement signals”) and the plurality of expected trajectories (para. [0016]: "known movement patterns 42") and identifies the selected trajectory based on the difference. (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patters 42. This comparison of sensed signals to known patterns 42 can be achieved through a neural network, pattern recognition, or other method known to those skilled in the art. Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46. Then, a dynamic simulation of limb movement is generated 50 based on the command signals 46.” A comparison between the signal and movement patterns would include the differences between data points. Differences would be found during comparison).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 1, as taught by Ramos, with the processor that computes the difference between expected and actual trajectories, as taught by Davoodi. Doing so would be an obvious improvement to the device that would allow it to record accurately select and generate command signals. Further, one of ordinary skill would recognize that the technique of computing a difference between an actual trajectory and expected trajectory to generate command signals for a limb would improve limb actuation to include the most accurate. It would have also been obvious the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Regarding claim 4, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). Ramos further discloses a feedback signal indicating that the selected action was the intended action of the human (para. [0023]-[0029]: " The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data, and to determine second motion commands depending on the determined trajectory data to be followed by the paretic limb…During initial and intermediate (i.e. non-final) rehabilitation stages, the first motion commands may represent an incorrect motion of the paretic limb and the second motion commands may represent a correction of said incorrect motion aimed at redirecting the limb towards a valid trajectory according to the predefined exercise data…This hybrid motion may cause a visual and/or proprioceptive feedback on the patient when guiding the patient to finally complete the exercise. This feedback may provide the patient with information about the correctness of his/her muscular activations and hence, may establish a closed-loop system that might lead to the activation of neuro-plastic mechanisms that can restore the motor function. Other types of feedback that can be provided are e.g. haptic, auditory, etc…The generator and rehabilitation systems proposed herein permit predicting kinematic (or motion) signals from the neuromuscular activity of the paretic limb and, therefore, establishing a continuous myoelectric control of the body actuator (e.g. an exoskeleton).”). However, Ramos does not expressly teach an input device adapted to receive a feedback signal and communicate the feedback signal to the processor.
Davoodi also discloses an input device (para. [0014]: “actuators”) adapted to receive a feedback signal and communicate the feedback signal to the processor (para. [0014]: “The movements produced by the actuators can also be sensed in order to provide feedback information to adjust the control signals to the actuators in order to achieve the desired limb movement. The control system integrates these sources of command and feedback information to compute continuously the output to the various actuators according to a control algorithm.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to further include the actuator and feedback mechanism of Davoodi. One of ordinary skill would recognize that feedback mechanism would improve control to achieve desired limb movement. It would have been an obvious improvement to implement the input device to improve the device of Ramos.
Regarding claim 5, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). Ramos further discloses wherein the processor (para. [0056]: "In a fifth aspect, a computing system is provided comprising a memory and a processor…"), generates the pre-trained expected trajectory based on a training set of motions (para. [0053]: "The rehabilitation method additionally comprises determining trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data, and determining second motion commands depending on the determined trajectory data to be followed by the paretic limb.").
Regarding claim 6, Ramos, in combination with Davoodi, discloses the device of claim 5 (see above). Ramos further discloses wherein the one or more stimulation signals to perform the at least one selected intentional action comprises a pattern of stimulation signals (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals.") and wherein the pattern of stimulation signals is determined from muscle displacements sensed during the training set of motions (The pattern is determined from the healthy limb which is mapped into electrostimulation signals. This is determined by trajectories of motion signals and exercise data; para. [0052]: "The rehabilitation method additionally comprises determining trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data, and determining second motion commands depending on the determined trajectory data to be followed by the paretic limb.").
Regarding claim 7, Ramos, in combination with Davoodi, discloses the device of claim 6 (see above). Ramos further discloses wherein the muscle displacements are sensed using one or more of an electromyogram sensor (para. [0040]: "In some examples of the generator system and/or the rehabilitation system, the neuromuscular sensors may comprise one or more electromyography (EMG) sensors…"), a camera (para. [0092]: "The mobile base 202 may have, in some examples, three degrees of freedom and may (optionally) include a camera for tracking bi-dimensional movements of the base on a plane of reference"), an inertial motion unit (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), a bend/joint angle sensor, and a force sensor (para. [0100]: " In any case, with respect to motion sensors of either a generator or rehabilitation system or a combination thereof, a motion condition (sensed by a motion sensor) may comprise e.g. at least one of a position, a velocity, an acceleration, a torque, a force etc. in a given degree of freedom.").
Regarding claim 8, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). Ramos further discloses wherein the processor performs the comparison using one or more of a support vector machine (SVM) algorithm (para. [0122]: "A diversity of mapping methods may be used to generate the neuromuscular-to-motion decoder, such as e.g. machine learning methods, statistical methods, datamining methods, etc. or a combination of at least some of them. In particular, linear regression, non-linear regression, Lasso regression, ridge regression, Kalman filter, support vector machine, neural network, fuzzy logic, etc. may be employed for that purpose."), a hand-writing recognition algorithm, a dynamic time warping algorithm, a deep learning algorithm, a recursive neural network, a shallow neural network, convolutional neural network, a convergent neural network, or a deep neural network.
Regarding claim 33, Ramos teaches a device (Fig. 1: Generator system 100) comprising: one or more motion sensors (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), the sensors configured to generate one or more respective motion signals indicative of movement of a first body part of a human (para. [0084]: "Suitable connections between the motion sensors and the computing system 109 may also exist for the computer 109 to receive motion signals generated by the motion sensors."; Fig. 1 shows sensors 101 disposed on the forearm of the subject); a muscle stimulator (para. [0106]: "the rehabilitation system may comprise another type of body actuator, such as e.g. a neuromuscular stimulation system or a combination thereof. Examples of neuromuscular stimulation systems are e.g. a Functional electrical stimulation (FES) system..."), wherein the muscle stimulator is configured to generate a predetermined sequence of stimulation signals (para. [0024] shows the patient performs predefined exercises: " to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb." The predefined exercises are based on inputs from neuromuscular signals of the paretic arm (Fig. 4, 401). The sequence of signals are predetermined based on the predefined exercise mapped to the neuromuscular signal.) to cause one or more muscles to displace a second body part to perform a selected intentional action by the human (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals." The paretic limb is considered to be the second body part.); and a processor connected with the one or more motion sensors and the muscle stimulator (para. [0085]: "[0085] The computing system 109 may comprise a memory and a processor. The memory may store a computer program comprising instructions that are executable by the processor for causing the performance of a generator method for generating a neuromuscular-to-motion decoder from a healthy limb." The decoder uses data obtained from the motion signals (see para. [0013]).), the processor including data storage (para. [0085]; The memory included in the computing system is a form of data storage), the data storage including a pre-trained expected trajectory (para. [0023]: "The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data"; the trajectory data from the predefined exercise data is considered pre-trained since the data comes from previous motions of the healthy limb) associated by the human with an intention of the human to perform intentional actions (Fig. 4, step 402 shows that the signal data is input into a neuromuscular decoder, which associates intended motion from the signals of the paretic arm in 401.) wherein the selected intentional action is associated with a selected pre-trained trajectory (Fig. 4, step 402; The intentional actions, from the signals in step 401, are associated with a trajectory during the decoding process; Further, they are associated with pre-trained trajectories since they are decoded using the predefined exercise data of the healthy limb), and wherein the processor: receives the one or more signals from the one or more motion sensors (para. [0012]: "The controller system (of the generator system) is further configured to receive motion signals obtained by the motion sensors associated to predefined positions of the healthy limb."); calculates an actual trajectory of the first body part (Abstract: "to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb"); and actuates the muscle stimulator (Fig. 4; step 407) to apply the pre-determined sequence of stimulation signals to displace the second body part to perform the at least one selected intentional action (para. [0024]: "The controller system (of the rehabilitation system) is still additionally configured to determine final motion commands depending on the first motion commands and the second motion commands, and to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb."; The predefined exercise is being performed with the paretic limb, meaning it is selected based on the signals from the paretic arm, making it intentional. Further, since the actions are mapped (decoded), and the patient is stimulated according to the mapping, the stimulation signals can be considered to be a predetermined sequence that is based on the signal associated with the stimulation.) analyses the feedback signal to determine that the selected intentional action was the intended action of the human from among the plurality of intended actions. (para. [0023]: " The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data, and to determine second motion commands depending on the determined trajectory data to be followed by the paretic limb.").
However, Ramos does not explicitly teach a plurality of pretrained expected trajectories, where the movements are in two or three dimensions, a comparison between the actual trajectory with the plurality of pre-trained expected trajectories and, based on the comparison identifying the actual trajectory with the selected pre-trained trajectory, or an input device adapted to receive a feedback signal and communicate the feedback signal to the processor.
Although Ramos does not explicitly disclose a plurality of trajectories and intentional actions, it would have been obvious for one of ordinary skill in the art to modify the processor and memory of Ramos to include a plurality of pretrained expected trajectories and a plurality of intentional actions. One of ordinary skill would recognize that the processor of Ramos would be able to store a plurality of exercise trajectories, not just a single exercise trajectory. They would also recognize that the processor and memory of Ramos would be able to store a plurality of intentional exercise trajectories associated with the expected trajectories. Referencing MPEP (VI)(B): “…the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.” In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).” Since Ramos discloses a pretrained expected trajectory associated by the human with an intention of the human to perform a respective intentional action, it would have been obvious to duplicate the expected trajectories to capabilities for a plurality of exercises and trajectories and their associated actions. Further, para. [0015] of the instant specification discloses at least one expected trajectory and para. [0016] discloses a plurality of expected trajectories. Therefore, the instant specification does not express criticality for a plurality of trajectories, and using a plurality of trajectories instead of a singular trajectory is a matter of choice.
Although Ramos does not explicitly disclose movement of a first body part of a human in two or three dimensions, or that the plurality of trajectories are in two or three dimensions, Ramos discloses generating a neuromuscular to motion decoder for each of all or some degrees of freedom or permitted motions (para. [0119] – [0120]: “At block 303, a neuromuscular-to-motion decoder may be generated for each of all or some of the degrees of freedom (in exoskeleton-based examples) or permitted motion directions (in examples based on a stimulation system) under consideration… Given a particular degree of freedom (or permitted motion direction), a neuromuscular-to-motion decoder may be generated by mapping the received neuromuscular signals and motion signals corresponding to said degree of freedom (or permitted motion direction). It is also possible to generate a single neuromuscular-to-motion decoder with different outputs corresponding to different degrees of freedom.”). It would have been obvious for one of ordinary skill to record the movement signals in two or three dimensions and for the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses comparing an actual trajectory (para. [0016]: "sensed voluntary movement signals") with a plurality of pre-trained expected trajectories (para. [0016]: "known movement patterns 42"); and, based on the comparison (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patterns 42."), identifies the actual trajectory with the selected pre-trained trajectory (para. [0016]: "Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to include the technique of comparing trajectories for selecting limb movement of Davoodi. One of ordinary skill would recognize that the technique of Davoodi is used to select limb movement actuation. One would recognize that the comparison technique for predicting and selecting limb movement could be implemented into the device of Ramos to yield the predictable result of correctly identifying the trajectory of the limb based on the sensed signal and known patterns. Therefore, it would have been obvious to implement this technique in the device of Davoodi.
Davoodi also discloses an input device (para. [0014]: “actuators”) adapted to receive a feedback signal and communicate the feedback signal to the processor (para. [0014]: “The movements produced by the actuators can also be sensed in order to provide feedback information to adjust the control signals to the actuators in order to achieve the desired limb movement. The control system integrates these sources of command and feedback information to compute continuously the output to the various actuators according to a control algorithm.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to further include the actuator and feedback mechanism of Davoodi. One of ordinary skill would recognize that feedback mechanism would improve control to achieve desired limb movement. It would have been an obvious improvement to implement the input device to improve the device of Ramos.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), and Hewage et al. (WO 2014113813 A1, “Hewage”).
Regarding claim 9, Ramos, in combination with Davoodi, teaches the device of claim 7 (see above). However, Ramos does not explicitly teach wherein the processor performs a comparison using a Long Short-Term Memory type recursive neural network.
Davoodi teaches where the comparison can be performed with a neural network (para. [0016]: “Then, the sensed voluntary movement signals are compared to known movement patterns 42. This comparison of sensed signals to known patterns 42 can be achieved through a neural network, pattern recognition, or other method known to those skilled in the art.”). However, Davoodi does not disclose using an LSTM recursive neural network.
Hewage, in the same field of endeavor of processing biological signals with neural networks, dicslcoses using neural networks to interface with humans. Hewage discloses wherein the processor performs the comparison using a Long Short-Term Memory type recursive neural network. (para. [0033]: "Preferably, the computer implemented method wherein at least one of the first one or more ML technique(s) comprise at least one or more ML technique(s) or combinations thereof from the group of:[…] long short term memory neural networks[…]").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 7, as disclosed by Ramos and Davoodi, with a Long Short-Term Memory type recursive neural network, as disclosed by Hewage. Hewage discloses using this type of neural network on signals from different biosensor types to estimate neural data. It would have been obvious for one of ordinary skill in the art to implement this type of neural network in the device of Ramos to perform a comparison since Hewage discloses using this neural network to make estimates. One would reasonably predict that this type of neural network could be used in the device of Ramos and Davoodi to compare motion signals and patterns to identify selected trajectories.
Claim 10, 13, 17, 31 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), and Rosenbluth et al. (WO 2014113813 A1, “Rosenbluth”).
Regarding claim 10, Ramos, in combination with Davoodi, discloses the device of claim 5 (see above). However, neither Ramos nor Davoodi does expressly disclose wherein the training set of motions are performed by a second human.
Rosenbluth, in the same field of endeavor of using machine learning on motion trajectory signals, discloses a system for stimulating a peripheral nerve to treat tremors. Rosenbluth discloses wherein the training set of motions are performed by a second human. (Para. [000211]: "2160 pooled analysis across multiple patients…").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 5, as disclosed by Ramos, with the processor that uses trajectory training sets performed by a second human, as disclosed by Rosenbluth. In doing so, the device would be able to increase the breadth of the motion data it can be compared to when training the neural network or machine learning algorithm. Rosenbluth discloses that obtaining data from an individual that is not the one using the device can be used as training data.
Regarding claim 13, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). However, neither Ramos nor Davoodi expressly disclose wherein at least one of the plurality of pre-trained expected trajectories is two-dimensional and in the shape of an alphanumeric character.
Rosenbluth discloses wherein at least one of the plurality of pre-trained expected trajectories is two-dimensional and in the shape of an alphanumeric character (para. [000193]-[000194]: “ The data from these tremor sensors is used measure the patient's current and historical tremor characteristics such as the amplitude, frequency and phase. These sensors may also be used to determine activities, such as to distinguish involuntary movements (e.g. tremor) from voluntary movements (e.g. drinking, writing) or the presence and absence of the tremor relative to the time of day or other detected activities such as sleep/wake cycles...engaging the patient in a tremor-induced writing task by analyzing a line traced on a smartphone screen."; the writing task could be alphanumeric characters. The writing task could be any characters, and the expected trajectories is the trajectory to trace the line, and the actual trajectory is the line the patient actually traced).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 1, as disclosed by Ramos, with the processor using a trajectories from a writing task, as disclosed by Rosenbluth. It is shown that the trajectories of a writing task can be measured and compared to data performed by another person doing the same task using machine learning, as demonstrated by Rosenbluth. It would have been obvious to make the trajectories a writing task since this is a technique that is shown to successfully demonstrate that alphanumeric characters can be used for expected trajectories. Further, one of ordinary skill would recognize the trajectories to be two dimensional since they are traced along a smartphone screen.
Regarding claim 31, Ramos teaches a device (Fig. 1: Generator system 100) comprising: one or more motion sensors (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), the sensors configured to generate one or more respective motion signals indicative of movement of a first body part of a human (para. [0084]: "Suitable connections between the motion sensors and the computing system 109 may also exist for the computer 109 to receive motion signals generated by the motion sensors."; Fig. 1 shows sensors 101 disposed on the forearm of the subject); a muscle stimulator (para. [0106]: "the rehabilitation system may comprise another type of body actuator, such as e.g. a neuromuscular stimulation system or a combination thereof. Examples of neuromuscular stimulation systems are e.g. a Functional electrical stimulation (FES) system..."), wherein the muscle stimulator is configured to generate a predetermined sequence of stimulation signals (para. [0024] shows the patient performs predefined exercises: " to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb." The predefined exercises are based on inputs from neuromuscular signals of the paretic arm (Fig. 4, 401). The sequence of signals are predetermined based on the predefined exercise mapped to the neuromuscular signal.) to cause one or more muscles to displace a second body part to perform a selected intentional action by the human (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals." The paretic limb is considered to be the second body part.); and a processor connected with the one or more motion sensors and the muscle stimulator (para. [0085]: "[0085] The computing system 109 may comprise a memory and a processor. The memory may store a computer program comprising instructions that are executable by the processor for causing the performance of a generator method for generating a neuromuscular-to-motion decoder from a healthy limb." The decoder uses data obtained from the motion signals (see para. [0013]).), the processor including data storage (para. [0085]; The memory included in the computing system is a form of data storage), the data storage including a pre-trained expected trajectory (para. [0023]: "The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data"; the trajectory data from the predefined exercise data is considered pre-trained since the data comes from previous motions of the healthy limb) associated by the human with an intention of the human to perform a respective intentional action (Fig. 4, step 402 shows that the signal data is input into a neuromuscular decoder, which associates intended motion from the signals of the paretic arm in 401.) wherein the selected intentional action is associated with a selected pre-trained trajectory (Fig. 4, step 402; The intentional actions, from the signals in step 401, are associated with a trajectory during the decoding process; Further, they are associated with pre-trained trajectories since they are decoded using the predefined exercise data of the healthy limb), and wherein the processor: receives the one or more signals from the one or more motion sensors (para. [0012]: "The controller system (of the generator system) is further configured to receive motion signals obtained by the motion sensors associated to predefined positions of the healthy limb."); calculates an actual trajectory of the first body part (Abstract: "to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb"); and, based on the comparison, identifies the actual trajectory with the selected pre-trained trajectory (fig. 4; step 406), and actuates the muscle stimulator (Fig. 4; step 407) to apply the pre-determined sequence of stimulation signals to displace the second body part to perform the at least one selected intentional action (para. [0024]: "The controller system (of the rehabilitation system) is still additionally configured to determine final motion commands depending on the first motion commands and the second motion commands, and to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb."; The predefined exercise is being performed with the paretic limb, meaning it is selected based on the signals from the paretic arm, making it intentional. Further, since the actions are mapped (decoded), and the patient is stimulated according to the mapping, the stimulation signals can be considered to be a predetermined sequence that is based on the signal associated with the stimulation. Ramos also teaches where the processor identifies the actual trajectory with the selected pre-trained trajectory (para. [0137]: “At block 404, trajectory data defining a trajectory to be followed by the paretic arm may be determined depending on a deviation between the motion signals and the predefined exercise data, in the different degrees of freedom (or permitted motion directions) under consideration.”), and actuates the muscle stimulator to displace the second body part to perform the at least one selected intentional action (para. [0138]: “The determined trajectory data may be seen as defining a corrected trajectory of the motion actually followed by the paretic arm (according to the received motion signals) for redirecting the motion of the arm towards a valid trajectory (according to the predefined exercise data).”). However, Ramos does not explicitly teach a plurality of pretrained expected trajectories, where the movements are in two or three dimensions, or a comparison between the actual trajectory with the plurality of pre-trained expected trajectories and, based on the comparison identifying the actual trajectory with the selected pre-trained trajectory. Ramos also does not teach wherein at least one of the plurality of pre-trained expected trajectories is in the shape of an alphanumeric character).
It would have been obvious for one of ordinary skill in the art to modify the processor and memory of Ramos to include a plurality of pretrained expected trajectories and a plurality of intentional actions. One of ordinary skill would recognize that the processor of Ramos would be able to store a plurality of exercise trajectories, not just a single exercise trajectory. They would also recognize that the processor and memory of Ramos would be able to store a plurality of intentional exercise trajectories associated with the expected trajectories. Referencing MPEP (VI)(B): “…the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.” In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).” Since Ramos discloses a pretrained expected trajectory associated by the human with an intention of the human to perform a respective intentional action, it would have been obvious to duplicate the expected trajectories to capabilities for a plurality of exercises and trajectories and their associated actions. Further, para. [0015] of the instant specification discloses at least one expected trajectory and para. [0016] discloses a plurality of expected trajectories. Therefore, the instant specification does not express criticality for a plurality of trajectories, and using a plurality of trajectories instead of a singular trajectory is a matter of choice.
Although Ramos does not explicitly disclose movement of a first body part of a human in two or three dimensions, or that the plurality of trajectories are in two or three dimensions, Ramos discloses generating a neuromuscular to motion decoder for each of all or some degrees of freedom or permitted motions (para. [0119] – [0120]: “At block 303, a neuromuscular-to-motion decoder may be generated for each of all or some of the degrees of freedom (in exoskeleton-based examples) or permitted motion directions (in examples based on a stimulation system) under consideration… Given a particular degree of freedom (or permitted motion direction), a neuromuscular-to-motion decoder may be generated by mapping the received neuromuscular signals and motion signals corresponding to said degree of freedom (or permitted motion direction). It is also possible to generate a single neuromuscular-to-motion decoder with different outputs corresponding to different degrees of freedom.”). It would have been obvious for one of ordinary skill to record the movement signals in two or three dimensions and for the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses comparing an actual trajectory (para. [0016]: "sensed voluntary movement signals") with a plurality of pre-trained expected trajectories (para. [0016]: "known movement patterns 42"); and, based on the comparison (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patterns 42."), identifies the actual trajectory with the selected pre-trained trajectory (para. [0016]: "Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to include the technique of comparing trajectories for selecting limb movement of Davoodi. One of ordinary skill would recognize that the technique of Davoodi is used to select limb movement actuation. One would recognize that the comparison technique for predicting and selecting limb movement could be implemented into the device of Ramos to yield the predictable result of correctly identifying the trajectory of the limb based on the sensed signal and known patterns. Therefore, it would have been obvious to implement this technique in the device of Davoodi.
However, Davoodi does not expressly teach the following limitations: wherein at least one of the plurality of pre-trained expected trajectories is in the shape of an alphanumeric character[…]).
Rosenbluth discloses wherein at least one of the plurality of pre-trained expected trajectories is in the shape of an alphanumeric character (para. [000194]: "engaging the patient in a tremor-indiced writing task by analyzing a line traced on a smartphone screen."; the writing task could be alphanumeric characters. The writing task could be any characters, but doesn't change the structure or function of the device.); And wherein the processor determines that the actual trajectory (the tracing of the line) has the shape of the alphanumeric character (the line being traced in the writing task of para. [000194] could be an alphanumeric character).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device Ramos with the processor using trajectories from a writing task that could be alphanumeric characters, as disclosed by Rosenbluth. The use of alphanumeric characters does not change the structure and function of the device, as one of ordinary skill would be able to configure a writing task to be performed with any characters, not just alphanumeric. Further, it would have been obvious to include writing alphanumeric characters as a simple substitution of a known type of trajectory motion of the hand, as Ramos discloses tracking motion of the wrist and fingers (para. [0093]). It would have been obvious to implement a writing task to track the motion of the wrist and fingers to provide a measurable trajectory for tracking performance.
Regarding claim 32, Ramos, in combination with Davoodi and Rosenbluth, discloses the device of claim 31 (see above). Neither Ramos nor Davoodi explicitly disclose wherein the shape of the alphanumeric character is selected from a C-shape, an S-shape, a sigma-shape, and epsilon-shape, a gamma- shape, a 3-shape, and an M-shape. However, Rosenbluth teaches that a writing task may be performed (para. [000194]). Including specific characters from the finite number of alphanumeric characters does not distinguish the structure and function of the device, as one of ordinary skill in the art would easily be able to program the writing task to be any character or shape.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the writing task of Rosenbluth with the device of Ramos since writing is a functional hand motion. By including this task, the trajectory could easily be measured and compared to an intended trajectory. Therefore, it would have been obvious to include the writing task in the device of claim 31.
Claim 11 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), and Smith et al. ("Development of an upper extremity FES system for individuals with C4 tetraplegia."; Published by IEEE on December 31, 1996, “Smith”).
Regarding claim 11, Ramos, in combination with Davoodi, discloses the device of claim 5 (see above). However, neither Ramos nor Davoodi disclose wherein the training set of motions are performed by the human using a laterally opposite body part of the first body part.
Smith, in the same field of endeavor of using machine learning and functional electrical stimulation (FES) to restore movement in a paretic limb, discloses a system for implementing FES to restore movement for a patient with tetraplegia. Smith discloses wherein the training set of motions are performed by the human using a laterally opposite body part of the first body part. (Abstract: "The subject controlled stimulation proportionally using contralateral shoulder motion sensed by a position transducer. Control of stimulated hand grasp and release were coupled with stimulated arm motions so that hand-to-mouth activities could be accomplished with one motion of the contralateral shoulder.") Smith et al. ("Development of an upper extremity FES system for individuals with C4 tetraplegia.")
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 5, as disclosed by Ramos and Davoodi, with the training data as disclosed by Smith. Smith demonstrates that the training data for machine learning comprising of motions from contralateral can be successfully implemented into machine learning algorithms to restore function. It would have been an obvious improvement with a reasonable expectation of success to use the motion data of Smith in the device of claim 5.
Regarding independent claim 28, Ramos teaches a device (Fig. 1: Generator system 100) comprising: one or more motion sensors (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), the sensors configured to generate one or more respective motion signals indicative of movement of a first body part of a human (para. [0084]: "Suitable connections between the motion sensors and the computing system 109 may also exist for the computer 109 to receive motion signals generated by the motion sensors."; Fig. 1 shows sensors 101 disposed on the forearm of the subject); a muscle stimulator (para. [0106]: "the rehabilitation system may comprise another type of body actuator, such as e.g. a neuromuscular stimulation system or a combination thereof. Examples of neuromuscular stimulation systems are e.g. a Functional electrical stimulation (FES) system..."), wherein the muscle stimulator is configured to generate a predetermined sequence of stimulation signals (para. [0024] shows the patient performs predefined exercises: " to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb." The predefined exercises are based on inputs from neuromuscular signals of the paretic arm (Fig. 4, 401). The sequence of signals are predetermined based on the predefined exercise mapped to the neuromuscular signal.) to cause one or more muscles to displace a second body part to perform a selected intentional action by the human (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals." The paretic limb is considered to be the second body part.); and a processor connected with the one or more motion sensors and the muscle stimulator (para. [0085]: "[0085] The computing system 109 may comprise a memory and a processor. The memory may store a computer program comprising instructions that are executable by the processor for causing the performance of a generator method for generating a neuromuscular-to-motion decoder from a healthy limb." The decoder uses data obtained from the motion signals (see para. [0013]).), the processor including data storage (para. [0085]; The memory included in the computing system is a form of data storage), the data storage including a pre-trained expected trajectory (para. [0023]: "The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data"; the trajectory data from the predefined exercise data is considered pre-trained since the data comes from previous motions of the healthy limb) associated by the human with an intention of the human to perform a respective intentional actions (Fig. 4, step 402 shows that the signal data is input into a neuromuscular decoder, which associates intended motion from the signals of the paretic arm in 401.) wherein the selected intentional action is associated with a selected pre-trained trajectory (Fig. 4, step 402; The intentional actions, from the signals in step 401, are associated with a trajectory during the decoding process; Further, they are associated with pre-trained trajectories since they are decoded using the predefined exercise data of the healthy limb), and wherein the processor: receives the one or more signals from the one or more motion sensors (para. [0012]: "The controller system (of the generator system) is further configured to receive motion signals obtained by the motion sensors associated to predefined positions of the healthy limb."); calculates an actual trajectory of the first body part (Abstract: "to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb"); identifies the actual trajectory with the selected pre-trained trajectory (fig. 4; step 406), and actuates the muscle stimulator (Fig. 4; step 407) to apply the pre-determined sequence of stimulation signals to displace the second body part to perform the at least one selected intentional action (para. [0024]: "The controller system (of the rehabilitation system) is still additionally configured to determine final motion commands depending on the first motion commands and the second motion commands, and to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb."; The predefined exercise is being performed with the paretic limb, meaning it is selected based on the signals from the paretic arm, making it intentional. Further, since the actions are mapped (decoded), and the patient is stimulated according to the mapping, the stimulation signals can be considered to be a predetermined sequence that is based on the signal associated with the stimulation. However, Ramos does not explicitly teach a plurality of pretrained expected trajectories, where the movements are in two or three dimensions, or a comparison between the actual trajectory with the plurality of pre-trained expected trajectories and, based on the comparison identifying the actual trajectory with the selected pre-trained trajectory. Ramos also fails to disclose the limitations: “wherein the motion sensor is adapted to be fixed to the first body part, wherein the first body part is a shoulder, a torso, and upper arm or hip; […]wherein the muscle stimulator is configured to generate a sequence of stimulation signals to cause one or more muscles to displace a second body part to perform a sequence of motions to perform a selected intentional action by the human, wherein the second body part is connected with the first body part by a natural joint, and wherein the second body part is a hand, lower leg, or foot of a human.”
It would have been obvious for one of ordinary skill in the art to modify the processor and memory of Ramos to include a plurality of pretrained expected trajectories and a plurality of intentional actions. One of ordinary skill would recognize that the processor of Ramos would be able to store a plurality of exercise trajectories, not just a single exercise trajectory. They would also recognize that the processor and memory of Ramos would be able to store a plurality of intentional exercise trajectories associated with the expected trajectories. Referencing MPEP (VI)(B): “…the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.” In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).” Since Ramos discloses a pretrained expected trajectory associated by the human with an intention of the human to perform a respective intentional action, it would have been obvious to duplicate the expected trajectories to capabilities for a plurality of exercises and trajectories and their associated actions. Further, para. [0015] of the instant specification discloses at least one expected trajectory and para. [0016] discloses a plurality of expected trajectories. Therefore, the instant specification does not express criticality for a plurality of trajectories, and using a plurality of trajectories instead of a singular trajectory is a matter of choice.
Although Ramos does not explicitly disclose movement of a first body part of a human in two or three dimensions, or that the plurality of trajectories are in two or three dimensions, Ramos discloses generating a neuromuscular to motion decoder for each of all or some degrees of freedom or permitted motions (para. [0119] – [0120]: “At block 303, a neuromuscular-to-motion decoder may be generated for each of all or some of the degrees of freedom (in exoskeleton-based examples) or permitted motion directions (in examples based on a stimulation system) under consideration… Given a particular degree of freedom (or permitted motion direction), a neuromuscular-to-motion decoder may be generated by mapping the received neuromuscular signals and motion signals corresponding to said degree of freedom (or permitted motion direction). It is also possible to generate a single neuromuscular-to-motion decoder with different outputs corresponding to different degrees of freedom.”). It would have been obvious for one of ordinary skill to record the movement signals in two or three dimensions and for the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses comparing an actual trajectory (para. [0016]: "sensed voluntary movement signals") with a plurality of pre-trained expected trajectories (para. [0016]: "known movement patterns 42"); and, based on the comparison (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patterns 42."), identifies the actual trajectory with the selected pre-trained trajectory (para. [0016]: "Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to include the technique of comparing trajectories for selecting limb movement of Davoodi. One of ordinary skill would recognize that the technique of Davoodi is used to select limb movement actuation. One would recognize that the comparison technique for predicting and selecting limb movement could be implemented into the device of Ramos to yield the predictable result of correctly identifying the trajectory of the limb based on the sensed signal and known patterns. Therefore, it would have been obvious to implement this technique in the device of Davoodi.
However, neither Ramos nor Davoodi discloses wherein the motion sensor is fixed to the first body part, wherein the first body part is proximal to a trunk of the human; wherein the muscle stimulator is configured to generate a sequence of stimulation signals to cause one or more muscles to displace a second body part to perform a sequence of motions to perform a selected intentional action by the human, wherein the second body part is connected with the first body part by a natural joint and wherein the second body part is distal of the first body part.
Smith discloses wherein the motion sensor is fixed to the first body part, wherein the first body part is a shoulder (Abstract: "The subject controlled stimulation proportionally using contralateral shoulder motion sensed by a position transducer.” The motion sensor is fixed to the shoulder, which is proximal to the trunk compared to the hand, which is being stimulated for grasping.); wherein the muscle stimulator is configured to generate a sequence of stimulation signals to cause one or more muscles to displace a second body part to perform a sequence of motions to perform a selected intentional action by the human, wherein the second body part is connected with the first body part by a natural joint and wherein the second body part a hand (Abstract: “Control of stimulated hand grasp and release were coupled with stimulated arm motions so that hand-to-mouth activities could be accomplished with one motion of the contralateral shoulder."; The hand is distal and connected by elbows and wrists (both natural joints), and the control stimulates a sequence to cause hand grasps.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the stimulation device of Ramos to further include sensors placed on a body part proximal to the trunk and stimulators placed on a body part distal of the first. In doing so, residual movement from the same side body part can be used to control the paretic or paralyzed limb. As disclosed by Smith, using this configuration of stimulators and sensors is predicted to successfully measure motion and provide movement with a reasonable expectation of success. It would have been obvious to include this configuration in a device that is also trying to restore function through stimulation.
Claim 12, 29, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), and Bouton (US 20180178008 A1, “Bouton”).
Regarding claim 12, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). However, neither Ramos nor Davoodi disclose where a motion sensor is located on an arm of the human and wherein the muscle stimulator is configured to generate the one or more stimulation signals to stimulate muscles to move one or more fingers of a hand of the human to perform a grasping motion.
Bouton, in the same field of endeavor of using machine learning to determine movement trajectories, discloses a system for interpreting neural signals to determine desired movement. Bouton discloses wherein a motion sensor is adapted to be located on an arm of the human (para. [0023]:” FIGS. 7A-18B are pictures of several different hand and arm motions obtained from an experiment in which an able-bodied user was asked to move his hand/arm into a given position. In each set, one picture shows an image of the hand/arm motion that the user performed with his limb, and the other picture shows a graphical hand representation of the user's limb position based on position sensor data collected from the sleeve.”; para. [0059]: “The body state observer can also accept data from body movement sensors 110 as input. Such sensors may provide information on the position, velocity, acceleration, contraction, etc. of a body part.”) and wherein the muscle stimulator is configured to generate the one or more stimulation signals to stimulate muscles to move one or more fingers of a hand of the human to perform a grasping motion (para. [0067] talks about the sequences of motion that can be produced, including grasping: “Another example of sequenced motions is a functional series of motions. Examples of functional series of motions include: teeth brushing, scratching, stirring a drink, flexing a thumb, cylindrical grasping, pinching, etc. These motions allow for manipulation of real-world objects of various sizes.”)
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to modify the device of claim 1, as disclosed by Ramos, with the motion sensor on the arm and stimulator that causes grasping, as disclosed by Bouton. Including these modifications would have been an obvious improvement to the device of claim 1 since it would allow the user to perform function movements. It would be an obvious improvement to give the device functional capabilities that allow the user to regain movements that are performed in everyday activities, such as grasping.
Regarding claim 29, Ramos teaches a device (Fig. 1: Generator system 100) comprising: one or more motion sensors (para. [0083]: "The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors…"), the sensors configured to generate one or more respective motion signals indicative of movement of a first body part of a human (para. [0084]: "Suitable connections between the motion sensors and the computing system 109 may also exist for the computer 109 to receive motion signals generated by the motion sensors."; Fig. 1 shows sensors 101 disposed on the forearm of the subject); a muscle stimulator (para. [0106]: "the rehabilitation system may comprise another type of body actuator, such as e.g. a neuromuscular stimulation system or a combination thereof. Examples of neuromuscular stimulation systems are e.g. a Functional electrical stimulation (FES) system..."), wherein the muscle stimulator is configured to generate a predetermined sequence of stimulation signals (para. [0024] shows the patient performs predefined exercises: " to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb." The predefined exercises are based on inputs from neuromuscular signals of the paretic arm (Fig. 4, 401). The sequence of signals are predetermined based on the predefined exercise mapped to the neuromuscular signal.) to cause one or more muscles to displace a second body part to perform a selected intentional action by the human (para. [0030]: "In some examples, a body actuator other than an exoskeleton may be used, in which case additional decoding(s) may be needed. For example if a Functional electrical stimulation (FES) system is used, the motion signals may be mapped into electrostimulation signals that may cause motions of the paretic limb according to the motion signals." The paretic limb is considered to be the second body part.); and a processor connected with the one or more motion sensors and the muscle stimulator (para. [0085]: "[0085] The computing system 109 may comprise a memory and a processor. The memory may store a computer program comprising instructions that are executable by the processor for causing the performance of a generator method for generating a neuromuscular-to-motion decoder from a healthy limb." The decoder uses data obtained from the motion signals (see para. [0013]).), the processor including data storage (para. [0085]; The memory included in the computing system is a form of data storage), the data storage including a pre-trained expected trajectory (para. [0023]: "The controller system (of the rehabilitation system) is additionally configured to determine trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the motion signals and the predefined exercise data"; the trajectory data from the predefined exercise data is considered pre-trained since the data comes from previous motions of the healthy limb) associated by the human with an intention of the human to perform a respective intentional actions (Fig. 4, step 402 shows that the signal data is input into a neuromuscular decoder, which associates intended motion from the signals of the paretic arm in 401.) wherein the selected intentional action is associated with a selected pre-trained trajectory (Fig. 4, step 402; The intentional actions, from the signals in step 401, are associated with a trajectory during the decoding process; Further, they are associated with pre-trained trajectories since they are decoded using the predefined exercise data of the healthy limb), and wherein the processor: receives the one or more signals from the one or more motion sensors (para. [0012]: "The controller system (of the generator system) is further configured to receive motion signals obtained by the motion sensors associated to predefined positions of the healthy limb."); calculates an actual trajectory of the first body part (Abstract: "to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb"); and actuates the muscle stimulator (Fig. 4; step 407) to apply the pre-determined sequence of stimulation signals to displace the second body part to perform the at least one selected intentional action (para. [0024]: "The controller system (of the rehabilitation system) is still additionally configured to determine final motion commands depending on the first motion commands and the second motion commands, and to send the final motion commands to a body actuator associated to the paretic limb for controlling the body actuator so as to stimulate (or induce) the patient to perform the predefined exercise with the paretic limb."; The predefined exercise is being performed with the paretic limb, meaning it is selected based on the signals from the paretic arm, making it intentional. Further, since the actions are mapped (decoded), and the patient is stimulated according to the mapping, the stimulation signals can be considered to be a predetermined sequence that is based on the signal associated with the stimulation. Further, Ramos teaches the additional limitation that the motion sensor may be an inertial sensor (para. [0083]: “The generator system may further comprise motion sensors (not shown) including e.g. inertial, magnetic or optical sensors (such as e.g. accelerometers, gyroscopes, etc.) arranged and configured to provide functionalities of motion detection and quantification.”) However, Ramos does not explicitly teach a plurality of pretrained expected trajectories, where the movements are in two or three dimensions, or a comparison between the actual trajectory with the plurality of pre-trained expected trajectories and, based on the comparison identifying the actual trajectory with the selected pre-trained trajectory. Ramos also does not expressly teach that the device is used for stimulating intentional action by a hand; that the sensor is an inertial motion sensor fixed to the wrist and is adapted to detect a trajectory of the wrist; where stimulation signals actuate motion of the hand; that the processor is configured to receive one or more signals from the inertial motion sensor and determines the trajectory of the hand from these signals; and that the processor energizes the stimulator to cause the hand to perform the selected hand motion.
It would have been obvious for one of ordinary skill in the art to modify the processor and memory of Ramos to include a plurality of pretrained expected trajectories and a plurality of intentional actions. One of ordinary skill would recognize that the processor of Ramos would be able to store a plurality of exercise trajectories, not just a single exercise trajectory. They would also recognize that the processor and memory of Ramos would be able to store a plurality of intentional exercise trajectories associated with the expected trajectories. Referencing MPEP (VI)(B): “…the court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.” In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960).” Since Ramos discloses a pretrained expected trajectory associated by the human with an intention of the human to perform a respective intentional action, it would have been obvious to duplicate the expected trajectories to capabilities for a plurality of exercises and trajectories and their associated actions. Further, para. [0015] of the instant specification discloses at least one expected trajectory and para. [0016] discloses a plurality of expected trajectories. Therefore, the instant specification does not express criticality for a plurality of trajectories, and using a plurality of trajectories instead of a singular trajectory is a matter of choice.
Although Ramos does not explicitly disclose movement of a first body part of a human in two or three dimensions, or that the plurality of trajectories are in two or three dimensions, Ramos discloses generating a neuromuscular to motion decoder for each of all or some degrees of freedom or permitted motions (para. [0119] – [0120]: “At block 303, a neuromuscular-to-motion decoder may be generated for each of all or some of the degrees of freedom (in exoskeleton-based examples) or permitted motion directions (in examples based on a stimulation system) under consideration… Given a particular degree of freedom (or permitted motion direction), a neuromuscular-to-motion decoder may be generated by mapping the received neuromuscular signals and motion signals corresponding to said degree of freedom (or permitted motion direction). It is also possible to generate a single neuromuscular-to-motion decoder with different outputs corresponding to different degrees of freedom.”). It would have been obvious for one of ordinary skill to record the movement signals in two or three dimensions and for the trajectories to be in two or three dimensions since Ramos is mapping motion in multiple directions and degrees of freedom, and that in doing so, it would have been obvious to record movement and trajectory data in multiple directions and degrees of freedom.
Davoodi, in the same field of endeavor of projecting limb trajectories, discloses a system for generating command signals for a prosthetic limb. Davoodi discloses comparing an actual trajectory (para. [0016]: "sensed voluntary movement signals") with a plurality of pre-trained expected trajectories (para. [0016]: "known movement patterns 42"); and, based on the comparison (para. [0016]: "Then, the sensed voluntary movement signals are compared to known movement patterns 42."), identifies the actual trajectory with the selected pre-trained trajectory (para. [0016]: "Then, the limb movement is predicted 44 based upon the sensed signal comparison 42. Based on the predicted limb movement 44, command signals are generated for simulated limb actuators 46").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Ramos to include the technique of comparing trajectories for selecting limb movement of Davoodi. One of ordinary skill would recognize that the technique of Davoodi is used to select limb movement actuation. One would recognize that the comparison technique for predicting and selecting limb movement could be implemented into the device of Ramos to yield the predictable result of correctly identifying the trajectory of the limb based on the sensed signal and known patterns. Therefore, it would have been obvious to implement this technique in the device of Davoodi.
However, neither Ramos nor Davoodi discloses where a device is used for stimulating intentional action by a hand; that the sensor is an inertial motion sensor fixed to the wrist and is adapted to detect a trajectory of the wrist; where stimulation signals actuate motion of the hand; that the processor is configured to receive one or more signals from the inertial motion sensor and determines the trajectory of the hand from these signals; and that the processor energizes the stimulator to cause the hand to perform the selected hand motion.
Bouton discloses where the device is used for stimulating intentional action by a hand (para. [0066]: “In this regard, the system is able to make the following isolated motions: pinky flexion; pinky extension; ring flexion; middle flexion; middle extension; index flexion; index extension; thumb flexion; thumb extension; thumb abduction; thumb adduction; thumb opposition; wrist flexion; wrist extension; wrist ulnar deviation; wrist radial deviation; forearm pronation; and forearm supination. These motions can be joined together to create combined/compound movements. For example, all of the fingers are flexed at the same time to create a “hand close” motion."); the sensor is an inertial motion sensor adapted to be fixed to the wrist and is adapted to detect a trajectory of the wrist (Inputs from body movement sensors 110); where stimulation signals actuate motion of the hand; that the processor is configured to receive one or more signals from the inertial motion sensor and determines the trajectory of the hand from these signals; and that the processor energizes the stimulator to cause the hand to perform the selected hand motion. (para. [0047]: “The computer then generates an electrical stimulation pattern which is delivered to a neurostimulation sleeve 152, which is worn by the patient on the arm and which includes electrodes. The electrical stimulation of the sleeve 152 results in the desired bending of fingers.”)
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to modify the device of Ramos with a device for stimulating the hand to actuate hand motions, as disclosed by Bouton. In doing so, the device would restore movement to the hand, which is a desirable for patients with loss of function in their hands. It would be an obvious improvement adapting the device of Ramos to be used on a hand specifically, as in the case with Bouton’s stimulation device.
Regarding claim 30, the combination of Ramos, Davoodi, and Bouton disclose the device of claim 29 (see above). Bouton further discloses wherein a selected hand motion comprises one or more of a key grip, a cylindrical grasp, and a vertical pinch (para. [0067]: “Examples of functional series of motions include: teeth brushing, scratching, stirring a drink, flexing a thumb, cylindrical grasping, pinching, etc.”).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to modify the device of Ramos with specific functional hand motions, as disclosed by Bouton. In doing so, the device would be able to restore these motions to a user, which would improve their ability to function independently. These movements, as disclosed by Bouton, are known to be functional and would allow the user to manipulate real-world objects of various sizes (see Bouton para. [0067].
Claims 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), Bouton (US 20180178008 A1, “Bouton”) and Rosenbluth et al. (WO 2014113813 A1, “Rosenbluth”).
Regarding claim 14, Ramos, in combination with Davoodi and Bouton, discloses the device of claim 12 (see above). However, Ramos, Davoodi, and Bouton do not disclose where a device is further comprising an orientation sensor connected with the processor and adapted to monitor an orientation of the first body part, wherein a force applied by the grasping motion depends on an amplitude of the stimulation ; signal, and wherein the processor adjusts an amplitude of the stimulation signal based, at least in part, on an output of the orientation sensor.
Rosenbluth discloses a stimulation device that comprises an orientation sensor (para. [000192]: " The device or system may include sensors. Sensors for monitoring the tremor may include a combination of single or multi-axis accelerometers, gyroscopes…") connected with the processor and adapted to monitor an orientation of the first body part (para. [000199]: "For example, a multi-axis accelerometer and gyroscope attached to the backside of the hand could be combined to reduce noise and drift and determine an accurate orientation of the hand in space. If a second pair of multi-axis accelerometer and gyroscope were also used on the wrist, the joint angle and position of the wrist could be determined during the tremor."), wherein a force applied by the grasping motion depends on an amplitude of the stimulation signal, and wherein the processor adjusts an amplitude of the stimulation signal based, it least in part, on an output of the orientation sensor. (Fig. 26A and B; para. [000115]: " A control system for the tremor device utilizes feedback to modify the stimulation. It is a closed loop in which the stimulation is adjusted based on measurement of the activity and tremor." para. [000214]: "The output (2690) modifies the stimulation. If the effector is electrical, this may include modifying the waveform, frequency, phase, location and/or amplitude of the stimulation. In the preferred embodiment (FIG. 15), the device contains an array of small electrodes and the output modifies the selection of which electrodes to use as the anode and cathode.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 12, as disclosed by Ramos and Bouton, with the orientation sensor and grasping motion adjusted by the orientation signal amplitude, disclosed by Rosenbluth. Including these features would be an obvious improvement that would allow the orientation sensor to dictate the stimulation based on trajectory. This is demonstrated, by Rosenbluth, to be effective in actuating hand movement, and therefore, would have been obvious to include in the device of claim 12.
Regarding claim 17, the combination of Ramos, Davoodi, and Bouton disclose the device of claim 12 (see above). However, the combination does not disclose wherein a processor further comprises a close delay timer, wherein the processor delays stimulating the grasping motion for a predetermined period at the end of the actual trajectory determined by the close delay timer.
Rosenbluth discloses wherein a processor further comprises a close delay timer, wherein the processor delays stimulating the grasping motion for a predetermined period at the end of the actual trajectory determined by the close delay timer (para. [000149]: "FIG. 8D illustrates various stimulation sites which can be subjected to stimulation that is delayed or offset by a predetermined fraction or multiple of the tremor period, T, as shown for example in FIG. 9.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 12, as disclosed by Ramos and Bouton, with the close delay, as disclosed by Rosenbluth. It would have been obvious to include this feature since the delay would allow the system to make adjustments before implementing the stimulation to close the grip. This would have been an obvious feature to include in the device of claim 12, as Rosenbluth discloses this in a stimulation device.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), Bouton (US 20180178008 A1, “Bouton”), Rosenbluth et al. (WO 2014113813 A1, “Rosenbluth”), and Hunter et al. (WO 2019234706 A1, “Hunter”).
Regarding claim 15, the combination of Ramos, Davoodi, Bouton, and Rosenbluth disclose the device of claim 14 (see above). However, the combination does not disclose wherein a processor adjusts the grasping motion to be a key grip, a cylindrical grasp, or a vertical pinch in response to the output of the orientation.
Hunter, in the same field of endeavor of producing functional motion, discloses controlling methods for a prosthetic device. Hunter discloses wherein the processor adjusts the grasping motion to be a key grip (Fig. 17 and pg. 9, ln. 44: "The hand is shown in 'key grip' mode"), a cylindrical grasp (pg. 11, ln. 45-46: "… control system identifies that the wearer is going to pick up… a heavier but more resilient object such as a coffee cup."; holding a coffee cup requires a cylindrical grasp), or a vertical pinch in response to the output of the orientation sensor (pg. 10, ln. 36-43: "As shown in Figure 23, the two sensors 302, 306 arranged on or in the palm of the hand detect the object and the control system interprets that the wearer’s intention is to pick it up, as before. However, the object 322 is too small to be picked up by the hand in all-fingers-gripping mode so the control system drives the actuators as appropriate to place the hand in a tripod-grip using only the thumb, the index and middle fingers." Pg. 11, ln. 25-27: "There are many further examples of objects that can be recognized by the control system to permit an appropriate grip mode to be selected. These include a coffee cup, a glass, a plate, cutlery, a pen, a remote control, keys, the steering wheel of a car and so on."; The sensors 302 and 306 detect a user’s intention, which could be considered to be an orientation).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 14, as disclosed by the combination of Ramos, Bouton, and Rosenbluth, with the functional hand motions disclosed by Hunter. It would have been an obvious improvement to include these hand motions since the hand motions are demonstrated to be replicated in prosthetics, as in the case of Hunter. In Hunter’s case, restoring means of functional motion is the goal, which is the same goal of Ramos, Bouton, and Rosenbluth. It would have been obvious to include these movements since it would allow the device of claim 14 to achieve functional movement restoration as well.
Claim 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), Bouton (US 20180178008 A1, “Bouton”), and Hunter et al. (WO 2019234706 A1, “Hunter”).
Regarding claim 16, the combination of Ramos, Davoodi, and Bouton disclose the device of claim 12 (see above). However, they do not disclose a camera connected with the processor and positioned proximate to the hand to capture an image of an object to be grasped, wherein the processor adjusts the grasping motion based in part on the image.
Hunter discloses a camera (Fig. 33, 406 and 408) connected with the processor (pg. 12, ln. 40: "The system has a controller 402 coupled…to one or more sensors 406…The controller 402 may be a microprocessor.") and adapted to be positioned proximate to the hand to capture an image of an object to be grasped (pg. 13, ln. 1: "…an optical camera 406 which is mounted on the palm of the prosthetic hand."), wherein the processor adjusts the grasping motion based in part on the image.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 12, as disclosed by the Ramos and Bouton combination, with the camera and images of Hunter. Doing so would be an obvious improvement to the device that would enhance the performance of the device by providing more references for the processor to use to adjust the grasping motion. It would have been obvious to include the camera since Hunter discloses using the camera for the same purpose.
Regarding claim 18, the combination of Ramos, Davoodi, and Bouton disclose the device of claim 12 (see above). However, neither reference discloses wherein the processor causes stimulation of the hand to perform a post-grasp activity in response to a post-grasp signal from the motion sensor.
Hunter discloses wherein the processor is configured to cause stimulation of the hand to perform a post-grasp activity in response to a post-grasp signal from the motion sensor. (Fig. 30, pg. 11, ln. 16-24: "Figure 28 shows the prosthetic hand approaching a computer mouse. The control system, in response the output from the sensor(s), determines that the object is a computer mouse and that the wearer intends to grip the mouse. The control system moves the thumb of the hand to the non-opposing position as shown in Figure 29 and the control system controls the hand to both grip the mouse and place the index finger of the prosthetic hand over the mouse button. This is shown in Figure 30. The second finger of the hand may also be placed over the “right click” button of the mouse or a scroll wheel (not shown).”; The control system does this in response to the output from sensors, which reads on “from the motion sensor”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 12, as disclosed by the Ramos and Bouton combination, with the post-grasp in response to a post-grasp signal from the motion sensor as disclosed by Hunter. Including this response would be an obvious improvement to the device of claim 12 since the device would enhance its abilities after grasping an object, which could improve resemblance to modeling human grasping function. It would have been obvious to use this in the device of claim 12 since Hunter aims to restore function in a similar manner.
Regarding claim 19, the combination of Ramos, Davoodi, Bouton, and Hunter disclose the device of claim 18 (see above). Hunter further discloses wherein the post-grasp activity is opening the hand to release the grasp (pg. 10, ln. 48 - pg. 11, ln. 2: "The control system interprets that the wearer's intention is to pick it up, as before…Consequently…the control system places the hand into the fully-open position as it approaches the ball.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 18, as disclosed by the Ramos, Bouton, and Hunter in combination, with the hand-opening release for the post grasp activity. Doing so would mimic the motion of human hand grasping more accurately. Therefore, it would have been obvious to include this in the device of claim 18, which aims to accurately mimic a hand trajectory.
Regarding claim 20, the combination of Ramos, Davoodi, Bouton, and Hunter disclose the device of claim 18 (see above). Hunter further discloses wherein the post-grasp signal is one or more taps of a grasped object against a surface. (Fig. 30 and pg. 11, ln. 19:…the control system controls the hand to both grip the mouse and place the index finger of the prosthetic hand over the mouse…The second finger of the hand may also be placed over the "right click" button of the mouse or a scroll wheel (not shown)"; the index finger over the mouse button and the second finger over the "right click" are used to tap (click) the grasped mouse.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of claim 18, as disclosed by the Ramos, Bouton, and Hunter in combination, with the tapping signal of Hunter. Tapping against an object surface is a common hand motion, and it would have been obvious to include this in the device of claim 18 since the device of claim 18 is looking to replicate common hand motions. Since Hunter discloses including tapping signals in a device that also replicates hand motion, it would have been obvious to include this in the device of claim 18.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Ramos Murguialday (US 20190269343 A1, “Ramos”), Davoodi (US 20070016265 A1, “Davoodi”), and Herr et al. (US 20170042467 A1, “Herr”).
Regarding claim 27, Ramos, in combination with Davoodi, discloses the device of claim 1 (see above). Ramos further discloses wherein the motion signals comprise acceleration data (para. [0188]: “ A generator system according to any of clauses 1 to 3, wherein the motion sensors are configured to generate the motion signals as velocity signals, and/or position signals, and/or torque signals, and/or force signals, and/or acceleration signals.”). However, neither Ramos nor Davoodi disclose integrating the acceleration data to determine a time-dependent position.
Herr, concerned with the same common problem of measuring limb movement and trajectories, discloses a system for modulating knee adduction based on kinematic measurements. Herr discloses integrating the acceleration data to determine a time-dependent position. (para. [0067]: “In another embodiment of a kinematic measurement system, inertial sensors are mounted on the joints so as to measure acceleration, obtaining position by integration from known or estimated initial conditions. To reduce drift, other sensors may be added, as is often accomplished using IMUs (inertial measurement units), which make use of gyroscopes to measure angular velocity and thereby improve location estimation accuracy.”).
It would have been obvious for one of ordinary skill in the art before the effective filling date of the claimed invention to include the technique of integrating acceleration data, as disclosed by Herr, with the device Ramos. One of ordinary skill would recognize that integration of acceleration is a known technique for calculating position data, and that one would expect a reasonable expectation of success in calculating position data from acceleration data using the technique of Herr.
Conclusion
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 OWEN LEWIS MARSH whose telephone number is (571)272-8584. The examiner can normally be reached 7-30am – 5pm (M-Th), 8am – noon (F).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer McDonald can be reached at (571) 270-3061. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/O.L.M./Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796
