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 .
Specification
The use of the terms Bluetooth, Python, Java, JavaScript, which are a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the terms should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6-9, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice.
Regarding Claim 1, Dechev discloses a prosthetic hand device mountable on a residual limb of an amputee (Fig 5, Prosthetic hand, 50), comprising:
a myoelectric hand comprising five mechanical fingers (see [44]) actuatable to provide multiple degrees of freedom of movement (See [104] 4 degrees of freedom, see [123] for explanation of ultrasound system, 10, monitoring tendon motion for movement of prosthetic appendages);
a control assembly (Fig 8, See [44] for controller 110 which includes microcontroller in communication with ultrasound system) comprising an ultrasound module (See [59] for ultrasound tracking system) as a human-machine interface (HMI) (See [85-89] for ultrasound system and use thereof, See [123] for example),
wherein the ultrasound module is configured to acquire ultrasound images of a region of the residual limb (See [95-102] for acquiring images and locating ideal location for image capture);
and the volitional movement is transmitted to the myoelectric hand to dynamically and proportionally control the five mechanical fingers based on at least the volitional movement (See [123] for ultrasound system forwarding data to perform desired task in prosthetic hand).
Dechev does not disclose an AI model to analyze the ultrasounds images, however Samadi discloses research on using AI on ultrasound images of the forearm, including
having a convolutional neural network (CNN) architecture for obtaining extracted features from the ultrasound images (see Page 278 section C for Neural Network Architecture for use of CNN);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Dechev does not does not teach the use of a transfer learning model, however the examiner takes Official Notice that it has long been well known to use transfer learning models as a type of machine learning as recited in claim 11.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine inherent use of transfer learning with the AI system of Dechev in view of Samadi as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Dechev in view of Samadi does not disclose specifics of the AI model to analyze the ultrasounds image, however Li discloses AI analysis of specific tendons in the forearm, including:
an artificial intelligence (AI) model (See 2.3.4 Wrist and Finger motion recognition, SVM, a version of AI) executed by one or more processors and configured to classify the extracted features from the ultrasound images for determining a volitional movement of the amputee in real-time (See 2.3.4 Wrist and Finger motion recognition, SVM is using pattern-recognition in ultrasound images) wherein:
the ultrasound module (See 2.2 Experiment Setup, commercial ultrasound system) is configured to capture the ultrasound images of flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and flexor pollicis longus (FPL) muscles for determining the volitional movement of the amputee (Fig 1C, see 2.2 Experiment Set up for tendons being imaged and 13 motions performed and imaged for training).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis of specific forearm tendons as taught by Li with the ultrasound system of the myoelectric prosthetic hand of Dechev as Li teaches of the advantages of using M-mode ultrasound recognition of wrist and finger motions compared to that of A-mode and B-mode.
Regarding Claim 2, Dechev further discloses that the prosthetic hand device of claim 1 further comprises a proportional control mechanism (See [100] for Speckle tracking and displacement calculation) enabling the amputee to dynamically control a speed of finger flexion and an angle of finger flexion (See [104-108] for motors controlling fingers, see [109] for breakdown of moving parts in fingers),
wherein the proportional control mechanism is configured to monitor a degree of muscular contraction using the ultrasound images for predicting a proportional change (See [100, 112-114] for motor control based of displacement data of the tendon).
Regarding Claim 3, Dechev does not disclose AI image analysis, however Samadi further discloses the transfer learning model further comprises a plurality of convolutional layers, a flatten layer, and a fully connected layer (see Page 278 Section C for Neural Network Architecture, see layers of models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Regarding Claim 6, Dechev does not disclose AI image analysis, however Samadi further discloses a machine learning model, wherein: the ultrasound images are separated into a training dataset and a validation dataset (See Section IV. Experiments and Results); the transfer learning model extracts features from the training dataset to obtain the extracted features for training the machine learning model (See Section IV. Experiments and Results); and the validation dataset is utilized to evaluate an accuracy of the AI model (See Section IV. Experiments and Results, explains dataset division into training, validation, and testing set).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Regarding Claim 7, Dechev does not disclose AI image analysis, however Li further discloses the machine learning model comprises one or more machine learning algorithms selected from the group consisting of random forest (RF), k-nearest neighbors classifier (KNN), and support vector machine (SVM) (See 2.3.4 Wrist and Finger Motion Recognition, see using SVM for pattern-recognition in ultrasound images).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis of specific forearm tendons as taught by Li with the ultrasound system of the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Regarding Claim 8, Dechev does not disclose AI image analysis, however Samadi further discloses the CNN architecture is selected from the group consisting of VGG16, VGG19, and Inceprion-Res-Net-V2 (See C. Neural Networks Architecture for Visual Geometry group network types).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Regarding Claim 9, Dechev further discloses a prosthetic hand device (Fig 5, Prosthetic hand, 50), wherein the myoelectric hand comprises an actuating system (See [44-46] for actuators connected to controller) to provide multiple degrees of freedom (See [104-108] for degrees of freedom with motor to "flex/extend'),
wherein the actuating system comprises plural artificial metacarpophalangeal (MCP) joints (Fig. 5, interphalangeal joint 70) at the five mechanical fingers, an additional MCP joint (Fig. 5, pivot assembly 78) at the first mechanical finger (interpreted as thumb), and plural artificial proximal interphalangeal (PIP) joints (Fig. 5, interphalangeal joint 70) at the second to the fifth mechanical fingers (See [109] for joints of fingers that allow for flexion and extension),
and wherein the additional MCP joint is rotatable about a second axis substantially orthogonal to a first axis of the MCP joint at the first mechanical finger to perform abduction and adduction (See [109] for joints and abduction and adduction of thumb using rotatable metacarpal 68).
Regarding Claim 16, Dechev further discloses a method for controlling a myoelectric hand using ultrasound images captured from a residual limb of an amputee (Fig 5, Prosthetic hand, 50, See [104] 4 degrees of freedom, see [123] for explanation of ultrasound system, 10, monitoring tendon motion for movement of prosthetic appendages), the method comprising:
performing, by one or more processors, real-time analysis on the extracted features for determining a volitional movement of the amputee (See [95-102] for acquiring images and processing and see [123] for ultrasound system forwarding data to perform desired task in prosthetic hand);
transmitting the volitional movement to a microprocessor in the myoelectric hand comprising five mechanical fingers (See [123] for ultrasound system forwarding data to perform desired task in prosthetic hand);
and providing instructions, by the microprocessor, to a control unit to actuate the five mechanical fingers dynamically and individually based on the volitional movement (See [104] for motor movement and [123] for example reference of hand operating).
Dechev does not disclose the use of AI for image analysis, however Samadi further discloses acquiring, using an ultrasound transducer, acoustic reflections from a region of the residual limb for generating the ultrasound images (See II. Dataset and Fig. 2 for ultrasound set up) that contribute to a training dataset and a validation dataset (See Section IV. Experiments and Results, explains dataset division into training, validation, and testing set);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Dechev does not disclose the use of AI for image analysis, however Samadi further discloses extracting, by a transfer learning model, features from the training dataset to obtain the extracted features, wherein the transfer learning model has a convolutional neural network (CNN) architecture (See Section IV. Experiments and Results, explains dataset division into training, validation, and testing set, as well as Section C. for Neural Network Architecture for CNN specification);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as Samadi teaches the use of CNN for control of prosthetic hands outperforms similar methods in terms of the number of errors (See I. Introduction).
Regarding Claim 17, Dechev does not disclose specifics of the AI analysis of the ultrasound images, however Li further discloses a method wherein the ultrasound transducer captures the ultrasound images of flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and flexor pollicis longus (FPL) muscles for determining the volitional movement of the amputee (Fig 1C, see 2.2 Experiment Set up for tendons being imaged and 13 motions performed and imaged for training).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis of specific forearm tendons as taught by Li with the ultrasound system of the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Regarding Claim 18, Dechev further discloses a method wherein the step of performing real-time analysis on the extracted features for determining the volitional movement of the amputee (See [100] for Speckle tracking and displacement calculation)
Dechev does not disclose training of an AI model to analyze the ultrasound images, however Samadi further discloses training a machine learning model using the extracted features for classifying different hand gestures (See Section IV. Experiments and results, explains dataset division into training, validation, and testing set).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis as taught by Samadi with the ultrasound system of the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Regarding Claim 19, Dechev does not teach the specifics of the types of acceptable AI models to use, however Li further discloses wherein the machine learning model comprises one or more machine learning algorithms selected from the group consisting of random forest (RF), k-nearest neighbors classifier (KNN), and support vector machine (SVM) (See 2.3.4 Wrist and Finger Motion Recognition, see using SVM for pattern-recognition in ultrasound images).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the AI image analysis of specific forearm tendons as taught by Li with the ultrasound system of the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Regarding Claim 20, Dechev further discloses a method further comprising the step of causing the ultrasound transducer to repeatedly and regularly generate acoustic waves which is directed into the residual limb of the amputee (See [91] for speed of transducer, see [92] for frequency).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice as applied to claims 1-3, 6-9, and 16-20 above, and further in view of Official Notice.
Dechev further discloses a prosthetic hand device wherein the ultrasound module comprises an ultrasound transducer and a control circuit (See [85-86] and Figs 1-3 for ultrasound system 10, computation hardware 18, circuit boards 32, See Fig 2 for transducer 12 See [85-89] for transducer specifics), wherein:
the control circuit is configured to cause the ultrasound transducer to repeatedly and regularly generate acoustic waves which is directed into the residual limb of the amputee (See [91] for speed of transducer, see [92] for frequency);
Dechev in view of Samadi and Li does not teach the inner workings of the ultrasound system, however the examiner takes Official Notice that it has long been well known that ultrasound transducers inherently work by measuring acoustic reflections for information to be used to generate the ultrasound images as recited in claim 4.2
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine inherent use of ultrasound with the ultrasound system of the myoelectric prosthetic hand of Dechev in view of Samadi and Li as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over [ US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice as applied to claim 4 above, and further in view of US 20200289089 A1 (“Nelson”).
Regarding Claim 5, Dechev in view of Samadi and Li does not teach the use of a silicone pad to enhance image quality, however Nelson teaches the use of an acoustic coupling pad with ultrasound devices:
the ultrasound module (See [30] sensor face with ultrasound sensors) further comprises a sticky silicone pad (See [36] for use of acoustic coupling pad, see [58] for use of silicone to manufacture acoustic coupling pad) placed between head of the ultrasound transducer and the residual limb for enhancing image quality (See [36] placement of pad and see [33] for image preservation with pad).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the acoustic coupling pad taught by Nelson with the ultrasound system of the myoelectric prosthetic hand of Dechev as Nelson teaches the use of the pad to minimize acoustic loss with out the use of ultrasound gel that needs to be continuously applied (See [33]).
Dechev in view of Samadi, Li and Nelson does not explicitly disclose the specifics on the hardness of the silicone of the pad, however, the recitation, “the sticky silicone pad is prepared by mixing silicones with 00 hardness and 05 hardness in a 3:1 ratio” reflects that the hardness ratio is selected based on the working range of the transducer arrays and acoustics of silicone in relation. Courts have found that where general conditions of claims are disclosed in prior art, it is not inventive to discover workable ranges by routine experimentation (see MPEP § 2144.05 subsection II). Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to use the recited ratio of silicone to optimize image quality and user comfort.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice as applied to claims 1-3, 6-9, and 16-20 above, and further in view of US 20160089251 A1 (“Mandl”).
Regarding Claim 10, Dechev further discloses the myoelectric hand comprises an artificial tendon (Fig. 9, See [114] artificial tendon relates to cable 136) and a control unit (Fig 8, See [44] for controller 110 which includes microcontroller in communication with ultrasound system) configured to actuate the artificial tendon to flex and extend an individual mechanical finger (Fig. 9, See [114] artificial tendon relates to cable 136, microcontroller 114 send electrical signals to motor amplifiers to control actuators), wherein:
the control unit comprises a motor (See [114] for DC motor), a motor shaft (motor shaft is inherent as gear box and lead screw require a motor shaft to be translate energy from motor) and a roller (Fig 9 sliders 134);
the motor is powered to cause the motor shaft and the roller to rotate to drive a pulling movement of the individual mechanical finger via the artificial tendon to cause the individual mechanical finger to flex or adduct (See [114] the motor rotates the gears which rotates the slider which pulls the cable causing the fingers to flex);
Dechev in view of Samadi does not disclose the use of the tension spring, however Mandl who teaches about artificial prosthetic fingers discloses:
the artificial tendon is attached to the tension spring at a first end (See [35] for first resetting spring which is fixed at one end of first member), through a fingertip of the individual mechanical finger to the roller at a second end (see [35] for first resetting spring is formed as a tension spring);
and the tension spring stores energy from flexion and releases the energy when the motor is driven in an opposite direction to cause the individual mechanical finger to extend or abduct (see [35] for first resetting spring is formed as a tension spring).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the tension or resetting spring taught by Mandl with the myoelectric prosthetic hand of Dechev as Mendl teaches the disclosure of the prosthetic finger is a simplified construction compared to analogous structures while improving the gripping of objects.
Regarding Claim 11, Dechev further discloses the control assembly is attached on a socket (See [13] Dechev discusses the prosthetic system worn on amputated limb which inherently implies a socket on limb) having a shape based on a normal human hand (See Fig 5 for hand shape);
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and the actuating system further comprises a wrist rotational joint (See annotated Fig. 5 to the right for wrist joint of prosthetic annotated to call out joint) provided between the socket and the myoelectric hand (See [13] for contemplation of rotational wrist, which in combination with annotated Fig. 5 would be inherently between the socket and myoelectric hand),
capture ultrasound images for determining an intended wrist movement and controlling the wrist rotational joint (See [123] for contemplation of ultrasound system sending information to prosthetic to preform desired task).
Dechev does not teach the use of A-mode ultrasound, however, Li further discloses: an A-mode ultrasound transducer arranged to capture ultrasound images for determining an intended wrist movement (See 1. Introduction for contemplation of using A-mode ultrasound).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the use of A-Mode ultrasound taught by Li with the ultrasound system of myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice as applied to claims 1-3, 6-9, and 16-20 above, and further in view of US 20150257903 A1 (“Perry”).
Regarding Claim 12, Dechev further discloses the myoelectric hand comprises a base portion (Fig 5 palm plate 80) connected to and provides support to the five mechanical fingers (Fig 5, Prosthetic hand, 50).
Dechev in view of Samadi and Li does not teach the materials of prosthetic device insides and covering, however, Perry teaches of a prosthetic arm device which discloses: each of the five mechanical fingers and the base portion are made of a base material and silicone (See [205, 207] for base material and silicone use); and
the base material is selected from the group of materials consisting of nylon, plastic, polypropylene (PP), Acrylonitrile Butadiene Styrene (ABS), and vinyl (See [207] for contemplation of materials abled to be used).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the materials of the prosthetic arm taught by Perry with the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Dechev in view of Samadi, Li and Perry does not explicitly disclose what specific hardness of the silicone to achieve gripping characteristics, however the recitation, “the silicone has a frictional gripping characteristic with a shore hardness value of 00-50” reflects that the shore hardness is based on the workable range of the grip required for the specific myoelectric hand in relation to the desired tasks and shape of prosthetic. Courts have found that where general conditions of claims are disclosed in prior art, it is not inventive to discover workable ranges by routine experimentation (see MPEP § 2144.05 subsection II). Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to use silicone with the recites frictional gripping characteristic in order to optimize gripping characteristics of the myoelectric hand.
Regarding Claim 13, Dechev further discloses a sensory feedback mechanism (See [117] for pressure sensors and rotational angle sensors that provide feedback to the microcontroller) comprising plural force sensors (See [117] for pressure sensors or touch sensors on fingertips), wherein:
each of the five mechanical fingers comprises a fingertip region (Fig 5. Fingertips at end of distal phalanx 66), and the force sensor is mounted in the fingertip region (See [117] for pressure sensors or touch sensors on fingertips);
and the five mechanical fingers are dynamically and individually actuated by a control unit (Fig 8, See [44] for controller 110 which includes microcontroller in communication with ultrasound system), which is controlled by a microprocessor (See [44] for microcontroller in communication with ultrasound tendon tracking) based on the ultrasound images and output voltages of the force sensors (See [117] for pressure sensors or touch sensors on fingertips).
Dechev in view of Samadi and Li does not disclose the silicone layer, however Perry further discloses the silicone layer covering the prosthetic hand (see [205] for fingers being covered inn silicone for additional grip).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the silicone layer for gripping of the prosthetic arm taught by Perry with the myoelectric prosthetic hand of Dechev in view of Samadi and Li as Perry teaches the use of silicone aids in gripping objects and adds additional friction.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and US 20150257903 A1 (“Perry”) and Official Notice as applied to claim 12 and 13 above, and further in view of US 20140277583 A1 (“Kuntaegowdanahalli”).
Dechev in view of Samadi, Li and Perry does not disclose nerve stimulation from the sensors, however Kuntaegowdanahalli teaches a prosthetic hand that involves nerve stimulation, disclosing: the sensory feedback mechanism (See [43] for sensory input from prosthetic sent to operator) is configured to stimulate different nerves of the amputee with different amplitudes and frequencies to allow the amputee to dynamically control a degree of flexion of each of the five mechanical fingers (citation), and decrease a phantom pain (See [35] for prosthetic hand movement based of analog sensors as well as nerve stimulation of the peripheral nerve of the residual limb).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the nerve stimulation of the prosthetic arm taught by Kuntaegowdanahalli with the myoelectric prosthetic hand of Dechev in view of Samadi, Li and Perry as Kuntaegowdanahalli teaches how nerve stimulation allows for the user of the myoelectric prosthesis to experience true sensation (See [6]).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20150112451 A1 (“Dechev”) in view of Hand Prosthesis: Finger Localization Based on Forearm Ultrasound Imaging (“Samadi”) and Wrist and finger motion recognition via M-mode ultrasound signal: A feasibility study (“Li”) and Official Notice and US 20150257903 A1 (“Perry”) as applied to claim 12 and 13 above, and further in view of US 20210252388 A1 (“VanWyk”).
Dechev does not disclose a part to transfer force to the force sensor, however VanWyk who teaches about sensor-rich controls, discloses a curved surface part, made of black nylon material, ABS, or PP, is fixed for transferring force to the force sensor (see [70] for molded plastic or other rigid material to transfer force to touch sensor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the sensor-rich controls with the structures to transfer force effectively to the sensors by VanWyk with the myoelectric prosthetic hand of Dechev to create better control and response from the sensors.
Dechev does not disclose the silicone layer, however Perry further discloses the silicone layer covering the prosthetic hand (see [205] for fingers being covered inn silicone for additional grip).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the silicone layer for gripping of the prosthetic arm taught by Perry with the myoelectric prosthetic hand of Dechev as courts have found combining prior art elements according to known methods to yield predictable results is considered obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lukas M Lehman whose telephone number is (571)272-5040. The examiner can normally be reached M-F 7:30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerrah C Edwards can be reached at (408)918-7557. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.M.L./Patent Examiner, Art Unit 3774
/JERRAH EDWARDS/Supervisory Patent Examiner, Art Unit 3774
1 F. Zhuang et al., "A Comprehensive Survey on Transfer Learning," in Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, Jan. 2021, doi: 10.1109/JPROC.2020.3004555.
keywords: {Transfer learning;Semisupervised learning;Data models;Covariance matrices;Machine learning;Adaptation models;Domain adaptation;interpretation;machine learning;transfer learning},
2 See Kripfgans, O. (2006). Ultrasonic Imaging. In Encyclopedia of Medical Devices and Instrumentation, J.G. Webster (Ed.). https://doi.org/10.1002/0471732877.emd254