DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
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.
Claims 1-2 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (CN 104991639 A; citations refer to machine translation) in view of Xie (US 20210060406 A1) and Mei (US 20190065687 A1).
With respect to claim 1, Zhang discloses an intelligent rehabilitation device (see page 4: virtual reality rehabilitation system), comprising a mobile cart (see page 7: device may include a movable small cart #20), wherein
the mobile cart is equipped with an embedded upper computer, a mobile unit, and a wearable unit, and both the mobile unit and the wearable unit are communicatively connected to the embedded upper computer (see page 5: system includes motion capture device #10, mobile device #20 and a processing device #30 which are all installed on movable equipment #20 to form a bedside integrally formed device and are communicatively connected);
the embedded upper computer comprises a rehabilitation training module, a rehabilitation assessment module, a data collection module, a data processing module, a mobile control module, and a storage module (see page 5: processing device #30 also includes a data receiving module #31, a processing module #32 and a display module #30 where a computer would also have a storage module);
the rehabilitation training module is configured to provide a scene training game designed based on a rehabilitation action, and the scene training game is used to train an upper limb, a lower limb, and a hand of a rehabilitation training patient (see page 6: processing device receives data for driving the three-dimensional figure model where the motion of the patient causes the sensor data change so as to reflect the action change of the three-dimensional character model);
the rehabilitation assessment module is configured to assess movement functions of the upper limb, the lower limb, and the hand of the rehabilitation training patient (see page 6: the display module is configured to display an action change of the three dimensional character model in real time which assesses movement function);
the data collection module is configured to collect information of the rehabilitation training patient, comprising feature vectors corresponding to a plurality of dimensions of the patient, wherein each feature vector is used to indicate patient information of each dimension, and the dimensions comprise a medical record, a lifestyle habit, an environmental factor, and a rehabilitation exercise of the patient (see page 6-7: the body movement data and training pose and training mode is acquired to obtain an evaluation result after the game is finished);
the mobile control module is configured to control, by obtaining a position of the rehabilitation training patient and measuring a distance from the rehabilitation training patient, the mobile cart to move and adjust a height (see page 5: main device body may comprise a platform and support surface with a height adjusting mechanism for adjusting the height of the main body by measuring the distance between the device and the patient); and
the mobile unit comprises a roller, an electric push rod, and a driver that are disposed on the mobile cart, wherein the driver is connected to the mobile control module, and through the driver, the mobile control module drives the roller to move and adjust a position, and controls the electric push rod to adjust a height (see page 5: main device body may comprise a platform and support surface with a height adjusting mechanism for adjusting the height of the main body by measuring the distance between the device and the patient where the height adjusting mechanism can be support column housing a cylinder).
Zhang does not disclose the data processing module is configured to: predict an impact of the patient information of each dimension on rehabilitation of the patient based on the feature vector of each dimension, obtain a first prediction vector corresponding to the feature vector of each dimension, and perform feature concatenation on each first prediction vector to obtain a target vector, wherein the first prediction vector is used to indicate the impact of the patient information of each dimension on the rehabilitation of the patient; predict, based on the target vector, a rehabilitation effect corresponding to the patient information of each dimension, and obtain a second prediction vector, wherein the second prediction vector is used to indicate the rehabilitation effect corresponding to the patient information of each dimension; and generate prescription information for the rehabilitation of the patient based on the second prediction vector, wherein the prescription information comprises medication information and a medication dosage; and the wearable unit comprises a rehabilitation training glove and a somatosensory sensor, wherein both the rehabilitation training glove and the somatosensory sensor are configured to perform rehabilitation training through the rehabilitation training module and send rehabilitation training data to the data collection module.
Xie teaches a wearable unit that comprises a rehabilitation training glove and a somatosensory sensor (see paragraph 0022 and 0027: force feedback glove and position tracker/VR headset), wherein both the rehabilitation training glove and the somatosensory sensor are configured to perform rehabilitation training through the rehabilitation training module and send rehabilitation training data to the data collection module (see paragraph 0027: glove is used for enhancing immersive training experience and improving patients reality in virtual environment and enhance rehabilitation effect).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang with the teachings of Xie to utilize a training glove and sensor because it would have resulted in the predictable result of giving a patient a training tactile feedback to enhance rehabilitation effect (Xie: see [0027]).
Zhang and Xie do not disclose the data processing module is configured to: predict an impact of the patient information of each dimension on rehabilitation of the patient based on the feature vector of each dimension, obtain a first prediction vector corresponding to the feature vector of each dimension, and perform feature concatenation on each first prediction vector to obtain a target vector, wherein the first prediction vector is used to indicate the impact of the patient information of each dimension on the rehabilitation of the patient; predict, based on the target vector, a rehabilitation effect corresponding to the patient information of each dimension, and obtain a second prediction vector, wherein the second prediction vector is used to indicate the rehabilitation effect corresponding to the patient information of each dimension; and generate prescription information for the rehabilitation of the patient based on the second prediction vector, wherein the prescription information comprises medication information and a medication dosage.
Mei teaches an recurrent neural network (RNN) and long short term memory (LSTM) architecture (see paragraph 0023) that predicts patient state from feature-vector inputs (see paragraph 0029-0048) and an optimizer that generates a recommended treatment action (see paragraph 0046-0065).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang and Xie with the teachings of Mei to predict patient state and generate treatment actions because it would have resulted in the predictable result of generating an optimized recommended treatment action that takes into account long term goals of a patient (Mei: see [0064]).
With respect to claim 2, all limitations of claim 1 apply in which Xie further teaches wherein the data collection module comprises a receiver and a camera (see paragraph 0020, visual sensor and VR headset is a receiver), wherein the receiver is wirelessly connected to both the rehabilitation training glove and the somatosensory sensor (see paragraph 0022, VR headset is a receiver where glove and position tracker are connected to it); the receiver is configured to receive movement data from the rehabilitation training glove and the somatosensory sensor, and the camera collects the movement data by capturing an action of the rehabilitation training patient (see paragraph 0022-0027, visual sensor records motion of patient and the position tracker is used for real time positing and posture information and the glove is used to provide tractile training feedback to patient); and
the data processing module is further configured to process the movement data based on multi-source information fusion (see paragraph 0022-0027, all data points are used to process the movement data of patient).
With respect to claim 8, all limitations of claim 1 apply in which Zhang in view of Xie further teaches wherein the mobile control module is connected to a radar detector and a positioning module (Xie: see paragraph 0022, position tracker and VR headset); the radar detector is configured to detect the position of the rehabilitation training patient (Xie: see paragraph 0022-0027); and the positioning module is configured to determine a current position of the mobile cart (Xie: see paragraph 0022-0027).
With respect to claim 9, all limitations of claim 1 apply in which Zhang further discloses wherein the mobile cart comprises a base and a connecting piece (see page 5: main device body may comprise a platform and support surface); and the roller is disposed at a bottom of the base, the electric push rod is disposed inside the connecting piece, and the embedded upper computer is connected to a top of the electric push rod (see page 5: main device body may comprise a platform and support surface with a height adjusting mechanism for adjusting the height of the main body by measuring the distance between the device and the patient where the height adjusting mechanism can be support column housing a cylinder).
Claims 3 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Xie and Mei as applied to claim 1 above, and further in view of Mason (US 20210134430 A1).
With respect to claim 3, all limitations of claim 1 apply in which Zhang, Xie and Mei do not teach wherein the intelligent rehabilitation device further comprises: a human-machine interaction column equipped with a card reading area and a button area, wherein the card reading area is configured to recognize an identity of the rehabilitation training patient, and the button area is equipped with a plurality of buttons to assist in the scene training game; and a power module, wherein the power module comprises a magnetic ring, a transformer, a filter, and a data line, and is configured to provide a power supply and process a power signal; the embedded upper computer is also communicatively connected to a cloud server; the cloud server stores an exercise prescription; and the embedded upper computer is further configured to download the exercise prescription or upload the rehabilitation training data through the cloud server.
Mason teaches a cloud server storing and transmitting treatment plans with a patient user interface (see paragraph 0003 and 0046-0047), a human-machine interaction column equipped with a card reading area and a button area, wherein the card reading area is configured to recognize an identity of the rehabilitation training patient, and the button area is equipped with a plurality of buttons to assist in the scene training game; and a power module, wherein the power module comprises a magnetic ring, a transformer, a filter, and a data line, and is configured to provide a power supply and process a power signal; the embedded upper computer is also communicatively connected to a cloud server; the cloud server stores an exercise prescription; and the embedded upper computer is further configured to download the exercise prescription or upload the rehabilitation training data through the cloud server (see paragraph 0085-0110).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie and Mei with the teachings of Mason because it would have resulted in the predictable result of having a training engine that is cloud based (Mason: see [0075]) to allow telemedicine or telehealth sessions between a patient and a provider (Mason: see [0036]).
With respect to claim 6, all limitations of claim 1 apply in which Zhang, Xie and Mei do not teach wherein the rehabilitation assessment module comprises an assessment assistance unit and an assessment unit, wherein the assessment unit comprises an intelligent scale assessment subunit and a compensatory-movement quantitative assessment subunit; the assessment assistance unit is configured to complete a task of recognizing a rehabilitation assessment action and obtain a recognition result; and the assessment unit is configured to obtain a corresponding assessment result based on the recognition result.
Mason teaches an assessment assistance unit and an assessment unit, wherein the assessment unit comprises an intelligent scale assessment subunit and a compensatory-movement quantitative assessment subunit (see paragraph 0085-0110); the assessment assistance unit is configured to complete a task of recognizing a rehabilitation assessment action and obtain a recognition result (see paragraph 0085-0110); and the assessment unit is configured to obtain a corresponding assessment result based on the recognition result (see paragraph 0085-0110).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie, and Mei with the teachings of Mason to have an assessment unit recognize a rehabilitation assessment action and obtain a result because it would have resulted in the predictable result of assessing patient rehabilitation treatment (Mason: see [0038]).
With respect to claim 7, all limitations of claim 1 apply in which Zhang, Xie and Mei do not teach wherein the embedded upper computer is interconnected with a terminal through a network, and configured for remote medical diagnosis or online rehabilitation training guidance.
Mason teaches a terminal through a network, and configured for remote medical diagnosis or online rehabilitation training guidance (see paragraph 0075 and 0036).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie and Mei with the teachings of Mason because it would have resulted in the predictable result of having a computer connected to a terminal via a network to be cloud based (Mason: see [0075]) to allow telemedicine or telehealth sessions between a patient and a provider (Mason: see [0036]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Xie and Mei as applied to claim 1 above, and further in view of Genis (US 20100262047 A1).
With respect to claim 4, all limitations of claim 1 apply in which Zhang, Xie and Mei do not specifically teach wherein the rehabilitation training glove comprises a glove body and a first housing installed on the glove body; and the first housing is equipped therein with a first battery, a first circuit board, and a bending sensor, and a first indicator light is installed on the first housing.
Genis teaches a rehabilitation training glove with a glove body and a first housing installed on the glove body (see Fig. 1A), where the first housing is equipped with a first battery, a first circuit board and a bending sensor and a first indicator light is installed on the first housing (see Fig. 1A, and see paragraph 0030-0051: sensor, battery and LED light with circuit board).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie and Mei with the teachings of Genis to have includes such a glove because it would have resulted in the predictable result of having a training glove that reduces healthcare costs because of its portable and compact construction and its ability to provide treatment within the privacy of a patient’s home (Genis: see [0074]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Xie and Mei as applied to claim 1 above, and further in view of Goodall (US 20170156662 A1).
With respect to claim 5, all limitations of claim 1 apply in which Zhang, Xie and Mei do not teach wherein the somatosensory sensor comprises a bandage and a second housing installed on the bandage; and the second housing is equipped therein with a second circuit board and a second battery, and a second indicator light is installed on the second housing.
Goodall teaches a sensor with a bandage (see paragraph 0127: sensor with attachment surface as a bandage) and a second housing (see paragraph 0096: epidermal electronics device with housing) with a second circuit board, a second batter and a second indicator light (see paragraph 0087-0097).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie and Mei with the teachings of Goodall to have included such a sensor because it would have resulted in the predictable result of having a sensor attachable to a patient with electronics attached (Goodall: see [0063]) to allow electronics to flex without being damaged and provide data.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Xie and Mei as applied to claim 1 above, and further in view of Casey (US 20050251292 A1)
With respect to claim 10, all limitations of claim 1 apply in which Zhang, Xie and Mei do not teach wherein the mobile control module is connected to an infrared sensor and an infrared signal transmitter, and when the intelligent rehabilitation device is in use, position and height adjustment is achieved through the infrared sensor, the infrared signal transmitter, and the mobile control module.
Casey teaches wherein the mobile control module is connected to an infrared sensor and an infrared signal transmitter (see paragraph 0018, infrared light source and detector), and when the intelligent rehabilitation device is in use, position and height adjustment is achieved through the infrared sensor, the infrared signal transmitter, and the mobile control module (see paragraph 0088).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Xie and Mei with the teachings of Casey to have included such a sensor and transmitter because it would have resulted in the predictable result of being designed to work well with all floor types and are inexpensive (Casey: see [0126]).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/JENNIFER ROBERTSON/Supervisory Patent Examiner, Art Unit 3791
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