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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “on the basis of a preset ultrasonic device”. It is unclear what the meaning is conveyed by the recitation of “preset”. The limitation is, thus, interpreted as “on the basis of an ultrasound device”, for purposes of the examination.
Claim 1 further recites “determining a training object”, the training object including “target muscle, non target muscle, skin, subcutaneous fat, fascia, ligament and joint and other soft tissue structures, as well as nearby blood vessels, nerves and bone and other non soft tissue structures according to the specification. It is, hence unclear if the limitation means determining the “target muscle…” as a training object, or that a determination is made of a presence/location/features of the tissue. For purposes of the examination, the limitation is being interpreted to mean that a “target muscle,…” is selected as a training object or object to be monitored.
Claim 2 recites “wherein, each frame of the dynamic image is used to represent the visual feedback signal of each time node”. There is insufficient antecedence basis for the recitation of “each time node”.
Claim 4 recites “the instaneous target training data meets a repetitive training condition”, which appears to include a typographical error.
Claim 9 recites “the mapping diagram is constructed based on the image to be processed using a preset image post-processing technology”. It is unclear what the meaning is conveyed by the recitation of “preset”. The limitation is, thus, interpreted as “the mapping diagram is constructed based on the image to be processed using an image post-processing technology”, for purposes of the examination.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 6-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Frich, et al., US 20200000433 A1.
Regarding claim 1, Frich teaches muscle training method for providing visual feedback by using ultrasonic imaging (the abstract disclose that “The invention relates to a method for determining deformation values of muscles, particularly skeletal muscles”), wherein, the method comprises:
on the basis of a preset ultrasonic device (ultrasound scanner 110 comprising a probe of [0069]), determining a training object (“FIG. 1 shows a processing device 100 configured to obtain an ultrasonography image sequence and determining deformation values indicative of contractility of a loaded muscle of a patient based on the image sequence” [0063]) and target training data ([0009] discloses setting a reference load) and associating the target training data with the training object ([0009] indicates setting a reference load to be exerted via a loaded action to be performed by the patient by use of the loaded muscle. Meaning the reference loading is set according to the muscle to be loaded and hence provides an association between the reference load and the loaded muscle);
acquiring ultrasonic images of a target muscle of the training object during contraction process ([0012] states “obtaining an ultrasonography image sequence of the muscle at least during the action period”), and respectively selecting regions of interest from ultrasonic image frames (“selecting an analysis location in at least a part of the ultrasonography image sequence subsequent to the measurement period” [0013]);
extracting muscle characteristic parameters of the target muscle from the selected regions of interest (“by providing a function for setting the reference to a percentage of the maximum load, the deformation test can be performed at different load levels for achieving a better characterization of the contractility” [0033] and “by providing a function for obtaining and e.g. storing the peak load values and determining the maximum load it becomes possible to evaluate the contractility at well-defined load levels below the maximum load” [0034] and [0095] states “The deformation curve 211 can be used to assess the contractility of the muscle under investigation. For example, deformation values below expected values for a given level of the load values 202 indicates low contractility”);
according to the muscle characteristic parameters, obtaining a training index value (“obtaining load values exerted by the patient during a measurement period comprising an action period in which the patient performs the loaded action” [0010], and “based on at least some of the ultrasonography images of the image sequence, determining the deformation values indicative of the contractility of the muscle as average deformation values based on determined displacements of different locations within the analysis location”), representing a degree of activation of the target muscle of the training object during the exercise ([0033]-[0034] describe expressing the muscle contractility as different load levels);
converting the training index value into visual feedback signals displayed in real time, and synchronously displaying the visual feedback signal and the ultrasonic images ([0077] states that “The reference load 201 is set in order to guide the patient to deliver a load close to the reference load. For example, the reference load 201 may be displayed together with the actually delivered load values 202 received from the exercise machine 101”, and [0097] states that “The user interface 300 also shows a video presenter 301 arranged for displaying the received ultrasonography image sequence”);
comparing the training index value with the target training data, and according to an instantaneous comparison result between the training index value and the target training data ([0078] states that “the guiding of the patient to exert the reference load may be based on the reference load 201 and at least some of the obtained load values 202”, meaning that because the load values 202 are displayed together with the reference load 201, the step of guiding the patient to exert the reference load inherently compares the reference load 201 to load value 202),
generating prompt information corresponding to the target muscle so as to guide the training object in real time to perform target muscle training ([0077] states “The reference load 201 is set in order to guide the patient to deliver a load close to the reference load. For example, the reference load 201 may be displayed together with the actually delivered load values 202 received from the exercise machine 101” and [0078] states “Accordingly, the guiding of the patient to exert the reference load may be based on the reference load 201 and at least some of the obtained load values 202”. [0082] also states that “the processing device 100 may initiate and end one or more measurement periods separated by relaxation periods in which the patient is instructed to exert a maximum force”).
Regarding claim 2, Frich further teaches wherein, the step of converting the training index values into visual feedback signals displayed in real time comprises: constructing the visual feedback signal varying with time by using the training index values in the form of dynamic images and numerical readings (“FIG. 2 together with FIG. 1, illustrates the principle of an embodiment of the invention. The coordinate systems show the load (F), the deformation (S) and EMG amplitude (EMG) as a function of time (t)” [0076]; “The user interface shows the determined deformation values 211” [0096], “The user interface 300 also shows a video presenter 301 arranged for displaying the received ultrasonography image sequence” [0097]),
wherein, each frame of the dynamic image is used to represent the visual feedback signal of each time node (“The measurement period 209 defines a period wherein the processing device 100 stores or samples the received load values 202, the ultrasonography image sequence and optionally the EMG values 221. The action period 208 is a sub-period of the measurement period 209 and defines the period where the patient performs the loaded action” [0079]), and the reading corresponding to the visual feedback signal is used to represent the degree of activation of the target muscle of the training object (“The processing device 100 receives and stores the load values 202, the ultrasonography image sequence and the optional EMG signal 221 in a synchronized way, i.e. so that the stores values are synchronized in time. Accordingly, a value of the deformation curve 211 at a given point in time can be directly compared with a value of the load curve 202 and/or the EMG signal 221” [0093], where the deformation values are indicative of the loading values, with [0095] stating that “The deformation curve 211 can be used to assess the contractility of the muscle under investigation. For example, deformation values below expected values for a given level of the load values 202 indicates low contractility. A low contractility could indicate low responsivity to a medical treatment or could indicate that that an injured muscle has not jet rehabilitated sufficiently”).
Regarding claim 3, Frich further teaches wherein, the preset ultrasonic device comprises a probe (ultrasound scanner 110 comprising a probe of [0069]);
accordingly, in the process of determining the training object based on the preset ultrasonic device, the positioning of the probe is guided by real-time feedback information (“the selection of an analysis location in at least a part of the ultrasonography image sequence means a part such as an area having substantially the same location and coverage in each of a plurality of the images in the image sequence” [0019], and [0098] states “The video presenter 301 may be used for selecting an analysis location 302 in the ultrasonography image sequence. The selection of the analysis location 302 may be performed manually or the selection may be assisted by a selection function of the processing device 100 or performed automatically based on image analysis”, meaning there is a real time visual feedback that guides the location selection and placement of the probe);
the feedback information is obtained by extracting at least one morphological parameter from skin layer, subcutaneous tissue layer and/or muscle layer and other soft tissues according to each frame of the ultrasonic images of the training object (“It is known to determine deformation values of muscles based on measured changes in muscle thickness. Compared to such methods the present method determines deformation values based on measured displacements of uniquely identifiable locations within a selected analysis location” [0020], the uniquely identifiable location being selected as the analysis location for the ultrasonography image sequence acquisitions according to [0098]-[0099]), and
the morphological parameter is used to characterize the coupling and perpendicularity between the probe and the skin to ensure the consistency of probe placement (“by tracking identifiable locations such as unique speckle patterns, within a selected analysis location 302 in an image of the image sequence from image to image, the deformation values 211 can be determined from tracked displacements of at least one identifiable location, preferably two or more different identifiable locations. By use of two or more identifiable locations, average deformations, i.e. average deformation values as a function of time, can be obtained” [0101] and [0112] states that “the image contained in the analysis location 302 may comprise muscle boundaries. This could be used as an identifier for subsequent ultrasound scanning procedures, e.g. to monitor an effect of a treatment over time, so that the deformation values 211 can be determined for the same muscle location”, hence at least suggesting that muscle morphologies such as location and boundaries determine the locations of measurements which determine the placement of the probe).
Regarding claim 4, Frich further teaches wherein, the step of according to an instantaneous comparison result between the training index value and the target training data, generating prompt information corresponding to the target muscle so as to guide the training object in real time to perform target muscle training, comprises:
according to the comparison result between the training index value and the target training data ([0078] states that “the guiding of the patient to exert the reference load may be based on the reference load 201 and at least some of the obtained load values 202”, meaning that because the load values 202 are displayed together with the reference load 201, the step of guiding the patient to exert the reference load inherently compares the reference load 201 to load value 202), determining whether the instantaneous relationship between the training index value calculated from the current ultrasonic image frame and the instaneous target training data meets a repetitive training condition (“Accordingly, by means of the start and stop functions of the trigger function the length of the measurement period can be adapted to the load activity. Furthermore, repeated contractility tests of a given patient can be performed with equal lengths of the measurement period 209” [0081], which at least suggests repeatability of the loading activity), and
generating the corresponding prompt information according to comparison results ([0077] states “The reference load 201 is set in order to guide the patient to deliver a load close to the reference load. For example, the reference load 201 may be displayed together with the actually delivered load values 202 received from the exercise machine 101” and [0078] states “Accordingly, the guiding of the patient to exert the reference load may be based on the reference load 201 and at least some of the obtained load values 202”. [0082] also states that “the processing device 100 may initiate and end one or more measurement periods separated by relaxation periods in which the patient is instructed to exert a maximum force”);
wherein, a comparison calculation before generating the prompt information is carried out in real time based on each frame of the ultrasonic image ([0093] states that “The processing device 100 receives and stores the load values 202, the ultrasonography image sequence and the optional EMG signal 221 in a synchronized way, i.e. so that the stores values are synchronized in time. Accordingly, a value of the deformation curve 211 at a given point in time can be directly compared with a value of the load curve 202 and/or the EMG signal 221”);
expression of the prompt information comprises at least one of tactile, auditory and visual prompts ([0077] states “The reference load 201 is set in order to guide the patient to deliver a load close to the reference load. For example, the reference load 201 may be displayed together with the actually delivered load values 202 received from the exercise machine 101. Alternatively, the reference load 201 may be used to generate a guiding sound informing the patient if the delivered load is within an acceptable range, too high or too low”).
Regarding claim 6, Frich further teaches wherein, the muscle characteristic parameters comprise biological information related to the degree of muscle activation ([0008] states “To better address one or more of these concerns, in a first aspect of the invention a method for determining deformation values indicative of contractility of a loaded muscle of a patient”),
the biological information represents an activation state of the target muscle and characterizes the contraction function of the target muscle ([0014] states “based on at least some of the ultrasonography images of the image sequence, determining the deformation values indicative of the contractility of the muscle as average deformation values based on determined displacements of different locations within the analysis location”),
the biological information comprises at least one of morphological parameters, physiological parameters, elastic parameters and image characteristic parameters ([0020] states “It is known to determine deformation values of muscles based on measured changes in muscle thickness. Compared to such methods the present method determines deformation values based on measured displacements of uniquely identifiable locations within a selected analysis location. Accordingly, the deformation values can be more accurate and are associated with a particular region in the muscle”);
accordingly the step of extracting muscle characteristic parameters of the target muscle from the selected regions of interest, comprises:
when the biological information is the morphological parameter, muscle thickness, muscle cross-sectional area, pinnate angle and muscle fiber length of the target muscle are extracted from the ultrasonic image by tracking and calculating the muscle displacement of the target muscle; when the biological information is the physiological parameter, the blood flow velocity, blood flow direction, blood flow intensity and heart rate of the training object are extracted from the Doppler image; when the biological information is the elastic parameter, Young's modulus, shear wave velocity, shear wave attenuation coefficient, shear modulus, bulk modulus, sound of speed, and ultrasonic attenuation coefficient are extracted from an echo signal and elastic image; when the biological information is the image characteristic parameter, color feature parameters, texture feature parameters, shape feature parameters, and spatial relationship feature parameters are extracted from a two-dimensional digital image ([0020] states that “It is known to determine deformation values of muscles based on measured changes in muscle thickness. Compared to such methods the present method determines deformation values based on measured displacements of uniquely identifiable locations within a selected analysis location. Accordingly, the deformation values can be more accurate and are associated with a particular region in the muscle” and [0097] states that “The user interface 300 also shows a video presenter 301 arranged for displaying the received ultrasonography image sequence. The image in the video presenter 301 shows a cross section of the muscle”).
Regarding claim 7, Frich further teaches wherein, the step of according to the muscle characteristic parameters, obtaining a training index value, representing a degree of activation of the target muscle of the training object during the exercise, comprises:
converting the muscle characteristic parameters into training index values through preset mathematical methods, and the training index values represent the degree of activation of the target muscle of the training object during the exercise; or directly representing the training index value by the muscle characteristic parameters (“obtaining load values exerted by the patient during a measurement period comprising an action period in which the patient performs the loaded action” [0010], and [0033]-[0034] describe expressing the muscle contractility as different load levels).
Regarding claim 8, Frich further teaches wherein, a carrier of the visual feedback signal is a feedback device, and the feedback device is used for outputting the visual feedback signal ([0096] discloses a user interface 300 for displaying the results of the scanning procedure, the displayed results being the reference load 201 displayed together with the actually delivered load values 202 received from the exercise machine 101 according to [0077]);
a type of the feedback device comprises a dial plat, a scale bar, an indicator light, a curve diagram and a mapping diagram (“The presentation of the deformation curve 211 together with load curve and/or the EMG signal 221 enables a clinician or other user of the processing device 100 to assess the validity of the determined strain curve 211, e.g. by assessing if the strain values 211 start increasing at the same time as the force values 202 and/or the values of the EMG signal 221” [0094]).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Frich in view of Wang, et al., US 20220211341 A1.
Regarding claim 5, Frich further teaches wherein, the ultrasonic image contains information related to the degree of muscle activation and is used to extract muscle characteristic parameters ([0099] states that “The selected analysis location 302 determines the location (e.g. location of an area, point, a line or other shape) in the ultrasonography image sequence obtained during the measurement period 209 from which the determination of the deformation values should be based”, with [0087] stating that “the processing device 100 determines deformation values of the loaded muscle by processing of the ultrasonography image sequence”. “FIG. 1 shows a processing device 100 configured to obtain an ultrasonography image sequence and determining deformation values indicative of contractility of a loaded muscle of a patient based on the image sequence” [0063]).
While Frich displays what appears to be an M-mode image in fig. 3 with speckle tracking ([0089]), Frich does not explicitly teach that the ultrasonic image comprises A-mode ultrasonic image, M-mode ultrasonic image, B-mode ultrasonic image, Doppler ultrasonic image or/and elastic ultrasonic image.
However, within the same field of endeavor, Wang teaches a muscle imaging system and methods [0048] that uses an ultrasound probe to image one or more muscles of a subject (abstract). According to [0077], the system acquires ultrasound images in B-mode to obtain structures features of interest. [0077] and [0078] include that the system also acquires Doppler image data to process temporally distinct signals arising from tissue movement and blood flow for the detection of moving substances, such as the flow of blood cells in the image field, acquiring measurements to asses a subjects muscles ([0051]) and hence teaching that the ultrasonic image comprises A-mode ultrasonic image, M-mode ultrasonic image, B-mode ultrasonic image, Doppler ultrasonic image or/and elastic ultrasonic image.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Frich wherein the ultrasonic image comprises A-mode ultrasonic image, M-mode ultrasonic image, B-mode ultrasonic image, Doppler ultrasonic image or/and elastic ultrasonic image, as taught by Wang, to improve an ease and accuracy of obtaining measurements of a subject’s muscles, e.g. for calculating an interpretation score of a medical assessment tool [0052], with a reasonable expectation of success, as Frich is also concerned with obtaining accurate data for assessing a subjects muscle activity [0020], [0028].
Regarding claim 9, Frich further teaches the mapping diagram is constructed in the following way: acquiring sampling regions in the selected region of interest, and determining the number and location of sampling regions in the selected region of interest (“the present method determines deformation values based on measured displacements of uniquely identifiable locations within a selected analysis location”, [0020], and [0101] states that “by tracking identifiable locations such as unique speckle patterns, within a selected analysis location 302 in an image of the image sequence from image to image, the deformation values 211 can be determined from tracked displacements of at least one identifiable location, preferably two or more different identifiable locations. By use of two or more identifiable locations, average deformations, i.e. average deformation values as a function of time, can be obtained”);
collecting the muscle characteristic parameters in each sampling region respectively, and converting the muscle characteristic parameters into the training index values ([0103]-[0104] describe successively tracking unique speckle patterns or traceable image patterns indicative of displacements at the locations, such displacements indicative of the deformation values which deformation values are in turn indicative of muscle contractility at the location [0014], [0020], [0102]) ;
mapping the individual training index values to the image to be processed according to a specific spatial position, and the mapping diagram is constructed based on the image to be processed using a preset image post-processing technology, ([0098] states that “The video presenter 301 may be used for selecting an analysis location 302 in the ultrasonography image sequence”, [0099] then states that “The selected analysis location 302 determines the location (e.g. location of an area, point, a line or other shape) in the ultrasonography image sequence obtained during the measurement period 209 from which the determination of the deformation values should be based”. The ultrasound image, an example of which is shown in fig. 3, is processed and presented according to conventional image processing, with [0087] stating that “the processing device 100 determines deformation values of the loaded muscle by processing of the ultrasonography image sequence”).
Frich fails to teach wherein, when the expression form of the visual feedback signal is the color coding change on the mapping diagram, wherein a gray scale or color coding in the mapping diagram represents the amplitude of the visual feedback signal.
However, Wang further teaches wherein, when the expression form of the visual feedback signal is the color coding change on the mapping diagram, wherein a gray scale or color coding in the mapping diagram represents the amplitude of the visual feedback signal ([0078] states that “In the case of B mode imaging, the brightness of pixel at a given coordinate is proportional to the amplitude of the RF signal received from that location…The scan converter can overlay a B mode structural image with colors corresponding to motion at points in the image field, where the Doppler-estimated velocities to produce a given color. The combined B mode structural image and color Doppler image depicts the motion of tissue and blood flow within the structural image field”. [0077] also states that “The B mode processor 26 performs amplitude detection on the received ultrasound signal for the imaging of structures in the body, such as organ tissue, muscles and blood vessels. In the case of line-by-line imaging, each line (beam) is represented by an associated RF signal, the amplitude of which is used to generate a brightness value to be assigned to a pixel in the B mode image. The exact location of the pixel within the image is determined by the location of the associated amplitude measurement along the RF signal and the line (beam) number of the RF signal”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Frich wherein, when the expression form of the visual feedback signal is the color coding change on the mapping diagram, wherein a gray scale or color coding in the mapping diagram represents the amplitude of the visual feedback signal, as taught by Wang, to improve an ease and accuracy of obtaining measurements of a subject’s muscles, e.g. for calculating an interpretation score of a medical assessment tool [0052], with a reasonable expectation of success, as Frich is also concerned with obtaining accurate data for assessing a subjects muscle activity [0020], [0028].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Frich in view of Berg, et al., US 20190261874 A1.
Regarding claim 10, Frich teaches all the limitations of claim 8 above.
Frich fails to teach wherein, the method further comprises: at an end of the muscle training of the training object, generating a score based on an exercise performance of the target muscle of the training object in a contraction cycle, according to a deviation between a historical data of the training index value and the current target training data.
However, within the same field of endeavor, Berg teaches a system for monitoring biometric signals of a user comprising: a garment configured to be worn by the user and comprising a mounting module having an array of connection regions; a set of biometric sensors coupled to the garment and configured to communicate with the array of connection regions to receive and transmit biometric signals indicative of muscle activity of the user (abstract), the system 100 preferably configured to perform at least a portion of a method 200 [0030], of monitoring biometric signals/muscle activity of a user [0095], wherein, the method further comprises: at an end of the muscle training of the training object, generating a score based on an exercise performance of the target muscle of the training object in a contraction cycle, according to a deviation between a historical data of the training index value and the current target training data ([0106] and [0107] describe providing a report to a user, the report including “synopses pertaining to one or more of: a muscle breakdown of work performed/output for specific muscles; a breakdown of a score given for a workout, wherein the score can be tracked over time to monitor progress of the user; a classification of exercise as cardio-based or strength-based; indications of muscle atrophy, indications of rehabilitation progress; indications of fatigue; indications of potential or actual injury; and any other suitable reported factor”. [0091] indicates that a database provides historical exercise information to the user for said monitoring of progress of the user.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Frich wherein, the method further comprises: at an end of the muscle training of the training object, generating a score based on an exercise performance of the target muscle of the training object in a contraction cycle, according to a deviation between a historical data of the training index value and the current target training data, as taught by Berg, to reduce artifacts, providing the user a robust manner of assessing the user’s training or exercise goals [0092], with a reasonable expectation of success, as the Frich is also concerned with providing useful biometric information [0004],[0006].
Conclusion
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/FAROUK A BRUCE/ Examiner, Art Unit 3797