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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. This action is responsive to communications: Application filed on August 8, 2024, and Drawings filed on August 8, 2024.
2. Claims 1–19 are pending in this case. Claim 1, 11 are independent claims.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim 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(s) 1-5, 7-14, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meng, 20140125600, in view of Jeppsson, Pub. No.; 2021/0103348.
With regard to claim 1:
Meng discloses a measurement application device comprising: a signal processing module configured to at least one of generate measurement signals, and acquire measurement signals (see fig. 3 for measurement signal, paragraph 15: “The waveform processing and sampling unit is connected to the touch control module and the display module of the touch screen display unit, has at least one signal input port to respectively receive at least one external signal to be tested, selectively processes one of the at least one external signal to be tested to a corresponding waveform image according to the set of waveform processing parameters, and outputs the waveform image to the display module to further display the waveform image on the display module.”); a user interface configured to acquire gesture-based user input (see fig. 3 for gesture based input paragraph 17: “Preferably, the touch control module has a touch panel, a touch controller and a CPU. The touch panel serves to detect at least one moving trajectory of touch point. The touch controller is connected to the touch panel, and outputs at least one set of continuous coordinates corresponding to the at least one moving trajectory of touch point. The CPU is connected to the touch controller, after receiving the at least one set of continuous coordinates from the touch controller, determines a touch gesture according to a variation of the at least one set of continuous coordinates, and generates the set of waveform processing parameters based on the determined touch gesture.”); and a processor coupled to the user interface, and executing a algorithm, wherein the algorithm is configured to analyze received gesture-based user input (the system determines the user input and then use the determination to control output based on the user gesture, paragraph 53: “With reference to FIG. 1B, given the oscilloscope with two signal input ports 21 as an example, the touch control module 11 has a touch panel 13, a touch controller 14 and a CPU 15. The touch panel 13 serves to detect at least one moving trajectory of touch point associated with the touch gesture when touched by a user. The touch controller 14 is connected to the touch panel 13 and the CPU 15, and outputs at least one set of continuous coordinates corresponding to the at least one moving trajectory of touch point. After receiving the at least one set of continuous coordinates, the CPU 15 determines a touch gesture according to a variation of the at least one set of continuous coordinates, and generates a corresponding set of waveform processing parameters based on the determined touch gesture. The set of waveform processing parameters has a vertical position value, a gain value, a sampling rate and a trigger position value.”), and to output respective control information for controlling the signal processing module or configuration information for configuration of the signal processing module (see fig. 6 for output based on the user gesture, paragraph 72: “With reference to FIG. 6, when the touch panel detects a single-point drag, the continuous coordinates of a moving trajectory of a single touch point may vary both horizontally and vertically. When receiving the continuous coordinates of the single touch point, the CPU 15 determines if a slanted angle between a moving direction of the single touch point and the horizontal axis falls within a range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), if negative, the CPU 15 neglects the coordinates with smaller changes along one of the horizontal and vertical directions, and if positive, the CPU 15 varies the vertical position value according to the variation of the continuous coordinates along the vertical direction and varies the trigger position value according to the variation of the continuous coordinates along the horizontal direction. By limiting the foregoing single-point drag aligning within the range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), the displayed waveform can be simultaneously moving horizontally and vertically and false operation arising from the slanted angle being too small can be avoided.”).
Meng does not disclose the algorithm is a machine learning algorithm.
However Jeppsson discloses a processor coupled to the user interface, and executing a machine learning algorithm, wherein the machine learning algorithm is configured to analyze received gesture-based user input (paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture.
With regard to claim 2:
Meng and Jeppsson disclose the measurement application device according to claim 1, wherein the user interface comprises a touchscreen; and wherein the algorithm is configured to analyze the gesture-based user input received from a user via the touchscreen (Meng paragraph 51 and 52 “The touch screen display unit 10 has a touch control module 11 and a display module 12. The touch control module 11 detects a touch gesture and generates a set of waveform processing parameters according to the touch gesture. The details regarding how to convert a touch gesture into a corresponding set of waveform processing parameters are described later. The waveform processing and sampling unit 20 is connected to the touch control module 11 and the display module 12 of the touch screen display unit 10, has at least one signal input port 21 to receive at least one external signal to be tested, selectively samples and then digitizes one of the at least one external signal to be tested and processes the digitized external signal to be tested to a corresponding waveform image according to the set of waveform processing parameters, and outputs the waveform image to the display module 12 to further display the waveform image on the display module 12. In the present embodiment, the waveform processing and sampling unit 20 has a vertical position adjustment module 22, an amplitude gain control module 23, an analog-to-digital (A/D) conversion module 24 and a digital signal processing module 25. The vertical position adjustment module 22 is connected to the signal input port 21.”); the algorithm is a machine learning algorithm (Jeppsson paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture.
With regard to claims 3 and 13:
Meng and Jeppsson disclose the measurement application device according to claim 1, wherein the user interface comprises at least one of a movement sensor, and a camera; and wherein the algorithm is configured to analyze the gesture-based user input acquired via the at least one of the movement sensor (Meng paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”), and the camera (paragraph 28: “FIG. 1 illustrates an example environment 100 in which techniques that enable facilitating user-proficiency in using radar gestures to interact with an electronic device can be implemented. The example environment 100 includes an electronic device 102, which includes, or is associated with, a persistent radar system 104, a persistent gesture-training module 106 (gesture-training module 106), and, optionally, one or more non-radar sensors 108 (non-radar sensor 108). The term “persistent,” with reference to the radar system 104 or the gesture-training module 106, means that no user interaction is required to activate the radar system 104 (which may operate in various modes, such as a dormant mode, an engaged mode, or an active mode) or the gesture-training module 106. In some implementations, the “persistent” state may be paused or turned off (e.g., by a user). In other implementations, the “persistent” state may be scheduled or otherwise managed in accordance with one or more parameters of the electronic device 102 (or another electronic device). For example, the user may schedule the “persistent” state such that it is only operational during daylight hours, even though the electronic device 102 is on both at night and during the day. The non-radar sensor 108 can be any of a variety of devices, such as an audio sensor (e.g., a microphone), a touch-input sensor (e.g., a touchscreen), a motion sensor, or an image-capture device (e.g., a camera or video-camera).”) the algorithm is a machine learning algorithm (Jeppsson paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture.
With regard to claims 4 and 14:
Meng and Jeppsson disclose measurement application device according to claim 1, wherein the algorithm is configured to output exactly one control information, or exactly one configuration information based on the gesture-based user input (Meng see fig. 6 wherein the only control information is the waveform displayed based on user input, paragrpah 72: “With reference to FIG. 6, when the touch panel detects a single-point drag, the continuous coordinates of a moving trajectory of a single touch point may vary both horizontally and vertically. When receiving the continuous coordinates of the single touch point, the CPU 15 determines if a slanted angle between a moving direction of the single touch point and the horizontal axis falls within a range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), if negative, the CPU 15 neglects the coordinates with smaller changes along one of the horizontal and vertical directions, and if positive, the CPU 15 varies the vertical position value according to the variation of the continuous coordinates along the vertical direction and varies the trigger position value according to the variation of the continuous coordinates along the horizontal direction. By limiting the foregoing single-point drag aligning within the range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), the displayed waveform can be simultaneously moving horizontally and vertically and false operation arising from the slanted angle being too small can be avoided.”); the algorithm is a machine learning algorithm (Jeppsson paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture.
With regard to claim 5:
Meng and Jeppsson disclose the measurement application device according to claim 1, wherein the machine learning algorithm is configured to output at least two suggestions of control information, or configuration information based on the gesture-based user input. (Jappsson paragraph 131 and 132: “FIG. 21 illustrates, at 2100, additional training screens that may be presented in the tips tutorial for snoozing alarms. For example, a detail view 2100-1 illustrates additional elements of the training screen described in the detail view 2000-4 that is presented after the user successfully performs the swipe (e.g., a direction-independent swipe or omni-swipe). The training screen of the detail view 2100-1 illustrates the smartphone with the “Nicely done!” textual message displayed in the text area 1810, the return control 1902, which allows the user 112 to exit the snooze alarms tutorial (e.g., the “Got it” icon), and the sound control 1814. In some implementations, the training screen may also present the smartphone display with one or both of a completion icon 2102 (e.g., a checkmark) or a restart control 2104 (e.g., a “Practice again” icon). Another detail view 2100-2 illustrates the training screen after the user 112 activates the return control 1902. In the detail view 2100-2, the animation ends, and the summary page is presented (e.g., the summary page described in the detail view 1900-4 of FIG. 19). The summary page can present text in the text area 1810 that reminds the user 112 that there are other gesture training options (“Try Quick Gestures for these actions”). The summary page can also present the tutorial controls 1904 that allow the user 112 to re-enter the tips environment for snoozing alarms or enter other tips tutorials for skipping songs and silencing calls. The summary page also includes the exit control 1908 that allows the user 112 to exit the tips tutorial environment (e.g., the “Finish” icon). The tutorial controls 1904 can be presented with the indicator 1906 (e.g., the check-mark icon), which lets the user 112 know which tutorials have been completed.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng and so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the displayed measurement.
With regard to claims 7 and 16:
Meng and Jeppsson disclose the measurement application device according to claim 1, wherein the processor is configured to output a user request to a user for repeating the gesture-based user input, if the machine learning algorithm calculates a confidence score for the gesture-based user input that is below a predetermined threshold (Jeppsson paragraph 90; “In some implementations, the visual element 122 and the associated instructions can also be used to increase the accuracy of the adjusted benchmark values and decrease the time it takes to generate the adjusted benchmark values. For example, the machine-learning technology can direct the gesture-training module 106 to present instructions, such as text or audio, that ask the user whether the user's movement is intended to be the requested gesture. The user can reply (e.g., using a radar gesture, touch input, or voice input), and the gesture-training module 106 can then ask the user to repeat the requested gesture until the machine-learning technology has enough data to generate the adjusted benchmark values.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture and to repeat user motion in order to accurately determine the user gesture for training.
With regard to claims 8 and 17:
Meng and Jeppsson disclose the measurement application device according to claim 1, wherein the machine learning algorithm is configured to output suggestions for at least one control information, or configuration information for association with a gesture-based user input based on the respective gesture-based user input in a configuration operating mode of the processor (Jeppsson paragraph 90; “In some implementations, the visual element 122 and the associated instructions can also be used to increase the accuracy of the adjusted benchmark values and decrease the time it takes to generate the adjusted benchmark values. For example, the machine-learning technology can direct the gesture-training module 106 to present instructions, such as text or audio, that ask the user whether the user's movement is intended to be the requested gesture. The user can reply (e.g., using a radar gesture, touch input, or voice input), and the gesture-training module 106 can then ask the user to repeat the requested gesture until the machine-learning technology has enough data to generate the adjusted benchmark values.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng and so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the display measurement date.
With regard to claims 9 and 18:
Meng and Jeppsson disclose the measurement application device according to claim 8, wherein, in the configuration operating mode of the processor, the processor is configured to request a user to repeat a gesture-based user input for training the machine learning algorithm (Jeppsson paragraph 90; “In some implementations, the visual element 122 and the associated instructions can also be used to increase the accuracy of the adjusted benchmark values and decrease the time it takes to generate the adjusted benchmark values. For example, the machine-learning technology can direct the gesture-training module 106 to present instructions, such as text or audio, that ask the user whether the user's movement is intended to be the requested gesture. The user can reply (e.g., using a radar gesture, touch input, or voice input), and the gesture-training module 106 can then ask the user to repeat the requested gesture until the machine-learning technology has enough data to generate the adjusted benchmark values.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng and so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the display measurement date.
With regard to claims 10 and 19:
Meng and Jeppsson disclose the measurement application device according to claim 1, further comprising: a measurement probe coupled to the signal processing module (Meng paragraph 59: “With reference to FIGS. 1B, 2A and 2B, the vertical position adjustment module 22 has two digital-to-analog (D/A) converters 221 and two voltage adders 222. The two D/A converters 221 are respectively connected to the voltage adders 222 and are connected to the CPU 15 of the touch control module 11. The CPU 15 outputs a vertical position value to each of the two D/A converters 221. After receiving the vertical position value, each D/A converter 221 outputs a corresponding DC voltage to one of the voltage adders 222. Each voltage adder 222 is connected to one of the signal input ports 21 to add the DC voltage outputted from a corresponding D/A converter 221 to a corresponding signal to be tested. Hence, when a touch point P1 moves downwards, the continuous vertical coordinates of a moving trajectory of the touch point P1 tend to decrease. The DC voltage added to the signal to be tested through the voltage adder 222 tends to decrease, and the displayed waveform corresponding to the signal to be tested moves downwards along the vertical axis. On the other hand, when the touch point P1 moves upwards, the continuous vertical coordinates of a moving trajectory of the touch point P1 tend to increase. The DC voltage added to the signal to be tested through the voltage adder 222 tends to increase, and the displayed waveform corresponding to the signal to be tested moves upwards along the vertical axis.”), wherein the processor is configured to configure the measurement probe based on the configuration information, or control information (Meng paragraph 59 to 61: “With reference to FIGS. 1B, 2A and 2B, the vertical position adjustment module 22 has two digital-to-analog (D/A) converters 221 and two voltage adders 222. The two D/A converters 221 are respectively connected to the voltage adders 222 and are connected to the CPU 15 of the touch control module 11. The CPU 15 outputs a vertical position value to each of the two D/A converters 221. After receiving the vertical position value, each D/A converter 221 outputs a corresponding DC voltage to one of the voltage adders 222. Each voltage adder 222 is connected to one of the signal input ports 21 to add the DC voltage outputted from a corresponding D/A converter 221 to a corresponding signal to be tested. Hence, when a touch point P1 moves downwards, the continuous vertical coordinates of a moving trajectory of the touch point P1 tend to decrease. The DC voltage added to the signal to be tested through the voltage adder 222 tends to decrease, and the displayed waveform corresponding to the signal to be tested moves downwards along the vertical axis. On the other hand, when the touch point P1 moves upwards, the continuous vertical coordinates of a moving trajectory of the touch point P1 tend to increase. The DC voltage added to the signal to be tested through the voltage adder 222 tends to increase, and the displayed waveform corresponding to the signal to be tested moves upwards along the vertical axis. 2. Adjustment of Vertical Scale: With reference to FIGS. 1B, 3A and 3B, the amplitude gain control module 23 has two amplifiers 231. The two amplifiers 231 are connected to the respective voltage adders 222 to receive the signals to be tested outputted from the corresponding voltage adders 222. Each amplifier 231 has a gain control terminal 232 connected to the CPU 15 of the touch control module 11 so that the amplifier 231 can amplify the signal to be tested according to the zoom levels mapped to by the respective gain value. When the continuous vertical coordinates of two touch points P1, P2 touched by two fingers depart from each other and a sum of two respective vertical displacements of the two touch points P1, P2 (i.e. | P1P1'|+| P2P2'|) is greater than the activation threshold, the CPU 15 sets up one of the zoom levels by one level up (for example from 2V/div to 1V/div) so that a scale of the displayed waveform is expanded by one zoom level up along the vertical axis. On the other hand, when the continuous vertical coordinates of two touch points P1, P2 touched by two fingers approach each other and a sum of two respective vertical displacements of the two touch points P1, P2 (i.e. | P1P1'|+| P2P2'|) is greater than the activation threshold, the CPU 15 sets up one of the zoom levels by one level down (for example from 1V/div to 2V/div) so that the scale of the displayed waveform is decreased with one zoom level down along the vertical axis.”).
With regard to claim 11:
Meng discloses a computer implemented method for operating a measurement application device (See fig. 3 for measurement signal, paragraph 15: “The waveform processing and sampling unit is connected to the touch control module and the display module of the touch screen display unit, has at least one signal input port to respectively receive at least one external signal to be tested, selectively processes one of the at least one external signal to be tested to a corresponding waveform image according to the set of waveform processing parameters, and outputs the waveform image to the display module to further display the waveform image on the display module.”), the method comprising: acquiring gesture-based user input (See fig. 3 for gesture based input paragraph 17: “Preferably, the touch control module has a touch panel, a touch controller and a CPU. The touch panel serves to detect at least one moving trajectory of touch point. The touch controller is connected to the touch panel, and outputs at least one set of continuous coordinates corresponding to the at least one moving trajectory of touch point. The CPU is connected to the touch controller, after receiving the at least one set of continuous coordinates from the touch controller, determines a touch gesture according to a variation of the at least one set of continuous coordinates, and generates the set of waveform processing parameters based on the determined touch gesture.”); analyzing the gesture-based user input to generate an analysis result (the system determines the user input and then use the determination to control output based on the user gesture, paragraph 53: “With reference to FIG. 1B, given the oscilloscope with two signal input ports 21 as an example, the touch control module 11 has a touch panel 13, a touch controller 14 and a CPU 15. The touch panel 13 serves to detect at least one moving trajectory of touch point associated with the touch gesture when touched by a user. The touch controller 14 is connected to the touch panel 13 and the CPU 15, and outputs at least one set of continuous coordinates corresponding to the at least one moving trajectory of touch point. After receiving the at least one set of continuous coordinates, the CPU 15 determines a touch gesture according to a variation of the at least one set of continuous coordinates, and generates a corresponding set of waveform processing parameters based on the determined touch gesture. The set of waveform processing parameters has a vertical position value, a gain value, a sampling rate and a trigger position value.”); and outputting control information for controlling a signal processing module of the measurement application device, or configuration information for configuration of the signal processing module based on the analysis result (see fig. 6 for output based on the user gesture, paragraph 72: “With reference to FIG. 6, when the touch panel detects a single-point drag, the continuous coordinates of a moving trajectory of a single touch point may vary both horizontally and vertically. When receiving the continuous coordinates of the single touch point, the CPU 15 determines if a slanted angle between a moving direction of the single touch point and the horizontal axis falls within a range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), if negative, the CPU 15 neglects the coordinates with smaller changes along one of the horizontal and vertical directions, and if positive, the CPU 15 varies the vertical position value according to the variation of the continuous coordinates along the vertical direction and varies the trigger position value according to the variation of the continuous coordinates along the horizontal direction. By limiting the foregoing single-point drag aligning within the range of 30.degree. to 60.degree. (or 120.degree. to 150.degree.), the displayed waveform can be simultaneously moving horizontally and vertically and false operation arising from the slanted angle being too small can be avoided.”).
Meng does not disclose analyzing the gesture-based user input with a machine learning algorithm to generate an analysis result.
However Jeppsson discloses the aspect of analyzing the gesture-based user input with a machine learning algorithm to generate an analysis result. (paragraph 3: “This document describes techniques and systems that enable facilitating user-proficiency in using radar gestures to interact with an electronic device. The techniques and systems use a radar field to enable an electronic device to accurately determine the presence or absence of a user near the electronic device and to detect a reach or other radar gesture the user makes to interact with the electronic device. Further, the electronic device includes an application that can help the user learn how to properly make the radar gestures that can be used to interact with the electronic device. The application can be a tutorial, a game, or another format that allows users to learn how to make radar gestures that are effective to interact with or control the electronic device. The application can also use machine-learning techniques and models to help the radar system and electronic device better recognize how different users make radar gestures. The application and machine-learning functionality can improve the user's proficiency in using radar gestures and allow the user to take advantage of the additional functionality and features provided by the availability of the radar gesture, which can result in a better user experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng so the system can be trained to improve accuracy at identifying user input wherein machine learning algorithm could user input data to model user behavior and learn to better recognize user gesture.
With regard to claim 12:
Meng and Jeppsson disclose the computer implemented method according to claim 11, wherein the gesture-based user input is acquired via a touchscreen (Meng paragraph 51 and 52 “The touch screen display unit 10 has a touch control module 11 and a display module 12. The touch control module 11 detects a touch gesture and generates a set of waveform processing parameters according to the touch gesture. The details regarding how to convert a touch gesture into a corresponding set of waveform processing parameters are described later. The waveform processing and sampling unit 20 is connected to the touch control module 11 and the display module 12 of the touch screen display unit 10, has at least one signal input port 21 to receive at least one external signal to be tested, selectively samples and then digitizes one of the at least one external signal to be tested and processes the digitized external signal to be tested to a corresponding waveform image according to the set of waveform processing parameters, and outputs the waveform image to the display module 12 to further display the waveform image on the display module 12. In the present embodiment, the waveform processing and sampling unit 20 has a vertical position adjustment module 22, an amplitude gain control module 23, an analog-to-digital (A/D) conversion module 24 and a digital signal processing module 25. The vertical position adjustment module 22 is connected to the signal input port 21.”).
Claims 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Meng, 20140125600, in view of Jeppsson, and further in view of Kruzick, Pub. NO.: 20220334686A1.
With regard to claim 6:
Meng and Jeppsson do not disclose the measurement application device according to claim 5, wherein the processor is configured to train the machine learning algorithm based on user feedback regarding the suggestions
However Kruzick discloses the measurement application device according to claim 5, wherein the processor is configured to train the machine learning algorithm based on user feedback regarding the suggestions (Kruzick paragraph 40: “Turning to FIG. 1B, a user has subsequently selected the spreadsheet application 104 to snap alongside the calendar application 102. Accordingly, the system has enlarged the spreadsheet application 104 to fill the second region of the display environment. A number of regions (e.g., two, three, four), as well as a size and a position of each of the regions to which the items are snapped into, may be preconfigured as an organized layout, or grid. In addition, the machine learning model 110 can receive and store user selection data 112 defining the user's choice of the spreadsheet application 104. As discussed above, and in further detail below, the machine learning model 110 can analyze the user selection data 112 to inform future snap assist recommendations and tailor the user experience to specific habits and contexts of individual users. In various examples, the machine learning model 110 can be initially trained using user data from many users (e.g., the global userbase) and reflect large-scale habits among a general user population. Furthermore, an individual instance of the machine learning model 110 can be associated with a particular user or user device and gradually adapt to the particular habits to provide a tailored experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Kruzick to Meng and Jeppsson so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the display measurement date and train based on the user responses to further improve the suggestions.
With regard to claim 15:
Meng and Jeppsson disclose aspect wherein the machine learning algorithm outputs at least two suggestions of control information, or configuration information based on the gesture-based user input (Jappsson paragraph 131 and 132: “FIG. 21 illustrates, at 2100, additional training screens that may be presented in the tips tutorial for snoozing alarms. For example, a detail view 2100-1 illustrates additional elements of the training screen described in the detail view 2000-4 that is presented after the user successfully performs the swipe (e.g., a direction-independent swipe or omni-swipe). The training screen of the detail view 2100-1 illustrates the smartphone with the “Nicely done!” textual message displayed in the text area 1810, the return control 1902, which allows the user 112 to exit the snooze alarms tutorial (e.g., the “Got it” icon), and the sound control 1814. In some implementations, the training screen may also present the smartphone display with one or both of a completion icon 2102 (e.g., a checkmark) or a restart control 2104 (e.g., a “Practice again” icon). Another detail view 2100-2 illustrates the training screen after the user 112 activates the return control 1902. In the detail view 2100-2, the animation ends, and the summary page is presented (e.g., the summary page described in the detail view 1900-4 of FIG. 19). The summary page can present text in the text area 1810 that reminds the user 112 that there are other gesture training options (“Try Quick Gestures for these actions”). The summary page can also present the tutorial controls 1904 that allow the user 112 to re-enter the tips environment for snoozing alarms or enter other tips tutorials for skipping songs and silencing calls. The summary page also includes the exit control 1908 that allows the user 112 to exit the tips tutorial environment (e.g., the “Finish” icon). The tutorial controls 1904 can be presented with the indicator 1906 (e.g., the check-mark icon), which lets the user 112 know which tutorials have been completed.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Jeppsson to Meng and so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the display measurement date.
Meng and Jeppsson do not disclose the aspect wherein the machine learning algorithm is trained based on user feedback regarding the at least two suggestions.
However Kruzick discloses the aspect wherein the machine learning algorithm is trained based on user feedback regarding the at least two suggestions (Kruzick paragraph 40: “Turning to FIG. 1B, a user has subsequently selected the spreadsheet application 104 to snap alongside the calendar application 102. Accordingly, the system has enlarged the spreadsheet application 104 to fill the second region of the display environment. A number of regions (e.g., two, three, four), as well as a size and a position of each of the regions to which the items are snapped into, may be preconfigured as an organized layout, or grid. In addition, the machine learning model 110 can receive and store user selection data 112 defining the user's choice of the spreadsheet application 104. As discussed above, and in further detail below, the machine learning model 110 can analyze the user selection data 112 to inform future snap assist recommendations and tailor the user experience to specific habits and contexts of individual users. In various examples, the machine learning model 110 can be initially trained using user data from many users (e.g., the global userbase) and reflect large-scale habits among a general user population. Furthermore, an individual instance of the machine learning model 110 can be associated with a particular user or user device and gradually adapt to the particular habits to provide a tailored experience.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Kruzick to Meng and Jeppsson so the system multiple suggestions of control to help the user navigate the interface and provide the user with options to control the display measurement date and train based on the user responses to further improve the suggestions.
Pertinent Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stearns, Patent No.: 8907910 B2: An instrument and method for operating the same is disclosed. The instrument includes an input port for receiving a signal, a processor for measuring a first parameter related to the signal, and a touch-enabled display. The touch-enabled display receives commands directed to the processor and displays the first parameter. The commands including commands that determine how the first parameter is displayed and commands that determine the manner in which the instrument operates. The commands are grouped into a plurality of contexts. Each command in a context is specified by a control gesture on the touch-enabled display. A first control gesture is used for a first command in a first context, the first control gesture is also utilized for a second command in a second context, the first context being different from the second context.
Marais, Pub. No.: 20130077820 A1: A virtual skeleton includes a plurality of joints and provides a machine readable representation of a human subject observed with a sensor such as a depth camera. A gesture detection module is trained via machine learning to identify one or more features of a virtual skeleton and indicate if the feature(s) collectively indicate a particular gesture.
Conclusion
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/DI XIAO/Primary Examiner, Art Unit 2178
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