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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in Korean parent Applications No. KR10-2022-0178409, filed on December 19, 2022 and KR10-2022-0129975, filed on October 11, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 2, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1, 4, 6-11 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee, United States Patent Application Publication No. US 2020/0012382 A1.
Regarding claim 1, Lee discloses an electronic device (Figs. 1-2, electronic apparatus, #200) comprising:
a display (Figs. 1-2, Detailed Description, [0062-0064], touch screen, #110);
a touch sensor (Figs. 2, touch sensor, #131; Detailed Description, [0071-0075]);
a memory (Figs. 2, memory, #135; Detailed Description, [0071-0075]) ; and
at least one processor (Figs. 2, touch control circuit, #133; Detailed Description, [0073=0075]), comprising processing circuitry;
wherein the memory includes instructions, and at least one processor, individually and/or collectively is configured to execute the instructions (Detailed Description, [0078-0080]) and to cause the electronic device to:
based on touch data received from the touch sensor, determine whether a force touch having an increasing variation of skin compression according to a touch area is recognized within a long touch detection time (Detailed Description, [0080-0081], “The touch input detector 132 may detect a touch input to obtain a touch result value based on the touch signals and the temperature signals sensed by the touch detector 131, and detect a time interval with the previously input touch input of the detected touch input, a touch area, a touch intensity (sensitivity), a distance between the touches, and a temperature upon touch of the touch input. The touch interval, the touch distance, the touch area, the touch intensity, and the temperature upon touch may be detected as measured values through the touch input detector 132.”; See also Figs. 2-3 generally and Detailed Definition, [0097] on user’s fingertip);
based on recognition of the force touch being maintained for a specified time, execute a force touch action function mapped to the force touch (Figs. 2-3, Detailed Description, [0080-0098], See also Fig. 8, S2400 and Detailed Description, [0228]); and
learn a malfunction situation for the force touch and, based on sensitivity adjustment of the force touch being required, display a sensitivity correction UI on the display (Fig. 2, touch type classifier, #136; AI model learner, #134 ; Detailed Description, [0083-0090], “The touch type classifier 136 may determine the touch type whether the touch input detected by the touch input detector 132 is a normal touch or a false touch through the AI model learned by the AI model learner 134. In another embodiment of the present disclosure, as described above, the touch type classifier 136 may be configured to determine whether the touch input is a normal touch or a false touch through the learned AI model received from the server 300....In an embodiment of the present disclosure, a false touch calibrating scheme may be at least one of a noise reduction filter adding scheme, a touch sensitivity adjusting scheme, or a touch frequency changing scheme. The noise reduction filter adding scheme may include a software component that calibrates a touch error by applying at least one noise reduction filter according to the number of error occurrences. The touch sensitivity adjusting scheme may also include a software component that calibrates a touch error by adjusting the touch sensitivity according to the number of error occurrences. In order to solve the noise problem, the frequency changing scheme allows a controller to filter out the noise of a common mode and avoid the noise problem by using a frequency hopping scheme.”).
Regarding claim 4, Lee discloses wherein at least one processor, individually and/or collectively, is configured control the display to display a force touch situation UI on the display, based on the recognition of the force touch (Figs. 4, Detailed Description, [0099-0110], “FIG. 4A is a detailed flowchart of a method for optimizing a screen by inferring the image quality of the screen or the content of the screen on the display 105… The touch input detector 132 or the server 300 that has received the data related to the touch input inputs the data related to the time interval with the previously input touch input of the detected touch input and the detected touch area to the AI model learned to determine the touch type of whether the detected touch input is a normal touch, a ghost touch, or an obscure touch (operation S1300). The touch control circuit 133 or the server 300 that has received the data related to or the touch input may also input the related elements of the additional touch inputs, for example, a touch intensity (pressure sensitive type touch screen), a touch temperature, and a touch distance additionally or independently in order to learn the AI model for determining the false touch.”; S1500 shows output result).
Regarding claim 6, Lee discloses wherein at least one processor, individually and/or collectively, is configured to observe whether a force touch occurs in a force touch observation interval before a long touch detection period, and perform a pressure calculation in a force touch determination interval to determine whether a force touch is recognized (Detailed Description, [0080-0098], “he AI model learner 134 may be configured to learn a normal/false touch classifying engine so as to output by inferring whether the detected touch input is a normal touch or a false touch by using at least two of the touch time interval of the previously input touch input with the previously input touch input of the detected touch inputs, the touch area of the detected touch inputs, the detected touch area, the touch distance between the detected touch inputs, the touch intensity of the detected touch inputs, or the temperature upon touch of the detected touch inputs, and the touch types labeled to the touch inputs as learning data…. and in the case of a pressure sensitive type touch screen, the touch interval and the touch intensity may be used as the environmental information of the touch input.”).
Regarding claim 7, Lee discloses wherein at least one processor, individually and/or collectively, is configured to, based on a variation of skin compression based on a touch area increasing over time, observe a corresponding touch as a force touch, and based on a variation of skin compression based on a touch area being maintained at a constant size over time, observe a corresponding touch as a long touch (Detailed Description, [0080-0098], “The AI model learner 134 may be configured to learn a normal/false touch classifying engine so as to output by inferring whether the detected touch input is a normal touch or a false touch by using at least two of the touch time interval of the previously input touch input with the previously input touch input of the detected touch inputs, the touch area of the detected touch inputs, the detected touch area, the touch distance between the detected touch inputs, the touch intensity of the detected touch inputs, or the temperature upon touch of the detected touch inputs, and the touch types labeled to the touch inputs as learning data”).
Regarding claim 8, Lee discloses wherein the force touch action function comprises an action function designatable for each application and/or a global action function applicable to an electronic device system (Detailed Description, [0080-0098]; See also Fig. 8 and S2400; Detailed Description, [0205-0229]).
Regarding claim 9, Lee discloses wherein at least one processor, individually and/or collectively, is configured to:
receive information on an application being executed and, based on the force touch being recognized in a state where the application is executed, reconfigure, based on the information on the application, a screen layout of the force touch action function to dynamically change and display a screen (Figs. 2-4, Detailed Description, [0080-0099], “The false touch determining device 100 may be executed in the form of a program or an application app in a smartphone, a tablet PC, an electric range, etc., and embedded in a home appliance, etc…. The false touch processor 138 may remove the touch input signal in the case of ghost touch, calibrate the obscure touch, and determine as the normal input to maintain it based on the result determined by the touch type classifier 136. In the case of the false touch (ghost touch, obscure touch), the false touch may be calibrated by using at least one of a noise reduction filter adding scheme, a touch sensitivity adjusting scheme, or a touch frequency changing scheme…. for optimizing a screen by inferring the image quality of the screen or the content of the screen on the display 105.”)
Regarding claim 10, Lee discloses wherein the sensitivity correction UI comprises a touch controller and is configured to enable touch sensitivity adjustment according to a position of the touch controller depending on a user input (Figs. 2-4, Detailed Description, [0080-0099], “. In the case of the false touch (ghost touch, obscure touch), the false touch may be calibrated by using at least one of a noise reduction filter adding scheme, a touch sensitivity adjusting scheme, or a touch frequency changing scheme.”)
Regarding claim 11, Lee discloses a method of improving a force touch operation of an electronic device (Lee, Figs. 1-8, generally), the method comprising the structural and functional aspects of claim 1. Thus, claim 11 is rejected under the same reasoning as claim 1.
Regarding claim 13, this is met by the rejection to claim 4.
Regarding claim 14, Lee discloses wherein the displaying of the sensitivity correction UI on the display further comprises, based on a user's touch release occurring in a state where the force touch situation UI is displayed or after the force touch being recognized, recording the malfunction situation (Detailed Description, [0080-0098], “The AI model learner 134 may generate the AI model by using supervised learning, but learn the normal/false touch classifying engine by using unsupervised learning or reinforcement learning. For example, the AI model learner 134 may learn the normal/false touch classifying engine through a machine learning algorithm of classification or regression analysis, and deep neural networks of a DNN, a CNN, and a RNN. Learning of the normal/false touch classifying engine by the classifying supervised learning is described in FIGS. 5A to 5E, and the neural network learning based on reinforcement learning is described in FIGS. 7A to 7C.”) and
wherein based on the malfunction situation being repeated a configured N or more times, a state where sensitivity adjustment of the force touch is required is recognized, and the sensitivity correction UI is displayed (Detailed Description, [0080-0098], “In an embodiment of the present disclosure, a false touch calibrating scheme may be at least one of a noise reduction filter adding scheme, a touch sensitivity adjusting scheme, or a touch frequency changing scheme. The noise reduction filter adding scheme may include a software component that calibrates a touch error by applying at least one noise reduction filter according to the number of error occurrences. The touch sensitivity adjusting scheme may also include a software component that calibrates a touch error by adjusting the touch sensitivity according to the number of error occurrences. In order to solve the noise problem, the frequency changing scheme allows a controller to filter out the noise of a common mode and avoid the noise problem by using a frequency hopping scheme.”).
Regarding claim 15, this is met by the rejection to claim 6.
Regarding claim 16, this is met by the rejection to claim 7.
Regarding claim 17, this is met by the rejection to claim 8.
Regarding claim 18, this is met by the rejection to claim 9.
Regarding claim 19, this is met by the rejection to claim 10.
Regarding claim 20, Lee discloses a non-transitory computer-readable recording medium storing instructions that cause an electronic device (Figs. 1-2, memory, #135; Detailed Description, [0232]) to perform a method comprising the elements of claim 11. Thus, claim 20 is rejected under the same reasoning as claim 11.
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.
Claim(s) 2, 3, 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Yoshida et al., United States Patent Application Publication No. US 2016/0147365 A1.
Regarding claim 2, Lee discloses every element of claim 1, and further discloses wherein the display comprises a flexible display having a variable display region configured to display visual information (Figs. 1-2, Detailed Description, [0070], “. As shown in FIG. 2A, the touch driving circuit 130 may be composed of one integrated circuit connected to a touch screen 110 such as the touch drive IC. At this time, the touch driving circuit 130 may be connected to the touch screen 110 through a flexible circuit board, and mounted on the flexible circuit board. The touch driving circuit 130 may be electrically connected to the touch part 111 and the temperature sensor 120 of the touch screen 110.”)
Lee does not explicitly disclose wherein at least one processor, individually and/or collectively, is configured to:
receive an electronic device form factor state and/or grip state from a sensor module and learn a touch recognition area according to the electronic device form factor state and/or grip state to dynamically adjust a threshold value of a force touch recognition model configured to determine skin compression.
Yoshida, in a similar field of endeavor, discloses an electronic device wherein at least one processor (Figs. 1-2, control unit, #50), individually and/or collectively, is configured to:
receive an electronic device form factor state and/or grip state from a sensor module and learn a touch recognition area according to the electronic device form factor state and/or grip state to dynamically adjust a threshold value of a force touch recognition model configured to determine skin compression (Figs. 1-3, use form determining unit, #55; Detailed Description, [0082-0090], “More specifically, the use form determining unit 55 determines a holding form in accordance with a touch position on the end face at which the touch operation is executed. The holding form is information representing a user's hand holding the display device 1, and the determining of a holding form is determining whether the user uses the display device 1 with holding it either using the right hand or using the left hand… The application executing unit 56 executes a process corresponding to the touch operation information transmitted from the input operation determining unit 52 in accordance with the application program, generates a screen on which the execution result is reflected, and outputs the generated screen to the display control unit 54. For example, in a case where the touch operation gives an instruction for generating a mail, the application executing unit 56 relating to the mail function generates a user interface (UI) screen for inputting the text of the mail and outputs the generated UI screen to the display control unit 54… 89] The predetermined end condition, in other words, a condition for releasing the setting of the insensitive areas is not particularly limited. For example, as the condition, at a time when the holding form determined by the use form determining unit 55 is changed or the like may be considered. Alternatively, since the insensitive areas have already been set, there is a possibility that a change in the holding form may not be detected well. Thus, it may be considered that, after a predetermined time (for example, about 0.5 to 2 seconds) elapses from the previous setting of the insensitive areas, the insensitive area setting unit 58 releases the previous setting of the insensitive areas at once. Accordingly, the use form determining unit 55 can newly determine the holding form again. The insensitive area setting unit 58 sets insensitive areas in accordance with the newly determined holding form. In other words, the insensitive area setting unit 58 may be configured to update the insensitive areas for each time at the interval of the predetermined seconds and reconfigures insensitive areas.”; See also Detailed Description, [0095-0100]).
It would have been obvious to one of ordinary skill in the art to have modified the processor within Lee to include the teachings of Yoshida to be configured to receive an electronic device form factor state and/or grip state from a sensor module and learn a touch recognition area according to the electronic device form factor state and/or grip state to dynamically adjust a threshold value of a force touch recognition model configured to determine skin compression. The motivation to combine these arts is to distinguish holding forms from input forms within touch sensitive devices (See Yoshida, Summary, [0008-0014]). The fact that both Lee and Yoshida are directed towards the same problem to be solved, namely detecting erroneous or inadvertent touches onto touch display devices, makes this combination more easily implemented.
Regarding claim 3, Lee in combination with Yoshida discloses every element of claim 2, and Lee further discloses wherein the force touch recognition model is configured to receive touch data as input data and output force touch data, based on an artificial intelligence network (Figs. 4, S1100 through S1500; Detailed Description, [0099-0106], “The touch control circuit 133 or the server 300 that has received the data related to or the touch input may also input the related elements of the additional touch inputs, for example, a touch intensity (pressure sensitive type touch screen), a touch temperature, and a touch distance additionally or independently in order to learn the AI model for determining the false touch….The AI model learner 134 of the false touch determining device 100 or the server 300 that has received the data related to the touch input applies the input data to the learned AI model (operation S1400)...The learned AI model outputs a touch type of whether the detected touch input is a normal touch or a false touch (operation S1500). In another embodiment, the learned AI model may output three classifications of whether the detected touch input is a normal touch, a ghost touch, or an obscure touch”).
Thus, it would have remained obvious to have combined Lee and Yoshida in the manner described in clam 2.
Regarding claim 5, Lee in combination with Yoshida discloses every element of claim 3, and Lee further discloses wherein at least one processor, individually and/or collectively, is configured to, based on a user's touch release occurring in a state where the force touch situation UI is displayed or after the force touch being recognized, record the malfunction situation and, based on the malfunction situation being repeated a configured N or more times, recognize that sensitivity adjustment of the force touch is required (Detailed Description, [0080-0098], “The AI model learner 134 may generate the AI model by using supervised learning, but learn the normal/false touch classifying engine by using unsupervised learning or reinforcement learning. For example, the AI model learner 134 may learn the normal/false touch classifying engine through a machine learning algorithm of classification or regression analysis, and deep neural networks of a DNN, a CNN, and a RNN. Learning of the normal/false touch classifying engine by the classifying supervised learning is described in FIGS. 5A to 5E, and the neural network learning based on reinforcement learning is described in FIGS. 7A to 7C….In an embodiment of the present disclosure, a false touch calibrating scheme may be at least one of a noise reduction filter adding scheme, a touch sensitivity adjusting scheme, or a touch frequency changing scheme. The noise reduction filter adding scheme may include a software component that calibrates a touch error by applying at least one noise reduction filter according to the number of error occurrences. The touch sensitivity adjusting scheme may also include a software component that calibrates a touch error by adjusting the touch sensitivity according to the number of error occurrences. In order to solve the noise problem, the frequency changing scheme allows a controller to filter out the noise of a common mode and avoid the noise problem by using a frequency hopping scheme.”).
Thus, it would have remained obvious to have combined Lee and Yoshida in the manner described in clam 2.
Regarding claim 12, this is met by the rejection to claim 3 with the combination of Lee and Yoshida.
Other References
The following references are also cited as relevant on the PTO-892 but may not be specifically relied upon with this Action: Pope et al. (US 2022/0066604 A1); Rosenberg et al. (US 2019/0018544 A1);
Whitt III et al. (US 10,013,030 B2)
Conclusion
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/KWIN XIE/Primary Examiner, Art Unit 2626