DETAILED ACTION
This action is in response to the original filling on 07/26/2024. Claims 1-20 are pending for examination.
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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “touch sensor configured to”, “touch sensor configured to”, “model configured to” in Claim 1, “computing devices are configured to” in Claims 3-5, “input sensor configured to”, and “machine-learned model configured to” in Claim 13, “computing devices are configured to” in Claims 16-18, and “machine-learned model configured to” in Claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over HWANG et al. (US 20220334670 A1, hereinafter Hwang) in view of Sensarn (US 11435836 B1, hereinafter Sensarn).
Regarding Claim 1, Hwang teaches an interactive object (Paragraph [0002] Embodiments of the disclosure relate to an electronic device for recognizing a user's touch, and an operating method of the electronic device.) comprising:
a touch sensor configured to generate sensor data in response to touch inputs; and (Paragraph [0044] In embodiments of the disclosure, a touch type of a touch input of a user includes a plurality of touch types. For example, the touch type of the touch input of the user may include a force touch type. Alternatively or additionally, the force touch may be identified based on a measurement of the strength applied by the user to the electronic device that may be measured using a pressure detection sensor in a non-limiting example. (Using a touch sensor to derive information (sensor data) from a touch input. Furthermore, paragraphs [0076]-[0078] describe application of labels to touch data through use of a pressure sensor (touch sensor which determines force of a touch)))
one or more computing devices configured to: input, to a machine-learned model configured to generate gesture inferences based on touch inputs to the touch sensor, sensor data associated with a first touch input to the touch sensor; (Paragraph [0042] In embodiments of the disclosure, touch data indicates data obtained from a touch input of a user via a touchscreen of an electronic device. Paragraph [0054] In an embodiment of the disclosure, the electronic device 2000 selects a plurality of touch recognition models in order to identify a touch type of the obtained touch data, and identifies the touch type of the touch input of the user by applying the obtained touch data to the selected touch recognition models. Paragraph [0096] The processor 2500 inputs first type sample touch data and second type sample touch data, obtained from the user, to the plurality of touch recognition models 2414, 2416 and 2418, and thus, outputs sample touch type probability values indicating a result of inference with respect to a touch type of the sample touch data. (An electronic (computing) device which acquires touch data from a user, then inputs that data into a machine learning model in order to generate an inference of the received touch type))
generate, based on a first output of the machine-learned model in response to the sensor data associated with the first touch input, first inference data indicating a negative inference corresponding to a first gesture; (Paragraph [0145] For example, the plurality of touch recognition models 620 and the base touch recognition model 626 may be AI models configured to receive an input of touch data of a user and to output a probability of a user's touch input being a first type touch. In another example, the plurality of touch recognition models 620 and the base touch recognition model 626 may be AI models configured to receive an input of touch data of a user and to output a result of inference with respect to a touch type of a user's touch input. Paragraph [0194] For example, an actual touch type label value of the first type sample touch data 1002 may be a ‘first type’ that is the touch type guided to the user by the electronic device 2000, and the electronic device 2000 may calculate how accurately the touch recognition model A 1011 identifies a touch type of the first type sample touch data 1002 as the ‘first type’, based on ‘first type’ that is a true label value indicating an actual touch type of the first type sample touch data 1002. Paragraph [0258] In another example, when the difference between the touch area of the sample touch data and the touch area of the real-use touch data is equal to or greater than the threshold value, the electronic device 2000 may determine that the real-use touch data and the sample touch data are not similar. When it is determined that the real-use touch data and the sample touch data are not similar, the electronic device 2000 may perform operation S1650. Paragraph [0261] In operation S1650, the electronic device 2000, according to an embodiment of the disclosure, labels the real-use touch data to a third label value different from a touch type of the sample touch data. (Using a touch recognition model (machine learning model) to generate inference data regarding a first touch input, including a determination of accuracy of the calculated inference by the model. This accuracy score is then used to determine which data inputs are viable candidates to update a touch recognition model (paragraph [0110]). The device also determines (i.e. makes an inference) that a real-use touch data type does not correspond (i.e. is a negative inference) to a sampled (stored) touch data type))
store the sensor data associated with the first touch input; (Paragraph [0101] If or when the touch input of the user is maintained, the processor 2500 obtains, at preset time intervals, touch data about an area in which the touch input of the user is received via a touchscreen. Touch data obtained at preset time intervals stored in the electronic device 2000 and/or a database of a server (not shown). (Storage of touch data))
input, to the machine-learned model, sensor data associated with a second touch input to the touch sensor, the second touch input being received by the touch sensor within a predetermined period after the first touch input; (Paragraph [0058] In some embodiments, the touch type of the touch input of the user identified by the electronic device 2000 may include a first type touch and a second type touch. Paragraph [0101] If or when the touch input of the user is maintained, the processor 2500 obtains, at preset time intervals, touch data about an area in which the touch input of the user is received via a touchscreen. Paragraph [0120] For example, as shown in the first graph 310, a touched area of a touch input of a first user may be input as a pattern in which the touched area is increased in a first time period (e.g., [0 ms, 5 ms]) and a second time period (e.g., [5 ms, 10 ms]) in which the touch input is received, and then is gently increased as an increase in the touched area is decreased in a time period thereafter. (A second touch type is recorded as input to the haptic sensor after a period of time))
train the machine-learned model based at least in part on the training data. (Paragraph [0043] In embodiments of the disclosure, a touch dataset indicates a set of a plurality of items of touch data. For example, a set of a plurality of items of sample touch data to be described below in embodiments may be referred to as a sample touch dataset, and a set of a plurality of items of training touch data to be described below in embodiments may be referred to as a training touch dataset. The plurality of items of touch data included in the touch dataset may indicate a plurality of items of touch data to which touch types are respectively labeled. Accordingly, as the touch types are labeled to the plurality of items of touch data included in the touch dataset, the plurality of items of touch data may be used to verify or train a touch recognition model. (A touch recognition model which is trained using obtained touch data))
Hwang does not teach: generate, based on an output of the machine-learned model in response to the sensor data associated with the second touch input, second inference data indicating a positive inference corresponding to the first gesture; in response to generating the positive inference subsequent to the negative inference, generate training data that includes at least a portion of the sensor data associated with the first touch input and one or more annotations that indicate the first touch input as a positive training example of the first gesture;
In the same field of endeavor, Sensarn teaches: generate, based on an output of the machine-learned model in response to the sensor data associated with the second touch input, second inference data indicating a positive inference corresponding to the first gesture; (Col. 3 Lines 64-67 The device 110 may detect (148) at least one gesture, for example by processing the output of the at least one trained model, for example by a tap manager 222, gesture manager 422, or other component Col. 4 Lines 43-67 As shown in FIGS. 2 and 3 the tap detector 120 may employ a first high-pass filter (HPF) 212 that receives (310) the motion data 211 and determines (312) first filtered motion data 213. The tap detector 120 may also employ a second HPF 214 that receives (310) the motion data 211 and determines (314) second filtered motion data 215. The first filtered motion data 213 may be processed by a trigger 216 to determine (318) if the motion data includes data above an amplitude trigger, which would result in an attempt to detect a tap input. The second filtered motion data 215 may be stored by a buffer 218 for eventual processing by downstream component(s) to determine whether a tap input is detected. (Generation of an inference of a gesture based on an output from a machine learning model (Col. 3 Lines 64-67). A tap (from a physical touch sensor) is detected at a first stage of input. Importantly, it is unknown at this stage if the first detected input corresponds to a tap (negative inference). Then, a second tap is inputted, confirming (by a machine learning model) that the first input was of the correct tap type (a positive inference which confirms an earlier negative)))
in response to generating the positive inference subsequent to the negative inference, generate training data that includes at least a portion of the sensor data associated with the first touch input and one or more annotations that indicate the first touch input as a positive training example of the first gesture; and (Col. 7 Lines 54-59 Thus the tap manager may determine whether a particular detected tap input may be considered part of a tap gesture by detecting a tap input, waiting a period of time (which may be a configurable number of milliseconds), and if another tap input (or tap inputs) is/are detected may determine the tap gesture corresponding to the number of tap inputs. Col. 10 Lines 37-59 The gesture detector CNN 420 may include a multi-class/universal classifier trained to process motion data and to determine if the motion data represents or does not represent one or more of a plurality of gestures. The particular and number of gestures the gesture detector CNN 420 is trained to detect is configurable. The gesture detector CNN 420 may be trained on data that includes many different examples of gestures and non-gestures as gathered from training operations resulting from the use of training devices by many different individuals of different age ranges, under different situations, wearing different devices on different hands, and under a variety of other conditions. The gesture detector CNN 420 may be configured to recognize any gestures that it is trained to recognize, even if such gestures may not be active at a particular time. The gesture manager 422 may track which gestures are active and if the gesture detector CNN 420 indicates detection of a gesture that is not active, the gesture manager 422 may simply ignore that detection and take no further action, whereas detection of an active gesture may result in the gesture manager 422 taking further action such as confirming (522) the gesture response and outputting gesture output data 430 indicating detection of the active gesture. (Respondent to a positive inference generated after a negative inference (Col. 7 Lines 54-59), the gathering of training data comprising sensor data and corresponding inferences occurs, including performing and non-performing gesture examples (positive and negative inferences) with positive examples confirmed (labeled as positive).))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have combined generating second inference data indicating a positive inference as taught by Sensarn into Hwang as both are in the same field of gesture recognition when served a form of touch inputs and doing so would be desirable in order to meet the needs of the increasing variety of touch sensitive user devices, as the increasing complexity of gesture detection poses new challenges in terms of reliability and accuracy (Sensarn Col. 1-2 Lines 5-15)
Regarding Claims 13 and 19-20, Claims 13 and 19-20 are device and method claims similar to the interactive object of Claim 1. As such, they are rejected for the same reasons above.
Regarding Claim 2, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the negative inference indicates non- performance of the first gesture based on the first touch input. (Hwang Paragraph [0086] For example, when the probability value is equal to or greater than the threshold value and thus it is determined that a touch type identification result from the base touch recognition model 2412 is reliable, the processor 2500 may identify the touch type, based on the probability value output from the base touch recognition model 2412. Also, when the output probability value is less than the threshold value and thus it is determined that the touch type identification result from the base touch recognition model 2412 is unreliable, the processor 2500 may select, according to a preset criterion, touch recognition models included in the plurality of touch recognition models 2414, 2416 and 2418 to identify the touch type of the touch input of the user. (A probability value which identifies a touch input as being unrecognized, comprising non-performance))
Regarding Claim 3, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the one or more computing devices are configured to generate the training data that includes at least the portion of the sensor data associated with the first touch input and (Hwang Paragraph [0043] The plurality of items of touch data included in the touch dataset may indicate a plurality of items of touch data to which touch types are respectively labeled. Accordingly, as the touch types are labeled to the plurality of items of touch data included in the touch dataset, the plurality of items of touch data may be used to verify or train a touch recognition model. (Generation of model training data using touch sensor inputs))
the one or more annotations that indicate the first touch input as a positive training example of the first gesture in response to generating the positive inference subsequent to two or more negative inferences within the predetermined period (Hwang Paragraph [0097] The processor 2500 calculates accuracy of the sample touch type probability values indicating the result of inference with respect to the touch type of the sample touch data. Paragraph [0147] Label values of the plurality of items of touch data included in the touch dataset 616 of the entire time period may be true label values labeled using a pressure sensor and indicating whether respective items of touch data are a force touch type or another touch type. Paragraph [0261] In operation S1650, the electronic device 2000, according to an embodiment of the disclosure, labels the real-use touch data to a third label value different from a touch type of the sample touch data. When it is determined that real-use touch data has a low similarity to the plurality of items of sample touch data, the electronic device 2000 may determine that the real-use touch data is touch data being input by a finger which is not stored in the electronic device 2000. For example, the electronic device 2000 may not determine a touch type of the input real-use touch data, and thus, may label the real-use touch data to a label value of other types. (Annotations (labels) which identify sensor inputs as positive (true label) training examples based on a calculated probability (the result of an inference). Some training data comprising unknown touch types which are least similar to a particular desired touch type (in the example given, data which is least similar to a force-touch type), comprising negative inferences))
the two or more negative inferences comprising the first inference data and at least one additional negative inference. (Sensarn Col. 6 Lines 45-60 The data used to train the tap detector CNN 220 may include many different examples of tap inputs and non-tap inputs as gathered from training operations resulting from the use of training devices by many different individuals of different age ranges, under different situations, wearing different devices on different hands, and under a variety of other conditions. (Training data which comprises both positive and negative examples))
Regarding Claims 16, Claims 16 is a device claim similar to the interactive object of Claim 3. As such, they are rejected for the same reasons above.
Regarding Claim 4, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the first inference data includes a confidence value associated with the first gesture, and (Hwang Paragraph [0086] For example, when the probability value is equal to or greater than the threshold value and thus it is determined that a touch type identification result from the base touch recognition model 2412 is reliable, the processor 2500 may identify the touch type, based on the probability value output from the base touch recognition model 2412. (The first probability (inference result) has an associated confidence threshold which serves to identify it as a given touch type))
wherein the one or more computing devices are configured to: determine that the confidence value associated with the first gesture is below a first confidence value threshold; (Hwang Paragraph [0086] Also, when the output probability value is less than the threshold value and thus it is determined that the touch type identification result from the base touch recognition model 2412 is unreliable, the processor 2500 may select, according to a preset criterion, touch recognition models included in the plurality of touch recognition models 2414, 2416 and 2418 to identify the touch type of the touch input of the user. (Calculating that the inference is below a confidence threshold))
in response to determining that the confidence value associated with the first gesture is below the first confidence value threshold, generate the negative inference. (Hwang Paragraph [0261] In operation S1650, the electronic device 2000, according to an embodiment of the disclosure, labels the real-use touch data to a third label value different from a touch type of the sample touch data. When it is determined that real-use touch data has a low similarity to the plurality of items of sample touch data, the electronic device 2000 may determine that the real-use touch data is touch data being input by a finger which is not stored in the electronic device 2000. For example, the electronic device 2000 may not determine a touch type of the input real-use touch data, and thus, may label the real-use touch data to a label value of other types. (Determining that the input is not the same as a stored input, and then identifying it as a negative example of the first stored input))
Regarding Claims 17, Claims 17 is a device claim similar to the interactive object of Claim 3. As such, they are rejected for the same reasons above.
Regarding Claim 5, the combination of Hwang and Sensarn teaches all the limitations of Claim 4, including: wherein the one or more computing devices are configured to generate the training data that includes at least the portion of the sensor data associated with the first touch input and (Hwang Paragraph [0100] In an embodiment of the disclosure, the processor 2500 executes the training data collection module 2440, thereby obtaining touch data corresponding to a touch input of a user when the touch input of the user is received. (Generating training data with at least a portion of it being from input of a user))
the one or more annotations that indicate the first touch input as a positive training example of the first gesture further in response to the confidence value associated with the first gesture being above a second confidence value threshold, (Hwang Paragraph [0043] Accordingly, as the touch types are labeled to the plurality of items of touch data included in the touch dataset, the plurality of items of touch data may be used to verify or train a touch recognition model. Paragraph [0185] The electronic device 2000, according to an embodiment of the disclosure, may update a threshold value for identifying a touch type of the user, based on the sample touch data. Paragraph [0239] In order to update touch recognition models using the plurality of items of obtained real-use touch data, the electronic device 2000 labels a label value indicating a touch type to the plurality of items of obtained real-use touch data, and thus, generates training touch data. Paragraph [0263] The electronic device 2000 determines a final label value of the training touch data 1700, based on the obtained touch type probability value. Paragraph [0273] The electronic device 2000, according to an embodiment of the disclosure, identifies training touch data having high reliability by evaluating reliability of training touch data, and updates a touch recognition model using the identified training touch data having high reliability. (The first touch inputs are identified as reliable training examples after surpassing a threshold))
the second confidence value threshold being less than the first confidence value threshold. (Hwang Paragraph [0304] That is, the electronic device 2000 improves accuracy of touch type identification by updating a threshold value for identifying a touch type from the existing threshold value 2014 to the modified threshold value 2019. (The device updates an existing (first) threshold to an updated (second) threshold. This is done in order to find the optimum threshold to identify the type of touch being used as input, and this second threshold is lowered or raised accordingly.))
Regarding Claims 18, Claims 18 is a device claim similar to the interactive object of Claim 3. As such, they are rejected for the same reasons above.
Regarding Claim 6, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the negative inference includes inference data indicating non-performance of any gesture that the machine-learned model is trained to recognize. (Sensarn Col. 5 Lines 46-67 If the motion data is above the amplitude trigger (318:Yes), the device 110 may determine if (320) a trained model detects a tap input using the buffered samples. The specific amplitude trigger is configurable by the system but may be a threshold value that is stored by the device 110. The trigger component 216 may also be configured to use different threshold values where at least one tap input in a tap gesture must exceed a higher threshold to prevent an error in gesture detection for double and triple taps as compared to single taps. (The negative inference is one wherein the tap input (which the model is trained to recognize) fails to surpass an internal threshold))
Regarding Claim 7, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the predetermined period is a predetermined period of time immediately prior to the second touch input. (Sensarn Col. 6-7 Lines 61-6 The output of the tap detector CNN 220 (which may include data indicating the likelihood a tap was detected) may be processed by the tap manager 222. The tap manager 222 may operate a quasi-state machine or perform other operations to determine when a detected tap input corresponds to a tap gesture, when multiple tap inputs combined result in a multi-tap gesture (e.g., a double-tap), or when a gesture is concluded. For example, the tap manager 222 may track how long it has been since a previous tap input, if a second (or third) tap input is detected within a certain time window of a previous (e.g., first or second) tap, manage a tap counter (for example by incrementing or clearing the tap counter), and/or performing other operations. (The output is determined via a series of inputs received within a certain predetermined period of time))
Regarding Claim 8, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the predetermined period is a period since a most recent positive inference was received. (Sensarn Col. 7 Lines 7-32 If the score exceeds the threshold, the tap manager 222 may determine that a tap input has occurred. Alternatively, the output of the tap detector CNN 220 may simply be a binary indicator that a tap input was detected. If a tap input was not detected (320:No) the tap manager 222 may start a tap cooldown 322 during which the amplitude trigger may be disabled to avoid restarting processing by the tap detector CNN 220. Such a cooldown limits the number and frequency of CNN inferences. The time of the cooldown is configurable. If a tap input was detected (320:Yes) the tap manager 222 may increment a tap counter (324) and start a further tap timer 326. (The time period is one wherein it is a measurement of a last received confirmed (positive inference) input))
Regarding Claim 9, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the machine-learned model is trained based on a periodic schedule. (Hwang Paragraph [0290] The electronic device 2000 may calculate the class weight using the validation dataset/training dataset. Also, the electronic device 2000 may perform fine-tuning on a touch recognition model by re-training the touch recognition model, based on the calculated class weight. Until training results are converged, the electronic device 2000 may repeat a process of calculating the class weight, and performing fine-tuning on the touch recognition model. (The model is trained periodically until training results converge))
Regarding Claim 10, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the sensor is a capacitive touch sensor. (Hwang Paragraph [0003] As an input means via which an electronic device can recognize a touch input from a user, a user interface such as a touchscreen, a touchpad, or the like is provided. (One embodiment of the sensor is a touch screen, which is a capacitive touch sensor))
Regarding Claim 11, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the interactive object is deformable. (Hwang Paragraph [0067] In other embodiments, the display 2310 may include at least one of a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode display, a flexible display, a three-dimensional (3D) display, or an electrophoretic display. (One aspect of the device includes a deformable flexible screen))
Regarding Claim 12, the combination of Hwang and Sensarn teaches all the limitations of Claim 1, including: wherein the machine-learned model comprises a convolutional neural network or a recurrent neural network. (Sensarn Col. 2 Lines 37-46 As described herein, data output from the accelerometer may be filtered and processed (for example by one or more trained models/neural networks such as a convolutional neural network (CNN)) to detect when a gesture has been performed. (The neural network is a CNN (convolutional network)))
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Hwang in view of Sensarn, as applied in Claim 13, and in further view of Zhang et al. (US 12154011 B2, hereinafter Zhang).
Regarding Claim 14, the combination of Hwang and Sensarn teaches all the limitations of Claim 13. Hwang and Sensarn do not teach: wherein the input sensor comprises a RADAR sensor and wherein the first user input and the second user input comprise a first touch-free gesture input and a second touch-free gesture input.
In the same field of endeavor, Zhang teaches: wherein the input sensor comprises a RADAR sensor and wherein the first user input and the second user input comprise a first touch-free gesture input and a second touch-free gesture input. ((Col. 4 Lines 32-43) With all of the embodiments described herein, the imaging sensors 102 may be any suitable sensors for capturing images. For example, the imaging sensors 102 may include, without limitation, infrared (IR) imaging sensors, thermal imaging sensors, charge-coupled devices (CCD's), complementary metal oxide semiconductor (CMOS) devices, active-pixel sensors, radar imaging sensors, fiber optics, and other known imaging device technology (A RADAR input sensor, which is touch free)).
It would have been obvious before the effective filing date of the claimed invention to have combined touch free radar imaging sensors to capture gesture information as taught by Zhang into the combination of Hwang and Sensarn as all three are in the same field of gesture recognition and machine learning, and doing so would be desirable in order to allow for non-obtrusive imaging techniques when recognizing gestures (Zhang Col. 1 Lines 15-35).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gillian et al. (US 20230061808 A1) discusses gesture recognition.
Xu et al. (US 20240296697 A1) discusses recognition of gestures of the hand and updating/making inferences of a prior inference.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at (571) 272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
