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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
Status of Claims
Claims 1-19 are currently pending and are being hereby examined herein. Claims 1-3, 5, 7-9, and 11-12 are amended. Claims 13-19 are new.
Response to Amendments / Remarks / Declaration
Any reference to the prior office action refers to the non-final rejection dated 13 November 2025.
The objections from the prior office action not listed below are withdrawn.
The rejections under 35 U.S.C. 112(b) from the prior office action that are not listed below are withdrawn. Responses to specific arguments are as follows: the rejection under 35 U.S.C. 112(b) for the indefiniteness of “a neural network operator” has been withdrawn due to “a general-purpose computing on graphics processing unit (GPGPU)” being structure of hardware. The rejection under 35 U.S.C. 112(b) for the indefiniteness of including both “a collision of a robot manipulator…” and “a collision between the plurality of joints an external object” in Claim 1 has been withdrawn due to Applicant’s persuasive arguments. However, a 35 U.S.C. 112(b) rejection for Claim 1 (and the claims that depend on Claim 1) remains because no arguments nor amendments were found to overcome Examiner’s statements that “it is indefinite if only a collision at a joint (i.e., not a collision with a shaft) is the intent of this claim limitation”. The rejection under 35 U.S.C. 112(b) for “the cycle” and “the defined cycle” are withdrawn due to Applicant’s clarifying statements. Applicant’s arguments regarding the rejections under 35 U.S.C. 112(b) of the term “a collision” in Claims 9-11 are not persuasive; one of ordinary skill in the art would not be able to determine the metes and bounds of the claims because they would not know if the limitations refer specifically to the collisions detected in Claim 1 and should have antecedent basis from Claim 1. Examiner has suggested language below to overcome the rejections
The rejections under 35 U.S.C. 101 from the prior office action are withdrawn because each of the claims, as currently presented, is interpreted to not recite an abstract idea and/or recite significantly more than any recited abstract idea and/or provide a practical application to any recited abstract idea.
The declaration under 37 CFR 1.132 filed 13 February 2026 is sufficient to determine there is a 35 U.S.C. 102(b)(1)(A) exception for the reference “Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach” (Heo et al.).
The arguments regarding the prior art rejections from the prior office action are moot because the prior art rejections presented in this office action do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new rejections were necessitated by amendments changing the scope of the claims.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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), 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) 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 limitation(s) is/are:
A neural network operator in Claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Based on structure for “neural network operator” found in paragraph [39], Examiner is interpreting this as “a general-purpose computing on graphics processing unit (GPGPU)” or equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
Claim Objections
The claims are objected to for the following informalities:
Claim 1: “to generalize the neural network” should be “to improve generalization performance of
Claim 3: “from the first waypoint” should be “from a[[the]] first waypoint”.
Claim 3: “the last waypoint” should be “a[[the]] last waypoint”.
Claim 5: “plurality of the joint actuators” should be “the plurality of the joint actuators”.
Claim 6: An Oxford comma is missing after the word “velocity”.
Claim 15: “the collision” should be “the collision between the robot manipulator and the external object” (this change would be consistent with the proposed amendments to overcome rejections under 35 U.S.C. 112(b) below, other changes are necessary if Examiner’s proposed changes are not incorporated).
Claim 16: “a plurality of designated waypoints” should be “[[a]] the plurality of designated waypoints”.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-11 and 13-18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1, and Claims 2-11 and 13-18 which depend from Claim 1, are rejected under 35 U.S.C. 112(b) because, in view of the specification, one of ordinary skill in the art would find it indefinite if only a collision at a joint (i.e., not a collision with a shaft) is the intent of the limitation “detect a collision between the plurality of joints and an external object”. Appropriate corrections are required.
Claim 9-11 and 18 are further rejected under 35 U.S.C. 112(b) because “a collision” (used twice in Claim 9, once in Claim 10, and once in Claim 11, once in Claim 18) is indefinite. One of ordinary skill in the art would not be able to determine if these refer should be “the [[a]] collision…” (with antecedent basis from Claim 1) or something else. For the purposes of compact prosecution, the examiner will assume these can refer to either of the collisions mentioned in Claim 1 or separate collisions. Appropriate corrections are required.
Examiner suggests the following amendments that would overcome the rejections under 35 U.S.C. 112(b) (Examiner notes these amendments would change the scope of the claims):
Claim 1: change “detect a collision between the plurality of joints and an external object” to “detect a collision between the robot manipulator and an external object”.
Claim 9: change “a true signal when a collision occurs, and a false signal when a collision does not occur” to “a true signal when the collision between the robot manipulator and the external object is detected the collision between the robot manipulator and the external object is not detected
Claims 10 and 18: change “a collision” to “a training collision”.
Claim 11: change “a collision occurs” to “the [[a]] collision between the robot manipulator and the external object occurs”.
Claim Rejections - 35 USC § 102 / Claim Rejections - 35 USC § 103
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 13 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over “Manipulator Collision Detection and Collided Link Identification Based on Neural Networks” (Sharkawy et al., hereinafter, Sharkawy) first available online 29 September 2018 (first cited as additional relevant art the prior office action).
Regarding Claim 1, Sharkawy discloses A system for detecting a collision of a robot manipulator based on an artificial neural network (see at least abstract), the system comprising:
a plurality of joint actuators provided in a plurality of joints of the robot manipulator and configured to actuate each of the plurality of joints (see at least Fig. 1: KUKA LWR);
a plurality of encoders provided at respective ones of the plurality of joint actuators and configured to measure angles of the plurality of joints, respectively (see at least section 1 on page 4 and Fig. 1: joint position sensors); and
a neural network operator configured to train the artificial neural network and perform inference to detect a collision between the plurality of joints and an external object based on a preprocessing operation, wherein the preprocessing operation comprises cycle normalization to generalize the artificial neural network (see at least section 2 on page 5, section 3 on page 6, section 4 on page 6, Fig. 1, and Fig. 2: a collision of a hand between the first and second joint is detected by the neural network; the neural network is trained on sinusoidal motion signals; some of the signals are for training, some for validation, some for testing; the same motion is repeated with and without collisions; additional joint ranges are used for generalization; the current and previous position error are preprocessed inputs).
Either it is inherent or it would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, for the neural network operator of Sharkawy to be a GPGPU or equivalent thereof (with the motivation of quick processing on a system known in the art).
Regarding Claim 2, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses wherein the cycle normalization defines a plurality of designated waypoints (see at least section 2 on page 5, section 3 page 6, and section 4 on page 6: the current position and the past position would both be known to the processing of current position error and previous position error; the training data includes motion without a collision and the same motion with a random collision).
Regarding Claims 3 and 4, Sharkawy discloses all the limitations of Claim 2. Furthermore, Sharkawy discloses “the same motion is performed” both with and without collisions (see at least section 3 on page 6) therefore, wherein one cycle is defined as a path for movement of going through the plurality of designated way points from the first waypoint to the last waypoint one after another and returning back to the first waypoint and further comprising a memory configured to store information related to the cycle, wherein an entire signal of the defined cycle is defined as a reference cycle and stored in the memory would have been inherent or obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art (with the motivation of defining the data already being used).
Regarding Claim 5, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses further comprising a controller configured to apply a signal related to a control input and perform an operation to control plurality of the joint actuators (see at least section 2 on page 4: KUKA Robot Controller).
Regarding Claim 6, Sharkawy discloses all the limitations of Claim 5. Furthermore, Sharkawy discloses wherein the signal comprises a joint angle, a joint angular velocity and a control torque, which are obtained in every control cycle (see at least section 2 on page 5 and Fig. 2: inputs include angular joint velocity, measured joint torque, angular position error).
Regarding Claim 7, Sharkawy discloses all the limitations of Claim 6. Furthermore, Sharkawy discloses wherein the signal is used as an input to the artificial neural network, and a sliding technique is used to analyze a pattern of the signal (see at least section 2 on page 5 and Fig. 2: the inputs include the current and previous position error (i.e., “sliding technique”)).
Regarding Claim 8, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses wherein the artificial neural network learns about a control signal through supervised learning based on learning data in which a signal and a label form a pair (see at least page section 3 on page 6: two sets of data, with collision and without collision).
Regarding Claim 9, Sharkawy discloses all the limitations of Claim 8. Furthermore, Sharkawy discloses wherein the artificial neural network is configured to output a binary signal comprising a true signal of when a collision occurs, and a false signal of when a collision does not occur (see at least section 2 on page 4 and Table 1: detects a collision).
Regarding Claim 10, Sharkawy discloses all the limitations of Claim 8. Furthermore, Sharkawy discloses wherein learning about the control signal comprises measuring a collision based on contact with a pressure sensor or force sensitive resistor (FSR) provided at one side of the plurality of joints (see at least Fig. 1, section 4 on page 7, and Fig. 3: evaluation of force sensor versus neural network).
Regarding Claim 13, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses wherein each of the plurality of joints includes a respective joint actuator of the plurality of joint actuators and a respective encoder of the plurality of encoders provided at one side of the respective joint actuator, the respective encoder configured to measure an angle of the respective joint (see at least section 1 on page 4 and Fig. 1: KURA LWR manipulator).
Claims 11, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sharkawy.
Regarding Claim 11, Sharkawy discloses all the limitations of Claim 8. Furthermore, Sharkawy discloses receiving an output value from a neural network, determining a collision threshold, and evaluating the output value of the neural network to the collision threshold to determine if a collision occurred (see section 2 on page 4, section 2 on page 5, and Fig. 2). Based on Sharkawy, wherein a last layer among layers of the artificial neural network comprises a probability value between 0 and 1, and the neural network operator identifies that a collision occurs when the probability value is higher than or equal to 0.5 based on inference of the artificial neural network would have been obvious, with a reasonable expectation of success, to one having ordinary skill in the art at the time the invention was made, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980). The instant application provides no unexpected results based on this limitation, and provides 0.5 as merely an example in one embodiment (see paragraph [87] of the specification).
Regarding Claim 16, Sharkawy discloses all the limitations of Claim 2. Furthermore, Sharkawy has position error as an input to the neural network. Here, Sharkawy includes “current position error …between the designated and actual joint position” (see section 2 on page 3), while Sharkawy appears to be subtracting the actual position from the commanded position as the input, there are three positions available for the motion cycle: the commanded position, the position without collision, and the position with collision (see section 3 on page 6) and therefore, one of ordinary skill in the art would find it obvious to try subtracting any combination of the position values for the same point as inputs, so wherein the cycle normalization comprises storing a reference cycle signal corresponding to robot signals obtained during a first motion cycle through a plurality of designated waypoints, and computing a difference between an execution cycle signal and the reference cycle signal at corresponding timing positions to produce a normalized signal for input to the artificial neural network would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of trying different inputs through experiments and trials to determine the best output results (see Sharkawy page 5).
Regarding Claim 18, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses “the collisions are performed randomly by the human hand touching the end-effector and the link between the two joints” (see at least section 2 on page 4) which explicitly discloses wherein the artificial neural network is trained through supervised learning based on learning data obtained by randomizing timing at which a collision occurs during motion of the robot manipulator…, the learning data comprising pairs of robot signals and collision labels obtained from the collision measurer (see at least section 2 on page 4 and FIG. 1: “the collisions are performed randomly”); furthermore, randomizing…a motion program of the plurality of joints, an attached position of a collision measurer would have been obvious to try before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art with the motivation of providing training data that covered a wider variety of scenarios by randomizing some of the finite number of user-changeable data generation inputs (see section 2 on page 4).
Claims 12, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharkawy in view of U.S. Pub. No. 2020/0376666 (Briquet-Kerestedjian et al., hereinafter, Briquet-Kerestedjian).
Regarding Claim 12, Sharkawy discloses A method of detecting a collision of a robot manipulator based on an artificial neural network (see at least abstract), the method comprising:
defining a plurality of waypoints and allowing a neural network operator to perform a preprocessing operation through cycle normalization (see at least section 2 on page 5, section 3 on page 6, section 4 on page 6, Fig. 1, and Fig. 2: a collision of a hand between the first and second joint is detected by the neural network; the neural network is trained on sinusoidal motion signals; some of the signals are for training, some for validation, some for testing; the same motion is repeated with and without collisions; additional joint ranges are used for generalization; the current and previous position error are preprocessed inputs);
obtaining a control signal from a collision measurer; training the artificial neural network based on the control signal (see at least section 1 on page 4: “For the training of the NN, the measurements of the collision force mapped to the joints torque is required”).
Sharkawy does not explicitly disclose in response to the trained artificial neural network detecting a collision in real time, performing control by applying a control input to at least one joint actuator to stop motion of the robot manipulator.
Briquet-Kerestedjian, in the same field of robotics, and therefore analogous art, teaches in response to the trained artificial neural network detecting a collision in real time, performing control by applying a control input to at least one joint actuator to stop motion of the robot manipulator (see at least [0031] and [0047]-[0055]: “The robot system may further comprise a controller for controlling motion of the robot arm, the mode of operation of which is variable depending on an operation condition signal output by the neural network”; “an emergency stop mode in which the robot arm 1 is immediately brought to a halt”; “The neural network 11 is trained off-line”; “Based on training data which indicate a link in which contact occurred, these output layers 24 can be trained to distinguish between normal operation and a condition where a contact, deliberate or accidental, occurred in link adjacent to their associated joint”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to combine the teachings of Sharkawy with stopping the robot arm when an accidental collision is detected of Briquet-Kerestedjian to enable safe interaction between a human and robot when an accidental contact occurs (see at least Briquet-Kerestedjian [0003]-[0004]).
Regarding Claim 15, Sharkawy discloses all the limitations of Claim 1. Sharkawy does not explicitly disclose further comprising a controller configured to implement a safety stop of the robot manipulator in response to the neural network operator detecting the collision.
Briquet-Kerestedjian, in the same field of robotics, and therefore analogous art, teaches further comprising a controller configured to implement a safety stop of the robot manipulator in response to the neural network operator detecting the collision (see at least [0031] and [0047]-[0055]: “The robot system may further comprise a controller for controlling motion of the robot arm, the mode of operation of which is variable depending on an operation condition signal output by the neural network”; “an emergency stop mode in which the robot arm 1 is immediately brought to a halt”; “The neural network 11 is trained off-line”; “Based on training data which indicate a link in which contact occurred, these output layers 24 can be trained to distinguish between normal operation and a condition where a contact, deliberate or accidental, occurred in link adjacent to their associated joint”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to combine the teachings of Sharkawy with stopping the robot arm when an accidental collision is detected of Briquet-Kerestedjian to enable safe interaction between a human and robot when an accidental contact occurs (see at least Briquet-Kerestedjian [0003]-[0004]).
Regarding Claim 19, the Sharkawy and Briquet-Kerestedjian combination teaches all the limitations of Claim 12. Furthermore, Sharkawy has position error as an input to the neural network. Here, Sharkawy includes “current position error …between the designated and actual joint position” (see section 2 on page 3), while Sharkawy appears to be subtracting the actual position from the commanded position as the input, there are three positions available for the motion cycle: the commanded position, the position without collision, and the position with collision (see section 3 on page 6) and therefore, one of ordinary skill in the art would find it obvious to try subtracting any combination of the position values for the same point as inputs, so wherein the cycle normalization comprises storing a reference cycle signal corresponding to robot signals obtained during a first motion cycle through the plurality of waypoints, and normalizing robot signals in subsequent execution cycles based on a difference between execution cycle signals and the reference cycle signal at corresponding timing position would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of trying different inputs through experiments and trials to determine the best output results (see Sharkawy page 5).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Sharkawy in view of U.S. Pub. No. 2018/0107174 (hereinafter, Takahashi).
Regarding Claim 14, Sharkawy discloses all the limitations of Claim 1. Sharkawy does not explicitly disclose wherein the neural network operator comprises a general-purpose graphics processing unit (GPGPU) configured to execute the artificial neural network.
Takahashi, in the same field of robot controls, and therefore analogous art teaches wherein the neural network operator comprises a general-purpose graphics processing unit (GPGPU) configured to execute the artificial neural network (see at least [0053]: “a general-purpose computer or processor may be used as the machine learning device 3, but using GPGPU (general-purpose computing on graphics processing units), a large-scale PC cluster and the like allow an increase in processing speed”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art to substitute the GPGPU of Takahashi into Sharkawy with the motivation of improving processing speeds (see at least Takahashi [0053]).
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Sharkawy in view of U.S. Pub. No. 2020/0086497 (Johnson et al., hereinafter, Johnson).
Regarding Claim 17, Sharkawy discloses all the limitations of Claim 1. Furthermore, Sharkawy discloses the preprocessing operation further comprises applying a sliding window to robot signals comprising joint angles, joint angular velocities, and control torques to generate an input to the convolutional neural network (See at least section 2 on page 5 and Fig. 2: the sliding window is the current result of angular velocity and torque, and the current and previous errors for joint position angles).
Sharkawy does not explicitly disclose wherein the artificial neural network comprises a convolutional neural network.
Johnson, in the same field of robot controls, and therefore analogous art teaches wherein the artificial neural network comprises a convolutional neural network(see at least [0062], [0068], and [0077]).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art to try the substitution of the specific known model of a convolutional neural network of Johnson for the neural network of Sharkawy because there are a finite number of machine learning models that are known to one of ordinary skill in the art with different benefits for each model, as known by one of ordinary skill in the art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.R.M./Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658
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