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 .
Claims 1, 11, 19, 21 and 24-26 have been amended. Claims 3 and 22 have been canceled. Claims 1-2, 4-21 and 23-26 have been examined.
Response to Arguments/Amendments
Applicant’s arguments, see pp. 8-9, filed 5/4/2026, with respect to 35 USC 101 have been fully considered and are persuasive. The rejection of claims 1-26 under 35 USC 101 has been withdrawn.
Applicant's remaining arguments on pp. 10-15 filed 5/4/2026 have been fully considered but they are not persuasive.
At the top of p. 12, Applicant argues that “There is no disclosure or suggestion by Zhang of assigning different neural networks to different future time steps, nor training them independently.” The rejection cites Zhang, ¶ 0148, “For example, as shown in FIG. 6, the first recurrent unit 622a predicts a vehicle speed 624a at time T0+1, the second recurrent unit 622b predicts a vehicle speed 624b at time T0+2, and the third recurrent unit 622c predicts a vehicle speed 624c at time T0+3.” Applicant has not addressed this citation and has failed to further explain why Zhang fails to meet the claimed limitations. Applicant’s argument is not persuasive.
Applicant continues and argues that Zhang is limited to prediction of vehicle speed, but fails to disclose or suggest estimation of “broader system ‘behavior.’” In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “broader system behavior”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The claims are given their broadest reasonable interpretation. In this case, the prediction of vehicle speed taught by Zhang applies to a broad but reasonable interpretation of the claimed limitation. Applicant’s argument is not persuasive.
Applicant continues on p. 12 of the remarks and argues “Zhang does not disclose or suggest using predicted outputs to control system operation.” However, with respect to former claim 3, the action cites Zhang ¶ 0027, “As another example, the agent may be an autonomous or semi-autonomous vehicle navigating through the environment. In these implementations, the actions may be control inputs to control the robot or the autonomous vehicle.” Applicant has not addressed this citation and has failed to further explain why Zhang fails to meet the claimed limitations. Applicant’s argument is not persuasive.
At the top of p. 14, Applicant argues that “There is no disclosure or suggestion by Zhang of assigning different neural networks to different future time steps, nor training them independently.” The rejection cites Zhang, ¶ 0148, “For example, as shown in FIG. 6, the first recurrent unit 622a predicts a vehicle speed 624a at time T0+1, the second recurrent unit 622b predicts a vehicle speed 624b at time T0+2, and the third recurrent unit 622c predicts a vehicle speed 624c at time T0+3.” Applicant has not addressed this citation and has failed to further explain why Zhang fails to meet the claimed limitations. Applicant’s argument is not persuasive.
Applicant continues and argues “Zhang does not disclose or suggest using predicted outputs to control system operation.” However, with respect to former claim 3, the action cites Zhang ¶ 0027, “As another example, the agent may be an autonomous or semi-autonomous vehicle navigating through the environment. In these implementations, the actions may be control inputs to control the robot or the autonomous vehicle.” Applicant has not addressed this citation and has failed to further explain why Zhang fails to meet the claimed limitations. Applicant’s argument is not persuasive.
Further arguments on pp. 12-14 are based upon previous arguments and are not persuasive for the reasons indicated above.
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 limitation(s) is/are: a controller configured to control operation of the aircraft behavior system in claim 1. Similar limitations are found in claim 11.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation “a controller configured to control operation …” (claims 1 and 11) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. In particular, the specification states the claimed function “control operation” is performed by a controller (see Fig. 2 element 250, Fig. 7 and ¶ 0062). There is no disclosure of any particular structure, either explicitly or inherently, to perform the control. The use of the term controller is not adequate structure for performing operation control because it does not describe a particular structure for performing the function. As would be recognized by those of ordinary skill in the art, the term controller broadly refers to anything that controls an operation and can be performed in any number of ways in hardware, software or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which filter structure or structures perform the claimed function. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As described above, the disclosure does not provide adequate structure to perform the claimed function of removing noise from the appearance signals. The specification does not demonstrate that applicant has made an Invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the Inventor had possession of the claimed Invention.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 11-12, 15-18, 21, 23 and 26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication 20210125076 by Zhang et al. ("Zhang").
In regard to claim 11, Zhang discloses:
1. A vehicle behavior system comprising: a computer system; Zhang Fig. 1, depicting a computer system.
an observation processor in the computer system, Zhang, Fig. 6, elements 606a, 606b and 606c, depicting an observation processor.
wherein the observation processor is configured to: receive observations for a vehicle system, wherein the observations are for a current time; extract features from the observations; Zhang, ¶ 0100, “Given a set of observations (e.g., x1, x2, x3, . . . , xn) and k clusters, a K-means algorithm can partition the data to minimize the sum of intra-cluster variances.” Also ¶ 0103, “each point is a vector of dependent features.” Also Fig. 6, elements 606a, 606b and 606c, depicting feature extraction using observation data 602a, 602b and 602c.
neural network layer systems in the computer system, Zhang, Fig. 6, elements 622a, 622b and 622c, depicting neural networks.
wherein the neural network layer systems are configured to:
receive the features from the observation processor; Zhang, Fig. 6, elements 614a, 614b, 614c and 616 provided to decoder 620.
estimate a behavior for the vehicle system for time steps in response to receiving the features extracted from the observations processed by the observation processor, Zhang, Fig. 6, elements 624a, 624b and 624c. Also ¶ 0148, “Each of the recurrent units 622a, 622b, and 622c may predict vehicle speed v.sub.t as an output (e.g., 624a, 624b, and 624c) at a future time step T.”
wherein each of the neural network layer systems is trained to estimate the behavior for the vehicle system for a different time step in the time steps; and Zhang, ¶ 0148, “For example, as shown in FIG. 6, the first recurrent unit 622a predicts a vehicle speed 624a at time T0+1, the second recurrent unit 622b predicts a vehicle speed 624b at time T0+2, and the third recurrent unit 622c predicts a vehicle speed 624c at time T0+3.”
a controller configured to control operation of the vehicle behavior system using the behavior estimated by the neural networks. Zhang ¶ 0027, “As another example, the agent may be an autonomous or semi-autonomous vehicle navigating through the environment. In these implementations, the actions may be control inputs to control the robot or the autonomous vehicle.”
In regard to claim 12, Zhang also discloses:
12. The vehicle behavior system of claim 11, wherein the neural network layer systems are neural networks. Zhang, Fig. 6, elements 622a, 622b and 622c, depicting neural networks.
In regard to claims 15-17, parent claim 11 is addressed above.
All further limitations of claims 15-17 are addressed in the following rejections of claims 8-10, respectively.
In regard to claim 18, Zhang also discloses:
18. The vehicle behavior system of claim 11, wherein the vehicle system is selected from at least one of a mobile platform, an aircraft, a fighter, a commercial airplane, a tilt-rotor aircraft, a tilt wing aircraft, a vertical takeoff and landing aircraft, an electrical vertical takeoff and landing vehicle, a personal air vehicle, a surface ship, a tank, a personnel carrier, a train, a spacecraft, a submarine, a spacecraft, or an automobile. Zhang, ¶ 0002, e.g. “cars.”
In regard to claim 21, Zhang discloses:
21. A method for determining a behavior for a vehicle system, the method comprising: Zhang, ¶ 0175, “methods described in this application.”
All further limitations of claim 21 have been addressed in the following rejection of claim 19.
In regard to claim 23, parent claim 22 is addressed above.
All further limitations of claims 23 have been addressed in the above rejection of claim 12.
In regard to claim 26, Zhang discloses:
26. A computer program product for estimating behavior, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer system to cause the computer system to: Zhang, ¶ 0046, “The storage 26 storing programs, instruction sets, and software that can be executed by the processors, such as the GPU 22 and the CPU 24, is an example of the storage 26 being a non-transitory computer-readable medium. The storage 26 may also be referred to generally as a non-transitory computer-readable medium.”
All further limitations of claim 26 have been addressed in the above rejection of claim 19.
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, 4-10, 14, 19-20 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of U.S. Patent Application Publication 20200379486 by Khosla et al. ("Khosla").
In regard to claim 1, Zhang discloses:
1. An … [vehicle] behavior system comprising: a computer system; Zhang Fig. 1, depicting a computer system.
Zhang does not expressly disclose: aircraft.
This is taught by Khosla. See Fig. 1, depicting an aircraft behavior system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Khosla’s aircraft with Zhang’s vehicle behavior system in order to provide automatic control of an aircraft with relatively-small amounts of human effort as suggested by Khosla (e.g. see ¶ 0023).
Zhang also discloses:
an observation processor in the computer system, Zhang, Fig. 6, elements 606a, 606b and 606c, depicting an observation processor.
wherein the observation processor is configured to: receive observations for an aircraft system, wherein the observations are for a current time; extract features from the observations; Zhang, ¶ 0100, “Given a set of observations (e.g., x1, x2, x3, . . . , xn) and k clusters, a K-means algorithm can partition the data to minimize the sum of intra-cluster variances.” Also ¶ 0103, “each point is a vector of dependent features.” Also Fig. 6, elements 606a, 606b and 606c, depicting feature extraction using observation data 602a, 602b and 602c.
neural networks in the computer system, Zhang, Fig. 6, elements 622a, 622b and 622c, depicting neural networks.
wherein the neural networks are configured to:
receive the features extracted from the observations; and Zhang, Fig. 6, elements 614a, 614b, 614c and 616 provided to decoder 620.
estimate a behavior for the aircraft system for time steps in response to receiving the features extracted from the observations processed by the observation processor, Zhang, Fig. 6, elements 624a, 624b and 624c. Also ¶ 0148, “Each of the recurrent units 622a, 622b, and 622c may predict vehicle speed vt as an output (e.g., 624a, 624b, and 624c) at a future time step T.”
wherein each of the neural networks is trained to estimate the behavior for the aircraft system for a different time step in the time steps. Zhang, ¶ 0148, “For example, as shown in FIG. 6, the first recurrent unit 622a predicts a vehicle speed 624a at time T0+1, the second recurrent unit 622b predicts a vehicle speed 624b at time T0+2, and the third recurrent unit 622c predicts a vehicle speed 624c at time T0+3.”
a controller configured to control operation of the aircraft behavior system using the behavior estimated by the neural networks. Zhang ¶ 0027, “As another example, the agent may be an autonomous or semi-autonomous vehicle navigating through the environment. In these implementations, the actions may be control inputs to control the robot or the autonomous vehicle.”
In regard to claim 4, Zhang does not expressly disclose:
4. The aircraft behavior system of claim 1, wherein the aircraft system is selected from a group comprising a single aircraft and a plurality of aircraft. This is taught by Khosla. See Khosla, ¶ 0073, “Each of worker threads 420a, 420b, 420c can operate concurrently and/or in parallel with its own copy of machine learning algorithm 130 and simulator 150, and its own copy of an agent needed to control the other aircraft (e.g., opponent aircraft 160), needed to control the vehicles in the environment.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Khosla’s multiple aircraft with Zhang’s vehicle in order to improve strategies and tactics involved with multiple aircraft while removing the necessity of a human pilot as suggested by Khosla (see ¶ 0023).
In regard to claim 5, Zhang does not expressly disclose:
5. The aircraft behavior system of claim 1, wherein the observation processor and the neural networks are located in an agent for the aircraft system. This is taught by Khosla. See Khosla ¶ 0019, e.g. “The machine learning algorithm can have a policy ANN, or policy network for short, that acts as an agent to select actions to control an aircraft conducive to a successful two-aircraft scenario outcome, where the action is selected based on a current state of an environment for the two-aircraft scenario.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Khosla’s agent with Zhang’s observation processor and neural networks in order to utilize a policy for controlling an aircraft as suggested by Khosla (see ¶ 0019).
In regard to claim 6, Zhang also discloses:
6. The aircraft behavior system of claim 1, wherein the observations are selected from at least one of a geometric observation, an environmental observation, or a status observation. Zhang, ¶ 0140, “environmental data.”
In regard to claim 7, Zhang also discloses:
7. The aircraft behavior system of claim 1, wherein the behavior is selected from at least one of a maneuver behavior, a non-maneuver behavior, a route vectoring, a route formation, an ingress vectoring, an ingress formation, an intercept, a missile intercept, a pure pursuit, a vectoring, a crank, a grinder, a pump, an egress, a first vector relative to a primary adversary aircraft, a second vector relative to a primary adversary aircraft centroid, or a third vector relative to a primary adversary missile centroid. Zhang, ¶ 0021, “Conventional systems and methods have been attempting to identify risky and aggressive driving behaviors. Examples of aggressive driving include, sudden changes in driving speed, speeding, harsh acceleration and braking, cutting off other drivers, brake checking, preventing other vehicles from merging or changing lanes, the ease of a driver becoming agitated and annoyed with other drivers, frequent honking, lane changes across one or more lanes of traffic in front of other vehicles, passing on the shoulder, and like driving behaviors.”
In regard to claim 8, Zhang also discloses:
8. The aircraft behavior system of claim 1, wherein the time steps are for the current time and a number of future time steps. Zhang ¶ 0129, “The time T0 represents the present, that is, the time the most recent data for the vehicle 71 is acquired. Times T0+n, where n is a positive integer, represents a time in the future that occurs after the time T0.”
In regard to claim 9, Zhang also discloses:
9. The aircraft behavior system of claim 1, wherein the observation processor is selected from at least one of a machine learning model, a neural network, a neural network layer, or a multi-layer perceptron, or a rule-based system. Zhang, ¶ 0139, “For example, as shown in FIG. 6, the recurrent units 608a, 608b, and 608c are illustrated as recurrent neural networks (RNNs).”
In regard to claim 10, Zhang also discloses:
10. The aircraft behavior system of claim 1, wherein the neural networks are selected from at least one of a proximal policy optimization neural network, recurrent neural network, a reinforcement learning neural network, or a multi-layer perceptron. Zhang, ¶ 0139, “For example, as shown in FIG. 6, the recurrent units 608a, 608b, and 608c are illustrated as recurrent neural networks (RNNs).”
In regard to claim 14, parent claim 11 is addressed below.
All further limitations of claim 14 have been addressed in the above rejection of claim 4.
In regard to claim 19, Zhang discloses:
19. A method for determining a behavior for an … [vehicle] system, the method comprising: Zhang, ¶ 0175, “methods described in this application.”
Zhang does not expressly disclose: aircraft.
This is taught by Khosla. See Fig. 1, depicting an aircraft behavior system. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Khosla’s aircraft with Zhang’s vehicle behavior system in order to provide automatic control of an aircraft with relatively-small amounts of human effort as suggested by Khosla (e.g. see ¶ 0023).
receiving, by a computer system, observations for the aircraft system, Zhang, Fig. 6, elements 606a, 606b and 606c, depicting an observation processor.
wherein the observations are for a current time; extracting, by the computer system, features from the observations; and Zhang, ¶ 0100, “Given a set of observations (e.g., x1, x2, x3, . . . , xn) and k clusters, a K-means algorithm can partition the data to minimize the sum of intra-cluster variances.” Also ¶ 0103, “each point is a vector of dependent features.” Also Fig. 6, elements 606a, 606b and 606c, depicting feature extraction using observation data 602a, 602b and 602c.
estimating, by the computer system, the behavior for the aircraft system for time steps using neural networks and the features extracted from the observations, Zhang, Fig. 6, elements 624a, 624b and 624c. Also ¶ 0148, “Each of the recurrent units 622a, 622b, and 622c may predict vehicle speed v.sub.t as an output (e.g., 624a, 624b, and 624c) at a future time step T.”
wherein each of the neural networks is trained to estimate the behavior for the aircraft system for a different time step in the time steps. Zhang, ¶ 0148, “For example, as shown in FIG. 6, the first recurrent unit 622a predicts a vehicle speed 624a at time T0+1, the second recurrent unit 622b predicts a vehicle speed 624b at time T0+2, and the third recurrent unit 622c predicts a vehicle speed 624c at time T0+3.”
controlling operation of the aircraft system using the behavior estimated by the neural networks. Zhang ¶ 0027, “As another example, the agent may be an autonomous or semi-autonomous vehicle navigating through the environment. In these implementations, the actions may be control inputs to control the robot or the autonomous vehicle.”
In regard to claim 20, parent claim 19 is addressed above.
All further limitations of claim 20 have been addressed in the above rejection of claim 4.
In regard to claim 25, parent claim 21 is addressed below.
All further limitations of claim 25 have been addressed in the above rejection of claim 4.
Claims 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Khosla as applied above, and further in view of U.S. Patent Application Publication 20240046070 by Zaheer et al. ("Zaheer").
In regard to claim 2, Zhang does not expressly disclose:
2. The aircraft behavior system of claim 1, wherein each of the neural networks has an input connected to the observation processor in which the input is configured to receive the features and an output that is configured to output the behavior for the different time step. This is taught by Zaheer. See Zaheer Fig. 1 elements 116, 124, 126 and 128 ¶ 0037, “Each auxiliary prediction neural network 124, 126, and 128 is configured to process an input set of features 118, 120, and 122 that is a proper subset of the features in the observation 108, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zaheer’s observation data with Zhang’s networks in order to improve the performance on a task as suggested by Zaheer (see ¶ 0048).
Claims 13 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Zaheer.
In regard to claim 13, Zhang does not expressly disclose:
13. The vehicle behavior system of claim 11, wherein the neural network layer systems are sets of layers in a neural network, wherein each set of layers has an input to receive the features from the observation processor and an output to output the behavior for the different time step. This is taught by Zaheer. See Zaheer Fig. 1 elements 116, 124, 126 and 128 ¶ 0037, “Each auxiliary prediction neural network 124, 126, and 128 is configured to process an input set of features 118, 120, and 122 that is a proper subset of the features in the observation 108, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zaheer’s observation data with Zhang’s networks in order to improve the performance on a task as suggested by Zaheer (see ¶ 0048).
In regard to claim 24, parent claim 21 is addressed above.
All further limitations of claim 24 have been addressed in the above rejection of claim 13.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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