Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The instant application having Application No. 18428822 has a total of 20 claims pending in the application, all of which are ready for examination by the examiner.
I. INFORMATION CONCERNING DRAWINGS
Drawings
The following drawings are objected to because they are faded and often illegible. Figures 7A-C, 8, and 9A-E are all too faded and blurry to be legible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
II. REJECTIONS NOT BASED ON PRIOR ART
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 8 is a machine type claim, and claim 15 is a manufacture type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“providing a scheduling environment” An office manager mentally or with pencil and paper sets up a schedule.
“using the scheduling environment … to simulate a fleet-level operational concept… and generate aircraft maintenance decisions and explanations for human operators” The office manager mentally or with pencil and paper sets up a description of the fleet-level maintenance schedule and makes decisions for the schedule with associated explanations.
Using … to maximize a mission accomplishment objective” The office manager mentally or with pencil and paper works to maximize their goal of the schedule.
“Using … to minimize a maintenance cost objective” The office manager mentally or with pencil and paper minimizes costs with their schedule.
“… obtain the aircraft maintenance decisions and corresponding mission accomplishment and maintenance cost rewards” The office manager mentally or with pencil and paper makes decisions based upon their intended goals.
“Providing a scheduling module” The office manager mentally or with pencil and paper considers their scheduling problem.
“Using the e scheduling module to arrange aircraft maintenance activities for a predetermined period” The office manager mentally or with pencil and paper arranges the schedule for the next month.
“providing an explainable module” The office manager mentally or with pencil and paper prepares explanations for any decisions.
“using the explainable module to get a reason to explain why the decisions are made and present a tradeoff between the decisions and non-selected alternatives” The office manager mentally or with pencil and paper writes down the reasons why decisions were made and explains the differences between the decisions.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
As per claims 2, and 4-6, this claim contains similar mental steps to claim 1, and is rejected for similar reasons.
As per claim 3, this claim contains similar mental steps to claim 1, and is rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (MPEP 2106.05(d)(II) indicate that merely “receiving or transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed displaying step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 7, this claim has similar mental steps to claims 1 and 3, and similar displaying to claim 3, and is rejected for similar reasons.
As per claim 8,
2A Prong 1:
“providing a scheduling environment” An office manager mentally or with pencil and paper sets up a schedule.
“using the scheduling environment … to simulate a fleet-level operational concept… and generate aircraft maintenance decisions and explanations for human operators” The office manager mentally or with pencil and paper sets up a description of the fleet-level maintenance schedule and makes decisions for the schedule with associated explanations.
Using … to maximize a mission accomplishment objective” The office manager mentally or with pencil and paper works to maximize their goal of the schedule.
“Using … to minimize a maintenance cost objective” The office manager mentally or with pencil and paper minimizes costs with their schedule.
“… obtain the aircraft maintenance decisions and corresponding mission accomplishment and maintenance cost rewards” The office manager mentally or with pencil and paper makes decisions based upon their intended goals.
“Providing a scheduling module” The office manager mentally or with pencil and paper considers their scheduling problem.
“Using the e scheduling module to arrange aircraft maintenance activities for a predetermined period” The office manager mentally or with pencil and paper arranges the schedule for the next month.
“providing an explainable module” The office manager mentally or with pencil and paper prepares explanations for any decisions.
“using the explainable module to get a reason to explain why the decisions are made and present a tradeoff between the decisions and non-selected alternatives” The office manager mentally or with pencil and paper writes down the reasons why decisions were made and explains the differences between the decisions.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
An electronic device, one or more processors, a memory (mere instructions to apply the exception using a generic computer component);
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
An electronic device, one or more processors, a memory (mere instructions to apply the exception using a generic computer component)
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
As per claims 9, and 11-13, these claims contain similar mental steps to claim 8, and is rejected for similar reasons.
As per claim 10, this claim contains similar mental steps to claim 8, and is rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (MPEP 2106.05(d)(II) indicate that merely “receiving or transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed displaying step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 14, this claim has similar mental steps to claims 8 and 10, and similar displaying to claim 10, and is rejected for similar reasons.
As per claim 15,
2A Prong 1:
“providing a scheduling environment” An office manager mentally or with pencil and paper sets up a schedule.
“using the scheduling environment … to simulate a fleet-level operational concept… and generate aircraft maintenance decisions and explanations for human operators” The office manager mentally or with pencil and paper sets up a description of the fleet-level maintenance schedule and makes decisions for the schedule with associated explanations.
Using … to maximize a mission accomplishment objective” The office manager mentally or with pencil and paper works to maximize their goal of the schedule.
“Using … to minimize a maintenance cost objective” The office manager mentally or with pencil and paper minimizes costs with their schedule.
“… obtain the aircraft maintenance decisions and corresponding mission accomplishment and maintenance cost rewards” The office manager mentally or with pencil and paper makes decisions based upon their intended goals.
“Providing a scheduling module” The office manager mentally or with pencil and paper considers their scheduling problem.
“Using the e scheduling module to arrange aircraft maintenance activities for a predetermined period” The office manager mentally or with pencil and paper arranges the schedule for the next month.
“providing an explainable module” The office manager mentally or with pencil and paper prepares explanations for any decisions.
“using the explainable module to get a reason to explain why the decisions are made and present a tradeoff between the decisions and non-selected alternatives” The office manager mentally or with pencil and paper writes down the reasons why decisions were made and explains the differences between the decisions.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory compute readable storage medium, one or more processors, an electronic device, (mere instructions to apply the exception using a generic computer component);
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory compute readable storage medium, one or more processors, an electronic device, (mere instructions to apply the exception using a generic computer component)
“as a reinforcement learning (RL) environment”, “train an RL agent”, “a decomposed reward Deep Q-learning network (DRDQN) algorithm, “a plurality of Deep Q-networks (DQNS), comprising a first DQN and a second DQN” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic recitations of well-known algorithms with no additional details or information beyond generic, off the shelf algorithms.
As per claims 16, and 18-19, these claims contain similar mental steps to claim 8, and is rejected for similar reasons.
As per claim 17, this claim contains similar mental steps to claim 8, and is rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements individually or in combination that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“presenting the summary text in a graphical user interface (GUI)” (MPEP 2106.05(d)(II) indicate that merely “receiving or transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed displaying step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 20, this claim has similar mental steps to claims 8 and 10, and similar displaying to claim 10, and is rejected for similar reasons.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote examiners note. This will be placed to further explain a rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Silva et al (“Adaptive Reinforcement Learning for task scheduling in aircraft maintenance”) in view of Erwig et al (“Explaining Deep Adaptive Programs via Reward Decomposition”).
As per claims 1, 8, and 15, Silva discloses, “An … Deep Reinforcement Learning … based method for aircraft maintenance scheduling, comprising” (Abstract; EN: this denotes using reinforcement learning for scheduling maintenance for aircraft. Pg.2-3, particularly the “Deep Q-Learning” section; This denotes the reinforcement learning making use of buffers and memory, which denotes the use of any computer equipment such as processors, memory, and computer readable storage mediums that are required to run machine learning algorithms like reinforcement learning).
“providing a scheduling environment” (pg.5, particularly the “Adaptive Scheduling algorithm” section; EN: this denotes the system being used for scheduling).
“Using the scheduling environment as a reinforcement (RL) environment” (pg.5, particularly the “Adaptive Scheduling algorithm” section, Paragraphs 2-3; EN: This denotes using reinforcement learning with the scheduling process). “to simulate a fleet-level operational concept” (pg.5, particularly the “Adaptive Scheduling algorithm” section, first paragraph; EN: this denotes working with a fleet of aircraft). “train an RL agent” (Pg.6, particularly the Maintenance Scenarios Simulation, paragraph 1; EN: this denotes training the reinforcement learning algorithm). “and generate aircraft maintenance decisions… for human operators” (pg.5, particularly the “Adaptive Scheduling algorithm” section, second paragraph; EN: this denotes the system scheduling tasks and updating the maintenance plan).
“Using the …DQN to maximize a mission accomplishment objective” (pg.5, particularly the “Adaptive Scheduling algorithm” section, first paragraph; EN: this denotes the goal of making efficient maintenance plans).
“Using the …DQN to minimize a maintenance cost objective” (abstract; EN: this denotes the goal of reducing the cost of maintenance).
“Using the trained … agent to obtain the aircraft maintenance decisions and corresponding mission accomplishment and maintenance cost rewards” (Pg.6, particularly the third paragraph; EN: this denotes the reward function, with time slack and task duration being the mission accomplishment, and additional cost c being the cost reward aspect).
“Providing a scheduling module” (Pg.6, particularly the Maintenance Scenarios simulation; EN: This denotes using the scheduling system).
“using the scheduling module to arrange aircraft maintenance activities for a predetermined period” (Pg.6, particularly the Maintenance Scenarios simulation, first paragraph; EN: this denotes planning for a month).
However, Silva fails to explicitly disclose, “explainable deep reinforcement learning (XDRL)”, “and explanations for human operators”, “providing a decomposed reward Deep Q-Network (drDQN) algorithm, the drDQN algorithm including a plurality of Deep Q-Networks (DQNs) comprising a first DQN and a second DQN”, “First DQN”, “second DQN”, “providing a trained drDQN agent”, “Using the trained drDQN agent…”, “providing an explainable module”, and “using the explainable module to get a reason to explain why the decision are made and present a tradeoff between the decisions and non-selected alternatives”
Erwig discloses, “explainable deep reinforcement learning (XDRL)” (abstract; EN: this denotes the use of Explainable reinforcement learning).
“and explanations for human operators” (pg.1, C2, last paragraph; EN: this denotes the explanations being for humans).
“providing a decomposed reward” (pg.2-3, particularly section 3; EN: this denotes reward decomposition). “Deep” (Pg.1, particularly C2, first paragraph; EN: this denotes the neural networks being deep). “Q-Network (drDQN) algorithm” (Pg.2, particularly C2, paragraph 3; EN: this denotes the use of Q-Functions with the neural networks). “the drDQN algorithm including a plurality of Deep Q-Networks (DQNs) comprising a first DQN and a second DQN” (Pg.2, particularly C2, paragraph 3; EN: this denotes each decomposed variable having its own Q-function and associated neural network)
“first DQN”, “Second DQN” (Pg.2, particularly C2, paragraph 3; EN: this denotes each decomposed variable having its own Q-function and associated neural network. In the case of Silva, this denotes allowing the breaking up of the reward function into time and costs using the separate neural networks of the Erwig reference).
“providing a trained drDQN agent” (pg.2, particularly C2, third paragraph; EN: this denotes the system as trained).
“Using the trained drDQN agent…” (pg.2, particularly C2, third paragraph; EN: this denotes the system as trained).
“providing an explainable module” (pg.2-3, particularly section 3; EN: this denotes the system explaining the reasons behind the decisions).
“using the explainable module to get a reason to explain why the decision are made and present a tradeoff between the decisions and non-selected alternatives” (pg.1, particularly C2, third paragraph; EN: this denotes the system explaining the tradeoffs going into the decisions made by the system).
Silva and Erwig are analogous art because both involve deep reinforcement learning.
Before the effective filing date it would have been obvious to one skilled in the art of deep reinforcement learning to combine the work of Silva and Erwig in order to make use of explainable AI with reinforcement learning.
The motivation for doing so would be to “provide a finer-grained view of the trade-offs going into the decisions and significantly more insight” (Erwig, Pg.1, C2, third paragraph) into the outputs of the algorithm, or in the case of Silva, allow the system to provide explanations as to why maintenance decisions were made and how the rewards were manipulated to inform the users of the reinforcement learning system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of deep reinforcement learning to combine the work of Silva and Erwig in order to make use of explainable AI with reinforcement learning.
As per claims 2, 9, and 16, Silva disclose, “… to obtain the aircraft maintenance…” (Abstract; EN: this denotes using reinforcement learning for scheduling maintenance for aircraft).
Erwig discloses, “obtaining reward difference Explanation (RDX)” (Abstract; EN: this denotes the use of RDX with the system). “and minimal Sufficient explanation (MSX)” (pg.4, particularly C1, second paragraph; EN: this denotes MSX used with the system).
“Using the RDX and MSX to obtain the … explanations” (Pg.2-4, particularly section 3; EN: this denotes using RDX and MSX for decision making and explanations).
As per claims 3, 10, and 17, Erwig discloses, “using a plurality of RDX values to obtain a summary text generated in natural language” (Pg.4, particularly Figures 4-6 and associated paragraphs; EN: this denotes text summarizing the explanation of differences in the rewards between different actions).
“Presenting the summary text ….” (Pg.4, particularly Figures 4-6 and associated paragraphs; EN: this denotes text summarizing the explanation of differences in the rewards between different actions and displaying them in graph form).
However, Silva and Erwig fail to explicitly disclose, “in a graphical user interface (GUI)”. The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art of machine learning systems to make use of basic I/O systems like graphical user interfaces to input data into the system and display results as needed, in order to allow the user to control the machine learning process and see and interpret results of that machine learning process.
As per claims 4, and 11, Silva discloses, “where the scheduling environment…” (pg.5, particularly the “Adaptive Scheduling algorithm” section; EN: this denotes the system being used for scheduling).
Erwig discloses, “is created using an OpenAI Gym toolkit” (Pg.2, particularly C1, section 2, second paragraph; EN: this denotes setting up the problem in Open AI gym environments).
As per claims 5, 12, and 18, Silva discloses, “calculating a total reward using the mission accomplishment and maintenance cost rewards” (Pg.6, particularly the third paragraph; EN: this denotes the reward function, with time slack and task duration being the mission accomplishment, and additional cost c being the cost reward aspect).
As per claims 6, 13, and 19, Silva discloses, “calculating the total reward using …the mission accomplishment and maintenance cost rewards…” (Pg.6, particularly the third paragraph; EN: this denotes the reward function, with time slack and task duration being the mission accomplishment, and additional cost c being the cost reward aspect).
Silva fails to explicitly disclose, “calculating the total reward using a first weight and a second weight of the mission accomplishment and maintenance cost rewards, the first weight being larger than the second weight.”
Erwig discloses, “calculating the total reward using a first weight” (pg.3, particularly C2, second paragraph; EN: this denotes a weight that is positive). “and a second weight of … cost rewards” (Pg.3, particularly c1, third paragraph; EN: this denotes one of the weights in the reward being negative). “the first weight being larger than the second weight” (pg.3, particularly C2, second paragraph; Pg.3, particularly c1, third paragraph; EN: these paragraphs denote a positive weight and a negative weight, with the positive weight “larger” than the negative as it is a positive number).
As per claims 7, 14, and 20, Silva discloses, “presenting … and the mission accomplishment and maintenance cost rewards …” (Pg.7, particularly the Results section and table 2; EN: this denotes the inputs used with the system and the results of the system).
Erwig discloses, “the total reward…. (Pg.4, particularly Figures 4-6 and associated paragraphs; EN: this denotes text summarizing the explanation of differences in the rewards between different actions and displaying them in graph form).
However, Silva and Erwig fail to explicitly disclose, “in a graphical user interface (GUI)”. The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art of machine learning systems to make use of basic I/O systems like graphical user interfaces to input data into the system and display results as needed, in order to allow the user to control the machine learning process and see and interpret results of that machine learning process.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
Prosecution Insights