DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-17 are pending.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 12/20/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
4. The drawings have been reviewed and are accepted as being in compliance with the provisions of 37 CFR 1.121.
Priority
5. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in This application is a 371 of PCT/EP2022/066975, filed in FRANCE 06/22/2022.
Claim Objections
6. Claims 7, 10, 12, and 15 are objected to because of the following informalities: the claims are objected to because they include abbreviations which are not enclosed within parentheses. For example: human-machine interfaces (HMI), Claim 7, lacks the connotation of HMI, and base of operator behavior (BCCO), lacks the enclosed parenthesis at least once in claims 10 and 15. Reference characters corresponding to recited connotation of the same element or group of elements in the claims should be enclosed within parentheses so as to avoid confusion with other numbers or characters which may appear in the claims. Appropriate correction is required. See MPEP § 608.01(m).
Claim 12 objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim dependency. Appears to be a device claiming the features of Claim 1 (method claim), further comprising means for implementing the seps for the method 2 (method claim also, which depends on Claim 1) See MPEP § 608.01(n). Accordingly, the claim 12 is not been further treated on the merits. Appropriate correction is required.
Claim Rejections - 35 USC § 112
7. 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.
8. Claims 1-17 are Regarding claims 1-17, the phrase "mission" renders the claim indefinite because it is unclear what the specific use of the word is intended to define the claim to.
The term “complex system” in claim 1-17 is used by the claim to mean “pilots”, aircraft”; while the accepted meaning could be any “complex” “sum of factors” on any system. The term is indefinite because the specification does not clearly redefine the term. The Examiner suggests to be more explicit as to what the claim is to encompass certain features of an “aircraft” and the human behavior corresponds to “pilot”. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 1-10 and 14-17 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Step 1: Claim 1 and 15 recite “A method for…”; the claim recites a series of steps and therefore is a process. Claims 8 and 16-17 recite “A device…” therefore the claim is a machine, “predictive analysis”, therefore the claim is a manufacture.
Step 2A Prong One: Claims 1, 8, and 15 recite the limitations "determining" and specifically “collecting data…; analyzing…” and generating” a model of human behavior configured for modeling the behavior of said operator being observed with regard to the cognitive and procedural aspects…”
These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components.
That is, other than reciting a "implemented by a computer", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “collecting” and “analyzing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, grouping and evaluating data, if the token is inexistent then store it.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements “applied interaction"; “modeling.” And The “capitalized in a knowledge base” in complex systems, based on a determination of the existence of data, this limitation is a mere generic transmission and presentation of collected and analyzed data (MPEP 2106.05(g).
A claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they are considered insignificant extra-solution activity. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities listed above, including “engine for prediction” this limitation is a mere generic transmission and presentation of collected and analyzed data. The limitations performed by an “artificial intelligence” being a tool.; generates a combination of data or “output” based on data and update them based on comparison of data ( it is recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (see MPEP 2106.04(a)(2). There are no additional elements that amount to significantly more than the above-identified judicial exception (abstract idea).
Koninklijke KPN N.V. v.Gemalto M2M GmbH, 942 F.3d 1143, 1149 (Fed. Cir.2019) (quoting Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1257 (Fed. Cir. 2016)). In the context of software patents (which includes machine learning patents), the step-one inquiry determines “whether the claims focus on ‘the specific asserted improvement in computer capabilities . . . or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool.’” Id. (alteration in original) (quoting Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303 (Fed. Cir. 2018)).
As per Claims 2-10 and 13-17 , The claim recites the additional limitations “determining from the collected data” “modeling”, influence by behavior and actions, analyzing prediction data, identifying risks; generating and adapting from human behavior, constructing databases or “knowledge base” as claimed. The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components, specifically doing comparison of data, from different sources. (See COFFELT V. NVIDIA CORPORATION, The calculations claimed can be done by a human mentally or with a pen and paper.” It further added that “analyzing information by steps people [can] go through in their minds, or by mathematical algorithms, without more . . . [are] mental processes within the abstract-idea category.
Claim Rejections - 35 USC § 103
11. 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.
12. Claim(s) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sequeira et al (US 2020/0320435), in view of COULMEAU et al (US 2017/0013061).
As per Claim 1, Sequeira discloses:
A method for predictive analysis of the behavior of an operator interacting with a complex system during a real or simulated mission, (Par [0004], “artificial intelligence assistants, and augmented reality or virtual reality applications” and par [0115] Reinforcement learnings methods are also starting to be applied in control problems across industrial systems for resource management, e.g., to control power systems, computer network routing, or even to perform automated trade execution. In general, these systems are trained to control the flow of some quantity (such as information, power, or orders) across a large and complex network of devices…” and Figures 2- 4) the method being implemented by computer and comprising steps of: collecting data characterizing actions of observation, of manipulation and of communication of the operator, physiological data relating to the operator and contextual data combining data on the state and the dynamics of the complex system, environmental data, and data relating to the context of the mission; (Par [0006], “The multi-level introspection framework applies statistical analysis and machine learning methods to interaction data collected during the RL agent's interaction with the environment. The multi-level introspection framework may include a first (“environment”) level that analyzes characteristics of one or more tasks to be solved by the RL agent to generate elements, a second (“interaction”) level that analyzes actions of the RL agent when interacting with the environment to generate elements, and a third (“meta-analysis”) level that generates elements by analyzing combinations of elements generated by the first level and elements generated by the second level.” Par [0007], “As another example, the multi-level introspection framework may avoid having to make assumptions with regards to optimality of the observed behavior—it captures important aspects specific to a given history of interaction independently of whether the agent was exploring the environment or exploiting its knowledge after learning.” And see Figures 1-4) using the collected data as inputs of an engine for predicting behavior in order to generate prediction data representing actions and behaviors that said operator could carry out in the short term; (Par [0024], “RL agent 111 may obtain observation data 124 via one or sensors such as cameras, feedback devices, proximity sensors, infrared sensors, accelerometers, temperature sensors, and so forth (not shown) operating in the environment to observe the environment. RL agent 111 may or may not be located within the environment. The environment may or may not include a physical space but is generalizable as a task to be solved, and RL agent 111 may be considered “within” the environment when it is operating to solve the task in learning mode or other mode.” And see Figures 1-4). and analyzing the prediction data in order to determine whether technical adaptations are to be applied to the interaction between the operator and the complex system; (Par [0035-0037], “RL agent 111 generates some of the interaction data 122 using value-based RL methods, e.g., the Q function. RL agent 111 generates some of the interaction data 122 using model-based algorithms…” and par [0082-0084], “ Observation-action value outliers: These elements 150 correspond to (z, a) pairs that are significantly more or less valued” the prediction outliers determine all situations and prediction related to the observed data”)
wherein the step for generating prediction data comprises, via said engine for predicting behavior, in implementing, with the collected data, a model of human behavior configured for modeling the behavior of said operator being observed with regard to the cognitive and procedural aspects, said model of human behavior instantiated for said observed operator having been learnt, in a learning phase, by the application of artificial intelligence techniques to cognitive models and to procedural models, using, over many simulations, a plurality of teaching data of the same nature as said collected data, but for various operators, the teaching data being capitalized in a knowledge base of the behavior of operators of complex systems. (Par [0021], “a multi-level introspection framework to generate elements that characterize interactions of the RL agent with an environment and that may enable human operators or designers to correctly understand an RL agent's aptitude in a specific task, i.e., both its capabilities and limitations, whether innate or learned. The multi-level introspection framework relies on generic data that is already collected by standard RL algorithms and on a factored-state structure, which may enable domain independence and avoidance of manual adjustments. The framework may further provide analysis of reasons for an RL agent's behavior to provide insights about the agent's decision-making. The framework may also enable automatic detection of situations requiring human intervention and output a request for such intervention, e.g., in the form of a decision.” Par [0108], “Interaction data 122 also characterizes one or more interactions of RL agent 111 with the environment performed according to trained policies for RL agent 111. RL model 112 may include the trained policies.” And see Figures 1-4).
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 2, the rejection of Claim 1 is incorporated and further Sequeira discloses:
wherein the step for implementing the model of human behavior for said operator comprises steps of: determining, from a sub-set of the collected data, one or more cognitive states of the operator; characterizing, from a sub-set of the collected data, the perception of the situation by said operator, for the operational phase during the mission; using the data on cognitive states as parameters of human factors of influence for the determination of behaviors and of actions of the operator, as a function of his/her perception of the situation;
the model of human behavior being represented as a hierarchical graph
comprising modules of cognitive behaviors and modules of tasks relating to a mission, (Par [0040], par [0044], par [0052], “For some elements 150, explanation system 130 may generate visual tools such graphs and images to highlight important situations—including relevant observation features, such as showing identified sequences within an environment that are represented in elements 150.” The behavior tracking and the actions complying with the “cognitive behaviors” as claimed) the modules of cognitive behaviors and the modules of tasks being decomposed into modules of behaviors, the modules of behavior being decomposed into modules of actions, the actions being elementary actions observable on the operator, the graph comprising an output level corresponding to a selection of elementary actions. (Par [0051], “Explanation for autonomous agents requires determining not just what to explain but also how and when. Elements 150 described here provide the content for explanation. This disclosure describes insights on how different types of elements 150 can be used to expose the behavior of RL agent 111 to a human user, justifications regarding its decisions, situations in which input from the user might be needed, etc.” and par [0108], “[0108] Explanation system 130 obtains interaction data 122 generated by RL agent 111 (1002). Interaction data 122 characterizes one or more tasks in an environment. Interaction data 122 also characterizes one or more interactions of RL agent 111 with the environment performed according to trained policies for RL agent 111. RL model 112 may include the trained policies.” Par [0117], different modules; and See Figures 1-4).
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 3, the rejection of Claim 2 is incorporated and further Sequeira discloses:
wherein the parameters of human factors of influence on the determination of behaviors and of actions of the operator are used at several levels of the hierarchical graph. (Par [0040] and par [0046], “to train RL agent 111 to perform one or more actions within the environment. In an example where reinforcement learning model 112 is a Deep Q Network, reinforcement learning engine 110 may update one or more Q-value network parameters of reinforcement learning model 112 based on training data.” And par [0059], “This analysis thus highlights the certain and uncertain elements of the transitions, actions, and observation features of RL agent 111. Uncertain elements are especially important as people tend to resort to “abnormal” situations for the explanation of behavior.” The behavior tracking and the actions complying with the “cognitive behaviors” as claimed)
As per Claim 4, the rejection of Claim 3 is incorporated and further Sequeira discloses:
wherein the parameters of human factors of influence are used at a first level of the graph to determine cognitive behaviors, at a second level of the graph to determine behaviors and actions, and at a third level of the graph to determine a selection of actions. (Par [0040], “…or acquired preferences for RL agent 111, and the local minima states denotes highly undesirable situations that the RL agent sought to avoid. Based on this information, introspection framework 131 may generate a graph representing likely transitions from any given situation (e.g., state) to a sub-goal (e.g., a local maxima). In turn, this allows the identification of situations where RL agent 111 can predict its near future (as well as how to achieve it) and situations in which the future of RL agent 111 is uncertain. Meta-analysis level 144 may automatically determine contradictory situations in which RL agent 111 behaved in unexpected or surprising ways. Finally, meta-analysis level 144 may also perform a differential analysis between two behaviors of two different instances of RL agent 111, which allows tracking the development of learning by RL agent 111 or comparing the behavior of a novice RL agent 111 against that of an expert RL agent 111 in the task. Different instances of RL agent 111 may represent different “runs” through an environment resulting in different RL models 112 for the different instances. Environment analysis level 140 may store representations of the results of its analyses as elements in elements 150.” The behavior tracking and the actions complying with the “cognitive behaviors” as claimed)
As per Claim 5, the rejection of Claim 1 is incorporated and further Sequeira discloses:
wherein the step for analyzing the prediction data in order to determine whether technical adaptations are to be applied to the interaction between the operator and the complex system comprises identifying possible risks for the mission, linked to predicted actions and behaviors of the operator. (Par [0091], “… where the variance of the difference in value to possible next observations is significantly higher or lower, as defined by a threshold for example. These elements 150 are important to identify highly-unpredictable and especially risky situations, i.e., in which executing actions might lead to either lower- or higher-valued next state”)
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 6, the rejection of Claim 1 is incorporated and further Sequeira discloses:
further comprising determining adaptation suggestions as regards the interaction between said observed operator and the complex system. (Par [0041], “Machine learning system 102 may process training scenarios 164 generated from elements 150 to train reinforcement learning model 112. Training scenarios 164 may be in the form <s, a, s′, r>, including an initial state s of the environment, an action a to be performed by RL agent 111, a resulting state s′ of the environment, and a resulting reward r for RL agent 111.” And see Figures 3-4, providing introspection).
As per Claim 7, the rejection of Claim 6 is incorporated and further Sequeira discloses:
further comprising steps of: generating written and/or visual and/or audible warnings, intended for said observed operator and/or for co-operators and/or for control services; and/or generating written and/or visual and/or audible assistance suggestions, intended for said observed operator and/or for third-parties; and/or adapting / reconfiguring HMIs used by said observed operator in order to facilitate his/her actions; and/or adapting / modifying an operation within the complex system in order for a predicted action not to be realized or to be carried out differently. (Par [0041], “Explanation system 130 may output result data 152 for display to a display device 160 used by user 162. User 162 may be an operator of a system that includes RL agent 111, an RL agent 111 trainer, or other user. In some examples, explanation system 130 may derive explanations about the learned strategy of RL agent 111 by visualizing relevant sequences to learned goals given any situation, as represented in selected elements 150, and output this as result data” and par [0079], “Certain/uncertain observations and features: besides transition certainty, observation-action frequency analysis 308 can calculate how certain or uncertain each observation is with regards to action execution. Observations where many different actions have a high count (high evenness) are considered uncertain…”).
As per Claim 8, the rejection of Claim 2 is incorporated and further Sequeira discloses: further comprising supplying the collected data to the input of the model of human behavior as teaching data. (Par [0041-0042], “…may generate training scenarios 164 for such states. Machine learning system 102 may process training scenarios 164 generated from elements 150 to train reinforcement learning model 112. Training scenarios 164 may be in the form <s, a, s′, r>, including an initial state s of the environment, an action a to be performed by RL agent 111, a resulting state s′ of the environment, and a resulting reward r for RL agent 111.” The “training data” is the “teaching data” as claimed).
As per Claim 9, the rejection of Claim 1 is incorporated and further Sequeira discloses: comprising initial steps of an automatic learning of models of human behavior. (Par [0040-0041], “Meta-analysis level 144 may automatically determine contradictory situations in which RL agent 111 behaved in unexpected or surprising ways. Finally, meta-analysis level 144 may also perform a differential analysis between two behaviors of two different instances of RL agent 111, which allows tracking the development of learning by RL agent 111 or comparing the behavior of a novice RL agent 111 against that of an expert RL agent 111 in the task.” And see Figures 3-4).
As per Claim 10, the rejection of Claim 9 is incorporated and further Sequeira discloses: wherein the step for automatic learning of the models of human behavior comprises steps of: constructing a knowledge base of operator behavior BCCO using data coming from operators interacting with the complex system; (Par [0040-0041] and par [0070], and See Figures 1-3)
using the data from the BCCO to construct by learning a database of cognitive
models; (Par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”; Par [0040-0041] and See Figures 1-3)
using the data from the BCCO to construct by learning a database of models
specific to each operator or to each category of operators; (Par [0040-0041] and See Figures 1-3)
using the data from the BCCO, the data from the database of cognitive models,
the data from the database of specific models to construct by learning:
cognitive state models allowing the trend over time of the human factors
parameters to be modeled as a function of the context represented by the operator and the task that he/she is performing using the complex system; (Par [0040-0041] and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”)
mission models integrating into their rules of operation parameters of human
factors of influence coming from the cognitive state models, the parameters being taken into account according to three levels of influence; (Par [0040-0041] and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies” and See Figures 1-3) the combination of cognitive state models and of mission models constituting models of human behavior for operators or for categories of operators. (Par [0037], “…multi-level introspection framework…” par [0040-0041], and par [0104] Finally, differential analysis 318 can apply the differential analysis to data captured by a novice, e.g., learning agent, and an expert in the task.” and See Figures 1-3).
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 15, Sequeira discloses:
A method for constructing models of human behavior, comprising steps of: constructing a knowledge base of operator behavior BCCO using data coming from operators interacting with the complex system; (Par [0070], “Each observation-transaction is then repeatedly added to a database for a number of times according to its frequency, as given by n(z). Observation frequency analysis 306 may create a frequent-pattern tree (FP-tree) using a Frequent Pattern Tree or other algorithm that facilitates the systematic discovery of frequent combinations between the items in a database.” And see Figure 1)
using the data from the BCCO to construct by learning a database of cognitive models; (Figure 1 and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”)
using the data from the BCCO to construct by learning a database of models
specific to each operator or to each category of operators; (Figure 1 and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”)
using the data from the BCCO, the data from the database of cognitive models,
the data from the database of specific models to construct by learning:
cognitive state models allowing the trend over time of parameters of human
factors of influence to be modeled, as a function of the context represented by the operator and the task that he/she is performing in his/her interaction with the complex system; (Par [0006], includes “interaction data”, see Figure 1 and par [0070], “action data” being the “cognitive data” as claimed; par [0083], tracks prediction error, learning, and perceptual limitations; and see par [0108], “RL mode” includes interaction data, environment, and trained policies”)
mission models integrating into their rules of operation the parameters of human
factors of influence coming from the cognitive state models, the parameters being taken into account according to three levels of influence; (Par [0037], “…multi-level introspection framework…” and par [0104] Finally, differential analysis 318 can apply the differential analysis to data captured by a novice, e.g., learning agent, and an expert in the task. This can be useful to “debug” the RL agent 111 behavior and identify its learning difficulties, assess how its acquired goals differ from those of the expert, etc. Notably, our analyses framework can be used to capture interestingness elements of observed human behavior—in particular, among all the interaction data 122 that is required, only the analysis over the prediction error cannot be captured or estimated given data of a human interacting with an RL environment. This means that the differential analysis 318 can compare the behavior of a learning agent with that of a human, which allows for automatically identifying unexpected or surprising behaviors” and see Figures 1-4)
the combination of cognitive state models and of mission models constituting
models of human behavior for operators or for categories of operators. (Par [0040-0041], see figures 1-4).
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 11-12, being the device Claim corresponding to the method Claims 1 and 2 further Sequeira discloses: (Par [0115]).
As per Claim 13, being the device Claim corresponding to the method Claim 11 further Sequeira discloses: for the predictive analysis of the behavior of an operator interacting with an aircraft platform during a real or simulated mission. (Par [0022], “a task or simulation to be solved by RL agent 111. An environment may include, for example, a digital assistant task, a software-controlled network, operations for an autonomous vehicle, a robotic device or a factory with many such robotic devices, a medical therapy, or a home automation environment. Accordingly, RL agent 111 may be included within a digital conversational assistant, a network controller, an autonomous vehicle, an industrial control system, a home automation system, or a medical device such an automated pump or defibrillator, among other examples.”).
However, Sequeira do not specifically discloses the “aircraft platform” real or simulated.
COULMEAU discloses the above claimed features as follows: (Par [0099], “The peripheral computer 6 for managing or coordinating the tasks of the FIM application comprises an inputs/outputs interface 24 for exchanging operational requests and responses with an operator environment 26 consisting for example of a pilot 28 and an AOC (Airline Operational Communication) or ATC (Air Traffic Control) ground station.” And see Figures 1-3 and 7).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claim 14, being the computer program product executed by a processor corresponding to the method Claim 1 further Sequeira discloses: (Par [0115]).
As per Claim 15, Sequeira discloses:
A method for constructing models of human behavior, comprising steps of: constructing a knowledge base of operator behavior BCCO using data coming from operators interacting with the complex system; (Par [0070], “Each observation-transaction is then repeatedly added to a database for a number of times according to its frequency, as given by n(z). Observation frequency analysis 306 may create a frequent-pattern tree (FP-tree) using a Frequent Pattern Tree or other algorithm that facilitates the systematic discovery of frequent combinations between the items in a database.” And see Figure 1)
using the data from the BCCO to construct by learning a database of cognitive models; (Figure 1 and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”)
using the data from the BCCO to construct by learning a database of models
specific to each operator or to each category of operators; (Figure 1 and par [0070], “action data” being the “cognitive data” as claimed and par [0108], “RL mode” includes interaction data, environment, and trained policies”)
using the data from the BCCO, the data from the database of cognitive models,
the data from the database of specific models to construct by learning:
cognitive state models allowing the trend over time of parameters of human
factors of influence to be modeled, as a function of the context represented by the operator and the task that he/she is performing in his/her interaction with the complex system; (Par [0006], includes “interaction data”, see Figure 1 and par [0070], “action data” being the “cognitive data” as claimed; par [0083], tracks prediction error, learning, and perceptual limitations; and see par [0108], “RL mode” includes interaction data, environment, and trained policies”)
mission models integrating into their rules of operation the parameters of human
factors of influence coming from the cognitive state models, the parameters being taken into account according to three levels of influence; (Par [0037], “…multi-level introspection framework…” and par [0104] Finally, differential analysis 318 can apply the differential analysis to data captured by a novice, e.g., learning agent, and an expert in the task. This can be useful to “debug” the RL agent 111 behavior and identify its learning difficulties, assess how its acquired goals differ from those of the expert, etc. Notably, our analyses framework can be used to capture interestingness elements of observed human behavior—in particular, among all the interaction data 122 that is required, only the analysis over the prediction error cannot be captured or estimated given data of a human interacting with an RL environment. This means that the differential analysis 318 can compare the behavior of a learning agent with that of a human, which allows for automatically identifying unexpected or surprising behaviors” and see Figures 1-4)
the combination of cognitive state models and of mission models constituting
models of human behavior for operators or for categories of operators.
Sequeira do not specifically discloses the “mission”
COULMEAU discloses the above claimed feature as follows:
(Par [0147], “In the current architectures and whatever the aircraft, the “Flight Planning” and “optimized trajectory” part is generally included in a dedicated computer called the “FMS” for “Flight Management System” (or flight management computer). These functions constitute the FM business core. This system can also host part of the “Location” and of the “Guidance”. In order to ensure its mission, the FMS is connected to numerous other computers (a hundred or so)”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date to incorporate the teachings of COLMENAU specifically inferring a certain “mission” into the method of Sequeira to take advantage on applying the respective mission to make the behavior specifically related to a task. The modification would have been obvious because one of the ordinary skills in the art would implement providing certain system and task to a user or operator of an aircraft for planning purposes.
As per Claims 16-17, being the device claims corresponding to the method claims 15 respectively and rejected under the same reason set forth in connection of the rejections of Claims 15 and further Sequeira discloses: (Par [0116]).
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Heath; Stephan (US-10096033-B2) relates to platform for providing a social context to software applications, predictive resource identification and phased delivery of structured documents, presenting social networking events via a television receiver, processing social relation oriented service, profile rating and verification system, promoting shopping information on a network based social platform,
Iordanov, Vassil US-20030167454-A1, relates to The human behavior simulator and method with metacognitive capability enhances the quality of simulation for behavioral simulation and decision making processes by generating, storing and updating information on internal aspects of the resources that are involved in processing cognitive tasks. A cognitive proprioception unit detects any internal change among the resources and updates symbolic knowledge that represent the internal aspects of the resources.
DUTT; RAJEEV US-20160293133-A1, relates to Predictive caching is based on the ability to make “predictions” as to what data will be needed or what event will occur (or is likely to occur) next and to (pre)generate and store the data, system state, or values that are most likely to be needed or used.
Bettles; James W. H. US-20200125225-A1, relates to collection of data with a real-time analytics engine including at least one predictive model to identify at least one drilling operation event associated with a drilling rig from the synchronized data, applying logic rules to calibrate outputs of the real-time analytics engine to determine a set of key performance indicators for the at least one drilling operation event from the drilling rig,
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/ANGELICA RUIZ/Primary Examiner, Art Unit 2154 June 21, 2026
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