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
This action is response to the communication filed on January 30, 2024. Claims 1-14 are pending.
Preliminary Amendment
The preliminary amendment of the drawing filed on April 11, 2025 has been entered.
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 1-13 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 1 the second limitation recited “the internal state”, the third limitation recited “the cognitive state” and “the artificial operator”, the fourth limitation recited “the progress”, and “the new cognitive state”, the last limitation recited “the simulated operator”, “the evolution”, and “the choices”. All of the recited phrases start with article “the”. There is insufficient antecedent basis for the above limitations in the claim. Appropriate correction is required.
Further, the last limitation of claim 1 recited “this influence”, “this cognitive state”, and “this new cognitive sate”. It is not clear what is meant by the recited phrases. The phrase “this” make those limitation ambiguous. Appropriate correction is required.
Again, the construction of the last limitation of claim 1 is vague. It’s a run on sentence and does not make sense. Appropriate correction is required.
The dependent claims are rejected based on their respective dependency.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites gathering a plurality of data concerning the current situation of the complex system; supplying the gathered data as input for a data processing module comprising a behavior generation engine instantiating a human behavior model in order to cognitively model the internal state of an operator and to procedurally model formalized or observed operating procedures for the complex system, said human behavior model being a single hybrid modeling structure built by learning from trade expertise and from interaction data of handling, observing and communicating between one or more real operators and the complex system, the data processing module operating with the gathered data by following steps of: updating the cognitive state of the artificial operator, the cognitive state of the artificial operator being represented by a set of psychophysiological variables representing human factors that can influence the behavior and decision-making of real operators; updating the progress of the tasks instantiated in the behavior generation engine, taking into account the new cognitive state of the artificial operator, in order to carry out the mission; using, based on the new cognitive state of the artificial operator and the progress of the tasks in the mission, the human factors modeled by the cognitive state of the simulated operator in order to influence a breakdown of the mission into a tree of new tasks ending with the generation of behavior and action data, this influence of the human factors being able to be exerted by generating new behaviors in the tree directly linked to the evolution of this cognitive state, by orienting the choices of behaviors or actions of the artificial operator during the breakdown of the mission and by modifying the effectiveness of the selected actions as a function of this new cognitive state.
The claim recited the limitation of “updating the cognitive state of the artificial operator, the cognitive state of the artificial operator being represented by a set of psychophysiological variables representing human factors that can influence the behavior and decision-making of real operators; updating the progress of the tasks instantiated in the behavior generation engine, taking into account the new cognitive state of the artificial operator, in order to carry out the mission” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally update cognitive state by observing the operator and mentally keep track (progress) task which is a mental process.
The claim recited three additional elements: gathering --, supplying ---, and using ---. The gathering step as recited amounts to mere data gathering, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362(utilizing an intermediary computer to forward information)). Similarly, the supplying and using steps as recited is nothing but data processing and manipulation which is an insignificant extra-solution activity. Accordingly, even in combination, the additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of gathering, supplying, and using steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of wherein the hybrid human behavior model instantiated for the artificial operator has been learned in a learning phase, by applying artificial intelligence techniques to cognitive models and to procedural models using, over numerous simulations, a plurality of learning data for different operators, the learning data being capitalized in a knowledge database of the behavior of operators of complex systems, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of wherein the step of updating the cognitive state of the artificial operator takes into account the current situation that includes data concerning the situation perceived by the artificial operator and mission and environment context data, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of wherein the hybrid human behavior model is represented as a hierarchical graph comprising cognitive behavior modules and task modules relating to a mission, the cognitive behavior modules and the task modules being broken down into behavior modules, the behavior modules being broken down into action modules, the actions being elementary actions that can be observed by the artificial operator, the graph comprising an output level corresponding to a selection of elementary actions, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 4 and includes all the limitations of claim 4. Therefore, claim wherein the mission breakdown step involves using, on the hierarchical graph, information originating from the current situation and from the parameters of human influence factors generated by the updated cognitive state recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of [], which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 5 and includes all the limitations of claim 5. Therefore, claim 6 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of wherein the parameters of human influence factors are used on multiple levels of the hierarchical graph, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 6 and includes all the limitations of claim 6. Therefore, claim 7 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of wherein the parameters of human influence factors are used on a first level of the graph in order to determine cognitive behaviors, on a second level of the graph in order to determine behaviors and actions, and on a third level of the graph in order to determine a selection of actions, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of a device for generating the behavior of an artificial operator interacting with a complex system during a mission, the mission being a real or simulated mission, the device comprising means for implementing the steps of the method as claimed in claim 1, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 9 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 9 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of generating the behavior of an artificial pilot interacting with a simulated aircraft or interacting with a real drone during a mission, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 10 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of executing the steps of the method as claimed in claim 1, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 11 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of building human behavior models, said preliminary steps involving: building an operator behavior knowledge database BCCO based on data derived from operators interacting with a complex system; using the BCCO data to build a database of cognitive models by learning; using the BCCO data to build a database of models specific to each operator or to each category of operators by learning; using the BCCO data, the data from the database of cognitive models, and the data from the database of specific models, to build the following by learning: cognitive state models allowing the evolution of parameters of human influence factors to be modeled, as a function of the context represented by the operator and the task they are carrying out when they are interacting with the complex system; mission models incorporating the parameters of human influence factors originating from cognitive state models into their operating rules, with the parameters being taken into account according to three levels of influence; the combination of cognitive state models and of mission models forming hybrid modeling structures representing human behavior models for an operator or a category of operators, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 12 is dependent on claim 11 and includes all the limitations of claim 11. Therefore, claim 12 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of device for generating the behavior of an artificial operator interacting with a complex system during a mission, the mission being a real or simulated mission, the device comprising means for implementing the steps of the method as claimed in claim 11, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 13 is dependent on claim 12 and includes all the limitations of claim 12. Therefore, claim 13 recites the same abstract idea of generating behavior of an operator interacting with a complex system. The claim recites the limitations of implement artificial intelligence techniques in order to perform machine learning, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claim 14, it has similar limitations as of claim 11 above. Hence, claim 14 is rejected under the same rational as of claim 11 above.
Claim Rejections - 35 USC § 102
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 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 1-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iordanov et al. (Pub. No. : US 20030167454 A1)
As to claim 1 Iordanov teaches a method for generating the behavior of an artificial operator interacting with a complex system during a mission, the mission being a real or simulated mission, the method being computer-implemented and comprising the steps of:
gathering a plurality of data concerning the current situation of the complex system (paragraph [0015]: "The major data stores in HOS-V are the Simulation Library 19, Customization Library 20, and Object Library 22, which contain the data used to specify each simulation and control its execution ... ");
supplying the gathered data as input for a data processing module comprising a behavior generation engine (figure 2: "HOS-V Data"; paragraph [25]: " ... HOS-V Data 30 may be roughly sorted into threegroupings: Object Descriptions 31, Low-level Operator Models32, and Task Analysis 34 for high levelsimulation control...") instantiating a human behavior model in order to cognitively model the internal state of an operator (paragraph [0004]: " ... Another source of human behavioral representations in CGFs lies in the psychology and cognitive science research in cognitive modeling. This body of work has created very sophisticated models of human cognition based on theories of underlying cognitive architecture and information processing ... ", paragraph [0076]: " .. .It focuses on modeling real-time, multi-tasking human cognition at an expert level but in a minimally embodied framework. .. ") and to procedurally model formalized or observed operating procedures for the complex system (paragraph [0009]: " ... The task hierarchy would start at the mission level, which the user would decompose iteratively into subordinate procedures until a bottom level of procedure specification was reached ... ", paragraph [0076]: " .. .It has proven quite robust and flexible in capturing and simulating human strategies in complex environments such as Na val Command and Control (Zachary, Ryder, and Hicinbothom, 1998) and telecommunications operations (Ryder, Szczepkowski, Weiland, and Zachary, 1998), among others ... "), said human behavior model being a single hybrid modeling structure built by learning from trade expertise (paragraph [0051]: "The overall strategy for representation of each of these types of expertise is driven by the focus of the overall COGNET system as discussed earlier-on expert-level competence in complex real-time environments ... ") and from interaction data of handling, observing and communicating between one or more real operators and the complex system (paragraph [0084]: "Paradoxically, the purely cognitive HBR models also often exhibit this same limitation, behaving optimally in an individual way, but failing to reason and act cooperatively. An HBR development framework should be able to generate cooperative and team behaviors, as well as individual reasoning/decision-making and environmental sensory/motor behaviors ... "; paragraph [0378]: "This section discusses ways in which CGF-COGNET seeks to eliminate this limitation, allowing the modeling of team-work and other cooperative behaviors, as well as individual task-work ... "), the data processing module operating with the gathered data by following steps of:
updating the cognitive state of the artificial operator (paragraph [0079]: " ... The ability to 'plug and play' component process models would allow the overall HBR to be more readily updated to reflect improved data and/or refinements of understanding of the component process without requiring larger changes in the remainder of the HBR model..."), the cognitive state of the artificial operator being represented by a set of psychophysiological variables representing human factors that can influence the behavior and decision-making of real operators (paragraph [0065]: " ... providing real-time adaptive decision support, including: apredetermined set of resources for accomplishing a set of predetermined tasks; a cognitive module connected to the resources for executing at least one of the tasks ... in response to the cognitive module the metacognitive module updating symbolic information on self-awareness of the resources in the metacognition memory in response to any change that is related to the resources ... "; paragraph [426]: " ... This ability is crucial to enable the flexible use of the growing body of component models from psychology human factors computer-human interaction, and cognitive science research to solve applied problems in engineering simulation and design ... "; paragraph [427]: " ... a general technology for modeling and simulation of human capabilities in complex realtime environments, which are of central importance in many industries, including (non-military) aerospace, process control, manufacturing, medicine, financial services, transportation, and telecommunications ... creating and incorporating models of human information processing into the development of training systems, into the design evaluation/validation processes, into the development of decision support and performance support systems, and into the creation of intelligent task automation solutions ... ");
updating the progress of the tasks instantiated in the behavior generation engine, taking into account the new cognitive state of the artificial operator, in order to carry out the mission (paragraph [0430]:"Performance robustness - a simulated or synthetic system operator (i.e., synthetic human) need to handle the interruptions and unforeseen events that arise in the context of both routine activities and unusual acti vies (e.g, during emergencies). The synthetic system operator, like the person being simulated (in the engineering setting) or replaced/supported (in the operational setting) will have to be able to deal with interruptions and novel settings and recover or adapt its behavior to meet its (mission) goals in these novel settings ... ");
using, based on the new cognitive state of the artificial operator and the progress of the tasks in the mission, the human factors modeled by the cognitive state of the simulated operator in order to influence a breakdown of the mission into a tree of new tasks ending with the generation of behavior and action data (paragraph [0009]: " ... The task hierarchy would start at the mission level, which the user would decompose iteratively into subordinate procedures until a bottom level of procedure specification was reached (i.e., the task element level) at which all actions could be specified in terms of a few action verbs which had predefined connections to a set of general human performance micro-models ... "; paragraph [0017]:"Customization Editors 6 allow HOS-V users to customize selection models and micro-models, which describe a simulated operator's behavior on a low-level second-by-second basis. These models are intended to be used in a modular fashion within higher-level descriptions of operator behavior at the task and subtask level, so that once created, they will need to be altered only occasionally ... "), this influence of the human factors being able to be exerted by generating new behaviors in the tree directly linked to the evolution of this cognitive state (paragraph [ 0424]: "A software implementation of the CGF-COGNET architecture, incorporating advanced behavioral simulation infrastructure, new behavioral representation capabilities (including performance time and accuracy prediction), and self-awareness of internal processing states and the ability to modify cognitive and motor processing on the basis of this self-awareness ... "), by orienting the choices of behaviors or actions of the artificial operator during the breakdown of the mission and by modifying the effectiveness of the selected actions as a function of this new cognitive state (paragraph [0430]: " ... The synthetic system operator, like the person being simulated (in the engineering setting) or replaced/supported (in the operational setting) will have to be able to deal with interruptions and novel settings and recover or adapt its behavior to meet its (mission) goals in these novel settings ... "; paragraph [408]:" Compensatory actions. Compensatory teamwork action is a reactive version of the proactive guidance behavior. That is, the cognitive system identifies a problem caused as a result of an action taken by a teammate, and then reacts to it. Most of this processing can actually be accomplished by first order cognitive processes (e.g., via cognitive tasks within the COGNET framework), as the problem is first perceived and then internalized, at which point it may stimulate a corrective or compensatory action ... ").
As to claim 2 Iordanov teaches wherein the hybrid human behavior model instantiated for the artificial operator has been learned in a learning phase, by applying artificial intelligence techniques to cognitive models and to procedural models using, over numerous simulations, a plurality of learning data for different operators, the learning data being capitalized in a knowledge database of the behavior of operators of complex systems (paragraph [0004]).
As to claim 3 Iordanov teaches wherein the step of updating the cognitive state of the artificial operator takes into account the current situation that includes data concerning the situation perceived by the artificial operator and mission and environment context data (paragraph [0009]).
As to claim 4 Iordanov teaches wherein the hybrid human behavior model is represented as a hierarchical graph comprising cognitive behavior modules and task modules relating to a mission, the cognitive behavior modules and the task modules being broken down into behavior modules, the behavior modules being broken down into action modules, the actions being elementary actions that can be observed by the artificial operator, the graph comprising an output level corresponding to a selection of elementary actions (paragraph [0015]).
As to claim 5 Iordanov teaches wherein the mission breakdown step involves using, on the hierarchical graph, information originating from the current situation and from the parameters of human influence factors generated by the updated cognitive state (paragraph [0009]).
As to claim 6 Iordanov teaches wherein the parameters of human influence factors are used on multiple levels of the hierarchical graph (paragraph [0172]).
As to claim 7 Iordanov teaches wherein the parameters of human influence factors are used on a first level of the graph in order to determine cognitive behaviors, on a second level of the graph in order to determine behaviors and actions, and on a third level of the graph in order to determine a selection of actions (paragraph [0172]).
As to claim 9 Iordanov teaches generating the behavior of an artificial pilot interacting with a simulated aircraft or interacting with a real drone during a mission (paragraph [0038]).
As to claim 8 and 10, they have similar limitations as of claim 1 above. Hence, they are rejected under the same rational as of claim 1 above.
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 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Iordanov et al. (Pub. No. : US 20030167454 A1) in the view of BALAKRISHNA et al. (Pub. No. US 20220244062 A1)
11. (Currently Amended) The method as claimed in claim 1, comprising preliminary steps of building human behavior models, said preliminary steps involving:
building an operator behavior knowledge database BCCO based on data derived from operators interacting with a complex system (paragraph [0015]: The major data stores in HOS-V are the Simulation Library 19, Customization Library 20, and Object Library 22, which contain the data used to specify each simulation and control its execution.);
using the BCCO data to build a database of cognitive models by learning (paragraph [0004], [0305], [0431]: cognitive models that are embedded into interactive applications such as decision support will need to be able to adapt their information processing to the actions and/or characteristics of their human users and behave in a cooperative manner);
mission models incorporating the parameters of human influence factors originating from cognitive state models into their operating rules, with the parameters being taken into account according to three levels of influence (paragraph [0065], [0426]-[0427]: providing real-time adaptive decision support, including: a predetermined set of resources for accomplishing a set of predetermined tasks; a cognitive module connected to the resources for executing at least one of the tasks ... in response to the cognitive module the metacognitive module updating symbolic information on self-awareness of the resources in the metacognition memory in response to any change that is related to the resources);
the combination of cognitive state models and of mission models forming hybrid modeling structures representing human behavior models for an operator or a category of operators (paragraph [0051]: "The overall strategy for representation of each of these types of expertise is driven by the focus of the overall COGNET system as discussed earlier-on expert-level competence in complex real-time environments ... ").
Iordanov does not explicitly disclose but BALAKRISHNA teaches using the BCCO data to build a database of models specific to each operator or to each category of operators by learning (paragraph [0069]: The Behavior and Cognitive Models for Navigation is illustrated in FIG. 5 which illustrates components used to establish training data using human-in-the-loop systems to collect behavioral data and based on that data, find user cognitive load points and optimize machine learning behavioral models which are used by the system to understand a user);
using the BCCO data, the data from the database of cognitive models, and the data from the database of specific models (paragraphs [0067], [0070]: behavior and cognitive model), to build the following by learning:
cognitive state models allowing the evolution of parameters of human influence factors to be modeled, as a function of the context represented by the operator and the task they are carrying out when they are interacting with the complex system (paragraphs [0066], [0070]: A simulated driving simulator 440 provides a machine learning model to mimic the human user 405 driving in an environment and provides and receives data to and from the BCMN. The BCMN 450 itself includes the behavioral models 425, the cognitive models 430, and the BCMN visualization and simulation 435).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Iordanov adding above limitation as taught by BALAKRISHNA to improve the cognitive models (to improve the cognitive models, paragraph [0073]).
As to claim 13 Iordanov together with BALAKRISHNA teaches a device according to claim 11. BALAKRISHNA teaches wherein the means implement artificial intelligence techniques in order to perform machine learning.
As to claims 12 and 14, they have similar limitations as of claim 11 above. Hence, they are rejected under the same rational as of claim 11 above.
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169
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