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 Office Action is in response to the application filed on 12/29/2023.
Claims 1-2 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 01/22/2024 has been considered (see form-1449, MPEP 609).
Priority
Applicant’s claim of foreign priority on Turkish application 2021/014085 filed 09/08/2021, under 35 U.S.C. 119(a)-(d) or (f) is acknowledged.
Drawings
The drawings filed on 12/29/2023 are accepted.
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 1-2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The Claim recites the language of “developing an optimal behavior model with deep learning approaches for
intelligent virtual entities controlled with supervised learning algorithms,
connecting simulations with intelligent virtual assets to a training
interface in training algorithms and thus intelligent virtual assets behaving as
an interface, and
learning the appropriate behavior model by trial and error according to
punishment-reward or objective function with reinforcement learning after a supervise learning training;
wherein the intelligent virtual entities generalize a simulation world map by
restarting from different locations at the beginning of the episode, and
the intelligent virtual entities have an ability to perform parallel
multi-training by generalizing between scenarios simultaneously.”
Claim 1 recites the limitation of “developing an optimal behavior model with deep learning approaches for intelligent virtual entities controlled with supervised learning algorithms”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, “developing” in the context of this claim encompasses the user manually creating/instruction/developing a behavior learning. Similarly, the limitation of connecting simulations with intelligent virtual assets to a training
interface in training algorithms and thus intelligent virtual assets behaving as
an interface, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “connecting” in the context of this claim encompasses the user manually connecting/putting/gathering information. Similarly, the limitation of learning the appropriate behavior model by trial and error according to punishment-reward or objective function with reinforcement learning after a supervise learning training, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “learning” in the context of this claim encompasses the user manually learning/acknowledge information. Similarly, the limitation of wherein the intelligent virtual entities generalize a simulation world map byrestarting from different locations at the beginning of the episode, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “generalize” in the context of this claim encompasses the user manually learning different level/episode. Also Similarly, the limitation of the intelligent virtual entities have an ability to perform parallel
multi-training by generalizing between scenarios simultaneously, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “perform” in the context of this claim encompasses the user manually perform multiple training. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” (Which define in the specification as a software, and simulation that is a mental process learning different ways) grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using artificial intelligence model to perform the developing, connecting, learning, generalize, perform steps. The entities with an artificial intelligence model in those steps is recited at a high-level of generality (i.e., as a generic computer function or software of learning) such that it amounts no more than mere instructions to apply the exception using an artificial intelligence model. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an 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 using an intelligent virtual entities of developing, connecting, learning, generalize, perform steps amounts to no more than mere instructions to apply the exception using a generic computer component. 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 independent claim 1 and includes all the limitations of claim 1. Claim 2 recites “training algorithms are deep learning algorithms”. The claim language provides only further learning which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Accordingly, the claims 1-2 are not patent eligible.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in 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 that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Lindkvist et al. (US PGPUB 2019/0340956, hereinafter Lindkvist), in view of Chen et al. (US PGPUB 2023/0352133, hereinafter Chen).
As per as claim 1, Lindkvist discloses:
(Currently Amended) A self-improvement method of virtual entities with an artificial intelligence model in training simulators, comprising the steps of:
developing an optimal behavior model with deep learning approaches for
intelligent virtual entities controlled with supervised learning algorithms (Lindkvist, e.g., [0022-0023], “...simulations can be used to train medical practitioners to develop skill sets for performing the medical procedures and/or to assist trained medical practitioners with maintaining their skill sets... information that can assist the medical practitioners with developing and/or maintaining their skill sets (e.g., by outputting information that analyzes the performance of the medical practitioners during the simulations and/or providing recommendations for advancing the skill sets of the medical practitioners)”,
connecting simulations with intelligent virtual assets to a training
interface in training algorithms and thus intelligent virtual assets behaving as
an interface (Lindkvist, e.g., [0025-0026], “...the simulation interface devices may include full body mannequins that simulate patients, devices that have human-shaped figures integrated into upper surfaces of the simulation interface devices, and/or portable, box-shaped simulation interface devices...”, and
Lindkvist does not explicitly disclose the features of “learning the appropriate behavior model by trial and error according to punishment-reward or objective function with reinforcement learning after a supervise learning training;
wherein the intelligent virtual entities generalize a simulation world map by
restarting from different locations at the beginning of the episode, and
the intelligent virtual entities have an ability to perform parallel
multi-training by generalizing between scenarios simultaneously.”
However Chen, in an analogous art, discloses “learning the appropriate behavior model by trial and error according to punishment-reward or objective function with reinforcement learning after a supervise learning training” (Chen, e.g., [0009-0010], [0111], “...The one or more medical models may be trained using deep learning. The deep learning may be supervised, unsupervised, or semi-supervised. The one or more medical models may be trained using reinforcement learning or transfer learning. The one or more medical models may be trained using image thresholding or color-based image segmentation. The one or more medical models may be trained using clustering...” and [0203-0204], “...The one or more medical models may be trained using deep learning. The deep learning may be supervised, unsupervised, or semi-supervised. The one or more medical models may be trained using reinforcement learning or transfer learning. The one or more medical models may be trained using image thresholding or color-based image segmentation. The one or more medical models may be trained using clustering...), “wherein the intelligent virtual entities generalize a simulation world map by
restarting from different locations at the beginning of the episode”, (Chen, e.g., [0138-0139], “...one or more landmarks may correspond to one or more locations or regions of interest in the surgical scene...”, [0173-0174], “...algorithms or models may identify and/or track the locations of certain structures as the surgeon is performing a surgical task near such structures...”), “the intelligent virtual entities have an ability to perform parallel multi-training by generalizing between scenarios simultaneously” (Chen, e.g., [0108-0110], [0189], [0220], “...one or more medical training tools may be used and/or deployed to train one or more doctors, surgeons, nurses, medical assistants, medical staff, medical workers, medical students, medical residents, medical interns, or healthcare providers. The one or more medical training tools may be configured to provide best practices or guidelines for performing one or more surgical procedures... The one or more medical training tools may be configured to provide procedure training or medical instrument training. The one or more medical training tools may be configured to provide outcome-based training for one or more surgical procedures. In some cases, the one or more medical training tools may comprise a training simulator. The training simulator may be configured to provide a trainee with a visual and/or virtual representation of a surgical procedure...” (the examiner asserts doctors, surgeons, nurses, medical assistants, medical staff, medical worker, students, medical residents, interns and so on taking multiple training as the same time which is equivalent to perform parallel multi-training by generalizing between scenarios simultaneously ) Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Chen and Lindkvist to learn medical data for various patients and procedures may be compiled and analyzed to aid in the diagnosis and treatment of different medical conditions in order for doctors and surgeons may utilize medical data compiled from various sources to make informed judgments about how to perform different medical operations (Chen, e.g., [0002]).
As per as claim 2, the combination of Chen and Lindkvist disclose:
(Currently Amended) A-The self-improvement method according to claim 1,
characterized in that wherein the training algorithms are deep learning algorithms (Chen, e.g., [0009], “...The one or more medical models may be trained using deep learning. The deep learning may be supervised, unsupervised, or semi-supervised. The one or more medical models may be trained using reinforcement learning or transfer learning...” and [0111]).
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to artificial intelligence model is provided virtual entities used in educational training simulations are transitioned from a rule-based behavior function to an artificial intelligence-based learning behavior function and thereby the virtual entities improve themselves, and a method in which the virtual entities improve themselves. Further the autonomous virtual entities that are trained with supervised learning and reinforcement learning algorithms as the training algorithm by being interacted with the simulation are designed.
a. Limor Grossman Avrahan (US PGPUB 2022/0394052, herein after Grossman Avrahan); “Method and System For Online User Security Information Event Management” discloses providing an automated response to user behavior which receiving, by a computer system, data of user actions taken on a computer of the user, the computer of the user in communication with the computer system; analyzing the received data against the knowledge level of the user as determined by the computer system, and/or, the user's responses to simulations generated by the computer system, to determine a score for the user; and, in response to the score, making a behavior recommendation for the user and/or making a decision to take an action associated with the computer of the user”.
Crossman Avrahan discloses simulated phishing attempts, simulated emails, any training and/or education as to company policy/cyber security, the user's position in the company, any permissions or credentials the user has, the accuracy and/or credibility of reports the user makes, the frequency of reports made by the user, any reports about the user, or behaviors of the user [0084].
Crossman Avrahan further teaches organization's policy, with a user's level/rating of his cybersecurity awareness training and behavior [0130], expert [0151].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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