DETAILED ACTION
This action is responsive to the Application/amendment filed on 09/11/2024. Claims 1-12 are pending in the case. Claims 1, 8, and 12 are independent claims.
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 .
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.
Priority
Receipt of certified copies of papers required by 37 CFR 1.55 and priority of 09/11/2023 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-12 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.
Claims 1, 8 and 12 recites the limitation the associated results. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret the limitation as a associated results.
Claims 2 recites the limitation the resulting training. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret the limitation as a resulting training.
Claims 2 recites the limitation the assignment has taken place. There is insufficient antecedent basis for this limitation in the claim. Examiner will interpret the limitation as a assignment has taken place.
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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claims include both physical storage and transitory signals. Specifically, a computer-readable medium encoding instructions.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract concepts without significantly more.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites to identify one or more relationships between the identification and/or function data and the associated results which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and evaluation to determine how concepts/inputs-outputs are related. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer-implemented recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification and/or function data of a plurality of field devices, and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
feeding the training data to the AI model which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
training the AI model using machine learning based upon the training data recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (b) and (c) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) and (d) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 2:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein a user manually assigns a result to each of the field devices which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing a value. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the resulting training data are fed individually to the AI model after the assignment has taken place which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
Subject Matter Eligibility Analysis Step 2B:
Additional element (a) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 3:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the training data are fed to the AI model collected as at least one training data set which amount to mere extra solution activity of storing information in memory, see MPEP §2106.05(g).
Subject Matter Eligibility Analysis Step 2B:
Further, additional element (a) is well understood, routine, and conventional activity of “storing information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015))
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 4:
The rejection of claim 3 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the identification and/or function data are divided into subsets, wherein a user assigns a result to each subset, wherein the training data set or the training data sets each consist of one of the subsets of the identification and/or function data and the result assigned to this subset which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and evaluation to determine how concepts/inputs-outputs are related. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5:
The rejection of claim 4 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the division of the identification and/or function data into the subsets is carried out with the aid of a further AI model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 6:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the AI model is based upon a neural network specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 7:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the identification and/or function data comprise one or more of the following data categories: naming of a measuring point; network address of the field device; manufacturer name; device type; serial number; device name specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites to identify one or more relationships between the identification and/or function data and the associated results which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and evaluation to determine how concepts/inputs-outputs are related. See 2106.04.(a)(2).III.C.
The claim recites checking the identification and/or function data by the user management system…to determine whether the field device is allowed to log on to the user management system
Subject Matter Eligibility Analysis Step 2A Prong 2:
use of an AI model which has been trained by means of a process recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification and/or function data of a plurality of field devices, and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
feeding the training data to the AI model which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
training the AI model using machine learning based upon the training data recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
making a user management system known in an automation system by sending invitation telegrams to at least one field device used in the system; sending a registration query of the field device to the user management system in response to the invitation telegram, which registration query contains identification and/or function data of the field device which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
using the AI model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
registering the field device with the user management system in the case where the check produces a successful result recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" that is performed based on a decision (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (d ) (f) and (g) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (b) (c) and (e) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) (d) (e) (f) and (g) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 9:
The rejection of claim 8 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein, before the step of checking the identification and/or function data by the user management system, a user first manually checks…whether the field device is allowed to log on to the user management system which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing a value and/or evaluating a value. See 2106.04.(a)(2).III.C.
The claim recites wherein the field device is only logged on to the user management system if the check by the user and the check by the AI model each produce a successful result which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using evaluation to determine a successfully result to make a decision. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
with the help of the AI model recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 10:
The rejection of claim 8 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the at least one field device and the user management system are in communications connection via a local network or via the Internet specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 11:
The rejection of claim 10 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the communications connection is wired or wireless specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites to identify one or more relationships between the identification and/or function data and the associated results which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and evaluation to determine how concepts/inputs-outputs are related. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
computer-readable medium encoding instructions recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification and/or function data of a plurality of field devices, and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
feeding the training data to the AI model which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)
training the AI model using machine learning based upon the training data recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional element (b) and (c) recites receiving and sending inputs/outputs which is a well-understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
The additional element(s) (a) (b) (c) and (d) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7 and 12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vavilala et al(US20220247786A1).
Regarding claim 1:
1. A computer-implemented process for training an AI model, comprising:
Vavilala discloses providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification(Vavilala, [0049], “in addition, the devices may be grouped by attributes that are not based on behavior patterns. Examples of attributes that are not based on behavior patterns include…a type of the device” where the type of device corresponds to identification of the device) and/or function data of a plurality of field devices(Vavilala, [0025], “Device evaluation logic 132 analyzes device attributes, such as …functions performed by the device, applications running on the device…” where the functions performed by the device and applications running on the device corresponds to function data of field devices ), and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted(Vavilala, ABSTRACT, “ For each cluster of devices, the security manager trains a machine learning model to indicate whether a particular data flow associated with a device in the particular cluster of devices is allowed or denied” where data flow being allowed or denied corresponds to whether or not logging into a user management system was permitted)
Vavilala discloses feeding the training data to the AI model(Vavilala, ABSTRACT, “Training logic 151 uses the training data sets 141 to train machine learning (ML) models 154 ” where using the training data sets to train a machine learning model corresponds to feeding training data to AI models)and training the AI model using machine learning based upon the training data to identify one or more relationships between the identification and/or function data and the associated results(Vavilala, [0065], “The machine learning engine applies a machine learning algorithm to the training set to train a machine learning model to identify, for each cluster of devices: (1) data flows that are to be allowed or denied access to the device cluster, and (2) the enforcement point(s) where security policies to allow or denied data flows are to be enforced (Operation 306). In one embodiment, applying the machine learning algorithm to the training set results in output values, for a respective data flow and device cluster, indicating whether the data flow is allowed or denied and where the policy to allow or deny the data flow is to be enforced” where categorizing devices into clusters and identifying clusters of devices to allow or deny corresponds to identifying one or more relationships between the identification and/or function data and the associated results )
Regarding claim 2:
The rejection of claim 1 with prior art is incorporated and further:
Vavilala discloses wherein a user manually assigns a result to each of the field devices, wherein the resulting training data are fed individually to the AI model after the assignment has taken place(Vavilala, [0081], “The user may modify results, such as by changing a particular type of data flow from “allowed” to “denied”…The security manager 450 may update the training data sets 441 based on the user feedback, and the training logic 451 may generate a new set of ML models 454 based on the updated training data sets 441.”)
Regarding claim 3:
The rejection of claim 1 with prior art is incorporated and further:
Vavilala discloses wherein the training data are fed to the AI model collected as at least one training data set(Vavilala, [0081], “The security manager 450 may update the training data sets 441 based on the user feedback, and the training logic 451 may generate a new set of ML models 454 based on the updated training data sets 441” where the updated training data sets correspond to a new training data set created using user feedback that is fed into the AI model.)
Regarding claim 4:
The rejection of claim 3 with prior art is incorporated and further:
Vavilala discloses wherein the identification and/or function data are divided into subsets, wherein a user assigns a result to each subset(Vavilala, [0019], “The data flows are either system-labeled or user-labeled as allowed or denied” where user-labeled corresponds to a user assigned result), wherein the training data set or the training data sets each consist of one of the subsets of the identification and/or function data and the result assigned to this subset(Vavilala, [0064], “The machine learning engine generates training data sets based on the historical data (Operation 304). The training data sets may include subsets of the historical data selected to represent different types of data flows and different device clusters destinations of the data flows”)
Regarding claim 5:
The rejection of claim 4 with prior art is incorporated and further:
Vavilala discloses wherein the division of the identification and/or function data into the subsets is carried out with the aid of a further AI model(Vavilala, [0029], In one or more embodiments, the clustering module 133 utilizes one or more machine learning models to generate the device clusters. In one embodiment, the clustering module 133 utilizes unsupervised machine learning processes, such as k-means clustering, to group all of the devices in the system into clusters according to respective attributes of the devices” where the utilization of unsupervised machine learning processes for clustering corresponds to AI model aiding in the division of identification and/or function data into subsets)
Regarding claim 7:
The rejection of claim 1 with prior art is incorporated and further:
Vavilala discloses wherein the identification and/or function data comprise one or more of the following data categories: naming of a measuring point; network address of the field device; manufacturer name; device type; serial number; device name(Vavilala, [0060], “The security manager generates a policy syntax for the identified enforcement point according to the type of device” where generating a policy for the type of device corresponds to identification data comprising a device type)
Regarding claim 12:
Vavilala discloses computer-readable medium(Vavilala, [0106], “Computer system 500 also includes a main memory 506, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 502 for storing information and instructions to be executed by processor 504.”) encoding instructions(Vavilala, [0115], “Computer system 500 can send messages and receive data, including program code, through the network(s)”)
Vavilala discloses providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification(Vavilala, [0049], “in addition, the devices may be grouped by attributes that are not based on behavior patterns. Examples of attributes that are not based on behavior patterns include…a type of the device” where the type of device corresponds to identification of the device) and/or function data of a plurality of field devices(Vavilala, [0025], “Device evaluation logic 132 analyzes device attributes, such as …functions performed by the device, applications running on the device…” where the functions performed by the device and applications running on the device corresponds to function data of field devices ), and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted(Vavilala, ABSTRACT, “ For each cluster of devices, the security manager trains a machine learning model to indicate whether a particular data flow associated with a device in the particular cluster of devices is allowed or denied” where data flow being allowed or denied corresponds to whether or not logging into a user management system was permitted)
Vavilala discloses feeding the training data to the AI model(Vavilala, ABSTRACT, “Training logic 151 uses the training data sets 141 to train machine learning (ML) models 154 ” where using the training data sets to train a machine learning model corresponds to feeding training data to AI models)and training the AI model using machine learning based upon the training data to identify one or more relationships between the identification and/or function data and the associated results(Vavilala, [0065], “The machine learning engine applies a machine learning algorithm to the training set to train a machine learning model to identify, for each cluster of devices: (1) data flows that are to be allowed or denied access to the device cluster, and (2) the enforcement point(s) where security policies to allow or denied data flows are to be enforced (Operation 306). In one embodiment, applying the machine learning algorithm to the training set results in output values, for a respective data flow and device cluster, indicating whether the data flow is allowed or denied and where the policy to allow or deny the data flow is to be enforced” where categorizing devices into clusters and identifying clusters of devices to allow or deny corresponds to identifying one or more relationships between the identification and/or function data and the associated results )
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Regarding claim(s) 6:
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vavilala et al. (US20220247786A1), henceforth known as Vavilala, in view of Tedaldi et al. (US20210126833A1), henceforth known as Tedaldi.
Regarding claim 6:
The rejection of claim 1 with prior art is incorporated and further:
Vavilala does not explicitly disclose wherein the AI model is based upon a neural network
Tedaldi discloses wherein the AI model is based upon a neural network (Tedaldi, [0044], “Example machine learning techniques that device classification process 248 can employ may include, but are not limited to… neural networks (e.g., reservoir networks, artificial neural networks, etc.)…multi-layer perceptron (MLP) artificial neural networks (ANNs)”)
References Vavilala and Tedaldi are analogous art because they are from the problem-solving area of using ML-assisted management of networked devices.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Vavilala and Tedaldi before him or her, to modify the AI model of Vavilala to include a neural network of Tedaldi as Tedaldi discusses neural networks being a suitable for device classification. The suggestion/motivation for doing so would have been Tedaldi, [0044], “Example machine learning techniques that device classification process 248 can employ may include, but are not limited to… neural networks (e.g., reservoir networks, artificial neural networks, etc.)…”)
Regarding claim(s) 8-11:
Claim(s) 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vavilala et al. (US20220247786A1), henceforth known as Vavilala, in view of Schmoetzer et al. (US20190188008A1), henceforth known as Schmoetzer.
Regarding claim 8:
Vavilala discloses providing training data, wherein the training data comprise input data and output data, wherein the input data comprise identification(Vavilala, [0049], “in addition, the devices may be grouped by attributes that are not based on behavior patterns. Examples of attributes that are not based on behavior patterns include…a type of the device” where the type of device corresponds to identification of the device) and/or function data of a plurality of field devices(Vavilala, [0025], “Device evaluation logic 132 analyzes device attributes, such as …functions performed by the device, applications running on the device…” where the functions performed by the device and applications running on the device corresponds to function data of field devices ), and wherein the output data each comprise a result associated with the field devices as to whether or not logging into a user management system was permitted(Vavilala, ABSTRACT, “ For each cluster of devices, the security manager trains a machine learning model to indicate whether a particular data flow associated with a device in the particular cluster of devices is allowed or denied” where data flow being allowed or denied corresponds to whether or not logging into a user management system was permitted)
Vavilala discloses feeding the training data to the AI model(Vavilala, ABSTRACT, “Training logic 151 uses the training data sets 141 to train machine learning (ML) models 154 ” where using the training data sets to train a machine learning model corresponds to feeding training data to AI models)and training the AI model using machine learning based upon the training data to identify one or more relationships between the identification and/or function data and the associated results(Vavilala, [0065], “The machine learning engine applies a machine learning algorithm to the training set to train a machine learning model to identify, for each cluster of devices: (1) data flows that are to be allowed or denied access to the device cluster, and (2) the enforcement point(s) where security policies to allow or denied data flows are to be enforced (Operation 306). In one embodiment, applying the machine learning algorithm to the training set results in output values, for a respective data flow and device cluster, indicating whether the data flow is allowed or denied and where the policy to allow or deny the data flow is to be enforced” where categorizing devices into clusters and identifying clusters of devices to allow or deny corresponds to identifying one or more relationships between the identification and/or function data and the associated results )
Vavilala discloses checking the identification and/or function data by the user management system using the AI model to determine whether the field device is allowed to log on to the user management system; (Vavilala, [0035], “ …For example, the machine learning engine 150 may apply a machine learning algorithm to a training data set 141 and output values for whether a particular flow should be allowed to be, or prevented from being, transmitted to a particular device cluster and where the security policy should be enforced. The security manager 130 may display results to the user via the user interface 136. The user may modify results, such as by changing a security tag for a particular type of data flow from “allowed” to “denied.”… In addition to providing feedback regarding data flows analyzed by the machine learning engine 150, a user may identify a new data flow as a data flow that should be allowed or denied”)
Vavilala discloses and registering the field device with the user management system in the case where the check produces a successful result(Vavilala, [0027], “…In addition, if the security manager 130 detects a new device accessing the monitored network in the particular room, the security manager may automatically add the device to the device cluster. ” where being added to a device cluster corresponds to registering a field device with a user management system)
Vavilala does not explicitly disclose the following limitations:
making a user management system known in an automation system by sending invitation telegrams to at least one field device used in the system
sending a registration query of the field device to the user management system in response to the invitation telegram, which registration query contains identification and/or function data of the field device;
Schmoetzer discloses making a user management system known in an automation system(Schmoetzer, [0006], “It is an object of the invention to provide a method for the automatic configuration of an exchange field device in a process control system. It is a further object of the invention to provide an appropriate configuration provider, an appropriate automatically configurable field device, and a system having a configuration provider and one or more automatically configurable field devices which are designed to execute the method according to the invention.”) by sending invitation telegrams to at least one field device used in the system(Schmoetzer, [0013], “According to a further aspect, the method may comprise the recurrent sending of an introducing message. The introducing message can be used to introduce the configuration provider to the field devices. The configuration provider may be designed to send a recurring introducing message to the field devices to introduce itself”)
References Vavilala and Schmoetzer are analogous art because they are from the same field of endeavor of using multiple network-connected devices with a centralized management component with device identification/attributes and having the centralized management component perform automated actions concerning the devices.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Vavilala and Schmoetzer before him or her, to modify the communication of the centralized system of Vavilala to include the broadcast message of Schmoetzer to expose the centralized management system and devices to each other and provide consistent information. The suggestion/motivation for doing so would have been Schmoetzer, [0074]- [0075], “The recurring provider-available broadcast S205 comprises a recurring sending of an introducing message…from the configuration provider CP to the field devices FD A to FD X of the process control system 1, in order to introduce the configuration provider CP to the field devices FD A to FD X. The introducing message…is a message to all field devices, a so-called broadcast message… By sending the registration request rBDOB, it can be ensured that the information regarding the field devices FDA to FD X in the process control system 1 (in particular the second list L2) of the configuration provider CP is consistent”
Schmoetzer discloses sending a registration query of the field device to the user management system in response to the invitation telegram, which registration query contains identification and/or function data of the field device(Schmoetzer, [0016], “According to a further aspect, the method may comprise the registration of the field devices with and by the configuration provider. The field devices may be designed to send a message to the configuration provider to register with the configuration provider. The message can contain identification data of the respective field device. The configuration provider may be designed to register field devices which answer the configuration provider. This enables an efficient and resource saving communication.”)
Regarding claim 9:
The rejection of claim 8 with prior art is incorporated and further:
Vavilala discloses wherein, before the step of checking the identification and/or function data by the user management system, a user first manually checks with the help of the AI model whether the field device is allowed to log on to the user management system, wherein the field device is only logged on to the user management system if the check by the user and the check by the AI model each produce a successful result(Vavilala, [0035], “ …For example, the machine learning engine 150 may apply a machine learning algorithm to a training data set 141 and output values for whether a particular flow should be allowed to be, or prevented from being, transmitted to a particular device cluster and where the security policy should be enforced. The security manager 130 may display results to the user via the user interface 136. The user may modify results, such as by changing a security tag for a particular type of data flow from “allowed” to “denied.”… In addition to providing feedback regarding data flows analyzed by the machine learning engine 150, a user may identify a new data flow as a data flow that should be allowed or denied. ”)
Regarding claim 10:
The rejection of claim 8 with prior art is incorporated and further:
Vavilala discloses wherein the at least one field device and the user management system are in communications connection via a local network or via the Internet(Vavilala, [0090], “…a client may be local to and/or remote from a computer network. The client may access the computer network over other computer networks, such as a private network or the Internet”)
Regarding claim 11:
The rejection of claim 10 with prior art is incorporated and further:
Vavilala discloses wherein the communications connection is wired or wireless(Vavilala, [0104], “According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices. The special-purpose computing devices may be hard-wired to perform the techniques”)
Relevant Prior Art
While not used in the current rejection Examiner found the following relevant prior art:
Huffman et al, US20210352110A1, that discusses automated device classification
Mermoud et al, US20200160100A1, that discusses automated traffic telemetry datapoint classifcation
Byers et al, US20200267543A1, that discusses automated IoT network server registration
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES JEFFREY JONES JR whose telephone number is (703)756-1414. The examiner can normally be reached Monday - Friday 8:00 - 5:00 EST.
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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
