DETAILED ACTION
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 .
Status of Claims
In response to communications filed on 07 March 2024, claims 1-16 are presently pending in the application, of which, claims 1 and 9 are presented in independent form.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07 March 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings, filed 08 August 2025, have been reviewed and accepted by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Regarding claims 1-16, under Step 2A claims 1-8 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites:
determining a first local model parameter representing a machine learning (ML) model of the at least first client edge based on the sensor data;
collecting, by the at least one master edge, the first local model parameter from the at least first client edge; and
generating, by the at least one master edge, a global ML model based on the at least first local model parameter, wherein the global ML model is used for monitoring a system performance or a condition of the system.
These limitations recite mental processes, such as concepts performed in the human mind (see: 2019 PEG, p. 52). This is because the each of the limitations above recite a series of steps that may be mentally performed by which an evaluation is made for an abstract data. For example, the limitations of ‘determining a first local model parameter representing a machine learning (ML) model of the at least first client edge based on the sensor data; collecting, by the at least one master edge, the first local model parameter from the at least first client edge; and generating, by the at least one master edge, a global ML model based on the at least first local model parameter, wherein the global ML model is used for monitoring a system performance or a condition of the system,’ illustrate a judgement being performed to find matching results and does not perform any technical operation. This represents a judgement or decision which are concepts performed in the human mind and falls under certain methods of mental processes. Accordingly, under step 2A (prong 1) the claim recites an abstract idea because the claim recites limitations that fall within the “Certain methods of mental processes” grouping of abstract ideas (see again: 2019 PEG, p. 52).
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including hardware processing circuitry, such as edge device.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application (see again: 2019 Revised Patent Subject Matter Eligibility Guidance).
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. That is, the limitations of ‘receiving sensor data from the at least first client edge sent by at least one sensor device via a second communication interface; and storing the first local model parameter in a data storage of the at least first client edge,’ are additional elements that are insignificant extra solution activities that that do not amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(lI)), including at least:
• receiving or transmitting data over a network, and/or
• storing and retrieving information in memory
• performing repetitive calculations
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Dependent claim 2 also does not integrate the abstract idea into a practical application. Notably, claim 2 recites ‘ wherein the at least one master edge generates a global model parameter that is provided to the at least first client edge and/or to the at least second client edge to update respective local ML models present in the at least first or second client edge,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 2 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 2 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 2 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 2 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 3 also does not integrate the abstract idea into a practical application. Notably, claim 3 recites ‘wherein the global ML model is built by aggregating the first local model parameter with a second local model parameter of an ML model provided by the at least second client edge,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 3 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 3 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 3 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 3 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 4 also does not integrate the abstract idea into a practical application. Notably, claim 4 recites ‘wherein the first local model parameter corresponds to a first machine learning weight value, the second local model parameter corresponds to a second machine learning weight value and the global model parameter corresponds to a third machine learning weight value of the corresponding ML models,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 4 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 4 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 4 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 4 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 5 also does not integrate the abstract idea into a practical application. Notably, claim 5 recites ‘wherein the at least one master edge, the at least first client edge and the at least second client edge distribute their respective model parameters with each other,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 5 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 5 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 5 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 5 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 6 also does not integrate the abstract idea into a practical application. Notably, claim 6 recites ‘wherein the global ML model is updated each time a change of the local model parameter has occurred,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 6 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 6 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 6 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 6 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 7 also does not integrate the abstract idea into a practical application. Notably, claim 7 recites ‘wherein collecting is performed in at least one of an event-based approach, and/or a time-based approach,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 7 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 7 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 7 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 7 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 8 also does not integrate the abstract idea into a practical application. Notably, claim 8 recites ‘wherein generating the resulting global ML model by the at least one master edge uses an information about a topology of the network to build the global ML model,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 8 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 8 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 8 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 8 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Claims 9-16 appears to include similar subject matter as in claims 1-8 as discussed above. More specifically, independent claim 1 additionally recites ‘a method comprising…’ which is recited at a high level of generality and are recited as performing mere generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system in addition to merely indicating a field of use or technological environment in which the judicial exception do not amount to significantly more than the exception itself. All the comments made with respect to the rejection of claims 1-8 equally apply and therefore stand rejected.
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 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.
(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 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable by Milletari, Fausto, et al (U.S. Patent 11,804,050 and known hereinafter as Milletari).
As per claim 1, Milletari teaches a method for efficient performance monitoring of a system in a hierarchical network of distributed edges comprising at least one master edge, at least a first client edge, wherein the at least first client edge is connected via a first communication interface with the at least one master edge, at least a second client edge connected via the first communication interface to the at least one master edge, and wherein the at least one master edge is connected to the system, the method comprising:
receiving sensor data from the at least first client edge sent by at least one sensor device via a second communication interface (e.g. Milletari, see paragraph (0199) which discloses controllers provide signals for controlling one or more devices in response to sensor data received from one or more sensors.);
determining a first local model parameter representing a machine learning (ML) model of the at least first client edge based on the sensor data (e.g. Milletari, see paragraph (0061-0062), which discloses receiving data from one or more parameters from a training node, where a parameter determiner may determine one or more values of one or more parameters of machine learning models by aggregating one or more values of the one or more corresponding parameters from one or more training nodes based on the sensor data received.);
storing the first local model parameter in a data storage of the at least first client edge (e.g. Milletari, see paragraph (0087), which discloses aggregating or combining values, updates and/or contributions of parameters for each training node.);
collecting, by the at least one master edge, the first local model parameter from the at least first client edge (e.g. Milletari, see paragraphs (0160-0162), which discloses aggregating MLM parameter information associated with the same training node to be analyzed by the system.); and
generating, by the at least one master edge, a global ML model based on the at least first local model parameter (e.g. Milletari, see paragraph (0119, 0120-0124) which discloses generating a trained model based on the set of weight values to a maxima point as an amount of consensus between a training node and a consensus of training nodes.), wherein the global ML model is used for monitoring a system performance or a condition of the system (e.g. Milletari, see paragraph (0258) which discloses monitoring status and health of the controllers).
As per claim 9, Milletari teaches a computer comprising a processor configured to execute computer executable instructions stored on tangible media, wherein, upon execution, the computer executable instructions are configured to monitor a system in a hierarchical network of distributed edges comprising at least one master edge, at least a first client edge, wherein the at least first client edge is connected via a first communication interface with the at least one master edge, at least a second client edge connected via the first communication interface to the at least one master edge, and wherein the at least one master edge is connected to the system, the computer executable instructions being further configured to cause:
receiving sensor data from the at least first client edge sent by at least one sensor device via a second communication interface (e.g. Milletari, see paragraph (0199) which discloses controllers provide signals for controlling one or more devices in response to sensor data received from one or more sensors.);
determining a first local model parameter representing a machine learning (ML) model of the at least first client edge based on the sensor data (e.g. Milletari, see paragraph (0061-0062), which discloses receiving data from one or more parameters from a training node, where a parameter determiner may determine one or more values of one or more parameters of machine learning models by aggregating one or more values of the one or more corresponding parameters from one or more training nodes based on the sensor data received.);
storing the first local model parameter in a data storage of the at least first client edge (e.g. Milletari, see paragraph (0087), which discloses aggregating or combining values, updates and/or contributions of parameters for each training node.);
collecting, by the at least one master edge, the first local model parameter from the at least first client edge (e.g. Milletari, see paragraphs (0160-0162), which discloses aggregating MLM parameter information associated with the same training node to be analyzed by the system.); and
generating, by the at least one master edge, a global ML model based on the at least first local model parameter (e.g. Milletari, see paragraph (0119, 0120-0124) which discloses generating a trained model based on the set of weight values to a maxima point as an amount of consensus between a training node and a consensus of training nodes.), wherein the global ML model is used for monitoring a system performance or a condition of the system (e.g. Milletari, see paragraph (0258) which discloses monitoring status and health of the controllers).
As per claims 2 and 10, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein the at least one master edge generates a global model parameter that is provided to the at least first client edge and/or to the at least second client edge to update respective local ML models present in the at least first or second client edge (e.g. Milletari, see paragraph (0119, 0120-0124) which discloses generating a trained model based on the set of weight values to a maxima point as an amount of consensus between a training node and a consensus of training nodes.).
As per claims 3 and 11, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein the global ML model is built by aggregating the first local model parameter with a second local model parameter of an ML model provided by the at least second client edge (e.g. Milletari, see paragraph (0061-0062), which discloses receiving data from one or more parameters from a training node, where a parameter determiner may determine one or more values of one or more parameters of machine learning models by aggregating one or more values of the one or more corresponding parameters from one or more training nodes based on the sensor data received.).
As per claims 4 and 12, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein the first local model parameter corresponds to a first machine learning weight value, the second local model parameter corresponds to a second machine learning weight value and the global model parameter corresponds to a third machine learning weight value of the corresponding ML models (e.g. Milletari, see paragraph (0119, 0120-0124) which discloses generating a trained model based on the set of weight values to a maxima point as an amount of consensus between a training node and a consensus of training nodes.).
As per claims 5 and 13, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein the at least one master edge, the at least first client edge and the at least second client edge distribute their respective model parameters with each other (e.g. Milletari, see paragraph (0199) which discloses controllers provide signals for controlling one or more devices in response to sensor data received from one or more sensors.).
As per claims 6 and 14, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein the global ML model is updated each time a change of the local model parameter has occurred (e.g. Milletari, see paragraph (0131) which discloses time based training of nodes to aggregate parameter values from machine learning models into machine learning models.).
As per claims 7 and 15, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein collecting is performed in at least one of an event-based approach, and/or a time-based approach (e.g. Milletari, see paragraph (0131) which discloses time based training of nodes to aggregate parameter values from machine learning models into machine learning models.).
As per claims 8 and 16, Milletari teaches the method according to claim 1 and the computer according to claim 9, respectively, wherein generating the resulting global ML model by the at least one master edge uses an information about a topology of the network to build the global ML model (e.g. Milletari, see Figure 5 which discloses generating a machine learning model based on the training nodes in the topology.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 August 12, 2026
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