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 .
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 10) and a non-transitory storage medium (claims 11 - 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 1,
Step 2A, Prong One
The claim recites in part:
constructing, by the central node, a distance matrix, using the support matrices and the agreement matrices received from the edge nodes;
using, by the central node, the distance matrix to cluster the edge nodes into one or more cliques.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person could compare information, determine relationships or similarities, and group related items together.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
transmitting labeling functions, by a central node to each edge node in a group of edge nodes;
receiving, by the central node, a respective support matrix and agreement matrix from each of the edge nodes;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The recitation of labeling functions, respective support matrix, and agreement matrix amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
transmitting labeling functions, by a central node to each edge node in a group of edge nodes;
receiving, by the central node, a respective support matrix and agreement matrix from each of the edge nodes;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The recitation of labeling functions, respective support matrix, and agreement matrix amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein information in the support matrices and the agreement matrices serves as a proxy for respective underlying sample data distributions at each of the edge nodes in one of the cliques.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein information in the support matrices and the agreement matrices serves as a proxy for respective underlying sample data distributions at each of the edge nodes in one of the cliques.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
wherein, for one of the edge nodes, the support matrix for that edge node comprises a score that indicates how frequently each pair of the labeling functions are applied together to a local data sample of that edge node.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
wherein, for one of the edge nodes, the agreement matrix for that edge node comprises a score that indicates how frequently each pair of the labeling functions agree on a class assigned to a local data sample of that edge node.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein one of the labeling functions is a single class labeling function
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein one of the labeling functions is a single class labeling function
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein one of the labeling functions is a multi-class labeling function
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein one of the labeling functions is a multi-class labeling function
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
wherein the distance matrix indicates respective distances between each pair of the edge nodes
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
wherein each of the labeling functions is configured to either assign a class to respective data samples of the edge nodes, or abstain from assigning a class to the respective data samples of the edge nodes.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the support matrices and the agreement matrices, individually and collectively, do not include enough information to enable reconstruction, of respective data samples of the edge nodes, at the central node, or at any of the edge nodes.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein the support matrices and the agreement matrices, individually and collectively, do not include enough information to enable reconstruction, of respective data samples of the edge nodes, at the central node, or at any of the edge nodes.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 10,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the support matrix of one of the edge nodes is used to filter the agreement matrix of that edge node.
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein the support matrix of one of the edge nodes is used to filter the agreement matrix of that edge node.
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The non-transitory storage medium and one or more hardware processors are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
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 3, 4, 13, and 14 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.
As to claims 3 and 4, the limitations “each pair of the labeling functions” is not understood by the examiner as there is insufficient antecedent basis for this limitation in the claim.
Claims 13 and 14 depend on claims 3 and 4, respectively, therefore the claims are also rejected.
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.
Claim(s) 1 – 6, 8 – 16, and 18 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalyan et al (US 2021/0383187) in view of Ghesu et al (US 2023/0154164).
As to claim 1, Kalyan et al teaches a method, comprising:
transmitting labeling functions, by a central node to each edge node in a group of edge nodes (paragraph [0025]…the one or more group managers 140 are configured to manage the one or more corresponding learning groups by providing authentication, authorization and harmonization between the one or more learning nodes 120. The platform server 130 also includes a central connection manager 150 operatively coupled to the one or more group managers 140. The central connection manager 150 orchestrates communication among the one or more learning groups for harmonization of the decentralized learning based on a decision for participation of the one or more learning groups via multiple selective group learning flags at a group level)(Examiner’s Note: “the one or more group managers 140 are configured to manage the one or more corresponding learning groups by providing authentication, authorization and harmonization between the one or more learning nodes 120” reads on “transmitting labeling functions, by a central node to each edge node in a group of edge nodes”);
receiving, by the central node, a respective support matrix and agreement matrix from each of the edge nodes (paragraph [0027]…The platform server 130 also manages the communication between the central connection manager 150, the one or more learning groups 110 and the one or more learning nodes 120 via a communication management module. The communication mechanism involves weight matrices of the one or more machine learning models that flow from the one or more learning nodes 120 to the one or more corresponding group managers 140 and the weight updates back to the one or more learning nodes 120 after computations on the weight matrices)(Examiner’s Note: “The communication mechanism involves weight matrices of the one or more machine learning models that flow from the one or more learning nodes 120 to the one or more corresponding group managers 140” reads on “receiving, by the central node, a respective support matrix and agreement matrix from each of the edge nodes”);
constructing, by the central node, a distance matrix, using the support matrices and the agreement matrices received from the edge nodes (paragraph [0027]…the communication management component is responsible for collecting all the events across the one or more learning nodes 120, determining the latest events to harmonize across the one or more learning nodes 120 and then sending the latest weight matrix to all the learning nodes. The communication management component includes a server-side module as well as a client-side connector module that helps in determining the latest weight matrix and synchronize the communication with the epoch cycle during the ML Process execution to make the communication efficient)(Examiner’s Note:” the communication management component is responsible for collecting all the events across the one or more learning nodes 120, determining the latest events to harmonize across the one or more learning nodes 120 and then sending the latest weight matrix to all the learning nodes” reads on “constructing, by the central node, a distance matrix, using the support matrices and the agreement matrices received from the edge nodes”); and
using, by the central node, the distance matrix to cluster the edge nodes into one or more cliques (paragraph [0029]…Each of the one or more learning nodes 120 needs to register with the one or more corresponding group managers 140 to be able to send model updates in form of the weight matrix and also to receive harmonized models updates back from the corresponding one or more group managers 140 taking into account the multiple selective learning flags which decide, what to apply based on a selected preference of the one or more learning nodes 120. The one or more group managers 140 help in managing and controlling the one or more learning nodes 120 which are distributed for each tenant and provides authentication and authorization from a central location)(Examiner’s Note: “receive harmonized models updates back from the corresponding one or more group managers 140 taking into account the multiple selective learning flags which decide, what to apply based on a selected preference of the one or more learning nodes 120. The one or more group managers 140 help in managing and controlling the one or more learning nodes 120 which are distributed for each tenant and provides authentication and authorization from a central location” reads on “using, by the central node, the distance matrix to cluster the edge nodes into one or more cliques”).
Kalyan et al fails to explicitly show/teach that the weight matrix comprises respective support matrix and agreement matrix.
However, Ghesu et al teaches weight matrix comprises respective support matrix and agreement matrix (paragraph [0071]…In embodiment shown in FIG. 9, the input layer 902 comprises 36 nodes 912, arranged as a two-dimensional 6×6 matrix. The convolutional layer 904 comprises 72 nodes 914, arranged as two two-dimensional 6×6 matrices, each of the two matrices being the result of a convolution of the values of the input layer with a kernel. Equivalently, the nodes 914 of the convolutional layer 904 can be interpreted as arranges as a three-dimensional 6×6×2 matrix, wherein the last dimension is the depth dimension).
Therefore, it would have obvious for one having ordinary skill in the art at the time the invention was made, for Kalyan et al’s weight matrix to comprise respective support matrix and agreement matrix, as in Ghesu et al, for the purpose of continuously optimized with the plurality of subsets.
As to claim 2, modified Kalyan et al teaches a method, wherein information in the support matrices and the agreement matrices serves as a proxy for respective underlying sample data distributions at each of the edge nodes in one of the cliques (paragraph [0027]…Each of the one or more learning nodes 120 needs to register with the one or more corresponding group managers 140 to be able to send model updates in form of the weight matrix and also to receive harmonized models updates back from the corresponding one or more group managers 140 taking into account the multiple selective learning flags which decide, what to apply based on a selected preference of the one or more learning nodes 120. The one or more group managers 140 help in managing and controlling the one or more learning nodes 120 which are distributed for each tenant and provides authentication and authorization from a central location).
As to claim 3, modified Kalyan et al teaches a method, wherein, for one of the edge nodes, the support matrix for that edge node comprises a score that indicates how frequently each pair of the labeling functions are applied together to a local data sample of that edge node (paragraph [0029]…the node management module is responsible for maintaining a list of the one or more learning nodes 120 involved in the decentralized learning and status of the one or more learning nodes such as offline/inactive/active. Each of the one or more learning nodes 120 needs to register with the one or more corresponding group managers 140 to be able to send model updates in form of the weight matrix and also to receive harmonized models updates back from the corresponding one or more group managers 140 taking into account the multiple selective learning flags which decide, what to apply based on a selected preference of the one or more learning nodes 120).
As to claim 4, modified Kalyan et al teaches a method, wherein, for one of the edge nodes, the agreement matrix for that edge node comprises a score that indicates how frequently each pair of the labeling functions agree on a class assigned to a local data sample of that edge node (paragraph [0029]…the node management module is responsible for maintaining a list of the one or more learning nodes 120 involved in the decentralized learning and status of the one or more learning nodes such as offline/inactive/active. Each of the one or more learning nodes 120 needs to register with the one or more corresponding group managers 140 to be able to send model updates in form of the weight matrix and also to receive harmonized models updates back from the corresponding one or more group managers 140 taking into account the multiple selective learning flags which decide, what to apply based on a selected preference of the one or more learning nodes 120).
As to claim 5, Kalyan et al in view of Ghesu et al discloses the claimed invention except for wherein one of the labeling functions is a single class labeling function. It would have been an obvious matter of design choice for one of the labeling functions to be a single class labeling function, since the applicant has not disclosed that the one of the labeling function being a single class labeling function solves any stated problems or is for any particular purpose and it appears that the invention would perform equally well any type of labeling function.
As to claim 6, Kalyan et al in view of Ghesu et al discloses the claimed invention except for wherein one of the labeling functions is a multi-class labeling function. It would have been an obvious matter of design choice for one of the labeling functions to be a multi-class labeling function, since the applicant has not disclosed that the one of the labeling function being a multi-class labeling function solves any stated problems or is for any particular purpose and it appears that the invention would perform equally well any type of labeling function.
As to claim 8, modified Kalyan et al teaches the method wherein each of the labeling functions is configured to either assign a class to respective data samples of the edge nodes, or abstain from assigning a class to the respective data samples of the edge nodes (paragraph [0019]…FIG. 1 is a block diagram of a decentralized machine learning system 100 in accordance with an embodiment of the present disclosure. The system 100 includes one or more learning groups 110 which includes one or more learning nodes 120. The one or more learning groups 110 are formed via one or more learning group formation protocols based on a decision for participation of the one or more learning nodes 120 in decentralized learning via multiple selective node learning flags at a node level. As used herein, the term ‘decentralized learning’ is defined as the learning process that enables individual learning or group learning for multiple decentralized nodes or groups within one or more multiple networks. As used herein, the term ‘one or more learning nodes’ are defined as one or more self-contained logical units such as one or more machine learning pipelines in one or more physical nodes of the network. In such embodiment, the one or more learning nodes 120 may store at least one of multiple datatypes used for the one or more machine learning processes, an asset catalog or a combination thereof. In such embodiment, the multiple datatypes may include one or more categories, wherein the one or more categories include relational or structured data category, unstructured or semi-structured text category and media data such as images and videos category).
As to claim 9, modified Kalyan et al teaches the method, wherein the support matrices and the agreement matrices, individually and collectively, do not include enough information to enable reconstruction, of respective data samples of the edge nodes, at the central node, or at any of the edge nodes (paragraph [0027]…The platform server 130 also manages the communication between the central connection manager 150, the one or more learning groups 110 and the one or more learning nodes 120 via a communication management module. The communication mechanism involves weight matrices of the one or more machine learning models that flow from the one or more learning nodes 120 to the one or more corresponding group managers 140 and the weight updates back to the one or more learning nodes 120 after computations on the weight matrices. The communication management component is responsible for collecting all the events across the one or more learning nodes 120, determining the latest events to harmonize across the one or more learning nodes 120 and then sending the latest weight matrix to all the learning nodes. The communication management component includes a server-side module as well as a client-side connector module that helps in determining the latest weight matrix and synchronize the communication with the epoch cycle during the ML Process execution to make the communication efficient).
As to claim 10, Ghesu et al teaches the method, wherein the support matrix of one of the edge nodes is used to filter the agreement matrix of that edge node (paragraph [0071]…In embodiment shown in FIG. 9, the input layer 902 comprises 36 nodes 912, arranged as a two-dimensional 6×6 matrix. The convolutional layer 904 comprises 72 nodes 914, arranged as two two-dimensional 6×6 matrices, each of the two matrices being the result of a convolution of the values of the input layer with a kernel. Equivalently, the nodes 914 of the convolutional layer 904 can be interpreted as arranges as a three-dimensional 6×6×2 matrix, wherein the last dimension is the depth dimension).
It would have been obvious for the support matrix of one of the edge nodes is used to filter the agreement matrix of that edge node, for the same reasons above
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kalyan et al (US 2021/0383187) in view of Ghesu et al (US 20230154164) and in further view of Chalk et al (US 2024/0143794).
As to claim 7, Sharma et al teaches a distance matrix.
Kalyan et al and Ghesu et al both fail to explicitly show/teach that the distance indicates respective distances between each pair of the edge nodes.
However, Chalk et al teaches a distance indicates respective distances between each pair of the edge nodes (paragraph [0140]…The model is then retrained using the altered and/or inserted data (at 2030), and the weights between the models may be compared (at 2040). As with other analyses, the degree of difference between the weight space of the original model and the retrained model may be indicative of an exfiltration attempt, as there will be a larger weight difference when data is being stored within the weight matrix as compared against a model where only legitimate weights are updated in response to the data changes. A comparison is thus done between the calculated difference in weights against a configured threshold (at 2050). A fraud/exfiltration event is detected when the distance value is above the threshold (at 2060), or the model is determined to be ‘clean’ (at 2070) when the distance in the weights is below the threshold).
Therefore, it would have obvious for one having ordinary skill in the art at the time the invention was made, for Kalyan et al’s distance indicates respective distances between each pair of the edge nodes, as in Chalk et al, for the purpose of filtering out bad data that is above a threshold.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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