Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/30/2026 has been entered.
DETAILED ACTION
3. This Office Action is in response to the filing with the office dated 03/30/2026.
Claims 1, 7, 8, 11 and 16 have been amended. Claims 3, 5, 9, 10, 15 and 20 have been cancelled. New claim 26 has been added. Claims 1, 11 and 16 are independent claims. Claims 1, 2, 4, 6-8, 11-14, 16-19 and 21-26 are presented in this office action.
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.
4. Claims 1-20 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 1,11 and 16 recites the term “complementary data”. The term “complementary data” is unclear and is indefinite because the specification [0040] … data collectors 100 and may obtain a complementary data statistic based on the inferences. The paragraph does not clearly define the term what the complementary data is.
For examination purpose Examiner interprets complementary data as statistics. Examiner is available for an interview to discuss these matters at applicant’s convenience.
Response to amendment/arguments
5. Applicant’s amendments to claim 7, with respect to the claim objection has been fully considered. As a result claim objection has been withdrawn.
6. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. See response to arguments section. The rejection has been maintained.
7. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) have been fully considered but are moot in view of the new grounds of rejection. Please see the office action below.
Response to 101 Arguments
8. Applicants arguments on page 10 regarding 101 rejection states “Finding that the claims under appeal, which relate to the machine learning technology, sufficiently reflect an improvement to the computer or similar technology described in the specification of the subject application, the ARP vacated a § 101 rejection raised sua sponte by the Patent Trial and Appeal Board (PTAB). Id. at 8-10. Under Enfish, and consistent with Desjardins, Applicant submits that the claims are patent- eligible at least because the claims recite non-abstract elements that reflect an improvement to the computer technology, thereby integrating the alleged judicial exception into a practical application.
Examiner respectfully disagree as the amended claim limitations “distributing, by the data aggregator…. the trained inference models” is Insufficient to overcome Patent Eligibility. distributing the trained machine learning model to collect data in a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. Limitations “training a model …”, “using the trained models…” under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. There is nothing in the claim element which precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer components, or a programmed computer or a generic display or a set of processing nodes does to train the models and using those models does not take the claim limitation out of the mental processes grouping. The combination of these additional elements is no more than mere instructions to apply the exception using series of steps. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
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.
9. Claims 1,2. 4, 6-8, 11-14, 16-19 and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106; See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Pursuant to Step 1, claims 16-19 recite a system which are directed to the statutory category of a machine. Claims 11-14 recite a non-transitory computer readable medium, which are directed to a manufacture.
Regarding Claims 1, 11 and 16 Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Under the 2019 PEG, claims are deemed to be directed to an abstract idea if they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Here, claims 1, 11 and 16 are directed to an abstract idea categorized under mental processes. Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” MPEP 2016(a)(2)(III). Courts also consider a mental process as one that can be performed in the human mind and is merely using a computer as a tool to perform the concept. MPEP 2016(a)(2)(III)(C)(3). Claim 1 recites a mental process because the recited steps recite the actions for storing and manipulating data but is recited at a high level of generality that merely used computers as a tool to perform the processes. See MPEP 2106(a)(2)(III). For example, Claim 1 recites limitations of “determining….”, “distributing trained models…” is a process, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations are essentially steps of generating and manipulating data at a high level of generality, which can be performed by a person using a computer as a tool. These limitations, at the high level of generality as drafted, would encompass a user to determine the grouping of the nodes based on their similarities, distributing the trained models to the users, which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the recited abstract idea is integrated into a practical application. In this case, as explained above, claims 1, 11 and 16 merely recite a mental process. These limitations of “training inference model….”, “obtaining….”, “collecting…”, “distributing the trained models….”, “using the trained models….”. While limitations recite additional components in the form of “processor”, “memory”, “data aggregator”, “distributed system”, “communication system”, are recited at a high level of generality as generic computer components. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). The additional elements “obtaining….”, “collecting…” information amount to mere data gathering which is insignificant extra-solution activity. Similarly limitations “training a model …”, “using the trained models…” is Insufficient to overcome Patent Eligibility. training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and data gathering of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation of generic computing components is still mere instructions to apply the exception under MPEP 2106.05(f) and does not provide significantly more. The “obtaining….”, “collecting…”elements that was identified as insignificant extra-solution activity as mere data outputting when re-evaluated still does not provide significantly more, since this generic data gathering steps. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
Regarding claims 2, 3, 12, 13, 14, 17, 18, 19 recites “making a determination that the weight of the at least one edge falls below a threshold; and based on the determination: discarding the at least one edge from the similarity graph”, “wherein discarding the at least one edge indicates that the at least two nodes connected by the at least one edge are set up with different data collection schemas.”, “making a determination that the weight of the at least one edge is within a threshold; and based on the determination: retaining the at least one edge on the similarity graph” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user to determine the grouping of the nodes based on their similarities and based on the predefined thresholds discarding the edge and training the model. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 6 recites “updating, by the data aggregator, the similarity graph based on the collected data by updating the relationship based on a change in the properties of the data collected by the at least two nodes connected by the at least one edge” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user to update relationship based on the change in the properties of the data collected. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 7, 8, 26 recites “making a determination ….”, “selecting ….” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user select a model. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 21 recites “wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge is based comprise at least one selected from a group consisting of: a type of the data, a value of the data, a distribution of the data, a collection method of the data, a range of time over which the data is collected, and an environment in which the data is collected” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user select properties based on a type of the data, a value of the data, a distribution of the data, a collection method of the data, a range of time over which the data is collected, and an environment in which the data is collected and group them. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 22 recites “wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge is based comprise at least a numerical range of the data.” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user to select properties based on a at least a numerical range of the data is mere data gathering. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Regarding claim 23, 24, 25 recites “…one edge is based comprise at least a numerical range of the data.”, “…at least two nodes overlaps…”, “… at least one edge indicate one or more conditions in which the data is collected by each of the at least two nodes.”, “…,the nodes are positioned in disparate ambient conditions” which depends on the same abstract idea as claim 1. This limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. These limitations, at the high level of generality as drafted, would encompass a user to select properties based on a at least a numerical range of the data and which overlaps and the edge indicates a disparate ambient condition is mere data gathering. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper, in the context of this claim encompasses a user to evaluate or make a judgement on from where the request is being performed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
10. Claims 1, 2,4, 6-8, 11-14, 16-19 and 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over Harris; Theodore (US 20210027182 A1) herein referred as Harris182, in view of JOO; Mingyu (US 20210027179 A1) and in further view of Sadilek, Adam (US 20220351057 A1).
Regarding independent claim 1, Harris; Theodore (US 20210027182A1) teaches, a method for managing data collection in a distributed environment where data is collected in a data aggregator of the distributed environment and from sources operably connected to the data aggregator via a communication system (Paragraph [0100] teaches, Paragraph [0069]-[0071] discloses data collection in a distributed environment);
comprising: obtaining, by the data aggregator, a similarity graph, the similarity graph comprising: nodes based on data collected from the sources throughout the distributed environment and representing the sources, and a relationship between the nodes; the relationship being implemented with at least one edge connecting at least two nodes of the node (Paragraphs [0062], [0064], [0066] discloses, similarity graph/ strength comprising nodes and edges and relationship between nodes and edges based on the edge weight within a threshold);
determining, by the data aggregator, groupings of the nodes based on the relationship between the nodes (Paragraphs [0026], [0028] discloses, grouping the nodes based on relationship between nodes. Also see [0034]);
training, by the data aggregator, inference models for the groupings (Paragraphs [0025], [0026] discloses, training the prediction/ inference models for clustering/ grouping).
Harris182 fails to explicitly teach, the at least one edge comprising a weight that indicates whether the at least two nodes are set up with similar data collection schemas; training, by the data aggregator, inference models for the groupings to obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained, and the inference models being machine learning models; distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
JOO; Mingyu (US 20210027179 A1) teaches, the relationship being implemented with at least one edge connecting at least two nodes of the nodes, the at least one edge comprising a weight that indicates whether the at least two nodes are set up with similar data collection schemas (Paragraphs [0054]-[0056] discloses, relationship between two nodes. If the weighted values are within the limit than it indicates the two nodes are setup for similar data collection and if the values are different then the nodes are recognized to have different data collection schema);
obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained (Fig.8, Paragraphs [0067], [0075] discloses, trained models perspective for establishing a data collection strategy of a respective group, Also see [0064]), and the inference models being machine learning models (Paragraph [0130] discloses, the inference model easily learns and has the sufficient training data (i.e., inference models are machine learning models);
Harris182 and JOO et al fails to explicitly teach, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]]in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
Sadilek, Adam (US 20220351057 A1) teaches, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained (Paragraph [0023] discloses, deploying/ distributing the Machine-learned models to the user devices to make inferences and each inference model makes inference for different condition (Examiner interprets each ML model checks for different condition/ disease from group of conditions. See Paragraph [0046]);
each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group (Paragraph [0029] discloses, collecting statistics/ complementary data based on the actual inferences collected by the source of respective group/ ML model);
and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, (Paragraph [0031], [0032] discloses, generating inferences of the complementary/ statistics of the data collected by the sources and using the inferences in place of the statistics by using the generalized location/ removing the user location. Sending the inferences made by the ML model and never transmitting the statistics/ complementary data to the remote system/ data aggregator. Also see [0074]),
the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed (Paragraph [0032] discloses, the trained models being used to reduce a quantity of data transmitted and elimination the transmission of the statistics of user location (Examiner interprets reducing the transmitted as data as gathering data from different locations at different times and eliminating the user location by generalizing the location data).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182 and JOO et al by distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]]in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed., as taught by Sadilek et al (Paragraphs [0023], [0029], [0031], [0032]).
One of the ordinary skill in the art would have been motivated to make this modification, by deploying copies of the ML model for execution at the user devices can have either or both of the following technical effects. First, the privacy of users of the devices can be preserved by making inferences based on signals collected at the user device and then anonymizing the inferences prior to sending them to the remote system. In this manner, the signals collected at the user device are not sent directly to the remote system, thereby preserving sensitive information contained within those signals as taught by Sadilek et al (Paragraph [0030]).
Regarding dependent claim 2, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182 further teaches, further comprising: making a determination that the weight of the at least one edge falls below a threshold; and based on the determination: discarding the at least one edge from the similarity graph (Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold)).
Regarding dependent claim 3, Harris182, JOO et al and Sadilek et al teach, the method of claim 2.
Harris182 further teaches, wherein discarding the at least one edge indicates that(Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold) (i.e., edge weight determines if the nodes have similar or dissimilar data. if the edge value is within the threshold value then the nodes have similar data and if the edge value is less than the threshold value, it means that the nodes have dissimilar data). Also see [0064], [0066]).
Regarding dependent claim 4, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182 further teaches, further comprising: making a determination that the weight of the at least one edge is within a threshold; and based on the determination: retaining the at least one edge on the similarity graph (Paragraph [0068] discloses if the edge weight falls within the threshold then the edges are retained. Also see Paragraph [0079]).
Regarding dependent claim 6, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182 further teaches, further comprising: updating, by the data aggregator, the similarity graph based on the collected data by updating the relationship based on a change in properties of the data collected by the at least two nodes connected by the at least one edge (Paragraph [0109] If the monitor process determines that there is new data available as training data, indicating that there is new information (YES at 503), the then model building process continues to generate a topological graph, at 504, based on the new data (i.e., updating the relationship based on the change in similarity between the nodes such as new nodes added or existing ones deleted). The topological graph can be generated according to the methods discussed above. After generating the topological graph, a community detection algorithm can determine new communities structures within the topological graph, at 505, as discussed above. Also see Paragraph [0096]).
Regarding dependent claim 7, Harris182, JOO et al and Sadilek et al teach, the method of claim 6.
Harris182 further teaches, further comprising: making a determination, based on the updated relationship, that the groupings of the nodes has changed into new groupings; and based on that determination: selecting one of the trained inference models for each group of the new groupings, or(Paragraph [0112] If the percentage difference between the new and old models is greater than a predetermined threshold value, indicating that there is new information (YES at 511), then the model building process continues to generate decision rules corresponding to the new model, at 512, as discussed above. Then the modeling behavior tree used to drive the model building process can be tuned).
JOO et al also further teaches, further comprising: making a determination, based on the updated relationship, that the groupings of the nodes has changed into new groupings; and based on that determination: selecting one of the trained inference models for each group of the new groupings, or(Paragraphs [0135], [0136] discloses, if the actual result generated by the trained inference model does not match with the label, label error is generated. Accordingly, the correction of the label for the second type of data may be considered during the training).
Regarding dependent claim 8, Harris182, JOO et al and Sadilek et al teach, the method of claim 6.
Harris182 further teaches, further comprising: making a determination, based on the updated relationship, that the grouping of the nodes has not changed; and based on that determination: continuing the data collection from the sources utilizing the trained inference models (Paragraphs [0109], [0110] discloses, based on indication that there is new information and the grouping of nodes are within the threshold value, then the data collection continues receiving more new data).
JOO et al also further teaches, further comprising: making a determination, based on the updated relationship, that the grouping of the nodes has not changed; and based on that determination: continuing the data collection from the sources utilizing the trained inference models (Paragraph [0130] discloses, if the actual result generated by the inference model matches with the label, the plurality of inference models 500 may be considered to be well trained to well predict the first type of data (i.e., the grouping of the nodes have not changed).
Regarding independent claim 11, Harris; Theodore D (US 20210027182A1) teaches, a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing data collection in a distributed environment where data is collected in a data aggregator of the distributed environment and from sources operably connected to the data aggregator via a communication system (Paragraph [0100] teaches, Paragraph [0069]-[0071] discloses data collection in a distributed environment), the operations comprising: obtaining, by the data aggregator, a similarity graph, the similarity graph comprising: nodes based on data collected from the sources throughout the distributed environment and representing the sources, and a relationship between the nodes, the relationship being implemented with at least one edge connecting at least two nodes of the nodes (Paragraphs [0062], [0064], [0066] discloses, similarity graph/ strength comprising nodes and edges and relationship between nodes and edges based on the edge weight within a threshold);
determining, by the data aggregator, groupings of the nodes based on the relationship between the nodes (Paragraphs [0026], [0028] discloses, grouping the nodes based on relationship between nodes. Also see [0034]);
training, by the data aggregator, inference models for the groupings (Paragraphs [0025], [0026] discloses, training the prediction/ inference models for clustering/ grouping).
Harris182 fails to explicitly teach, the at least one edge comprising a weight that indicates whether the at least two nodes have a similar data collection schema; obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained, and the inference models being machine learning models; distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]] in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
JOO; Mingyu (US 20210027179 A1) teaches, the at least one edge comprising a weight that indicates whether the at least two nodes have a similar data collection schema (Paragraphs [0054]-[0056] discloses, relationship between two nodes. If the weighted values are within the limit than it indicates the two nodes are setup for similar data collection and if the values are different then the nodes are recognized to have different data collection schema);
obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained (Fig.8, Paragraphs [0067], [0075] discloses, trained models perspective for establishing a data collection strategy of a respective group, Also see [0064]), and the inference models being machine learning models (Paragraph [0130] discloses, the inference model easily learns and has the sufficient training data (i.e., inference models are machine learning models);
Harris182 and JOO et al fails to explicitly teach, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]] in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
Sadilek, Adam (US 20220351057 A1) teaches, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained (Paragraph [0023] discloses, deploying/ distributing the Machine-learned models to the user devices to make inferences and each inference model makes inference for different condition (Examiner interprets each ML model checks for different condition/ disease from group of conditions. See Paragraph [0046]);
each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group (Paragraph [0029] discloses, collecting statistics/ complementary data based on the actual inferences collected by the source of respective group/ ML model);
and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]] in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator (Paragraph [0031], [0032] discloses, generating inferences of the complementary/ statistics of the data collected by the sources and using the inferences in place of the statistics by using the generalized location/ removing the user location. Sending the inferences made by the ML model and never transmitting the statistics/ complementary data to the remote system/ data aggregator. Also see [0074]),
the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed (Paragraph [0032] discloses, the trained models being used to reduce a quantity of data transmitted and elimination the transmission of the statistics of user location (Examiner interprets reducing the transmitted as data as gathering data from different locations at different times and eliminating the user location by generalizing the location data).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182 and JOO et al by distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]]in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed., as taught by Sadilek et al (Paragraphs [0023], [0029], [0031], [0032]).
One of the ordinary skill in the art would have been motivated to make this modification, by deploying copies of the ML model for execution at the user devices can have either or both of the following technical effects. First, the privacy of users of the devices can be preserved by making inferences based on signals collected at the user device and then anonymizing the inferences prior to sending them to the remote system. In this manner, the signals collected at the user device are not sent directly to the remote system, thereby preserving sensitive information contained within those signals as taught by Sadilek et al (Paragraph [0030]).
Regarding dependent claim 12, Harris182, JOO et al and Sadilek et al teach, the non-transitory machine-readable medium of claim 11.
Harris182 further teaches, further comprising: making a determination that the weight of the at least one edge falls below a threshold; and based on the determination: discarding the at least one edge from the similarity graph (Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold)).
Regarding dependent claim 13, Harris182, JOO et al and Sadilek et al teach, the non-transitory machine-readable medium of claim 12.
Harris182 further teaches, wherein discarding the at least one edge indicates that (Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold) (i.e., edge weight determines if the nodes have similar or dissimilar data. if the edge value is within the threshold value then the nodes have similar data and if the edge value is less than the threshold value, it means that the nodes have dissimilar data). Also see [0064], [0066]).
Regarding dependent claim 14, Harris182, JOO et al and Sadilek et al teach, the non-transitory machine-readable medium of claim 11.
Harris182 further teaches, further comprising: making a determination that the weight of the at least one edge is within a threshold; and based on the determination: retaining the at least one edge on the similarity grap(Paragraph [0068] discloses if the edge weight falls within the threshold then the edges are retained. Also see Paragraph [0079]).
Regarding independent claim 16, Harris; Theodore D (US 20210027182 A1) teaches a data aggregator for managing data collection in a distributed environment where data is collected in the data aggregator of the distributed environment and from sources operably connected to the data aggregator via a communication system (Paragraph [0100] teaches, Paragraph [0069]-[0071] discloses data collection in a distributed environment), comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing the data collection, the operations comprising: obtaining, by the data aggregator, a similarity graph, the similarity graph comprising: nodes based on data collected from the sources throughout the distributed environment and representing the sources, and a relationship between the nodes, the relationship being implemented with at least one edge connecting at least two nodes of the nodes (Paragraphs [0062], [0064], [0066] discloses, similarity graph/ strength comprising nodes and edges and relationship between nodes and edges based on the edge weight within a threshold);
determining, by the data aggregator, groupings of the nodes based on the relationship between the nodes (Paragraphs [0026], [0028] discloses, grouping the nodes based on relationship between nodes. Also see [0034]);
training, by the data aggregator, inference models for the groupings (Paragraphs [0025], [0026] discloses, training the prediction/ inference models for clustering/ grouping)
Harris182 fails to explicitly teach, the at least one edge comprising a weight that indicates whether the at least two nodes are set up with similar data collection schemas; obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained, and the inference models being machine learning models; distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
JOO; Mingyu (US 20210027179 A1) teaches, the at least one edge comprising a weight that indicates whether the at least two nodes are set up with similar data collection schemas (Paragraphs [0054]-[0056] discloses, relationship between two nodes. If the weighted values are within the limit than it indicates the two nodes are setup for similar data collection and if the values are different then the nodes are recognized to have different data collection schema);
obtain trained inference models for the groupings where each group of the groupings is associated with one of the trained inference models, each of the inference models being trained using the data collected by the sources of a respective group of the groupings for which each of the inference models is trained (Fig.8, Paragraphs [0067], [0075] discloses, trained models perspective for establishing a data collection strategy of a respective group, Also see [0064]), and the inference models being machine learning models (Paragraph [0130] discloses, the inference model easily learns and has the sufficient training data (i.e., inference models are machine learning models);
Harris182 and JOO et al fails to explicitly teach, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed.
Sadilek, Adam (US 20220351057 A1) teaches, distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained (Paragraph [0023] discloses, deploying/ distributing the Machine-learned models to the user devices to make inferences and each inference model makes inference for different condition (Examiner interprets each ML model checks for different condition/ disease from group of conditions. See Paragraph [0046]);
each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group (Paragraph [0029] discloses, collecting statistics/ complementary data based on the actual inferences collected by the source of respective group/ ML model);
and using, by the data aggregator, a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator (Paragraph [0031], [0032] discloses, generating inferences of the complementary/ statistics of the data collected by the sources and using the inferences in place of the statistics by using the generalized location/ removing the user location. Sending the inferences made by the ML model and never transmitting the statistics/ complementary data to the remote system/ data aggregator. Also see [0074]),
the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed (Paragraph [0032] discloses, the trained models being used to reduce a quantity of data transmitted and elimination the transmission of the statistics of user location (Examiner interprets reducing the transmitted as data as gathering data from different locations at different times and eliminating the user location by generalizing the location data).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182 and JOO et al by distributing, by the data aggregator and after obtaining the trained inference models, each of the trained inference models to the respective group among the groupings for which each inference model is trained, each of the trained inference models facilitates collection of complementary data based on actual data collected by the source of the respective group; and using, by the data aggregator a copy of the trained inference models hosted by the data aggregator to generate inferences of the complementary data collected by the sources and using the inferences [[as]]in place of the complementary data collected by the source, the complementary data never being transmitted directly from the sources to the data aggregator, the trained inference models being used to reduce a quantity of data transmitted from the sources to the data aggregator through eliminating a portion of transmissions of the complementary data collected by the sources to the data aggregator after the trained inference models are obtained and distributed., as taught by Sadilek et al (Paragraphs [0023], [0029], [0031], [0032]).
One of the ordinary skill in the art would have been motivated to make this modification, by deploying copies of the ML model for execution at the user devices can have either or both of the following technical effects. First, the privacy of users of the devices can be preserved by making inferences based on signals collected at the user device and then anonymizing the inferences prior to sending them to the remote system. In this manner, the signals collected at the user device are not sent directly to the remote system, thereby preserving sensitive information contained within those signals as taught by Sadilek et al (Paragraph [0030]).
Regarding dependent claim 17, Harris182, JOO et al and Sadilek et al teach, the data aggregator of claim 16.
Harris182 further teaches, further comprising: making a determination that the weight of the at least one edge falls below a threshold; and based on the determination: discarding the at least one edge from the similarity graph (Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold)).
Regarding dependent claim 18, Harris182, JOO et al and Sadilek et al teach, the data aggregator of claim 17.
Harris182 further teaches, wherein discarding the at least one edge indicates that (Paragraph [0068], the optimization algorithm may remove (e.g., prune) existing structures that have weaker relationships (e.g., nodes connected by edging have a weight less than a threshold or a distance greater than a threshold) (i.e., edge weight determines if the nodes have similar or dissimilar data. if the edge value is within the threshold value then the nodes have similar data and if the edge value is less than the threshold value, it means that the nodes have dissimilar data). Also see [0064], [0066]).
Regarding dependent claim 19, Harris182, JOO et al and Sadilek et al teach, the data aggregator of claim 16.
Harris182 further teaches, further comprising: making a determination that(Paragraph [0068] discloses if the edge weight falls within the threshold then the edges are retained. Also see Paragraph [0079]).
Regarding dependent claim 22, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182 further teaches, wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge is based comprise at least a numerical range of the data (Paragraph [0018] discloses, degree of similarity between the properties of data collected as age range of a person based on the browsing history).
Regarding dependent claim 23, Harris182, JOO et al and Sadilek et al teach, the method of claim 22.
Harris182 further teaches, wherein a first numerical range of first data collected by a first of the at least two nodes overlaps with a second numerical range of second data collected by a second of the at least two nodes (Paragraph [0091] the learner can include multiple learners where single rules are generated by finding overlapping decision rule sets across learners. In some embodiments, if the model 280 is rebuilt using different training data, thereby causing a shift in the distribution of scores, the decision rules 290 can be re-determined).
Regarding dependent claim 26, Harris182, JOO et al and Sadilek et al teach, the method of claim 7.
Harris182 further teaches, further comprising: based on the determination that the groupings has changed into the new groupings, dynamically updating a threshold weight value for evaluating whether to retain or discard edges while determining the new groupings, the threshold weight value being updated based on a maximum number of members of the new groupings (Paragraph [0096] discloses, if the novel structures decreased the accuracy of the resulting model, they can be weighted less or be removed in the next model rebuild. This self-correction is advantageous because the parameters and settings used to run the algorithms are based on the incoming data, which can shift over time).
JOO et al also further teaches, further comprising: based on the determination that the groupings has changed into the new groupings, dynamically updating a threshold weight value for evaluating whether to retain or discard edges while determining the new groupings, the threshold weight value being updated based on a maximum number of members of the new groupings (Paragraphs [0135], [0136] discloses, if the actual result generated by the trained inference model does not match with the label, label error is generated. Accordingly, the correction of the label for the second type of data may be considered during the training).
11. Claims 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Harris; Theodore (US 20210027182 A1) herein referred as Harris182, in view of JOO; Mingyu (US 20210027179 A1), Sadilek, Adam (US 20220351057 A1), and in further view of Xu; Jianwu (US 20200285807 A1).
Regarding dependent claim 24, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182, JOO et al and Sadilek et al fails to explicitly teach, wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge indicate one or more conditions in which the data is collected by each of the at least two nodes.
Xu; Jianwu (US 20200285807 A1) teaches, wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge indicate one or more conditions in which the data is collected by each of the at least two nodes (Paragraph [0017] By using the same parameter settings to train the NMT model among each sensor pair in the system, we can use the training score to quantify the strength of the relationship between the source and target sensors. A higher score implies a stronger relationship between two sensors in the pair while a lower score implies a weaker relationship).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182, JOO et al and Sadilek et al by providing wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes from which the weight of the at least one edge indicate one or more conditions in which the data is collected by each of the at least two nodes, as taught by Xu et al (Paragraph [0017]).
One of the ordinary skill in the art would have been motivated to make this modification, By doing so, determine whether to discard an edge, including the use of a threshold for edge weights. Other methods may be utilized, including discarding edges in order to maintain a maximum and/or minimum number of nodes in a particular grouping by filtering out the redundant sensor data. The connection among different sensors generates clustering structure as taught by Xu et al (Paragraphs [0058], [0076]).
Regarding dependent claim 25, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182, JOO et al and Sadilek et al fails to explicitly teach, wherein, across the distributed environment, the nodes are positioned in disparate ambient conditions and the weight of the edge further indicates whether the at least two nodes are configured to collect data in similar conditions among the disparate ambient conditions.
Xu; Jianwu (US 20200285807 A1) teaches, wherein, across the distributed environment, the nodes are positioned in disparate ambient conditions and the weight of the edge further indicates whether the at least two nodes are configured to collect data in similar conditions among the disparate ambient conditions (Paragraph [0017] The sensors can be in, for example, a power plant or physical plant or some other type of structure. In an embodiment, the present invention constructs a comprehensive pairwise inter-dependence and inter-correlation model using the multivariate sensor data in relation to the system operating in a normal situation (i.e., nodes/ sensors are located in different type of structure/ ambient conditions). By using the same parameter settings to train the NMT model among each sensor pair in the system, we can use the training score to quantify the strength of the relationship between the source and target sensors. A higher score implies a stronger relationship between two sensors in the pair while a lower score implies a weaker relationship. With the quantified invariant relationship between sensors, we can then generate a sensor relationship network by treating nodes as sensors and edges as relationship).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182, JOO et al and Sadilek et al by providing wherein, across the distributed environment, the nodes are positioned in disparate ambient conditions and the weight of the edge further indicates whether the at least two nodes are configured to collect data in similar conditions among the disparate ambient conditions, as taught by Xu et al (Paragraph [0017]).
One of the ordinary skill in the art would have been motivated to make this modification, By doing so, determine whether to discard an edge, including the use of a threshold for edge weights. Other methods may be utilized, including discarding edges in order to maintain a maximum and/or minimum number of nodes in a particular grouping by filtering out the redundant sensor data. The connection among different sensors generates clustering structure as taught by Xu et al (Paragraphs [0058], [0076]).
12. Claims 24 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Harris; Theodore (US 20210027182 A1) herein referred as Harris182, in view of JOO; Mingyu (US 20210027179 A1), Sadilek, Adam (US 20220351057 A1), Xu; Jianwu (US 20200285807 A1) and in further view of BOUCHER; Christopher (US 20220355839 A1).
Regarding dependent claim 21, Harris182, JOO et al and Sadilek et al teach, the method of claim 1.
Harris182 further teaches, wherein the weight of the at least one edge further indicates a degree of similarity between properties of data collected by the at least two nodes, and the properties of the data collected by the at least two nodes (Paragraph [0068] discloses if the edge weight falls within the threshold then the edges are retained. Also see Paragraph [0079]).
Harris182, JOO et al and Sadilek et al fails to explicitly teach, comprise at least one selected from a group consisting of: a type of the data, a value of the data, a distribution of the data, a collection method of the data, a range of time over which the data is collected, and an environment in which the data is collected.
BOUCHER; Christopher (US 20220355839 A1) teaches, properties of the data comprise at least one selected from a group consisting of: a type of the data, a value of the data, a distribution of the data, a collection method of the data, a range of time over which the data is collected, and an environment in which the data is collected (Paragraph [0110] The component predicting step can comprise evaluating at least one model from the set of prediction models, wherein the at least one model represents a future development of at least one of the at least one condition of the component as a function of data of at least one data type selected from sensed data, load data, environment data, maintenance data, and specification data. The data of the at least one data type can relate to the component or to an asset that comprises the component).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Harris182, JOO et al and Sadilek et al, by providing properties of the data comprise at least one selected from a group consisting of: a type of the data, a value of the data, a distribution of the data, a collection method of the data, a range of time over which the data is collected, and an environment in which the data is collected, as taught by BOUCHER et al (Paragraph [0110]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, provide a method, system and computer program product for optimizing inspection activities and/or maintenance activities relating to the represented railway infrastructure system with regard to at least one of a need of resources to perform said inspection activities and/or maintenance activities, negative impacts on the represented railway infrastructure system caused by the inspection and/or maintenance activities, and at least one of a performance, reliability and availability of the represented railway network as taught by BOUCHER et al (Paragraphs [0036], [0037]).
Closest Prior art
13. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Xie; Di (US 20210073628 A1) teaches, [0062] The preset similarity measurement algorithm may include, but not limited to, Euclidean distance, Manhattan distance, Chebyshev distance, Minkowski distance, standardized Euclidean distance, Mahalanobis distance, included angle cosine, Hamming distance, etc. A similarity measurement matrix for task attributes may be generated through one of those algorithms. According to the similarity measurement matrix, if similarity of multiple task attributes is greater than the preset threshold, it means that the multiple task attributes are relatively similar, and the nodes that implement these task attributes may be assigned into a same category. It is possible to calculate the common part of these task attributes once, and the unique part of each of the task attributes is executed after the calculation of the common part of the task attributes. As a result, the redundancy in the neural networks can be effectively reduced, thereby improving the operation efficiency.
Edge; Darren K (US 20210019558 A1) teaches, [0076] With continued reference to FIG. 8, both nodes and edges may be cut from each resulting graph to reduce noise and emphasize recurrent, meaningful structure. For example, edges may be cut if their weight is below a threshold, or edges may be cut in increasing weight order (or other statistic such as betweenness centrality or mutual information) until the reduced graph exhibits desirable statistical properties (such as a target modularity score representing a clear partitioning of the graph into communities of closely related nodes).
14. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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