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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/8/2026 has been entered.
DETAILED ACTION
Claims 1-20 are presented for examination in this application, 17/850744, filed 2022-06-27 with an effective filing date of 2022-06-27.
The Examiner cites particular sections in the references as applied to the claims
below for the convenience of the applicant(s). Although the specified citations are
representative of the teachings in the art and are applied to the specific limitations within
the individual claim, other passages and figures may apply as well. It is respectfully
requested that, in preparing responses, the applicant(s) fully consider the references in
their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner.
Response to Arguments
Applicant’s arguments and remarks filed 2026-01-02 have been fully considered. The arguments and remarks regarding the 35 U.S.C 103 rejections were found to be persuasive however the amendments have necessitated a change in the references applied resulting in a new grounds of rejection. The 35 U.S.C 103 rejections have been maintained via new ground of rejection.
35 U.S.C 103
Applicant’s response:
Applicant asserts “In particular, Elkabetz is completely silent with regard to any of the data sources (namely, the sensors and other sources 344-349 shown in Figure 2) generating any reduced size data locally using the collected data that have been collected by these data sources. Rather, Elkabetz teaches that each of these data sources provide the collected data as-is to information collection and normalization server 310, which is the actual data aggregator of Elkabetz that processes and stores the collected data. This is contrast to at least "the data collector being a data source of the data and collects the data using one or more sensors as collected data," and "the data collector generating the reduced size data at a location of the data source using the collected data," as required by the amended independent claims. (Emphasis Added).
Additionally, Applicant asserts “Additionally, Elkabetz is completely silent with regard to any instances where the information collection and normalization server 310 (which is the or part of the actual data aggregator of Elkabetz's system) does not receive a full form instance of the collected data from the various data sources. Said another way, the complete and full form of the collected data (from each data source) is always provided by the data sources to the information collection and normalization server 310, which in turn saves the full form of the collected data provided from the data sources into database (DB) 320 for the full form of the collected data to be used by the other servers 360, 330, 370. This is in contrast to "obtaining, by the data aggregator, the reduced size data from the configured data collector without also obtaining a full form instance of the collected data from which the reduced size data is generated," and "the data aggregator not having access to the full form instance of the collected data to use as reference during the reconstructing," as required by the amended independent claims. (Emphasis Added).
Applicant submits that Moloney, Jia, Kulkarni, and Chen fail to supply that which Elkabetz lacks because, like Elkabetz, all of these other remaining cited prior art references are also completely silent with regard to at least the above emphasized (i.e., bolded) portions of the amended independent claims. As a result, Applicant submits that Elkabetz, Moloney, Jia, Kulkarni, and Chen, whether considered separately or in combination, cannot support an obviousness rejection of the amended independent claims.”.
Examiner’s response:
Applicant’s arguments and remarks are considered but are considered moot because the new ground of rejection does not rely on references applied in the prior rejection of record for any teaching or matter specifically challenged in the arguments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 5-8, 10-12, 16-18, and 21-23 are rejected under 35 U.S.C 103 as being unpatentable over Fathalla et al. (“An LSTM-based distributed scheme for data transmission reduction of IoT systems”, hereinafter Fathalla) Elkabetz et al. (WO2019126707A1, hereinafter Elkabetz).
Regarding claim 1 (currently amended):
Fathalla teaches a method for managing data collection in a distributed system where data collected by a data collector of the distributed system is provided to and collected in a data aggregator of the distributed system that is operably connected to the data collector via a communication system, the method comprising (see pg. 166 section ‘abstract’: “The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner”)
obtaining by the data aggregator, a data set for the data collector, the data collector being a data source of the data and collects the data using one or more sensors as collected data (see pg. 166 section ‘abstract’: “The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner”);
…
configuring, by the data aggregator, the data collector to send reduced size based on the data reduction plan, the data collector generating the reduced size data at a location of the data source using the collected data (see : “The problem of data transmission reduction can be addressed by utilizing a compression scheme”. Also see pg. 166 section ‘abstract’: “The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner”);
obtaining, by the data aggregator, the reduced size data from the configured data collector without also obtaining a full form instance of the collected data from which the reduced size data is generated (see : “In the first scenario, after the endpoint point checked that the predicted data at the cluster head or the fusion center side are within the accepted error threshold, the endpoint will not have to place a data transmission. Another scenario for huge network traffic mitigation is that the cluster head or the fusion center aggregates the data from the local endpoints and submits only the important information instead of transmitting all the collected data.”);
reconstructing, by the data aggregator, and using a first inference model that is a twin of a second inference model used by the data collector to implement the data reduction plan (see pg. 166 section ‘abstract’: “The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center”.)
the full form instance of the collected data from which the reduced size data is generated using the feature relationship inference model to obtain a representation of the data having error within the acceptable error thresholds (see pg. 169 section 2.3: “The proposed DPS in [14] deploys a GD-LMS model and an LSTM model on the CH and the sensor identically. The proposed DPS depends mainly on the GD-LMS model to predict the future readings. If the GD-LMS model’s predicted reading is not in the accepted error threshold, then the sensor checks the LSTM model’s predicted reading. If the LSTM model’s prediction was within the accepted error threshold, then a small-sized message (beacon) is sent from the sensor to the CH; this beacon notifies the CH to use the LSTM model’s prediction instead of the GD-LMS model’s prediction. If the sensors found that the prediction of the two models are not in the accepted error threshold, then the actual sensor reading is sent to the CH.”)
the data aggregator not having access to the full form instance of the collected data to use as reference during the reconstructing (see : “The proposed DPS in [14] deploys a GD-LMS model and an LSTM model on the CH and the sensor identically. The proposed DPS depends mainly on the GD-LMS model to predict the future readings. If the GD-LMS model’s predicted reading is not in the accepted error threshold, then the sensor checks the LSTM model’s predicted reading. If the LSTM model’s prediction was within the accepted error threshold, then a small-sized message (beacon) is sent from the sensor to the CH; this beacon notifies the CH to use the LSTM model’s prediction instead of the GD-LMS model’s prediction. If the sensors found that the prediction of the two models are not in the accepted error threshold, then the actual sensor reading is sent to the CH. ”)
Fathalla does not explicitly teach selecting, by the data aggregator and using the feature relationship inference model, a data reduction plan based on acceptable error thresholds associated with the features.
Elkabetz, however, analogously teaches selecting, by the data aggregator and using the feature relationship inference model, a data reduction plan based on acceptable error thresholds associated with the features (see [00139]: “Figure 21 illustrates an exemplary process flowchart for an exemplary cadence instance recalculation and propagation method for updating the forecast stacks to reflect newly collected data, according to an illustrative embodiment”. Also see [00459]-[00460]: “As shown in Figure 4, the cadence manager makes this determination each time the processing of the cadence changes processing stages; e.g. at the end of collection activities, at the end of post-collection activities, at the start of each forecast cycle, and the end of forecast processing (for all forecast cycles), prior to post-forecasting processing, and prior to each weather product program execution. [00460] At the end of collection activities, the cadence manager makes a determination whether the collected data and previous forecasts are in agreement by comparing corresponding collection and forecast data. This is done with the tile layer comparison program (910). If the collected data and a previous forecast are in agreement or are mostly in agreement, there is no need to rerun the whole forecast in order to create a new forecast”) [(Examiner’s note: the cadence manager selects a cadence (which taken as broadest reasonable interpretation is taken, inter alia, to be a data reduction plan as the cadence can determine the amount of time data is being transmitted). )].
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 1 to include attributes to select, by the data aggregator and using the feature relationship inference model, a data reduction plan based on acceptable error thresholds associated with the features in order to optimize the data reduction plan (see Elkabetz at para [00458]: “The cadence manager is responsible for optimizing run-time resource utilization during the processing of cadence instances.”)
Regarding claim 11:
Claim 11 recites analogous limitations to claim 1 and therefore the same rejection and rationale apply to it. In addition, claim 11 recites 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 system.
Fathalla does not explicitly teach 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 system.
Elkabetz, however, analogously 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 system. (see para [00175]: “Stored within persistent memories of the system may be one or more databases used for the storage of information collected and/or calculated by the servers and read, processed, and written by the processors under control of the program(s).”. Also see para [00176]: “Persistent memories may include disk, PROM, EEPROM, flash storage, and similar
technologies”.) ([Examiner note: the persistent memories from Elkabetz are consistent with the instant case’s disclosure of CSRM as stated at instant case’s para [00118]: “Computer-readable storage medium 509 may also be used to store some software functionalities described above persistently.” and at para [00123]: “A non-transitory machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine- readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices).”.)]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 1 to include attributes of 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 system in order
Regarding claim 16:
Claim 16 recites analogous limitations to claim 1 and therefore the same rejection and rationale apply to it. In addition, claim 16 recites a processor, a memory coupled to a processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing data collection.
Fathalla does not explicitly teach these aforementioned limitations.
Elkabetz, however, analogously teaches a processor and a memory coupled to a processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing data collection (see [00232]: “The forecasting program operates upon one or more of the current cadence instance’s forecast current cycle tile data (e.g. MiFforecast index forecast tile data (and optionally upon the current cadence instance collected data and/or past cadence instances collection and forecast generated data)), reading that data into the processor memory, performing calculations upon that data, creating new forecast generated data, and writing the resulting data to a data type specific database in order to create a new forecast generated data type tile layer associated with MiFforecast index.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 16 to include attributes of a processor and a memory coupled to a processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing data collection (see Elkabetz at para [00278]: “The programs are stored in or executed in transient or persistent memory of the server, and carry out the processing required on data stored in or executed in transient or persistent memory and/or system database (320).”).
Regarding claim 2:
Fathalla in view of Elkabetz teaches the method of claim 1.
Fathalla further teaches wherein the feature relationship inference model comprises a trained neural network (see pg. 170 section 3.2: “ The LSTM phase is the core phase of the proposed DPS, while the first two phases are utilized to collect the required readings for training the LSTM model. Using the available data, either the historical readings or the readings in the aggregate list collected through the previous two phases, the LSTM model is trained.”).
Regarding claim 17:
Claim 17 recites analogous limitations to claim 2 and is therefore rejected on the same grounds.
Regarding claim 5:
Fathalla in view of Elkabetz teaches the method of claim 1.
Fathalla does not explicitly teach wherein the data reduction plan indicates: a first subset of the features that are to be indicated by the reduced size data and a second subset of the features that are not to be indicated by the reduced size data, a quantization level for the first subset of the features, and a window duration that defines when the reduced size data is to be provided by the configured data collector to the data aggregator.
Elkabetz, however, analogously teaches wherein the data reduction plan indicates: a first subset of the features that are to be indicated by the reduced size data and a second subset of the features that are not to be indicated by the reduced size data (see para [00206]: “Cadence instances may include information generated by internal processes, such as machine learning models that are generated by the modelling and prediction server, and are then used to process collected data and produce new tile layers of data based, at least in part, upon the predictions. For example, For example, trained machine learning model, for example a neural network, may be used to calculate a probability of a forecast event being rain, mist, or fog based upon past historical weather data combined with current collected and forecast generated data. In an exemplary embodiment, the probability of rain, mist, or fog may be used by future processing steps, such as NowCasting and/or RVR calculations to determine the contribution of rain and/or fog to the precipitation and visibility forecasts.”. Also see [00208]: “The system may also enforce an interstitial delay between cadence cycles if desired. Cadence cycle timing may vary based upon weather or upon the results of one or more previous cadence cycle processing steps. For example, cadence cycle length and collection interval length may be increased during clear weather and decreased during stormy weather.”.);
a quantization level for the first subset of the features (see para [00414]: “o Q - a given quantization level in the specific pro forma satellite-to-earth station or satellite link segment”.);
and a window duration that defines when the reduced size data is to be provided by the configured data collector to the data aggregator (see para [00454]: “The server also implements one or more data management (e.g. applying transforms, tile layer compares, copying, and blending), weather inference, and weather forecast programs. These programs are used create aspects of the forecast data for the system. Generally, the modeling and prediction server programs retrieve data of the forecast program required type and time window from the system database (320) and from other sources of data provided by the system. ”. Also see table 2.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 5 to include attributes wherein the data reduction plan indicates: a first subset of the features that are to be indicated by the reduced size data and a second subset of the features that are not to be indicated by the reduced size data, a quantization level for the first subset of the features, and a window duration that defines when the reduced size data is to be provided by the configured data collector to the data aggregator in order to (see Elkabetz at para [00454] : “These programs are used create aspects of the forecast data for the system”).
Regarding claim 6 (currently amended):
Fathalla in view of Elkabetz teaches the method of claim 5.
Fathalla does not explicitly teach wherein the reduced size data comprises: representations of the first subset of the features for a period of time defined by the window duration and wherein the representations excluding portions of respective features based on a corresponding acceptable error threshold of the acceptable error thresholds.
Elkabetz, however, analogously teaches wherein the reduced size data comprises: representations of the first subset of the features for a period of time defined by the window duration (see para [00454: “The server also implements one or more data management (e.g. applying transforms, tile layer compares, copying, and blending), weather inference, and weather forecast programs. These programs are used create aspects of the forecast data for the system. Generally, the modeling and prediction server programs retrieve data of the forecast program required type and time window from the system database (320) and from other sources of data provided by the system. ”. Also see table 2.),
the representations excluding portions of respective features based on a corresponding acceptable error threshold of the acceptable error thresholds (see [00542]: “In an exemplary embodiment, the fog inference program includes an expert systems module (948) that retrieves one or more fog time series rule ML models from a system database (320) and implements the one or more fog time series rule ML models to process input data, for a current (Mi) fog LWC tile layer (2048) and one or more previous cadence instance fog LWC tile layers from fog inference data database (998), to produce output data including time series based fog inference decisions. The fog inference program (919) uses the time series rule ML models to confirm a preliminary fog inference and determine a confirmed fog inference (or in the absence of a confirmation, refute the preliminary fog inference). In an embodiment, the fog inference program increases a confidence indication associated with each confirmed fog inference, for example a medium confidence indication or a numerical value representing a medium confidence.”. Also see para [00160]: “Furthermore, the described systems supports efficiency optimizations in managing forecasts, and for deriving a second forecast from a first forecast without recalculating the entire forecast. These optimizations can improve the forecast calculation times by up to 95%, reducing a 10 minute forecast cycle to under 30 seconds. This improvement permits near real- time forecast generation.”.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 6 to include attributes wherein the reduced size data comprises: representations of the first set of the features for a period of time defined by the window duration and wherein the representations excluding portions of respective features based on a corresponding acceptable error threshold of the acceptable error thresholds in order to optimize efficiency (see para [00160]: “Furthermore, the described systems supports efficiency optimizations in managing forecasts, and for deriving a second forecast from a first forecast without recalculating the entire forecast.”)
Regarding claim 7:
Fathalla in view of Elkabetz teaches the method of claim 6.
Fathalla does not explicitly teach providing the configured data collector with a copy of the feature relationship inference model; and initiating refinement of the data reduction plan by the configured data collector using the feature relationship inference model and measurements obtained by the configured data collector during the window duration, at least one of the representations represents a feature of the second subset of the features.
Elkabetz, however, analogously teaches providing the configured data collector with a copy of the feature relationship inference model (see para [00466]: “Once a determination is made by the cadence manager (905) to perform a partial calculation and update within a cadence instance, several steps occur. First the portions of the tile layers to be copied to the cadence instance along with their corresponding prior tile layers selected from one or more of prior collected data tile layers, processed collected data tile layers, forecast tile layers, forecast post-processing tile layers, and weather product tile layers. The identified prior tile layers are propagated by copying to the current cadence instance.”. Also see paras [00492]-[00494]: “ If the cadence manager selects the option to copy and update a prior forecast tile layer, the cadence manager often still has to run one or more processing programs in order to complete some of the tile layers of the new cadence instance. For example, if a forecast is copied from once cadence cycle to another, the copied forecast will need is last forecast cycle run to complete the new forecast. [00493] Process and programs run by the cadence manager [00494] The cadence manager has a number of cadence specific programs that may be performed at specific times in the cadence cycle to create and manage the cadence data structures. These programs include collection, post-collection, pre-forecast processing programs as described herein. ”.) and
initiating refinement of the data reduction of the data reduction plan by the configured data collector using the feature relationship inference model and measurements obtained by the configured data collector during the window duration, at least one of the representations represents a feature of the second subset of the features (see para [00204]: “Processed data comprises data that has been previously associated with a cadence instance and has been further processed by one or more data processing programs of the system, with results of that processing stored in a system database. Processed data may include additional refinements to collected data, derivation of additional information from collected or forecast generated data, or data that is calculated by other systems and associated with one or more cadence instances.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 7 to include attributes providing the configured data collector with a copy of the feature relationship inference model; and initiating refinement of the data reduction plan by the configured data collector using the feature relationship inference model and measurements obtained by the configured data collector during the window duration, at least one of the representations represents a feature of the second subset of the features in order to optimize efficiency (see para [00160]: “Furthermore, the described systems supports efficiency optimizations in managing forecasts, and for deriving a second forecast from a first forecast without recalculating the entire forecast.”)
Regarding claim 8:
Fathalla in view of Elkabetz teaches the method of claim 7.
Fathalla does not explicitly teach wherein the data reduction plan is refined sequentially for data corresponding to respective window durations.
Elkabetz, however, analogously teaches wherein the data reduction plan is refined sequentially for data corresponding to respective window durations (see para [00204]: “Processed data comprises data that has been previously associated with a cadence instance and has been further processed by one or more data processing programs of the system, with results of that processing stored in a system database. Processed data may include additional refinements to collected data, derivation of additional information from collected or forecast generated data, or data that is calculated by other systems and associated with one or more cadence instances.”. Also see para [00311]: “In this way, the weather sensor data collection program filters out collected data points that are not required for further processing and aggregates large volumes of collected data points into statistical representations. These data set size reductions significantly reduce the amount of calculations required in subsequent forecasting by permitting forecast optimizations to be used.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 8 to include attributes of wherein the data reduction plan is refined sequentially for data corresponding to respective window durations in order to optimize the data reduction (see Elkabetz at para [00311] : “These data set size reductions significantly reduce the amount of calculations required in subsequent forecasting by permitting forecast optimizations to be used”).
Regarding claim 10:
Fathalla in view of Elkabetz teaches the method of claim 1.
Fathalla further teaches wherein the configured data collector is intermittently operably connected to the data aggregator by the communication system. (see pg. 168 section 2.2: “Another scenario for huge network traffic mitigation is that the cluster head or the fusion center aggregates the data from the local endpoints and submits only the important information instead of transmitting all the collected data. ”)
Regarding claim 12:
Fathalla in view of Elkabetz teaches the non-transitory machine-readable medium of claim 11.
Fathalla further teaches wherein the feature relationship inference model comprises a neural network (see pg. 168 section 2.3: “The prediction model can be designed as a neural network model”)
Regarding claim 21:
Fathalla in view of Elkabetz teaches the method of claim 1.
Fathalla further teaches wherein the second inference model is hosted by a computing device configured as the data collector, the computing device comprises the one or more sensors that collect the data as the collected data, and the second inference model being generated by and distributed to the data collector by the data aggregator (see pg. 170 section 3.2: “ The first phase role is to collect actual measurements so that the predictive model (i.e., an LMS adaptive filter or any predictive model) can predict the future measurements based on these actual measurements. This phase is a very short phase”. Also see pg. 170 section 3.2: “These measurements differences are saved to an aggregate list (denoted by 𝛾) for the next phase. The LMS phase: The role of the second phase is to start the prediction task as soon as possible”).
Regarding claim 22:
Fathalla in view of Elkabetz teaches the method of claim 21.
Fathalla further teaches wherein the reduced size data comprises only statistical information of the full form instance of the collected data (see pg. 174 section 4.3.1 : “The reduction of the data transmission instances can be understood from another perspective, the number of sent readings from the sensor to the CH out of thousands of actual measurements performed by the sensor.”. Also see fig. 5)
Regarding claim 23:
Fathalla in view of Elkabetz teaches the method of claim 23.
Fathalla further teaches wherein the reduced size data comprises only a difference between the full form instance of the collected data and a prediction of the full form instance of the collected data generated locally at the data collector by the second inference model hosted in the data collector (see pg. 168 section 2.3: “The sensor node transmits the actual value only if the difference between the predicted and actual data surpasses the predefined threshold.”).
Claims 3, 4, 13, 14, and 18 are rejected under 35 U.S.C 103 as being unpatentable over Fathalla et al. (“An LSTM-based distributed scheme for data transmission reduction of IoT systems”, hereinafter Fathalla) in view of Elkabetz et al. (WO2019126707A1, hereinafter Elkabetz) in further view of Moloney et al. (DE112019002589T5, hereinafter Moloney).
Regarding claim 3:
Fathalla in view of Elkabetz teaches the method of claim 2.
Fathalla further teaches wherein the trained neural network comprises hidden layers of nodes adapted to predict a first feature of the features based on a second feature of the features (see pg. 172 table 2 which shows the use of hidden layers. Also see pg. 171 section 3.2: “The future temperatures are predicted using the deployed LSTM model.”. Also see pg. 171 section 3.3: “For instance, temperature degrees change throughout an entire day according to whether it is the daytime or nighttime.”)
Fathalla in view of Elkabetz does not explicitly teach a self-supervised process.
Moloney, however, analogously teaches the trained neural network being trained with a self-supervised learning process (see para [0065]: “In some implementations, self-supervised learning can be performed on a machine learning model in a training phase, such as in the example above in Fig. 19 .”.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla, Elkabetz, and Moloney before him or her, to modify the method of claim 3 to include attributes of the trained neural network being trained with a self-supervised learning process in order to allow for datasets that contain unlabeled data (see Moloney at para [0065]: “In such an example, it is not necessary to have datasets with labeled soil survey data.”).
Regarding claims 13 and 18:
Claims 13 and 18 recite analogous limitations to claim 3 and therefore are rejected on the same grounds as claim 3.
Regarding claim 4:
Fathalla in view of Elkabetz in further view of Moloney teaches the method of claim 3.
Fathalla does not explicitly teach wherein the first feature comprises a first type of measurement data and the second feature comprises a second type of measurement data different from the first type of measurement data.
Elkabetz further teaches wherein the first feature comprises a first type of measurement data and the second feature comprises a second type of measurement data different from the first type of measurement data (see para [00496]: “The encoding and image feature comparison aspects of this technique may encode and compare only those tile layers and parts of tile layers that are specified for comparison by the cadence manager as contextually relevant. For example, by encoding only specific tiles from a tile layer, a region (part) of a tile layer, a complete tile layer, a set of tile layers, or a combination of specific regions of a specified set of tile layers, specific features of weather data may be exposed such as shapes of rain fields at specified precipitation rates, weather features such as convective storm cells, frontal boundaries, wind fields, and the like. Image feature analysis may be used to identify these weather features, which can then be stored in a weather objects database (350) and tracked across a plurality of these generated images”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla, Elkabetz, and Moloney before him or her, to modify the method of claim 3 to include attributes of a first type of measurement data and the second feature comprises a second type of measurement data different from the first type of measurement data in order to learn more of specific data (see Elkabetz para [00496]: “For example, by encoding only specific tiles from a tile layer, a region (part) of a tile layer, a complete tile layer, a set of tile layers, or a combination of specific regions of a specified set of tile layers, specific features of weather data may be exposed”).
Regarding claims 14 and 19:
Claims 14 and 19 recite analogous limitations to claim 3 and therefore are rejected on the same grounds as claim 4.
Claim 9 is rejected under 35 U.S.C 103 as being unpatentable Fathalla et al. (“An LSTM-based distributed scheme for data transmission reduction of IoT systems”, hereinafter Fathalla) in view of Elkabetz et al. (WO2019126707A1, hereinafter Elkabetz) in further view of Chen et al. (US20230332976A1, hereinafter Chen).
Regarding claim 9:
Fathalla in view of Elkabetz teaches the method of claim 1.
Fathalla further teaches predictability of the feature of the data set with the feature relationship inference model (see pg. 168 section 2.3 The proposed DPS in [14] deploys a GD-LMS model and an LSTM model on the CH and the sensor identically. The proposed DPS depends mainly on the GD-LMS model to predict the future readings”);
reconstructability of the features of the data set using twin inference models hosted by the configured data collector and the configured data aggregator (see : “The proposed DPS in [14] deploys a GD-LMS model and an LSTM model on the CH and the sensor identically. The proposed DPS depends mainly on the GD-LMS model to predict the future readings. If the GD-LMS model’s predicted reading is not in the accepted error threshold, then the sensor checks the LSTM model’s predicted reading. If the LSTM model’s prediction was within the accepted error threshold, then a small-sized message (beacon) is sent from the sensor to the CH; this beacon notifies the CH to use the LSTM model’s prediction instead of the GD-LMS model’s prediction. If the sensors found that the prediction of the two models are not in the accepted error threshold, then the actual sensor reading is sent to the CH. The authors proposed only to update the GD-LMS model. This decision makes the LSTM outdated and affects its performance. While the proposed hybrid models outperform the traditional LMS model in reducing the data transmission instance for one sensor node, they failed to do so for other sensor nodes.”).
Fathalla does not explicitly teach quantization of features of the data set, computing resource costs for transmitting the features of the data set from the configured data collector to the data aggregator.
Elkabetz, however, analogously teaches quantization of features of the data set (see para [00414]: “ o Q - a given quantization level in the specific pro forma satellite-to-earth station or satellite link segment.”.); and
computing resource costs for transmitting the features of the data set from the configured data collector to the data aggregator (see para [00457]: “The cadence manager determines which programs are to be processed next as part of a cadence instance, when they are to be processed, and in some embodiments, determines which resources are used by those programs (e.g. which processor a specific program is executed by)”. Also see para [00458]: “The cadence manager is responsible for optimizing run-time resource utilization during the processing of cadence instances.”.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla and Elkabetz before him or her, to modify the method of claim 9 to include attributes of quantization of features of the data set and computing resource costs for transmitting the features of the data set from the configured data collector to the data aggregator in order to optimize resource costs (see Elkabetz at para [00458]: “[00458] The cadence manager is responsible for optimizing run-time resource utilization during the processing of cadence instances. One important optimization by the cadence manager is the determination on whether a specific cadence instance can reuse some or all of prior collected data and forecasts or whether it is more efficient to fully process and calculate each element of the cadence instance. ”).
Fathalla does not explicitly teach wherein the data reduction plan is obtained using a genetic algorithm or the use of an objective function.
Chen, however, teaches in analogous wherein the data reduction plan is obtained using a genetic algorithm (see para [0311]: “FIG. 24 illustrates a flowchart for an evolutionary computation based optimization procedure with use of the genetic algorithm (GA), according to some embodiments.”.) and
and an objective function (see para [0312]: “At each generation, the fitness of each individual can be evaluated based on the user-defined objective function, and an updated population of solutions can be created by using genetic operators such as ranking, selection, crossover and mutation. This evolutionary computation approach can eliminate the need to calculate the first derivative and/or the second derivative (as done in some optimization methods) and is suitable to solve complex optimization problems.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fathalla, Elkabetz, and Chen, before him or her, to method of claim 9 to include attributes of genetic algorithms and objective functions in order to solve complex optimization problems, such as data reduction (see Chen at para [0312]: “This evolutionary computation approach can eliminate the need to calculate the first derivative and/or the second derivative (as done in some optimization methods) and is suitable to solve complex optimization problems.”.).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
“Two level data aggregation protocol for prolonging lifetime of periodic sensor networks” — Al-Qurabat et al. — discloses reducing data with quantization and sliding window techniques within the context of a data aggregator
“Not Every Bit Counts: Data-Centric Resource Allocation for Correlated Data gathering in Machine-to-Machine Wireless Networks” — Hsieh et al. — discloses using quantization techniques to reduce data within the context of a data aggregator
Conclusion
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/ANDREW BRACERO/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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