DETAILED ACTION
Status of Claims
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 9, 2026 has been entered.
Claims 1, 11 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 1 recites “applying the operations data to an improvement model executed by the artificial intelligence and machine-learning circuity […] and includes asset identification data generated from the registration data”. Applicant’s disclosure does not describe that the artificial intelligence and machine-learning circuity generates asset identification data generated from the registration data, which is included with the improved operations data set nor that asset identification data is generated from the registration data. Claim 1 also recites “generating and transmitting, to the corresponding asset, a machine-readable control instruction that causes the asset to adjust operations of one or more of the plurality of assets by adjusting at least one operating parameter of the corresponding asset”,. Applicant’s disclosure describes in ¶ 0053: “the operations processing system 140 is configured via hardware, software, firmware, and/or a combination thereof, to generate and/or transmit command(s) that control, adjust, or otherwise impact operations of one or more of the plurality of assets 102, the one or more databases 150, and/or the plurality of sensors 110.“ Applicant’s disclosure does not describe that an asset adjust operations of one or more of the plurality of assets. The same rationale applies to claims 11 and 20. Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
As per claim 1 recites generating and transmitting, to the corresponding asset, a machine-readable control instruction that causes the asset to adjust operations of one or more of the plurality of assets by adjusting at least one operating parameter of the corresponding asset, Examiner is not clear how an asset adjust the operations of one or more of the plurality of assets and then adjust at least one parameter of the corresponding asset, when the improved operations data is associated with a corresponding asset? Is the improved operations data for an asset? is the asset and the corresponding asset the same? The same The same rationale applies to claims 11 and 20. Appropriate correction is required.
Response to Arguments
Applicant’s arguments, see pages 9-13, filed on 4/9/2026, with respect to claim 1 have been fully considered and are persuasive. The 35 U.S.C. 101 of claims 1-20 has been withdrawn.
Applicant's arguments filed on 4/9/2026 have been fully considered but they are not persuasive.
With regard to the 35 U.S.C. 102, Applicant argues that Bose (1) “Bose does not disclose receiving or utilizing registration data that explicitly associates each sensor with a corresponding asset, nor does it disclose a system that dynamically maps unlabelled operations data, i.e., data "lacking identification data" to assets based solely on registration data. Further, Bose does not disclose processing incoming operations data that lacks identification data and then generating asset-specific outputs that include asset identification data derived from registration data” .(2) Bose does not teach or suggest the claimed pipeline in which operations data that lacks identification information is associated with assets "without requiring the plurality of sensors to transmit identification data," nor does it disclose generating a structured plurality of asset-specific improved operations data sets containing system-generated asset identification data derived from registration data.” (3) “Bose's disclosure of "recommendations," "control information," or "set-point suggestions" does not amount to generating and transmitting machine-readable control instructions that directly cause an asset to adjust operational parameters based on improved operations data.” And (4) “Bose fails to teach transmitting each improved operations data set to a database storage location associated with the corresponding asset as explicitly required by the amended independent claims of the present application.”
In response to applicant's argument (1) that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a system that dynamically maps unlabelled operations data , i.e., data “lacking identification data” to assets based solely on registration data) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Please see above the 35 U.S.C. 112(a) rejection. In addition, Examiner respectfully disagrees. Applicant’s disclosure paragraph 0070 describes that registration data may be the following “[i]n some embodiments, the registration data may be associated with the plurality of sensors 110. In some embodiments, for example, the registration data may indicate a device identification for each of the plurality of sensors 110 (e.g., a unique identification code for each sensor of the plurality of sensors 110 that uniquely identifies the sensor). As another example, the registration data may indicate an asset associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is associated with a first asset and a second sensor is associated with a second asset). As another example, the registration data may indicate a tenant (e.g., an operator and/or owner of an asset) associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is associated with a first tenant and a second sensor is associated with a second tenant). As another example, the registration data may indicate a sensor type associated with each sensor of the plurality of sensors 110 (e.g., the registration data may indicate that a first sensor is a temperature sensor and that a second sensor is an acceleration sensor).” Applicant’s disclosure paragraph 0073 describes that operations data may be the following “In some embodiments, for example, the operations data may include gas data (e.g., a flow rate of a gas associated with an asset captured by a gas sensor). As another example, the operations data may include temperature data (e.g., a temperature associated with the asset captured by a temperature sensor). As another example, the operations data may include humidity data (e.g., a humidity associated with an asset captured by a humidity sensor). As another example, the operations data may include material composition data (e.g., a composition of a material associated with an asset captured by the material composition sensor). As another example, the operations data may include vibration data (e.g., a vibration associated with an asset captured by a vibration sensor). As another example, the operations data may include acceleration data (e.g., an acceleration associated with an asset captured by the acceleration sensor). As another example, the operations data may include location data (e.g., a location associated with an asset captured by a location sensor).” Bose teaches receiving registration data that associates each sensor of a plurality of sensors with a corresponding asset of a plurality of assets in ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.” ¶ 0038: “the one or more software products 122 are operable to analyse the sensor data for determining an aggregate efficiency of operation of the asset 104 based upon a weighted combination of contributions from one or more apparatus 106 of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset. For example, the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104. Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” Bose also teaches includes asset identification data generated from the registration data in ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.” See also ¶ 0038.
In response to applicant's argument (2) that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., generating a structured plurality of asset-specific improved operations data sets) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Please see above the 35 U.S.C. 112(a) rejection. Bose as explained below teaches the generation of improved operations data sets and asset identification data generated from the registration data. Applicant’s arguments (2) directed to the newly added limitations “without requiring […] identification data” with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the updated rejection below as necessitated by amendments.
Applicant’s arguments (3) with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see above the 35 U.S.C. 112(a) rejection and the updated rejection below as necessitated by amendments.
Applicant’s arguments (4) with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see the updated rejection below as necessitated by amendments.
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 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.
Claims 1-2, 4, 7, 9, 11-12, 14, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose” in both view of Asher et al., (US 2018/0189332 A1) hereinafter “Asher” and Santoso et al., (US 2024/0288839 A1) hereinafter “Santoso”.
Claim 1:
Bose as shown discloses a computer-implemented method, the method comprising:
receiving registration data that associates each sensor of a plurality of sensors with a corresponding asset of a plurality of assets (¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.” ¶ 0038: “the one or more software products 122 are operable to analyse the sensor data for determining an aggregate efficiency of operation of the asset 104 based upon a weighted combination of contributions from one or more apparatus 106 of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset. For example, the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104. Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.”));
receiving, from the plurality of sensors, operations data representing operations of the plurality of assets, wherein the operations data is captured by the plurality of sensors; (¶ 0032: “The plurality of sensors 110 are optionally adjusted to monitor at given intervals for collection of appropriate amounts of data.” And ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.”);
Bose teaches in ¶ 0016: “For determining the one or more weighting factors, the analysis utilizes artificial intelligence, neural network analysis or both.” Bose is silent with regard to the following limitations. However, Asher in an analogous art of asset management for the purpose of providing the following limitations as shown does:
the operations data lacking identification data that identifies a particular sensor or asset; without requiring the plurality of sensors to transmit identification data (¶ 0056: “The set of consistent data 216 may include data generated by one or more analytics for a particular asset that is otherwise lacking a data record (e.g., a digital twin of that asset) or data associated with an asset identified as being similar to the asset with missing data” see also ¶ 0028: “the estimation or imputation of records of this core data from a set of raw sensor data received from the asset or related to that asset”);
executed by [the artificial intelligence] and machine-learning circuitry (¶ 0031: “ the term “analytic” refers to computer code that, when executed, receives a set of input data and applies statistical analysis and/or machine learning modeling techniques to that set of input data to generate a result. This computer code may be generated as a result of the application of a training set of data to a particular statistical or machine learning technique.” See also ¶ 0094: “Output of the records may include storing the reconciled records in a datastore for use by a predictive model or analytic, executing an analytic against those records, employing those records in a machine learning training model, transmitting those records to a client device or end user, or the like.”);
Both Bose and Asher teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Asher teaches in ¶ 0011: “The context data may include system context data, and the at least one characteristic may indicate a grouping of assets among the plurality of assets.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Asher would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Asher to the teaching of Bose would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the operations data lacking identification data that identifies a particular sensor or asset; without requiring the plurality of sensors to transmit identification data, executed by the artificial intelligence and machine-learning circuitry into similar systems. Further, as noted by Asher “ The system 100 provides for improved functionality of analytics by ensuring that the source data used to train those analytics and/or ingested by those analytics is consistent and accurate. To this end, the system 100 provides a data reconciliation framework 102 that is capable of receiving core data 106 from one or more assets 104, and using external data 108 to generate consistent data 110 and/or forecasted data 112.” (Asher ¶ 0041).
In addition, Bose teaches:
associating the operations data with the plurality of assets based at least in part on the registration data (Operation data: ¶ 0038: “Examples of various parameters include, but are not limited to, a combination and association of temperature, pressure, humidity, working conditions, and peak values pertaining to different operating conditions.” With the plurality of assets: ¶ 0038: “the one or more software products 122 analyse the various parameters associated with the pump, fan, compressors, cooling tower, HVAC and furnace of the asset 104.” Based at least in part on the registration data ¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors”);
applying the associated operations data to an improvement model to generate improved operations data based at least in part on the registration data and the operations data, wherein the improved operations data comprises a plurality of improved operations data sets, wherein each improved operations data set of the plurality of improved operations data sets is associated with one of the plurality of assets; (¶ 0038: “The one or more software products 122 are provided with simulation models of the one or more apparatus 106 of the asset 104 to which the configuration of sensors 110 is applied. The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104.” See also ¶ 0043: “the one or more software products 122 acquire data in real-time from the asset via a wireless communication network, analyses the acquired data to identify patterns and relationships in the acquired data, constructing a system model for the asset 104, applies simulation, for example Monte Carlo simulation, to determine where energy savings and/or increases in operating efficiency can be achieved and providing control information. The control information improves the efficiency of operation of the asset 104.” ¶ 0048: “The simulation models are employed for identifying adjustments that improve the efficiency of operation of the asset 104” and ¶ 0011: “identify adjustments that improve the efficiency of operation of the one or more assets and overall system.”);
and includes asset identification data generated from the registration data; and (¶ 0032: “These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines. […] The subsection of foundry optionally includes multiple machines which are monitored recovery via different types of sensors. Examples of these multiple machines include, but are not limited to, pumps, fans, compressors, rock crushers, screens, transporter belts, hoppers, cooling towers, HVAC and furnaces.” See also ¶ 0038);
initiating improved operations actions based at least in part on the improved operations data ¶ 0036: “analysing the sensor data for determining an efficiency of operation of the asset 104 and for providing one or more recommendations for improving the efficiency of operation of the asset 104. The recommendations may include instructions for the operator to set particular controls like valves, switches to certain positions to improve the performance of the system 100 as a whole. The one or more software products 122 trigger proactive and predictive actions/responses that are transmitted to the asset 104, thereby allowing the asset 104 to run more efficiently and accurately. More or less continuous set point recommendation for the asset 104 is provided through the operation of the BRAINS.APP software product to the operator to ensure the process runs efficiently with minimal waste or energy consumption. For example, the operator of In Situ Recovery mining facility may get recommendations from BRAINS.APP to set a number of flow restricting valves to certain setpoints in order to achieve improved flow from the injection wells to extraction wells in ISR process.”);
Bose teaches in ¶ 0044: “the cloud computing resource 124 generates response signals, namely containing adjustment data or recommendation, based on the analysis and/or simulation of the one or more software products 122. In addition, the one or more cloud computing resources 124 transmit the response signals and/or instructions to the control manager 108 to improve the efficiency of the operation of the asset 104.” Bose in view of Asher is silent with regard to the following limitations. However, Santoso in an analogous art of asset management for the purpose of providing the following limitations as shown does:
the artificial intelligence (¶ 0085: “The processor 130 may utilize data stored in the memory 140 as a neural network (also referred to herein as a machine learning network). The neural network may include a machine learning architecture. In some aspects, the neural network may be or include an artificial neural network (ANN).”);
the improved operations actions comprise at least: transmitting a respective improved operations data set to a database storage location associated with the corresponding asset; and (Figure 1, note devices 122a to 122d124-a to 124-d, ¶ 0073: “a device 122 (e.g., device 122-b) may be mechanically integrated with a corresponding device 124 (e.g., device 124-b). For example, the device 122 (e.g., device 122-b) and the corresponding device 124 (e.g., device 124-b) may be integrated within a shared housing.” ¶ 0074: “The devices 124 may be, for example, communication devices capable of transmitting and receiving signals (e.g., via wired or wireless communications). “ see also ¶ 0075, 0082 and ¶ 0114: “ applying advances in data storage technologies (e.g., implemented at the memory 140, database 115, and/or memory 165) with smart sensors (e.g., devices 124) to capture and store all the relevant data pertaining to any equipment/process. “);
generating and transmitting, to the corresponding asset, a machine-readable control instruction that causes the asset to adjust operations of one or more of the plurality of assets by adjusting at least one operating parameter of the corresponding asset (¶ 0361: “the process flow 1500 may include automatically or semi-automatically controlling the one or more operations of the asset in response to receiving the one or more recommended actions, wherein automatically or semi-automatically controlling the one or more operations of the asset includes using the second target value of the one or more second operational parameters.” And ¶ 0290: “The recommendation module may output the optimal controllable parameters (e.g., chilled water (CHW) supply temperature in a chiller system, motor speed (RPM) in a pump system, etc.) that may contribute to improving asset efficiency and power consumption savings for satisfying target criteria requirements (e.g., cooling load for a chiller system, flow for a pump system).”);
wherein the at least one operating parameter comprises at least one of temperature, humidity, vibration, acceleration, location, material composition, or flow rate (¶ 0124: “The operational parameters may include, for example, temperature, pressure, flow rate, humidity, vibration, full load current rating, power, and carbon dioxide level, but is not limited thereto.”);
Both Bose and Santoso teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Santoso teaches in the Abstract “ identifies a target value of one or more first operational parameters associated with an asset.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Santoso would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Santoso to the teaching of Bose in view of Asher would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the improved operations actions comprise at least: transmitting a respective improved operations data set to a database storage location associated with the corresponding asset; and generating and transmitting, to the corresponding asset, a machine-readable control instruction that causes the asset to adjust operations of one or more of the plurality of assets by adjusting at least one operating parameter of the corresponding asset, wherein the at least one operating parameter comprises at least one of temperature, humidity, vibration, acceleration, location, material composition, or flow rate the artificial intelligence into similar systems. Further, as noted by Santoso “provide improved user experience in association with asset simulation and analytics. For example, systems described herein may use machine learning models that are updated based on real-time data, and using the machine learning models, the system may provide analytics (e.g., simulation, prediction, diagnosis, etc.) for securing improved performance (e.g., operational efficiency, etc.) of equipment in a processing facility and reduced operational and other costs (e.g., reduced power consumption, etc.) associated with the equipment.” (Santoso, ¶ 0062).
Claims 11 and 20:
The limitations of claims 11 and 20 (¶ 0014) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 11 that differ from claim 1.
Claim 11:
Bose as shown discloses an apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to: (¶ 0035: Examples of one or more computing resources 124 include storage, processing, memory, network bandwidth, and virtual machines”);
Claim 2:
Bose as shown discloses the following limitations:
wherein applying the operations data to the improvement model to generate improved operations data occurs in real-time (¶ 0030: “The plurality of sensors 110 monitors and collects the data corresponding to the status/operating conditions of the plurality of apparatus 106 of the asset 104 in real time and transmits the data in real time in a form of signals to the server arrangement 112. ” see also ¶ 0031: “the system 100 has been applied to in-situ recovery mine. Combining real time flow rates and power consumption data of the submersible pumps allowed for identification of the pumps entering a “dry running” mode which is a damaging state for the pump. Real time identification of the dry running mode and addressing it by giving recommendations for the well workover timing in order to increase the well solution inflow would decrease the pump breakdown rate by about 15% and would lead to saving in energy up to about 35%.”);
Claim 12:
The limitations of claim 12 encompasses substantially the same scope as claim 2. Accordingly, those similar limitations are rejected in substantially the same manner as claim 2, as described above.
Claim 4:
Bose teaches in ¶ 0044: “the cloud computing resource 124 generates response signals, namely containing adjustment data or recommendation, based on the analysis and/or simulation of the one or more software products 122. In addition, the one or more cloud computing resources 124 transmit the response signals and/or instructions to the control manager 108 to improve the efficiency of the operation of the asset 104.” Bose in view of Asher is silent with regard to the following limitations. However, Santoso in an analogous art of asset management for the purpose of providing the following limitations as shown does:
wherein the one or more improved operations actions comprises: transmitting the improved operations data to a database, wherein each improved operations data set of the plurality of improved operations data sets is transmitted to a database storage location associated with an associated asset of the plurality of assets (Figure 1, note devices 122a to 122d124-a to 124-d, ¶ 0073: “a device 122 (e.g., device 122-b) may be mechanically integrated with a corresponding device 124 (e.g., device 124-b). For example, the device 122 (e.g., device 122-b) and the corresponding device 124 (e.g., device 124-b) may be integrated within a shared housing.” ¶ 0074: “The devices 124 may be, for example, communication devices capable of transmitting and receiving signals (e.g., via wired or wireless communications). “ see also ¶ 0075, 0082 and ¶ 0114: “ applying advances in data storage technologies (e.g., implemented at the memory 140, database 115, and/or memory 165) with smart sensors (e.g., devices 124) to capture and store all the relevant data pertaining to any equipment/process. “);
Both Bose and Santoso teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Santoso teaches in the Abstract “ identifies a target value of one or more first operational parameters associated with an asset.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Santoso would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Santoso to the teaching of Bose in view of Asher would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the one or more improved operations actions comprises: transmitting the improved operations data to a database, wherein each improved operations data set of the plurality of improved operations data sets is transmitted to a database storage location associated with an associated asset of the plurality of assets into similar systems. Further, as noted by Santoso “provide improved user experience in association with asset simulation and analytics. For example, systems described herein may use machine learning models that are updated based on real-time data, and using the machine learning models, the system may provide analytics (e.g., simulation, prediction, diagnosis, etc.) for securing improved performance (e.g., operational efficiency, etc.) of equipment in a processing facility and reduced operational and other costs (e.g., reduced power consumption, etc.) associated with the equipment.” (Santoso, ¶ 0062).
Claim 14:
The limitations of claim 14 encompasses substantially the same scope as claim 4. Accordingly, those similar limitations are rejected in substantially the same manner as claim 4, as described above.
Claim 7:
Bose as shown discloses the following limitations:
determining a computing resource consumption for each of the plurality of assets based at least in part on the improved operations data (¶ 0045: “the one or more cloud computing resources 124 transmit the response signals and/or instructions to one or more computing devices 130 of an administrator to take appropriate actions for increasing the efficiency of the asset 104. The analysis of the aggregate consumption data is performed online via the Internet or through wireless communication to the computing devices 130.”);
Claim 17:
The limitations of claim 17 encompasses substantially the same scope as claim 7. Accordingly, those similar limitations are rejected in substantially the same manner as claim 7, as described above.
Claim 9:
Bose as shown discloses the following limitations:
wherein the plurality of assets comprise at least one building, at least one plant, or at least one vehicle (¶ 0032: “Examples of the facility 102 include, but may not be limited to, micro-fabrication plants, manufacturing plants, steel mills, water treatment works, recovery assembly factories, power stations, oil and gas fields, quarries, mines, in-situ mining plants, water utilities, foundries, steel industry, petrochemicals industry, nuclear industry, transport facilities, water treatment works and food processing facilities. These facilities may include multiple assets having plurality of sensors to sense the parameters associated with various apparatus/machines.”);
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose”, Asher et al., (US 2018/0189332 A1) hereinafter “Asher” and Santoso et al., (US 2024/0288839 A1) hereinafter “Santoso” as applied to claims 1 and 11 above, further in view of Chambers et al., (US 2009/0132091 A1) hereinafter “Chambers”.
Claim 3:
Bose as explained above teaches a plurality of improved operations data sets. Santoso teaches in Figure 7C and optimized simulation. Bose in view of Asher and Santoso is silent with regard to the following limitations. However, Chambers in an analogous art of asset management for the purpose of providing the following limitations as shown does:
wherein the one or more improved operations actions comprises: causing each improved operations data set of the plurality of improved operations data sets to automatically be displayed on an improved operations data set interface of an associated asset of the plurality of assets (Figures 5-8 illustrates a plurality of improved operations data sets i.e., cost based settings, temperature based settings on a user interface);
Both Bose and Chamber teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Chamber teaches in the Abstract “sending component configured to send a signal to the plurality of assets to implement the business rule.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Chamber would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chamber to the teaching of Bose in view of Asher and Santoso would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the one or more improved operations actions comprises: causing each improved operations data set of the plurality of improved operations data sets to automatically be displayed on an improved operations data set interface of an associated asset of the plurality of assets into similar systems. Further, as noted by Chamber “utilization of the control system 104 with the environment 102 may allow more efficient utilization of assets within the environment 102 by controlling usage based on user preferences.” (Chamber ¶ 0029).
Claim 13:
The limitations of claim 13 encompasses substantially the same scope as claim 3. Accordingly, those similar limitations are rejected in substantially the same manner as claim 3, as described above.
Claims 5-6, 10, 15-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose”, Asher et al., (US 2018/0189332 A1) hereinafter “Asher” and Santoso et al., (US 2024/0288839 A1) hereinafter “Santoso” as applied to claims 1 and 11 above, further in view of Chet R. Douglas et al., (US 2005/0125563 A1) hereinafter “Douglas”.
Claim 5:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose in view of Asher and Santoso is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein receiving operations data representing operations of a plurality of assets is associated with a first bandwidth allocation and transmitting the improved operations data to a database is associated with a second bandwidth allocation (Figure 5 illustrates a plurality of devices with their respective bandwidths allocations);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose in view of Asher and Santoso would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as receiving operations data representing operations of a plurality of assets is associated with a first bandwidth allocation and transmitting the improved operations data to a database is associated with a second bandwidth allocation into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 15:
The limitations of claim 15 encompasses substantially the same scope as claim 5. Accordingly, those similar limitations are rejected in substantially the same manner as claim 5, as described above.
Claim 6:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose in view of Asher and Santoso is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein the second bandwidth allocation is greater than the first bandwidth allocation (Figure 4 illustrates when a device need extra bandwidth and Figure 5, note the total requested bandwidth for each device);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose in view of Asher and Santoso would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the second bandwidth allocation is greater than the first bandwidth allocation into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 16:
The limitations of claim 16 encompasses substantially the same scope as claim 6. Accordingly, those similar limitations are rejected in substantially the same manner as claim 6, as described above.
Claim 10:
Bose teaches in ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126. […] the cloud management module 126 is responsible for load management and cloud resources. The load management is optionally implemented through consideration to of a variety of factors, including user access level and/or total load in the cloud computing environment 120. Bose in view of Asher and Santoso is silent with regard to the following limitations. However, Douglas in an analogous art of resource distribution/allocation management for the purpose of providing the following limitations as shown does:
wherein the operations data is associated with a first data size and the improved operations data is associated with a second data size, wherein the second data size is greater than the first data size (Figure 5, see device 504, Active Bandwidth [504] =20 i.e., first data size, and Total Requested Bandwidth [504] = 60 i.e., second data size which is greater than the first data size);
Both Bose and Douglas teach resource distribution/allocation management. Bose teaches in the ¶ 0035: “The one or more computing resources 124 optionally communicate with one another to distribute resources, and such communication and management of distribution of resources are optionally controlled by a cloud management module 126.” Douglas teaches in the ¶ 0053 “Load balancer 304 may determine the number of active devices that require extra bandwidth by determining which of the active devices require more bandwidth than initially allocated.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Douglas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Douglas to the teaching of Bose in view of Asher and Santoso would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the operations data is associated with a first data size and the improved operations data is associated with a second data size, wherein the second data size is greater than the first data size into similar systems. Further, as noted by Douglas “a fair method of balancing the load on a plurality of devices that are trying to access a fixed amount of bandwidth.” (Douglas ¶ 0218).
Claim 19:
The limitations of claim 19 encompasses substantially the same scope as claim 10. Accordingly, those similar limitations are rejected in substantially the same manner as claim 10, as described above.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bose et al., (US 2015/0170090 A1) hereinafter “Bose”, Asher et al., (US 2018/0189332 A1) hereinafter “Asher” and Santoso et al., (US 2024/0288839 A1) hereinafter “Santoso” as applied to claims 7 and 17 above, further in view of Dozortsev et al., (US 2020/0090109 A1) hereinafter “Dozortsev”.
Claim 8:
Bose in view of Asher and Santoso is silent with regard to the following limitations. However, Dozortsev in an analogous art of asset management for the purpose of providing the following limitations as shown does:
transmitting a payment request to each of the plurality of assets based at least in part on the computing resource consumption for each of the plurality of assets (¶ 0091: “Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.”);
Both Bose and Dozortsev teach asset management. Bose teaches in the Abstract: “monitoring operation of an asset and creating a condition based preventive and predictive maintenance process for the individual asset and overall system.” Dozortsev teaches in the Abstract “controlling an electronic device based on mapping sensors to a physical asset.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Dozortsev would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Dozortsev to the teaching of Bose in view of Asher and Santoso would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as transmitting a payment request to each of the plurality of assets based at least in part on the computing resource consumption for each of the plurality of assets into similar systems. Further, as noted by Dozortsev “ the capacity to improve the technical field of the field of information technology asset management by utilizing cognitive learning and a series of rules to make inferences regarding the correct matching of sensors to assets.” (Dozortsev ¶ 0012).
Claim 18:
The limitations of claim 18 encompasses substantially the same scope as claim 8. Accordingly, those similar limitations are rejected in substantially the same manner as claim 8, as described above.
Conclusion
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
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