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 Amendment
This action is responsive to applicant RCE and amendments/remarks received 03/23/2026. Claims 1, 3, 11, 13 and 20 amended. Claims 1-5, 7-15 and 17-20 remain pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 7-9, 11-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Brueggen (US Patent No. 20140266685 A1), in view of Acharya (US Patent No. 20240288853 A1).
In re claim 1, Brueggen teaches A method comprising:
receiving, via one or more processors, at least one raw alarm corresponding to at least one asset, wherein each of the at least one raw alarm having one or more raw fields (Para [0025]: “Alarm messages generated by each facility alarm management system 112a, 112b, 112c in response to detecting one or more exception conditions can be transmitted, via the respective communication channel 114a, 114b, 114c, to the corresponding alarm message receiver 301a, 301b, 301c… The term alarm, as used herein, refers to information about a unique data point (e.g., data from a monitored facility system or fixture capable of having an exception condition) and the time that the exception condition associated with the data point occurred and/or was reported or received by the facility monitoring system. An alarm may include additional information, such as customized data relevant to the data point, the value of a set point (e.g., a high/low limit), and/or customized flags indicating a status of the associated facility system or fixture.” and para [0049]: “As discussed above with respect to FIGS. 2 and 4, the facility alarm management system 112 transmits inbound alarm messages 510 to a respective alarm receiver 301. One example of an alarm message 510 is depicted in FIG. 8 and indicated at 810.”);
mapping, via the one or more processors, each of the one or more raw fields with corresponding one or more fields of [[a]]the selected predefined template related to the at least one asset, to generate one or more normalized fields (Para [0032]: “In one example, one event may be generated from a single alarm message, where the alarm message includes information about a new alarm condition (e.g., a temperature out-of-range alarm just occurred).…The alarm loader 107 may include one or more processes to extract and transform vendor data 108 and one or more processes to load the transformed data into new or updated events 109. The extract and transform process 108 can utilize a set of rules 120 to transform disparate data sources into a normalized alarm format, which can be stored as event data in an event database.”);
receiving, via the one or more processors, asset information corresponding to the at least one raw alarm associated with the at least one asset (Para [0032]: “The alarm loader 107 can be configured to generate and/or update an event corresponding to one or more of the alarm messages. The term event, as used herein, is a logical construct (e.g., stored in a database) representing an exception condition of a facility system or fixture at one or more facilities 103a-e.” and para [0043]: “Events can be encrypted and/or generalized to hide or remove information that is specific to the condition of the facility system or fixture 101a-e, 102a-e that caused the event to be generated. Examples of such information include the manufacturer name, model number, device-specific error codes, and other information that can be used to identify particular characteristics of the facility system or fixture 101a-e, 102a-e. Users accessing the databases via terminals 304a-c can accordingly be restricted from viewing particular information contained in the alarm messages, e.g., only generalized information is accessible. In this manner, the user may, for example, only be able to determine that a circuit breaker has tripped in an HVAC system, but not be able to determine the manufacturer of the HVAC system or view any diagnostic information contained in the alarm message.”); and
generating, via the one or more processors, at least one normalized alarm based at least on the generated one or more normalized fields and the asset information (Para [0049]: “The alarm loader 501 extracts (e.g., reads) the alarm messages 510 from the alarm receiver 301 and transforms (e.g., normalizes) the alarm messages 510. Several examples of normalized alarm messages are depicted in FIG. 8 and indicated at 820.”) wherein generating the at least one normalized alarm comprises augmenting the one or more normalized fields with the asset information (Para [0032]: “In one example, one event may be generated from a single alarm message, where the alarm message includes information about a new alarm condition (e.g., a temperature out-of-range alarm just occurred).…The alarm loader 107 may include one or more processes to extract and transform vendor data 108 and one or more processes to load the transformed data into new or updated events 109. The extract and transform process 108 can utilize a set of rules 120 to transform disparate data sources into a normalized alarm format, which can be stored as event data in an event database.” and para [0043]: “Events can be encrypted and/or generalized to hide or remove information that is specific to the condition of the facility system or fixture 101a-e, 102a-e that caused the event to be generated. Examples of such information include the manufacturer name, model number, device-specific error codes, and other information that can be used to identify particular characteristics of the facility system or fixture 101a-e, 102a-e. Users accessing the databases via terminals 304a-c can accordingly be restricted from viewing particular information contained in the alarm messages, e.g., only generalized information is accessible. In this manner, the user may, for example, only be able to determine that a circuit breaker has tripped in an HVAC system, but not be able to determine the manufacturer of the HVAC system or view any diagnostic information contained in the alarm message.”).
Brueggen fails to teach selecting, via the one or more processors, from a plurality of predefined templates, a predefined template associated with the at least one asset based at least on one or more raw fields comprising at least one of a point address or a technical address, wherein each of the plurality of predefined templates is pre-saved and comprises multiple normalized fields related to the at least one asset;
wherein the at least one normalized alarm includes, from the asset information, a user- friendly name and a description corresponding to the at least one asset.
However, Acharya teaches selecting, via the one or more processors, from a plurality of predefined templates, a predefined template associated with the at least one asset based at least on one or more raw fields comprising at least one of a point address or a technical address, wherein each of the plurality of predefined templates is pre-saved and comprises multiple normalized fields related to the at least one asset (SEE BELOW);
wherein the at least one normalized alarm includes, from the asset information, a user- friendly name and a description corresponding to the at least one asset (Para [0116]: “In one or more embodiments, the data enrichment component 404 can receive, normalize, and enrich (e.g., contextualize) the raw streaming data 308 generated by the edge devices 161a-161n. In various embodiments, the data enrichment component 404 comprises a receiver, a normalizer, a streaming enricher, and/or a backfilling enricher.”, para [0118]: “In one or more embodiments, the streaming enricher contextualizes one or more portions of the raw streaming data 308 based on one or more corresponding flattened asset data objects associated with the edge devices 161a-161n. For instance, the data enrichment component 404 is configured to generate, based at least in part on the asset metadata derived from the hierarchical reference data associated with the edge devices 161a-161n, one or more flattened asset data objects corresponding to the edge devices 161a-161n. As such, the streaming enricher can be configured to contextualize the raw streaming data 308 with the one or more flattened asset data objects in order to generate contextualized asset data 312. In various embodiments, contextualizing the raw streaming data 308 with the one or more flattened asset data objects comprises joining, concatenating, formatting, generating respective data objects, and/or otherwise associating the raw streaming data 308 with the one or more flattened asset data objects in order to create contextualized asset data 312.”and para [0126]: “In an embodiment, a formatted version of the contextualized asset data 312 is formatted with one or more defined formats for the time series database 306. A defined format is, for example, a structure for data fields and/or a structure for data object such as a flattened asset data object. In one embodiment, a defined format is predetermined. For example, in one or more embodiments, a predominant type of structure (e.g., a predominant type of format, predominant type of procurement form, etc.) may be employed as a template for future use. In various embodiments, the data contextualization system 302 configures the raw streaming data 308, the contextualized asset data 312, asset metadata (e.g., data attributes), and/or hierarchical reference data to be displayed in an interactive tabular format such that one or more portions of data associated with the raw streaming data 308, the contextualized asset data 312, asset metadata (e.g., data attributes), and/or hierarchical reference data can be manipulated and/or otherwise updated.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Brueggen to incorporate the teachings of Acharya to provide selecting, via the one or more processors, from a plurality of predefined templates, a predefined template associated with the at least one asset based at least on one or more raw fields comprising at least one of a point address or a technical address, wherein each of the plurality of predefined templates is pre-saved and comprises multiple normalized fields related to the at least one asset; wherein the at least one normalized alarm includes, from the asset information, a user- friendly name and a description corresponding to the at least one asset with the ALARM PROCESSING SYSTEMS AND METHODS of Brueggen. Doing so enables displaying in an interactive tabular format such that one or more portions of data can be manipulated and/or otherwise updated, as recognized by Acharya (para [0126]).
System claim 11 and non-transitory machine-readable information storage medium claim 20 is rejected for the same reasons as method claim 1 for having similar limitations and begin similar in scope.
In re claim 2, Brueggen and Acharya teach all of the limitations of claim 1 where Brueggen further teaches wherein the one or more processors are configured to receive the at least one raw alarm from a monitoring area, wherein the monitoring area corresponds to at least one of a building, a warehouse, a storage unit, or an office space (Para [0029]: “FIG. 2 is a block diagram depicting a topology 100 of an alarm processing system including a network of facilities 103a-e. Some or all of the facilities 103a-e may be in the same physical location or in different physical locations.”).
System claim 12 is rejected for the same reasons as method claim 2 for having similar limitations and begin similar in scope.
In re claim 3, Brueggen and Acharya teach all of the limitations of claim 2 where Brueggen further teaches wherein the one or more raw fields comprises at least one of SEE FIG 8 and para [0049]: “As discussed above with respect to FIGS. 2 and 4, the facility alarm management system 112 transmits inbound alarm messages 510 to a respective alarm receiver 301. One example of an alarm message 510 is depicted in FIG. 8 and indicated at 810.”).
System claim 13 is rejected for the same reasons as method claim 3 for having similar limitations and begin similar in scope.
In re claim 4, Brueggen and Acharya teach all of the limitations of claim 1 where Brueggen further teaches wherein the one or more fields of the predefined template related to the at least one asset comprises at least a problem, a source, and a value (SEE FIG 8 and para [0049]: “The alarm loader 501 extracts (e.g., reads) the alarm messages 510 from the alarm receiver 301 and transforms (e.g., normalizes) the alarm messages 510. Several examples of normalized alarm messages are depicted in FIG. 8 and indicated at 820.”).
System claim 14 is rejected for the same reasons as method claim 4 for having similar limitations and begin similar in scope.
In re claim 5, Brueggen and Acharya teach all of the limitations of claim 2 where Brueggen further teaches wherein the asset information associated with the at least one asset comprises at least one of a source, a location, a type, a unit, or a description corresponding to the monitoring area (Para [0032]: “The alarm loader 107 can be configured to generate and/or update an event corresponding to one or more of the alarm messages. The term event, as used herein, is a logical construct (e.g., stored in a database) representing an exception condition of a facility system or fixture at one or more facilities 103a-e.” and para [0043]: “Events can be encrypted and/or generalized to hide or remove information that is specific to the condition of the facility system or fixture 101a-e, 102a-e that caused the event to be generated. Examples of such information include the manufacturer name, model number, device-specific error codes, and other information that can be used to identify particular characteristics of the facility system or fixture 101a-e, 102a-e. Users accessing the databases via terminals 304a-c can accordingly be restricted from viewing particular information contained in the alarm messages, e.g., only generalized information is accessible. In this manner, the user may, for example, only be able to determine that a circuit breaker has tripped in an HVAC system, but not be able to determine the manufacturer of the HVAC system or view any diagnostic information contained in the alarm message.”).
System claim 15 is rejected for the same reasons as method claim 5 for having similar limitations and begin similar in scope.
In re claim 7, Brueggen and Acharya teach all of the limitations of claim 1 where Brueggen further teaches wherein the at least one normalized alarm corresponds to a normalized alert having the augmented one or more normalized fields with the asset information (SEE FIG 8 and para [0049]: “The alarm loader 501 extracts (e.g., reads) the alarm messages 510 from the alarm receiver 301 and transforms (e.g., normalizes) the alarm messages 510. Several examples of normalized alarm messages are depicted in FIG. 8 and indicated at 820.”).
System claim 17 is rejected for the same reasons as method claim 7 for having similar limitations and begin similar in scope.
In re claim 8, Brueggen and Acharya teach all of the limitations of claim 1 where Brueggen further teaches further comprising
storing, via the one or more processors, the one or more fields of the predefined template, the asset information, and the one or more normalized fields in a memory communicatively coupled to the one or more processors (Para [0032]: “The extract and transform process 108 can utilize a set of rules 120 to transform disparate data sources into a normalized alarm format, which can be stored as event data in an event database.”).
System claim 18 is rejected for the same reasons as method claim 8 for having similar limitations and begin similar in scope.
In re claim 9, Brueggen and Acharya teach all of the limitations of claim 1 where Brueggen further teaches wherein the one or more processors are configured to receive the asset information from an asset module that is communicatively coupled to the one or more processors (SEE FIG 7, Facility Alarm Management System 112; asset information is received from facility alarm management system 112 through alarm message 510.).
System claim 19 is rejected for the same reasons as method claim 9 for having similar limitations and begin similar in scope.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Brueggen (US Patent No. 20140266685 A1), in view of Acharya (US Patent No. 20240288853 A1) and further in view of Nigam (US Patent No. 12013838 B2).
In re claim 10, Brueggen and Acharya teach all of the limitations of claim 1 stated above but fails to teach wherein the one or more processors are configured to generate the at least one normalized alarm using one or more Artificial Intelligence (AI)/ Machine Learning (ML) techniques.
However, Nigam teaches wherein the one or more processors are configured to generate the at least one normalized alarm using one or more Artificial Intelligence (AI)/ Machine Learning (ML) techniques (Abstract: “Systems and methods are provided to aggregate and analyze data from a plurality of data sources. The system may obtain data from a plurality of data sources. The system may also transform data from each of the plurality of data sources into a format that is compatible for combining the data from the plurality of data sources. The system uses a metadata-based data mapping template to match fields from one database system to the other. The system can generate and publish master data of a plurality of business entities.” and col 2, lines 53-61: “In some embodiments, the system comprises a mapping module that uses advanced machine learning techniques to extract and synchronize any kind of data. The mapping module is configured to prepare, validate and transform data to get standardized dimensions across data sources and create master data for further processing, with a possible subject matter expert (SME) approval. A feedback loop ensures that the platform learns and improves with every new user interaction.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Brueggen and Acharya to further incorporate the teachings of Nigam to provide wherein the one or more processors are configured to generate the at least one normalized alarm using one or more Artificial Intelligence (AI)/ Machine Learning (ML) techniques with the ALARM PROCESSING SYSTEMS AND METHODS of Brueggen as modified by Acharya. Doing so enables transforming data to get standardized dimensions across data sources and create master data for further processing, as recognized by Nigam (col 2, lines 53-61).
Response to Arguments
Applicants’ arguments filed 11/03/2025 with respect to the independent claims have been fully considered but are moot in view of the new ground(s) of rejection as necessitated by amendment.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES EDWARD MUNION whose telephone number is (571)270-0437. The examiner can normally be reached Monday-Friday 7:30-5:00.
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/JAMES E MUNION/Examiner, Art Unit 2688 05/16/2026
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