DETAILED ACTION
Claim 1-2 and 5-12 are presented for examination.
Claim 1, 7, and 10 were amended.
Claim 13 is new.
This is a Non-Final Action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/09/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 2, 5-12 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.
101 abstract idea has been obviated due to current 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 of this title, 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, 3, 5-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kiff et al. (US 20140032555) in view of Park et al. (US 20190094827 – (IDS)) and Kretz et al. (US 2013/0054621) further in view of Morris, II et al. (US 2016/0350671)
1. Kiff teaches, A computer-implemented method comprising:
processing the telemetry data in accordance with a plurality of context discovery operations (Fig 6:610,615,625,630,635,640, Paragraphs 73, 78 – teaches the goal of automatic context discovery is to map each point needed by the application… to their correct context – thus disclosing automatic context discovery operations performed on telemetry point data, Kiff);
determining, based on the processing of the telemetry data, for each context discovery operation of the plurality of context discovery operations, output data comprising one or more mapping structures indicative of a potential mapping for a respective data point of the plurality of data points (Fig 3, Paragraph 75 – teaches finding the concept tokens… mapping the tokens to potential concept terms and narrowing down which concept sets are probable matches – thus disclosing generating candidate mappings between telemetry tokens and ontology concepts, corresponding to mapping structures for potential mappings, Kiff), wherein each mapping structure comprises a confidence value indicative of a confidence of the potential mapping (Paragraphs 77 – teaches regular expression matches of tokens to terms in the lexicon with a calculated confidence factor; Paragraph 78 – teaches confidence for each match is assigned based on the number of characters matched/percentage of characters matched, and effectiveness of the algorithm and Paragraph 89 – teaches each match may be given a confidence levels- thus disclosing confidence levels to mapping matches, Kiff);
processing the output data by the context discovery system, the processing identifying one or more definitive mappings (Fig 6, Paragraphs 65 & 79 – teaches domain rules may then be applied to dismiss impossible combinations… only one legal path… identified as the correct match – describes resolving potential mappings into definitive mappings using ontology rules – thus disclosing domain rules to eliminate invalid mappings and select the valid mapping path, Kiff);
generating context data, based on the processing of the output data, for the asset system comprising the one or more definitive mappings of respective data points (Paragraphs 75 & 84 - a pointrole is collection of strongly typed meta data that together provides an unambiguous description of the context of a given piece of data – describes generating context data (pointrole) mappings comprising definitive mappings, Kiff).
Kiff does not explicitly teach,
receiving, by a context discovery system comprising a processor, a memory and context discovery circuitry, telemetry data comprising a plurality of data points associated with an asset system;
wherein the plurality of context discovery operations are executed in parallel and wherein each context discovery operation independently determines context about a respective data point of the plurality of data points;
wherein, the processing the output data further comprises: merging, at least a portion of the mapping structures generated by the plurality of context discovery operations using a probabilistic formula; and
generating a combined confidence value for the merged mapping structure; and
transmitting the context data to a semantic model generation application to automatically generate or update digital model of the asset system, wherein the generated or updated digital model is used to perform at least one analytical operation on the asset system, the at least one analytical operation comprising at least one of predictive maintenance operation, a fault detection operation, or a performance optimization operation on the asset system, wherein the generated or updated asset model provides one or more insights.
However, Park teaches,
receiving, by a context discovery system comprising a processor, a memory and context discovery circuitry, telemetry data comprising a plurality of data points associated with an asset system (Paragraph 102 – teaches OT data may include timeseries data received from IoT devices (e.g., sensor measurements, status indications, alerts, notifications)– thus describes receiving sensor and IoT operational technology data, which telemetry data points from devices of a physical asset system, Park);
transmitting the context data to a semantic model generation application to automatically generate or update digital model of the asset system (Abstract, Fig 10, Paragraph 103 – teaches the entity data can be created… described in relationships between entities – thus disclosing generating entity data representing relationships among devices and systems, corresponding to generating a semantic/digital model of the asset system, Furthermore, Paragraph 55 – teaches creating a smart entity, Paragraph 58 – teaches the smart entity is a virtual representation of a physical system or device -thus disclosing entity model to be same as semantic model and smart entity to be a digital model/digital twin, Park), wherein the generated or updated asset model provides one or more insights (Paragraphs 99 and 164 - teaches entity data describes the relationships between spaces, equipment, and other entities – thus disclosing entity model enabling analysis and understanding of system relationships corresponding to insights derived from the digital model, Park), wherein the generated or updated digital model is used to perform at least one analytical operation on the asset system (Fig 2 – teaches Analytic Service 224 receiving platform data/services in the BMS; Paragraph 89 – teaches the system includes platform services (e.g., timeseries service, an entity service, a security service, an analytics service, etc., Park), the at least one analytical operation comprising at least one of predictive maintenance operation, a fault detection operation, or a performance optimization operation on the asset system (Paragraph 212, Fig 15 – teaches data including an abnormal measurement is received from a first device, Transformer service 1108 identifies a second device that is related to the first device through relational objects; Analytics service 224 analyzes the data from the first and the second device; Analytics service 223 provides a recommendation from the analysis, Further teaching HVAC fault detection application and/or the analytics service 224 can further investigate into the other related entities to determine or infer the most likely cause of the fault, Park).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to allow Kiff and Park to be combined as taught because both prior arts are in the same field of endeavor of collection, processing and contextualization of telemetry data from asset system / automation systems addressing the same problem of converting raw telemetry into structured semantic context and the combination would yield predictable method of generating context data with definitive mappings.
However Kretz teaches,
wherein, the processing the output data further comprises: merging, at least a portion of the mapping structures generated by the plurality of context discovery operations using a probabilistic formula (Fig 3:311, Fig 4:408, 410, 414; Paragraph 8-9 – teaches similarity rules such as text based on associations semantic based associations and/or statistical significance correlations can bse used to generate similarity output and the number of association meeting certain confidence level threshold can be calculated, Kretz); and
generating a combined confidence value for the merged mapping structure (Fig 6:602,604,606, Paragraph 83 – teaches similarity outcome 222 may be based on similarity outcome score 225 of a number of text based associations and a number of semantic based associations, Kretz);
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to incorporate the confidence-based ontology similarity and scoring technique of Kretz into the context discovery system of Kiff operating on telemetry data as received by the building management system of Park in order to improve the reliability of determining correct mappings between telemetry data points and semantic concepts. The combination merely applies known statistical confidence evaluation techniques to candidate mappings to improve contextual modeling of asset systems, thus yielding predictable results.
However, Morris teaches,
wherein the plurality of context discovery operations are executed in parallel (Fig 2B – teaches feature discovery and runtime paths including aggregation, features, scoring, contextualization, training * validation, predictive models, and predictive outcome; Paragraph 57 – teaches feature discovery and runtime processes can be executed in prallel or concurrently by the predictive system; Paragraph 79 – teaches a plurality of ML models or classifiers can be trained in parallel based on the contextualization for use as the sub-models, Morris) and wherein each context discovery operation independently determines context about a respective data point of the plurality of data points.(Claim 11 – creating feature data context value comprising feature data having at least one contextual relationships and analyzing the feature data context values by application of a plurality of statistical models, independently, to each feature data context value, Morris).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify the automated telemetry context-discovery of Kiff, as applied to Park’s asset system smart-entity/digital-model environment and using Kretz’s confidence-based ontology alignment technique to execute multiple context discovery/modeling operations independently and in parallel as taught by Morris. Because a POSITA would have used Morris’s parallel independent processing architecture to improve Kiff’s telemetry context-discovery pipeline by reducing onboard latency and improving mapping confidence when processing large volumes of telemetry data from asset systems telemetry.
2. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, each data point of the plurality of data points comprising one or more of text data (Paragraph 51 – teaches generating tokens based on document sources (i.e. text data), Kiff), time-series data, and hierarchical data (Paragraphs 17 & 19 – teaches the smart entities… include data entities representing data generated by the physical building equipment devices… the data entities may include a timeseries representing data generated by the device – describes data points as time-series, Park).
5. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, the context data further comprising at least one mapping structure of the one or more mapping structures indicative of the potential mapping for the respective data point of the plurality of data points, the at least one mapping structure associated with a portion of the mapping structures not having undergone the merging (Paragraphs 79 & 87 – teaches matches that do not comply… are removed, stored regardless of their confidence – describes retaining non-merged mapping structures (potential mappings with low confidence), Kiff).
6. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, further comprising:
determining whether a confidence value associated with a merger of two or more mapping structures exceeds a predefined confidence threshold (Paragraphs 81-82 - teaches similarity rule 224 can be further based on threshold confidence level 230… similarity rule 224 is based on confidence level 227 associated with semantic-based association – thus disclosing evaluation of candidate ontology associations using confidence values and threshold confidence levels, corresponding to determining whether a combined confidence value exceeds a predefined threshold, Kretz); and
in accordance with the determination that the confidence value exceeds the predefined confidence threshold (Paragraph 83 - teaches similarity outcome 222 may be based on similarity outcome score… of a number of text-based associations and semantic-based associations greater than or equal to threshold confidence level – thus disclosing determining whether similarity outcomes satisfy a threshold confidence level between proceeding, corresponding to performing further operations based on the threshold determination, Kretz): identifying the merger of the two or more mapping structures as a definitive mapping (Paragraph 85 - teaches generating an alignment mapping between similar concepts in ontology pairing based on the similarity outcome – thus disclosing generating an alignment mapping when the similarity evaluation indicates sufficient confidence, corresponding to identifying the merged mapping as definitive mapping, Kretz).
7. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, the processing of the telemetry data in accordance with the plurality of context discovery operations comprising (Paragraph 75 - teaches the goal of automatic context discovery is to map each point needed by the application… to their correct context, Kiff) processing the telemetry data in accordance with one or more token interpretation operations (Paragraph 75 - teaches finding the concept tokens within the string that is the point name and/or point description – thus disclosing extracting tokens from telemetry point strings, corresponding to token interpretation operations, Kiff), one or more context translation operations (Paragraph 72 - teaches the validated tokens can then be mapped into specific roles by applying rules of the domain described by the ontology – thus teaching translates tokenized telemetry data into semantic roles within a domain ontology corresponding to context translation operations, Kiff), and one or more statistical classification operations (Paragraph 67 - teaches ontology concept string F-score based on computation of precision and recall – thus disclosing performing statistical similarity evaluation using F-scores and related metrics, corresponding to statistical classification operations used to evaluated candidate mappings, Kretz).
8. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 7, the processing of the telemetry data in accordance with the one or more token interpretation operations comprising:
identifying one or more tokens of text data associated with a respective data point of the plurality of data points (Paragraph 78 – teaches Points… inserted to a Trie structure… string processed to tokens – describes token identification, Kiff);
determining a mapping structure of at least one token of the one or more tokens to a predefined token of a predefined token set based on at least a portion of the at least one token matching the predefined token (Paragraph 72 – tokens compared to lexicon, mapped into specific roles, Kiff); and
determining a confidence value for the mapping structure based at least on a character length of the portion of the at least one token and a character length of the predefined token (Paragraph 78 - teaches confidence for each match based on number of characters matched/percentage of characters match, Kiff).
9. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, the processing of the output data comprising processing the output data in accordance with a point role template set (Paragraph 84 – teaches pointrole is a collection of strongly typed meta data unambiguous description of context – describes point role templates, Kiff) to generate one or more point role mapping structures, the processing comprising:
determining, for a respective data point and a respective point role template of a point role template set, a point role match confidence value based at least on an identification of a concept term associated with both a mapping structure associated with the respective data point and the respective point role template(Paragraph 78 - teaches Matches confidence for each match is assigned, evaluation of likelihood of resulting solution sets – describes assigning point role match confidence values, Kiff); and
based at least on the point role match confidence value, generate a point role mapping structure comprising at least an indication of the respective data point and an indication of the respective point role template, the context data further comprising the one or more point role mapping structures (Paragraph 84 - teaches point role, meta-model describes how elements, connected to ontology – describes generating point role mapping structure, Kiff).
10. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, further comprising:
causing transmission of the generated context data to the semantic model generation application (Paragraph 97 - teaches building management system 102 can be configured to ingest, process, store and/or publish data from a variety of data sources – thus disclosing a system that processes telemetry data and provides the resulting to to system components and applications, corresponding to transmitting generated context data to a semantic modeling applications, Park) comprises providing, along with the context data, metadata indicative of point roles (Paragraph 74 - teaches point role given context for the point value held by the process control system, Kiff), asset types (Paragraph 74 - teaches ElementType is Fan… PlantType is RoofTopUnit – thus disclosing equipment element types and plant types, corresponding to asset types for components of the system, Kiff), and hierarchical relationships (Paragraph 99 and 164 - teaches entity data describes the relationships between spaces, equipment and other entities – thus disclosing entity model representing hierarchical relationships among building equipment and spaces within an asset system, Park), and wherein the semantic model generation application integrates the context data with a domain ontology (Paragraph 70 - teaches the domain ontology provides the types, attributes and values that describes things and relationships in the domain, Kiff) to regenerate strongly typed entities and relationships for the digital model (Paragraphs 55 and 58 - teaches instructions cause the processors to create a smart entity… a virtual representation of a physical system or device, Park).
11. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, further comprising: generating a ranked list of one or more mapping structures associated with a particular asset in order of respective confidence values (paragraph 83 - similarity outcome 222 may be based on similarity outcome score 225… for ontology pairing – thus disclosing similarity outcome scores for candidate ontology associations. These scores allow candidate mappings to be ordered or ranked according to confidence value, corresponding to generating a ranked list of mapping structures, Kretz); and
determining, based on the ranked list, an asset type for the respective asset (Paragraph 5 - teaches mapping the tokens to specific roles utilizing rules of the domain ontology – thus teaching mapping tokens extracted from telemetry point names to ontology roles and equipment types. These mappings identify the type of equipment or asset represented by the telemetry point, corresponding to determine an asset type based on the mapping results, Kiff).
12. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, wherein in response to determining that the combined confidence value does not exceed a predefined confidence threshold (Paragraph 81 - teaches similarity rule 224 can be further based on threshold confidence level 230 – thus teaching evaluating similarity outcomes against predefined confidence thresholds, corresponding to determining whether a combined confidence value exceeds or fails to exceed a threshold, Kretz), including two or more mapping structures and their associated confidence values as suggested mappings in the context data (Paragraph 89 - teaches each match may be given a confidence level – thus disclosing assigning confidence values to candidate token-to-concept matches, thereby maintaining multiple candidate mappings and their associated confidence levels. These candidate mappings correspond to suggested mappings included in the context data when a definitive mapping cannot be determined, Kiff).
13. The combination of Kiff, Park, Kretz and Morris teach, The method of claim 1, further comprising:
receiving an indication confirming at least one definitive mapping of the one or more definitive mappings (Paragraph 66 – teaches once the context has been identified, it may be used by humans and machines to validate mappings, search and filter data, configure automation, generate displays and present data succinctly, Kiff; Paragraph 77 – teaches users or operating systems can also be queried at a period of time after generation of the predicted output 196 to obtain a confirmation that the predicted operational outcome of interest did (or did not) occur, Morris); and
disabling or deleting other mapping structures associated with a same asset as the at least one definitive mapping (Fig. 6 – teaches perform checks of aspect groups and roles, remove non-complying aspect matches and store matches; Paragraph 79 – teaches aspect matches that don’t comply with the rules within the ontology are removed, matches are then stored at 645, Kiff)
Conclusion
Lomdardi et al. (US 2020/0227178) – is directed to IoT integration automation and control architectures that connect and model physical assets using data streams, Avatars/logical models, field variables, sensors/actuators, machine data and asset-management/smart building domains. (Abstract, background, Fig 1).
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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