DETAILED ACTION
Claim 1-2 and 5-12 are presented for examination.
Claim 1, 6, and 10 were amended.
Claim 2 and 3 were cancelled.
Claim 11 and 12 are new.
This is a Final Action.
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.
With respect to 101 abstract idea, examiner respectfully disagrees with the applicant and maintains his rejection. Specifically, applicant makes the following arguments:
1. Applicant argues that the claimed operation cannot be performed by the human mind due to computational complexity and data scale.
Examiner respectfully disagrees with the applicant, this argument is not persusasive because the relevant inquiry under Step 2A is whether the claimed concepts can be performed mentally, not whether a human can practically perform them at scale. The steps of evaluating data, determining potential mappings, assigning confidence values, and selecting results based on probabilistic evaluation constitute mental processes involving observation, evaluation and judgement. Implementing such mental processes on a computer does not remove the claim from the abstract idea category.
Applicant argues that the claim requires a specialized context discovery system comprising a processor, memory and circuitry.
Examiner respectfully disagrees with the applicant, these elements represent generic computer components performing routine data processing functions, such as receiving data, processing data, and transmitting data. Reciting generic computing components to perform an abstract idea does not integrate the idea into a practical application.
Applicant asserts that probabilistic merging and confidence calculations require specialized machine algorithms.
Examiner respectfully disagrees with the applicant, the claims as recited merely applies mathematical operations (probabilistic formulas and confidence values) to analyze data relationships. These calculations can be performed mentally therefore recite abstract idea.
Applicant argues that the invention transforms raw telemetry data into structured context data.
Examiner respectfully disagrees with the applicant, transforming one form of information into another form of information does not constitute a technological improvement. The claim merely analyzes and organizes data, which is similar to the claims found in abstract in Electric Power Group.
Applicant argues that generating or updating a digital model of the asset system represents a technological improvement.
Examiner respectfully disagrees with the applicant, the generation of digital model is merely a result of the data analysis performed by the abstract idea. Limiting the abstract idea to a particular technological environment or application does not integrate the idea into a practical application.
Applicant argues that the claimed system provides a technological improvement over conventional asset management approaches.
Examiner respectfully disagrees with the applicant, the additional elements beyond the abstract idea rea limited to generic computer components performing routine data processing operations. When considered individually and as an ordered combination, the claim does not amount to significantly more than the abstract idea.
Claim Rejections - 35 U.S.C. §101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
With respect to independent claims 1 recites “processing the telemetry data in accordance with one or more context discovery operations;
determining, based on the processing of the telemetry data, for each context discovery operation, output data comprising one or more mapping structures indicative of a potential mapping for a respective data point of the plurality of data points, wherein each mapping structure comprises a confidence value indicative of a confidence of the potential mapping;
processing the output data, the processing identifying one or more definitive mappings;
merging, at least a portion of the mapping structures generated by the one or more context discovery operations using a probabilistic formula; and generating a combined confidence value for the merged mapping structure;
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”. These limitations could be reasonably and practically performed by the human mind, for example, processing data according to operations… determining output data… and processing by identifying a mapping/merging of data using mathematical calculations are activities that can be performed entirely in the human mind can be performed by a human based on the are observation/evaluation steps. Accordingly, the claim recites a mental process and mathematical relationships, which can be done utilizing pen and paper.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application, because the claim does not contain additional element “transmitting the context data to a semantic model generation application to automatically generate or update a digital model of the asset system, wherein the generated or updated asset model provides one or more insights.” is insignificant extra solution activity of data outputting (transmitting data and obtaining results) therefore does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim 1 at step 2B do not include additional elements that are sufficient to amount to significantly more than the judicial exception. No elements individually or in combination adds “significantly more” than the abstract idea hence are no more than well-understood, routine and conventional computer functions that merely apply the abstract idea on a generic computer. When viewed as an ordered combination, the additional elements “transmitting the context data to a semantic model generation application to automatically generate or update a digital model of the asset system, wherein the generated or updated asset model provides one or more insights.” is insignificant extra solution activity of data outputting therefore the claims do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself. According, claim 1 is ineligible under 101.
Claims 2, 5-12 are dependent claims and do not recite any additional elements that would amount to significantly more than the abstract idea. Specifically,
Claim 2. With respect to step 2A prong 1 “each data point of the plurality of data points comprising one or more of text data, time-series data, and hierarchical data.” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine a data structure just as data points of different types such as text, time-series or hierarchical.
Claim 5. With respect to step 2A prong 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.” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine merging portions of data based on relationships.
Claim 6. With respect to step 2A prong 1 “determining whether the combined confidence value associated with a merger of two or more mapping structures exceeds a predefined confidence threshold; and
in accordance with the determination that the combined confidence value exceeds the predefined confidence threshold: identifying the merger of the two or more mapping structures as a definitive mapping.” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine conditions based on threshold and based on thresholds merging data.
Claim 7. With respect to step 2A prong 1 “the processing of the telemetry data in accordance with the one or more context discovery operations comprising processing the telemetry data in accordance with one or more token interpretation operations, one or more context translation operations, and one or more statistical classification operations.” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine data based on statistical classifications and context.
Claim 8. With respect to step 2A prong 1 “identifying one or more tokens of text data associated with a respective data point of the plurality of data points;
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; 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.” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine relationships and values of data based on observation & evaluations.
Claim 9. With respect to step 2A prong 1 “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; 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. “ recites abstract idea of mental steps (observation & evaluation), a person can choose or determine relationships and values of data based on observation & evaluations.
Claim 10. With respect to step 2A prong 2 “transmission of the generated context data to the semantic model generation application comprises providing, along with the context data, metadata indicative of point roles, asset types, and hierarchical relationships,”, recites additional elements of insignificant extra solution activity of data outputting and data manipulation. With respect to step 2B the recited insignificant extra solution activity is recited at a high level of generality which are well-understood, routine and conventional as taught data outputting.
With respect to step 2A prong 1 “wherein the semantic model generation application integrates the context data with a domain ontology to generate strongly typed entities and relationships for the digital mode” recites abstract idea of mental steps (observation & evaluation), a person can choose or determining (generate) manipulated output based on integrating data from different data source to determine relationships between data.
Claim 3. With respect to step 2A prong 1 “each mapping structure comprising a confidence value indicative of a confidence of the potential mapping. recites abstract idea of mental steps (observation & evaluation), a person can choose or determine a mapping structure in determining multitude of data relationships including indication of confidence.
Claim 4. With respect to step 2A prong 1 “the processing of the output data comprising merging at least a portion of the one or more mapping structures. ” recites abstract idea of mental steps (observation & evaluation), a person can choose or determine merging portions of data based on relationships.
Claim 11. With respect to step 2A prong 1 “generating a ranked list of one or more mapping structures associated with a particular asset in order of respective confidence values; and determining, based on the ranked list, an asset type for the respective asset. ” recites abstract idea of mental steps (observation & evaluation), a person can generate a ranked list based on provided data and determine data types based on observations and evaluation of data.
Claim 12. With respect to step 2A prong 1 “wherein in response to determining that the combined confidence value does not exceed a predefined confidence threshold, including two or more mapping structures and their associated confidence values as suggested mappings in the context data. ” recites abstract idea of mental steps (observation & evaluation), a person can determine confidence values and combine confidence values based on predefined thresholds and determine additional aspects such as making relationship mapping based on conditional statements and context.
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, 5-12 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)) further in view of Kretz et al. (US 2013/0054621)
1. Kiff teaches, A computer-implemented method comprising:
processing the telemetry data in accordance with one or more context discovery operations (Paragraph 73 – 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, output data comprising one or more mapping structures indicative of a potential mapping for a respective data point of the plurality of data points (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 (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 (Paragraph 65 – 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), wherein the processing the output data further comprises:
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;
merging, at least a portion of the mapping structures generated by the one or more 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 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 (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).
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,
merging, at least a portion of the mapping structures generated by the one or more context discovery operations using a probabilistic formula (Paragraph 83 & Claim 13 - teaches similar outcome 222 may be based on similarity outcome score… of a number of text-based associations and semantic-based associations – thus teaching combining multiple associations and computes a score based on them, corresponding to merging mapping structures using statistical/probabilistic evaluations, Furthermore, Paragraph 67 – teaches ontology concept string F-score based on computation of precision and recall, F-score is a probabilistic statistical metrics, Kretz); and
generating a combined confidence value for the merged mapping structure (Paragraph 31 - teaches similarity rule 124 based on confidence level associated with semantic-based association – thus disclosing computing similarity outcome based on confidence levels of associations, corresponding to generating a combined confidence value, 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.
2. The combination of Kiff, Park and Kretz 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 and Kretz 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 and Kretz 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 and Kretz teach, The method of claim 1, the processing of the telemetry data in accordance with the one or more 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 and Kretz 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 and Kretz 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 and Kretz 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 and Kretz 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 and Kretz 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).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMRESH SINGH whose telephone number is (571)270-3560. The examiner can normally be reached Monday-Friday 8am-5pm.
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/AMRESH SINGH/Primary Examiner, Art Unit 2159