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 .
Priority
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/464,726, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. 18/658,549
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters:
"106" and "206" have both been used to designate "industrial site"
“106” is used to designate “industrial site” in paragraphs [0081] and [0082]
“206” is used to designate “industrial site” in paragraphs [0043], [0044], [0058], [0059], [0060], [0066], [0067], [0071], [0081], [0082], and [0086]
it is unclear if either “106” or “206” are referring to “industrial field” as “106” is not in the drawings and Figure 2 does not clarify.
"600" and "700" have both been used to designate "computer system"
“600” is used to designate “computer system” in paragraph [00121]
“700” is used to designate “computer system” in paragraph [00121], [00122], and [00123]
It is unclear if either “600” or “700” are referring to “computer system” as “600” is not in the drawings and Figure 7 has “700” as “computing system”
"110/120/130/140/150/160/170/180/190" and "206" have all been used to designate "industrial equipment"
"110/120/130/140/150/160/170/180/190" are used to designate “industrial equipment” in paragraphs [0036], [0037], [0038], and [0039]
“206” is used to designate “industrial equipment” in paragraphs [0040], [0042], [0045], and [0050]
It is clear that "110/120/130/140/150/160/170/180/190" refer to “industrial equipment” in Figure 1. It is unclear if “206” refers to “industrial equipment” as Figure 2 does not clarify.
"202" and "223" have both been used to designate "autonomous site model"
“202” is used to designate “autonomous site model” in paragraph [0084]
“223” is used to designate “autonomous site model” in paragraphs [0051], [0052], [0056], [0057], [0065], [0066], [0070], [0071], [0073], [0077], [0079], [0081], [0082], [0086], and [0094]
It is clear that "223" refers to “autonomous site model” in Figure 2. “202” refers to “autonomous site operator” in Figure 2.
"204" and "304" have both been used to designate "autonomous devices"
“204” is used to designate “autonomous devices” in paragraphs [0040], [0041], [0043], [0045], [0046], [0047], [0048], [0049], [0053], [0056], [0058], [0059], [0065], [00101], [00105], and [00115]
“304” is used to designate “autonomous devices” in paragraphs [0070] and [0072]
It is clear that "204" refers to “autonomous devices” in Figure 2. “304” refers to “action recommendations” in Figure 3.
"404a" and "404c" have both been used to designate "operating conditions"
“404a” is used to designate “operating conditions” in paragraph [0081]
“404c” is used to designate “operating conditions” in paragraph [0079]
It is unclear if either “404a” or “404c” are referring to “operating conditions” as “404a” refers to “normal operating conditions” and “404c” refers to “operating scenarios”
"504" and "506" have both been used to designate "scalable data store"
“504” is used to designate “scalable data store” in paragraphs [0095], [0096], [0097]
“506” is used to designate “scalable data store” in paragraph [0096]
It is unclear if either “504” or “506” are referring to “scalable data store” as “504” refers to “scalable data storage” and “506” refers to “data structure”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character:
“202” has been used to designate both "autonomous site operator" and "autonomous site model"
“Autonomous site operator” is designated as “202” in paragraphs [0040], [0047], [0051], [0079], [0080], [0081], [0082], [00101], and [00115]
“Autonomous site model” is designated as “202” in paragraph [0084]
According to Figure 2, “202” should designate “autonomous site operator”
“206” has been used to designate both "industrial equipment" and "industrial site"
“Industrial equipment” is designated as “206” in paragraphs [0040], [0042], [0045], and [0050]
“Industrial site” is designated as “206” in paragraphs [0043], [0044], [0058], [0059], [0060], [0066], [0067], [0071], [0081], [0082], and [0086]
According to Figure 2, it is unclear if “206” is referring to “industrial equipment” or “industrial field”
“208” has been used to designate both "5G network" and "autonomous sensor data"
“5G network” is designated as “208” in paragraph [0040]
“Autonomous sensor data” is designated as “208” in paragraph [0042]
According to Figure 2, “208” should designate “5G network”
“223” has been used to designate both "autonomous site model" and "anomalous site model"
“Autonomous site model” is designated as “223” in paragraphs [0051], [0052], [0056], [0057], [0065], [0066], [0070], [0071], [0073], [0077], [0079], [0081], [0082], [0086], and [0094]
“Anomalous site model” is designated as “223” in paragraph [00105]
According to Figure 2, “223” should designate “autonomous site model”
“304” has been used to designate both "action recommendations" and "autonomous devices"
“Action recommendations” is designated as “304” in paragraphs [0064], [0065], [0066], [0067], [0070], [0080], and [0082]
“Autonomous devices” is designated as “304” in paragraphs [0070] and [0072]
According to Figure 3, “304” should designate “action recommendations”
“404a” has been used to designate both "normal conditions" and "operating conditions"
“Normal conditions” is designated as “404a” in paragraph [0079]
“Operating conditions” is designated as “404a” in paragraph [0081]
According to Figure 4, “404a” should designate “normal operating conditions”
“404c” has been used to designate both "operating conditions" and "operating scenarios"
“Operating conditions” is designated as “404c” in paragraph [0079]
“Operating scenarios” is designated as “404c” in paragraph [0082]
According to Figure 4, “404c” should designate “operating scenarios”
“504” has been used to designate all of "scaled data storage", "scalable data storage", "scalable data store", and "data warehouse"
“Scaled data storage” is designated as “504” in paragraphs [0087], [0090], and [0092]
“Scalable data storage” is designated as “504” in paragraphs [0092], [0093], [0095], and [0096]
“Scalable data store” is designated as “504” in paragraphs [0095], [0096], and [0097]
“Data warehouse” is designated as “504” in paragraph [0090]
According to Figure 5, “504” should designate “scalable data storage”
“508” has been used to designate both "holding area" and "interim storage area"
“Holding area” is designated as “508” in paragraph [0089]
“Interim storage area” is designated as “508” in paragraph [0092]
According to Figure 5, “508” should designate “interim data storage”
“506” has been used to designate both "data structure" and "scalable data store"
“Data structure” is designated as “506” in paragraphs [0092], [0095], and [0096]
“Scalable data store” is designated as “506” in paragraph [0096]
According to Figure 5, “506” should designate “data structure”
“700” has been used to designate both "computing system" and "computer system"
“Computing system” is designated as “700” in paragraphs [00115] and [00116]
“Computer system” is designated as “700” in paragraphs [00121], [00122], and [00123]
According to Figure 7, “700” should designate “computing system.”
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference signs mentioned in the description:
In paragraphs [0081] and [0082],
“106” referring to the industrial site
In paragraph [00121],
“600” referring to the computer system.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference characters not mentioned in the description:
In Figure 3,
“320” referring to process setpoints
“322” referring to asset maintenance work orders
“324” referring to manual operation.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference characters in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The disclosure is objected to because of the following informalities:
Inconsistent length of paragraph numbers as they start with 4 digits [XXXX] then change to 5 digits [XXXXX]. For example, paragraphs [0001] – [0099] and then paragraphs [00100] – [00131] should be either paragraphs [0001] - [0131] or paragraphs [00001] - [00131]
Paragraph [0023] has inconsistent brackets for the enclosed phrase “e.g., of identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action.” The front bracket is “(“ while the back bracket is “}” where it should use “().”
Appropriate correction is required.
The use of the term Bluetooth Low EnergyTM (BLETM) in paragraph [0056], which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claims 1-20 and 22 are objected to because of the following informality:
Independent Claims 1 and 20 recites the following subject matter that is grammatically unclear:
“determining the at least one action use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, and/or constraint data about the industrial site, and supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field”.
The broadest reasonable interpretation in light of the specification, each of the following limitations are interpreted as a list of interchangeable elements: historical measurement, control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, constraint data about the industrial site, or supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field. See Specification at [0023] for support of “determining the at least one action} uses at least one of the types of data listed”.
Claims 2-19 and 22 are further objected for being dependent upon an objected base Claim 1. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-22 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.
Claim 1 recites the following, underlining limitations that have insufficient antecedent basis:
A method of autonomous anomaly management of an industrial site having industrial equipment in the field of the industrial site, the method comprising:
identifying industrial equipment to be inspected in the field of the industrial site;
obtaining select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment;
processing the autonomous sensor data to identify a potential anomaly; and in response to identifying a potential anomaly, taking at least one action to address the potential anomaly,
wherein, as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, and/or constraint data about the industrial site, and supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field.
It is unclear what the limitation “the field” is being referred to as a “field” is not mentioned before in Claim 1. It is unclear if the limitation “the autonomous sensor data” is being referred to “select autonomous sensor data” or “historical autonomous sensor data”. It is unclear if the limitation “the industrial equipment” is being referred to “industrial equipment” as recited in the preamble or “identifying industrial equipment” as recited in the body.
Claims 2-19 and 22 are further rejected for being dependent upon a rejected base Claim 1.
Claim 7 recites the following, underlining an additional limitation that has insufficient antecedent basis:
The method of claim 1, wherein the training includes:
injecting an anomalous scenario into a simulation of the industrial site;
monitoring operator actions responsive to the anomalous scenario;
monitoring respective outcomes of the operator actions; and
correlating the anomalous scenario, operator actions, and respective outcomes for future inferences.
It is unclear what the limitation “the training” is being referred to as “training” is not mentioned before in Claim 7 or in Claim 1 in which this claim depends on. It is interpreted by the examiner that Claim 1 that is depended on should actually be Claim 6.
Claim 8 recites the following, underlining an additional limitation that has insufficient antecedent basis:
The method of claim 1, further comprising:
locally storing batches of data of live measurement and/or control data from the industrial equipment, the historical measurement and/or control data from the industrial equipment, the autonomous sensor data, the extrinsic data, and the supervisory data;
processing the batches of data, for uniformity and/or normalization before and/or after storing the batches of data locally; and
storing the processed batches of data in a large data set in a data warehouse and/or data lake,
wherein the analyzing is performed on the large data set.
It is unclear what the limitation “the analyzing” is being referred to as “analyzing” is not mentioned before in Claim 8 or in Claim 1 in which this claim depends on.
Claims 9-11 are further rejected for being dependent upon a rejected base Claim 8.
Claim 20 recites the following, underlining limitations that have insufficient antecedent basis:
A system for performing autonomous inspections of equipment and/or processes in an industrial plant, comprising:
a memory configured to store a plurality of programmable instructions; and
a processing device in communication with the memory, wherein the processing device, upon execution of the plurality of programmable instructions is configured to:
identify industrial equipment to be inspected in the field of the industrial site;
obtain select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment;
process the autonomous sensor data to identify a potential anomaly; and in response to identifying a potential anomaly, take at least one action to address the potential anomaly,
wherein, as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, and/or constraint data about the industrial site, and supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field.
It is unclear what the limitation “the field” is being referred to as a “field” is not mentioned before in Claim 20. It is unclear what the limitation “the industrial site” is being referred to as an “industrial site” is not mentioned before in this claim only an “industrial plant” in the preamble. It is unclear if the limitation “the autonomous sensor data” is being referred to “select autonomous sensor data” or “historical autonomous sensor data”. It is unclear if the limitation “the industrial equipment” is being referred to “industrial equipment” as recited in the preamble or “identifying industrial equipment” as recited in the body.
Claim 21 recites the following, underlining limitations that have insufficient antecedent basis:
A method of autonomous anomaly management of an industrial site having industrial equipment in the field of the industrial site, the method comprising:
identifying industrial equipment to be inspected in the field of the industrial site;
obtaining select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment;
processing the autonomous sensor data to identify a potential anomaly;
in response to identifying a potential anomaly, recommending at least one action
to address the potential anomaly;
training using machine learning at least one of the identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action;
setting a confidence level in the recommended action, the confidence level being
a function of an amount of the training performed; and
in response to the confidence level being below a threshold, controlling the respective routes of the one or more autonomous mobile device to obtain additional autonomous sensor data related to the anomaly, wherein processing the additional autonomous sensor data increases the confidence level.
It is unclear what the limitation “the field” is being referred to as a “field” is not mentioned before in Claim 21.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 12, 13, 15-20, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Loychik (US-20190281431-A1).
Regarding Claim 1, Loychik teaches a method of autonomous anomaly management of an industrial site having industrial equipment in the field of the industrial site (smart communication device 10, fig.2 and [0053]; collecting and evaluating information about equipment distributed over a large area, e.g., over a large industrial plant or oil field; and more particularly relates to a technique for further processing such collected and evaluated information using a processor or processing module to determine the health/operability of the equipment [0001, 0053]), the method comprising:
identifying industrial equipment to be inspected in the field of the industrial site;
obtaining select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment (receive signaling containing information about sensed data from a multiplicity of data collectors D each associated with a respective piece of equipment E and configured to collect the sensed data related to the respective piece of equipment E when the smart communication device 10 is moved within a predefined proximity to automatically connect or pair with each data collector D to receive the signaling during movement of the smart communication device on a programmed route having multiple pieces of equipment E; and determine corresponding signaling containing information about sensed data related to each of the multiplicity of data collectors D for operating an app on the processor 10a, based upon the signaling received, fig.2 and [0054-0055]);
processing the autonomous sensor data to identify a potential anomaly (prioritized sensed data received may contain data or a subset of data related to the health or operability of the respective piece of equipment collected and evaluated by the smart communication device, including some combination of vibration data along one or more X, Y or Z axes, temperature data, alarm status data, trend data, historical data, time-based data, FFT evaluated data, etc.; and the scope of the invention is not intended to be limited to the type or kind of prioritized sensed data that is collected and/or evaluated in relation to the health or operability of the respective piece of equipment by the smart communication device [0058]); and
in response to identifying a potential anomaly, taking at least one action to address the potential anomaly (the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, [0019]; an alarm device that is configured to monitor equipment, e.g., including industrial, commercial or residential equipment, such as pumps, compressors, etc. [0095, 0097, 0110]),
wherein, as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, and/or constraint data about the industrial site, and supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field 1(upon reaching some proximity, a participant captures surface date and if the device/equipment is in alarm condition, then the participant connects for historical (trend) data [0016]; The processor may be configured to capture surface data, and if the respective piece of equipment is in an alarm state, then connect for historical (trend) data or immediate analytics [0029]).
Regarding Claim 2, Loychik teaches the method of claim 1, further comprising receiving live measurement and/or control data from the industrial equipment, wherein as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action uses the live measurement and/or control data (The processor may be configured to capture surface data, and if the respective piece of equipment is in an alarm state, then connect for historical (trend) data or immediate analytics [0029]; Time-based data, e.g., such as Historical like D/L historical data tailored to a desired analysis level, or immediate data by executing a command for data acquisition and analysis upon proximity [0092]).
Regarding Claim 12, Loychik teaches the method of claim 1, wherein the extrinsic data includes at least one of user profile of an operator performing control operations on the industrial site, governance information pertaining to the industrial site, and the at least one action customizes information for display to the operator based on the user profile and the industrial site (The app may include displaying a route report screen for allowing a user to generate a route report for a specific route, including providing information about devices in an alarm condition on the specific route and about alert devices on the specific route [0038]).
Regarding Claim 13, Loychik The method of claim 1, further comprising capturing the select autonomous sensor data (a robot (autonomous) which has access to a smart communication device [0008]; The prioritized sensed data received may contain data or a subset of data related to the health or operability of the respective piece of equipment collected and evaluated by the smart communication device, fig. 2 and [0058]).
Regarding Claim 15, Loychik teaches the method of claim 1, wherein processing the autonomous sensor data is performed by the mobile autonomous device (a robot (autonomous) which has access to a smart communication device [0008]; The prioritized sensed data received may contain data or a subset of data related to the health or operability of the respective piece of equipment collected and evaluated by the smart communication device, fig. 2 and [0058]).
Regarding Claim 16, Loychik teaches the method of claim 13, wherein the select autonomous sensor data includes processed image data to detect a defect or a phenomenon, read analog information from an analog measurement device, determine a position of an actuator device included with the industrial equipment and/or a component of the industrial site acted upon by the actuator device (including some combination of vibration data along one or more X, Y or Z axes, temperature data, alarm status data, trend data, historical data, time-based data, FFT evaluated data, etc.; and the scope of the invention is not intended to be limited to the type or kind of prioritized sensed data that is collected and/or evaluated in relation to the health or operability of the respective piece of equipment by the smart communication device, [0058]).
Regarding Claim 17, Loychik teaches the method of claim 13, wherein the select autonomous sensor data includes any of sensed temperature, pressure, radiation, a particular chemical, and motion (including some combination of vibration data along one or more X, Y or Z axes, temperature data, alarm status data, trend data, historical data, time-based data, FFT evaluated data, etc.; and the scope of the invention is not intended to be limited to the type or kind of prioritized sensed data that is collected and/or evaluated in relation to the health or operability of the respective piece of equipment by the smart communication device, [0058]).
Regarding Claim 18, Loychik teaches the method of claim 13, wherein the mobile autonomous device processes the autonomous sensor data and takes an action of the at least one action responsive to identification of the potential anomaly (the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, [0019]; an alarm device that is configured to monitor equipment, e.g., including industrial, commercial or residential equipment, such as pumps, compressors, etc. [0095, 0097, 0110]).
Regarding Claim 19, Loychik teaches the method of claim 18, wherein the action includes generating an alarm, capturing additional select autonomous sensor data, and/or adjusting its route (the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, [0019]; an alarm device that is configured to monitor equipment, e.g., including industrial, commercial or residential equipment, such as pumps, compressors, etc. [0095, 0097, 0110]).
Regarding Claim 20, Loychik teaches a system for performing autonomous inspections of equipment and/or processes in an industrial plant (smart communication device 10, fig.2 and [0053]; collecting and evaluating information about equipment distributed over a large area, e.g., over a large industrial plant or oil field; and more particularly relates to a technique for further processing such collected and evaluated information using a processor or processing module to determine the health/operability of the equipment [0001, 0053]), comprising:
a memory configured to store a plurality of programmable instructions; and
a processing device in communication with the memory, wherein the processing device (The smart communication device may include a memory for storing one or more databases, and the processor configured to store the programmed route in the database of the memory [0041-0042]; The processor 10a may be configured to provide the corresponding signaling in order to run the app on the smart communication device, e.g., which may include generating screens of the smart communication device, information displayed on the screens, updating information in one or more databases stored in the memory of the smart communication device [0059]), upon execution of the plurality of programmable instructions is configured to:
identify industrial equipment to be inspected in the field of the industrial site;
obtain select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment (receive signaling containing information about sensed data from a multiplicity of data collectors D each associated with a respective piece of equipment E and configured to collect the sensed data related to the respective piece of equipment E when the smart communication device 10 is moved within a predefined proximity to automatically connect or pair with each data collector D to receive the signaling during movement of the smart communication device on a programmed route having multiple pieces of equipment E; and determine corresponding signaling containing information about sensed data related to each of the multiplicity of data collectors D for operating an app on the processor 10a, based upon the signaling received, fig.2 and [0054-0055]);
process the autonomous sensor data to identify a potential anomaly (prioritized sensed data received may contain data or a subset of data related to the health or operability of the respective piece of equipment collected and evaluated by the smart communication device, including some combination of vibration data along one or more X, Y or Z axes, temperature data, alarm status data, trend data, historical data, time-based data, FFT evaluated data, etc.; and the scope of the invention is not intended to be limited to the type or kind of prioritized sensed data that is collected and/or evaluated in relation to the health or operability of the respective piece of equipment by the smart communication device [0058]); and
in response to identifying a potential anomaly, take at least one action to address the potential anomaly (the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, [0019]; an alarm device that is configured to monitor equipment, e.g., including industrial, commercial or residential equipment, such as pumps, compressors, etc. [0095, 0097, 0110]),
wherein, as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including enterprise data, customer relationship management data, guidance data, optimization data, and/or constraint data about the industrial site, and supervisory and control data generated and/or collected by supervisory control of the industrial site disposed remote from the field 2(upon reaching some proximity, a participant captures surface date and if the device/equipment is in alarm condition, then the participant connects for historical (trend) data [0016]; The processor may be configured to capture surface data, and if the respective piece of equipment is in an alarm state, then connect for historical (trend) data or immediate analytics [0029]).
Regarding Claim 22, Loychik teaches the method of claim 1, further comprising receiving dynamic autonomous device status data about individual and/or fleets of autonomous devices, wherein the autonomous device status data is used for selecting one or more autonomous devices to perform the at least one action (By way of example, the smart communication device may manage the download and upload process based upon priority, e.g., including where the participant may only be ‘passing through’ with limited connectivity so limited data would be transmitted; data collection would also be prioritized based upon the information that the participant or server wishes to collect in priority to other datasets; and/or completion of communication may be tracked and statused for the participant (for instance, so that the participant knows whether to continue with walk around or not) [0018]).
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 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Loychik (US-20190281431-A1) in view of Putman (US-20210194897-A1).
Regarding Claim 3, Loychik teaches the method of claim 1,
Loychik fails to teach further comprising determining a confidence level in identification of the potential anomaly or in identification of industrial equipment to be inspected in view of the identified potential anomaly, wherein the at least one action taken depends on the confidence level
However, Putman teaches further comprising determining a confidence level in identification of the potential anomaly or in identification of industrial equipment to be inspected in view of the identified potential anomaly, wherein the at least one action taken depends on the confidence level (In some examples, the method further includes assigning a threshold to a confidence level associated with the detection of the anomalous activity, and performing a predefined action when the threshold is reached. [0012]).
Loychik and Putman are in the same field as the invention as they have to do with industrial equipment in an environment. One of ordinary skill in the art would be able to combine assigning a confidence level associated with a detected anomaly in Putman with the identified issue in Loychik as the identified issue and detected anomaly are the same concept. Doing so for the purpose of quantifying the potential accuracy of the identified issue (In some aspects, the confidence level may be expressed as a numerical probability of accuracy for the prediction, in other aspects, the confidence level may be expressed as an interval or probability range, Putman: [0079]).
Regarding Claim 4, Loychik and Putman teach the method of claim 3, wherein when the confidence level is below a threshold, the at least one action includes controlling the respective routes of the one or more autonomous mobile device to obtain additional autonomous sensor data related to the anomaly, the method further comprising adjusting the confidence level in the detection of the anomaly as a function of the additional autonomous sensor data (An operator or algorithm can assign thresholds to the confidence levels associated with anomalous activities, as well as predefined actions to be performed when a threshold is met…whereas with anomalous activities receiving lower confidence level scores, an operator can be prompted to review the anomalous activity before an action is taken… In one embodiment, the confidence levels can be divided into three intervals: high, medium and low, and a threshold can be assigned to each interval. Further, actions to be performed can be assigned to each interval…for confidence levels that fall into the low confidence level interval, the anomalous activity can be flagged and sporadically checked… The thresholds and interval ranges can be reviewed and adjusted to minimize false positives or false negatives, Putman: [0080]; In the case of the participant's ability to respond to data based upon feedback from the machine/equipment, the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, Loychik: [0019]).
Regarding Claim 5, Loychik and Putman teach the method of claim 3, wherein when the confidence level is equal to or above a threshold, the at least one action includes a) controlling or recommending adjustment of a control parameter for controlling a process in the field and b) controlling or recommending application of a maintenance action to the industrial site (An operator or algorithm can assign thresholds to the confidence levels associated with anomalous activities, as well as predefined actions to be performed when a threshold is met. For example, for the anomalous activities receiving a high confidence level score, immediate action can be prescribed,… In one embodiment, the confidence levels can be divided into three intervals: high, medium and low, and a threshold can be assigned to each interval. Further, actions to be performed can be assigned to each interval. For example, for high confidence levels that fall into the high confidence interval an alert can be generated, for confidence levels that fall into the medium confidence interval, an operator can be prompted to review the anomalous activity, … The thresholds and interval ranges can be reviewed and adjusted to minimize false positives or false negatives, Putman: [0080]; In the case of the participant's ability to respond to data based upon feedback from the machine/equipment, the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, Loychik: [0019]).
Claims 6, 7, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Loychik (US-20190281431-A1) in view of Gupta (US-20220014422-A1).
Regarding Claim 6, Loychik teaches the method of claim 1,wherein the method further comprises:
Loychik fails to teach training using machine learning the at least one of the identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action; and
setting a confidence level as a function of an amount of the training performed
However, Gupta teaches training using machine learning the at least one of the identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action; and
setting a confidence level as a function of an amount of the training performed (At operation 1515, an artificial intelligence model may be trained (e.g., via means including an arithmetic and logic unit of a processor, machine learning processing circuitry, etc.) using the elements of the anomaly data to generate an impact score for the application, fig. 15 and [0128]).
Loychik and Gupta are in the same field as the invention as they have to do with industrial equipment in an environment. One of ordinary skill in the art would be able to combine the machine learning training and impact score using anomaly data done in the processor in Gupta with the smart communication device and anomaly data in Loychik as they both use processors to analyze data. Doing so for the purpose of using the training to address the anomaly later on if it happens again (generating the impact score for the application by evaluating current network metrics using the artificial intelligence model; and modifying an operational component of the network based on the impact score, Gupta: [0158]).
Regarding Claim 7, Loychik and Gupta teach the method of claim 63, wherein the training includes:
injecting an anomalous scenario into a simulation of the industrial site (At operation 1515, an artificial intelligence model may be trained (e.g., via means including an arithmetic and logic unit of a processor, machine learning processing circuitry, etc.) using the elements of the anomaly data to generate an impact score for the application, Gupta: fig. 15 and [0128]);
monitoring operator actions responsive to the anomalous scenario (At operation 1525, an operational component of the network may be modified (e.g., via means including a control unit of a processor, network communication circuitry, etc.) based on the impact score, Gupta fig. 15 and [0128]);
monitoring respective outcomes of the operator actions (A remediation directive may be received from an orchestrator of the orchestration layer and the operational component may be modified based at least in part on the remediation directive, Gupta: fig. 15 and [0130]); and
correlating the anomalous scenario, operator actions, and respective outcomes for future inferences (At operation 1525, an operational component of the network may be modified (e.g., via means including a control unit of a processor, network communication circuitry, etc.) based on the impact score. In an example, a network path of the application may be altered. In another example, a resource assignment for the application may be altered on a node of the network, Gupta: fig.15 and [0131]).
Regarding Claim 14, Loychik teaches the method of claim 13,
Loychik fails to teach wherein the at least one autonomous device is trained to identify the select autonomous data to be captured.
However, Gupta teaches wherein the at least one autonomous device is trained to identify the select autonomous data to be captured (At operation 1515, an artificial intelligence model may be trained (e.g., via means including an arithmetic and logic unit of a processor, machine learning processing circuitry, etc.) using the elements of the anomaly data to generate an impact score for the application, fig. 15 and [0128]).
Claims 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Loychik (US-20190281431-A1) in view of Guim (US-20210014047-A1).
Regarding Claim 8, Loychik teaches the method of claim 1, further comprising:
locally storing batches of data of live measurement and/or control data from the industrial equipment, the historical measurement and/or control data from the industrial equipment, the autonomous sensor data, the extrinsic data, and the supervisory data (Trend—Historical data comprised of equipment status, raw data or analytics stored on a data collector and able to be accessed by a participant, fig. 2 and [0012]; The present invention may also take the form of a method for running an app on a smart communication device having a processor and a memory for storing a database, comprising steps for receiving in the processor signaling containing information about sensed data from a multiplicity of data collectors each associated with a respective piece of equipment stored in a programmed route in the database of the memory and configured to collect the sensed data related to the respective piece of equipment when the smart communication device is moved within a predefined proximity to automatically connect or pair with each data collector to receive the signaling during movement of the smart communication device on the programmed route having multiple pieces of equipment distributed over a vast industrial, commercial or residential space [0042]);
processing the batches of data, for uniformity and/or normalization before and/or after storing the batches of data locally (Smart Communication Device—Phones, laptops, or subsystems of vehicles/robots, etc. Defined by the ability to perform generic processing and ability to convert data into a meaningful format for a Participant, fig.2 and [0011]; and determining with the processor corresponding signaling containing information about the sensed data related to each of the multiplicity of data collectors on the programmed route, based upon the signaling received [0042]); and
Loychik fails to teach storing the processed batches of data in a large data set in a data warehouse and/or data lake,
wherein the analyzing is performed on the large data set. (Data in the edge environment can be stored in data lakes. As used herein, a data lake refers to a storage and/or repository that can store both unstructured (e.g., raw) data and structured data at any scale, [0028])
However, Guim teaches storing the processed batches of data in a large data set in a data warehouse and/or data lake,
wherein the analyzing is performed on the large data set. (Data in the edge environment can be stored in data lakes. As used herein, a data lake refers to a storage and/or repository that can store both unstructured (e.g., raw) data and structured data at any scale, [0028])
Loychik and Guim are in the same field as the invention as they have to do with industrial equipment in an environment. One of ordinary skill in the art would be able to combine the storing and processing data in the Smart Communication Device in Loychik with storing data in a data lake in Guim as they both store data. Doing so for the purpose of secure data storage and reduced processing times (Partitioning of the data lake into data lake regions increases privacy of the data stored therein, as each tenant (e.g., user, entity requesting access, etc.) can be granted access only to particular regions of the data lake. Additionally, encryption and/or decryption of data can occur at the level of an individual data lake region to avoid having to encrypt and/or decrypt an entire corresponding data lake, thereby reducing processing times., Guim: [0079])
Regarding Claim 10, Loychik and Guim teach the method of claim 8, wherein storing the processed batches of data in the large data sets uses dynamically scaled compute resources and separated data structures that are separately refreshable (As used herein, a data lake refers to a storage and/or repository that can store both unstructured (e.g., raw) data and structured data at any scale. A data lake region refers to a region or partition of the data lake, where each data lake can be partitioned into any number of data lake regions of varying size…Partitioning of the data lake into data lake regions increases privacy of the data stored therein, as each tenant (e.g., user, entity requesting access, etc.) can be granted access only to particular regions of the data lake, Guim: [0028]).
Regarding Claim 11, Loychik and Guim teach the method of claim 8, comprising:
comparing frequency of queries, refresh times of stored data, and/or size of data structures used for storing the data in the data lake or data warehouse; and
selecting a method of handling the analysis based on a result of the comparison (Additionally or alternatively, the data lake manager 908 can expand or contract an existing data lake region (e.g., the data lake region A 917A). For example, the request analyzer 1000 receives a request from the EIO 935 to expand the data lake region A 917A. In such examples, the location selector 1002 defines a new address range 942A within the storage 914 of the second edge platform 904. In some examples, the new address range 942A corresponds to a storage location on a different edge platform (e.g., the first edge platform 902 and/or the third edge platform 906). The data lake table controller 1012 updates the entry of the data lake table 934 corresponding to the data lake region A 917A to include the new address range 942A. For examples in which the existing data lake region is expanded to a new edge platform (e.g., the third edge platform 906), the key distributor 1008 identifies the RDEK corresponding to the data lake region A 917A based on the data lake table 934, and transmits the RDEK to the new edge platform. Further, the data lake table controller 1012 updates the entry of the data lake table 934 corresponding to the data lake region A 917A to include the new edge platform in the data lake storage nodes 940, Guim: fig 9 and [0147]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Loychik (US-20190281431-A1) in view of Guim (US-20210014047-A1) in view of Musku (US-2020016237-A1).
Regarding Claim 9, Loychik and Guim teach The method of claim 8,
Loychik and Guim fail to teach wherein storing the batches of data locally uses a transactional database.
However, Musku teaches wherein storing the batches of data locally uses a transactional database. (The process 500 can continue with sequences 510, 512, and 514 in which the network devices can collect the streaming telemetry data for storage locally for a period of time based on the established subscriptions… The network management system can aggregate the data and store the data in a time series database (e.g., a type definition language (TDL) database and/or an in-memory transactional database), [0146])
Loychik, Guim, and Musku are in the same field as the invention as they have to do with industrial equipment in an environment. One of ordinary skill in the art would be able to combine the storing data locally and in a data lake in Loychik and Guim with storing data in a transactional database in Musku as they all store data. Doing so for the purpose storing time-series telemetry data based on subscriptions (The process 500 can continue with sequences 510, 512, and 514 in which the network devices can collect the streaming telemetry data for storage locally for a period of time based on the established subscriptions. At sequence 516, the network devices can transmit, to the network management system, the time series flow data using IP and ERSPAN and the congestion data using a streaming telemetry mechanism, Musku: [0079]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Loychik (US-20190281431-A1) in view of Putman (US-20210194897-A1) and further in view of Gupta (US-20220014422-A1).
Regarding Claim 21, Loychik teaches A method of autonomous anomaly management of an industrial site having industrial equipment in the field of the industrial site (smart communication device 10, fig.2 and [0053]; collecting and evaluating information about equipment distributed over a large area, e.g., over a large industrial plant or oil field; and more particularly relates to a technique for further processing such collected and evaluated information using a processor or processing module to determine the health/operability of the equipment [0001, 0053]), the method comprising:
identifying industrial equipment to be inspected in the field of the industrial site;
obtaining select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment (receive signaling containing information about sensed data from a multiplicity of data collectors D each associated with a respective piece of equipment E and configured to collect the sensed data related to the respective piece of equipment E when the smart communication device 10 is moved within a predefined proximity to automatically connect or pair with each data collector D to receive the signaling during movement of the smart communication device on a programmed route having multiple pieces of equipment E; and determine corresponding signaling containing information about sensed data related to each of the multiplicity of data collectors D for operating an app on the processor 10a, based upon the signaling received, fig.2 and [0054-0055]);
processing the autonomous sensor data to identify a potential anomaly (prioritized sensed data received may contain data or a subset of data related to the health or operability of the respective piece of equipment collected and evaluated by the smart communication device, including some combination of vibration data along one or more X, Y or Z axes, temperature data, alarm status data, trend data, historical data, time-based data, FFT evaluated data, etc.; and the scope of the invention is not intended to be limited to the type or kind of prioritized sensed data that is collected and/or evaluated in relation to the health or operability of the respective piece of equipment by the smart communication device [0058]);
in response to identifying a potential anomaly, recommending at least one action to address the potential anomaly (the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server, [0019]; an alarm device that is configured to monitor equipment, e.g., including industrial, commercial or residential equipment, such as pumps, compressors, etc. [0095, 0097, 0110]);
Loychik fails to teach alone training using machine learning at least one of the identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action;
setting a confidence level in the recommended action, the confidence level being a function of an amount of the training performed; and
in response to the confidence level being below a threshold, controlling the respective routes of the one or more autonomous mobile device to obtain additional autonomous sensor data related to the anomaly, wherein processing the additional autonomous sensor data increases the confidence level.
However, Putman teaches training using machine learning at least one of the identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the at least one action;
setting a confidence level in the recommended action, the confidence level being a function of an amount of the training performed; and (At operation 1515, an artificial intelligence model may be trained (e.g., via means including an arithmetic and logic unit of a processor, machine learning processing circuitry, etc.) using the elements of the anomaly data to generate an impact score for the application, fig. 15 and [0128]).
Loychik and Putman fail to teach alone in response to the confidence level being below a threshold, controlling the respective routes of the one or more autonomous mobile device to obtain additional autonomous sensor data related to the anomaly, wherein processing the additional autonomous sensor data increases the confidence level.
However, Loychik and Gupta teach in response to the confidence level being below a threshold, controlling the respective routes of the one or more autonomous mobile device to obtain additional autonomous sensor data related to the anomaly, wherein processing the additional autonomous sensor data increases the confidence level. (An operator or algorithm can assign thresholds to the confidence levels associated with anomalous activities, as well as predefined actions to be performed when a threshold is met…whereas with anomalous activities receiving lower confidence level scores, an operator can be prompted to review the anomalous activity before an action is taken… In one embodiment, the confidence levels can be divided into three intervals: high, medium and low, and a threshold can be assigned to each interval. Further, actions to be performed can be assigned to each interval…for confidence levels that fall into the low confidence level interval, the anomalous activity can be flagged and sporadically checked… The thresholds and interval ranges can be reviewed and adjusted to minimize false positives or false negatives. Gupta: [0080]; In the case of the participant's ability to respond to data based upon feedback from the machine/equipment, the smart communication device may be configured to identify issues that requires further data, inspection, or actions, and/or take action in any combination between the smart communication device, the participant, and the server. Loychik: [0019]).
Loychik, Putman, and Gupta are in the same field as the invention as they have to do with industrial equipment in an environment. One of ordinary skill in the art would be able to combine the machine learning training and impact score using anomaly data done in the processor in Gupta with the smart communication device and anomaly data in Loychik and assigning a confidence level associated with a detected anomaly in Putman as they all use processors to analyze data. Doing so for the purpose of using the training to address the anomaly later on if it happens again (generating the impact score for the application by evaluating current network metrics using the artificial intelligence model; and modifying an operational component of the network based on the impact score, Gupta: [0158]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA D CHAU whose telephone number is (571)270-0906. The examiner can normally be reached Monday-Friday: 8am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA DORA CHAU//J.D.C./Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
1 See Claim Objections for optional claim interpretation.
2 See Claim Objections for optional claim interpretation.
3 This limitation originally had claim 1 but it is interpreted by the examiner as claim 6 as noted for the 112(b)-rejection given above.
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