DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. Claims 2-20 are presented for examination. Claim 1 is canceled.
2. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 2-20 are rejected under 35 U.S.C. 101. Specifically, independent claims 2, 10 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claims 2, 10 and 17:
Yes, the claims 2 and 10 are directed to a computer implemented system; claim 17 is to a computer implemented method. which are a statutory category of invention.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. The limitations “create and deploy one or more data collection policies
to the industrial environment; automatically propagate the one or more data collection
policies through the plurality of industrial sensors train a machine learning model using the industry specific feedback to identify preferred sensor combinations for diagnosing conditions of the industrial environment; iteratively improve the machine learning model based on the industry-specific feedback from the industrial environment; and automatically adjust the one or more data collection policies based on outputs of the machine learning model; coordinate multiple data collection systems including
the plurality of industrial sensors by analyzing one or more collection patterns across the industrial environment”; and furthermore, in claim 10, “determine at least one current health state indicator associated with at least one industrial machine of the industrial environment, wherein the at least one current health state indicator includes a fault
condition of the at least one industrial machine; etc. The limitations above, as drafted, is a process or function that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This process is a mental process as described in MPEP 2106.04(a)(2)(III), because the recited processing is simple enough to be practically performed in the human mind.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No. The claim further recites “a plurality of industrial sensors configured to collect
operational data from an industrial environment; receive industry-specific feedback including at least one of: utilization metrics, yield metrics, or operational impact metrics; and adjust a collection of operational data by at least one of the plurality of industrial sensors” are amounts to extra-solution activity of receiving data, making judgment and adjusting data until the desire expectation of outcome is not satisfied (MPEP 2106.05 (g)): i.e. pre-solution activity of gathering data for use in the claimed process. When viewed individually or on combination, these additional elements do not integrate the recited judicial exception into a practical application.
Step 2B: Do the limitations add elements amounting to significantly more than the judicial exception?
No, the limitations do not add elements amounting to significantly more than the judicial exception. As recited above, the additional elements which are directed to insignificant extra‐solution activities.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of “a cloud-based policy automation engine; an adaptive intelligent system; a cognitive input selection system; and "an analytic system“ in claim 10; amount to insignificant extra‐solution activities. The automation engine, adaptive intelligent system, etc, are generic computer components used as a tool. They provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. Specifically mere data processing, and necessary outputting. These additional elements, when considered separately or in combination, are well‐understood, routine and conventional activities in the field (as shown in the court case, mere data gathering is considered routine and conventional activities. See In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196‐97 (CCPA 1982)) and do not add inventive concept into the claim.
Therefore, claims 1, 10 and 17 are directed to an abstract idea without significantly more, and is not patent eligible.
Regarding Claims 3-9, 11-16 and 18-20:
They depend on claims 2, 10 and 17, therefore recite the same abstract idea and additional elements of claims 1, 10 and 17. The claims 3-9, 11-16 and 18-20 recited other new limitations but they too can be practically performed in human’s mind hence are mental processes based abstract idea. Please note that a narrower abstract idea is still an abstract idea as in this case since the limitations of the claims 3-9, 11-16 and 18-20 are more narrowing the abstract idea of the claims 2, 10 and 17. Therefore, the claims 3-9, 11-16 and 18-20 fail to provide a practical application and an inventive step. Furthermore, the claims 3-9, 11-16 and 18-20 do not contain additional limitations that integrate the exception into a practical application or amount to significantly more than the exception. The claims 3-9, 11-16 and 18-20 are not patent eligible.
5. Examiner's note: To qualify as a § 101 statutory process, the claim should positively recite the particular machine to which it is tied, for example by identifying the apparatus that accomplishes the method steps, or positively recite the subject matter that is being transformed, for example by identifying the material that is being changed to a different state.
Claim Rejections - 35 USC § 112
6. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
7. Claims 2-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification, as originally filed, does not reasonably convey to one of ordinary skill in the art that inventors were in possession of these limitations at the time of filing, provide description for in claims 2 and 17 “automatically adjust the one or more data collection policies based on outputs of the machine learning model”; and in claim 10, “automatically adjust the one or more data collection policies based on outputs of the machine learning
model; and a cognitive input selection system configured to: select a data collection policy from the one or more data collection policies based on the fault condition of the
at least one industrial machine. It is unclear what is actually being described and thus what is actually being claimed to the point where one of ordinary skill in the art would be unable to make or use the invention.
Claims 3-9, 11-16 and 18-20 rejected because they incorporate the deficiencies of claims 2, 10 and 17 respectively.
Claim Rejections - 35 USC § 102
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.
8. 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.
9. Claims 2-9 and 17-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Cella et al. (Cella), US publication no. 2018/0284737 A1.
As per claim 2, Cella discloses A computer-implemented system for intelligent data collection and policy management [figure 1], comprising:
a plurality of industrial sensors configured to collect operational data from an industrial environment [para 219, 1177];
a cloud-based policy automation engine configured to: create and deploy one or more data collection policies to the industrial environment [para 341, 1806]; and automatically propagate the one or more data collection policies through the plurality of industrial sensors [para 374];
an adaptive intelligent system configured to: receive industry-specific feedback including at least one of: utilization metrics, yield metrics, or operational impact metrics [para 336, 338];
train a machine learning model using the industry specific feedback to identify preferred sensor combinations for diagnosing conditions of the industrial environment [para 336, 338];
iteratively improve the machine learning model based on the industry-specific feedback from the industrial environment [para 345, 1082]; and
automatically adjust the one or more data collection policies based on outputs of the machine learning model [para 228, 271, 1177]; and
a cognitive input selection system configured to: coordinate multiple data collection systems including the plurality of industrial sensors by analyzing one or more collection patterns across the industrial environment [para 338]; and
adjust a collection of operational data by at least one of the plurality of industrial sensors [para 228, 271].
Cella discloses:
[0228] Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in tum, be used for processing by the host system 112 as inputs to
components of the platform 100 and portions of the industrial IoT data collection, monitoring and control system 10, or the like. Learning may be human-supervised or fully automated, such as using one or more input sources 116 to provide a data set, along with information about the item to be learned. Machine learning may use one or more models, rules, semantic understandings, workflows, or other structured or semi-structured understanding of the world, such as for automated optimization of control of a system or process based on feedback or feed forward to an operating model for the system or process. One such machine learning technique for semantic and contextual understandings, workflows, or other structured or semi-structured understandings is disclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012, and hereby incorporated by reference as if fully set forth herein. Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such
as in a recursive process). Where sufficient understanding of the underlying structure or behavior of a system is not known, insufficient data is not available, or in other cases
where preferred for various reasons, machine learning may also be undertaken in the absence of an underlying model; that is, input sources may be weighted, structured, or the like within a machine learning facility without regard to any a priori understanding of structure, and outcomes (such as those based on measures of success at accomplishing various desired objectives) can be serially fed to the machine
learning system to allow it to learn how to achieve the targeted objectives. For example, the system may learn to recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic), or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, data types, objects, weights, nodes, links, or other factors based on feedback (such that successful elements emerge over a series of generations). For example, alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In embodiments, local machine learning may tum on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure,
contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like), contributing to optimization of one or more parameters,
identification of a pattern (such as relating to a threat, a failure mode, a success mode, or the like) or the like. For example, a system may learn what sets of sensors should be turned on or off under given conditions to achieve the highest value utilization of a data collector 102. In embodiments, similar techniques may be used to handle optimization of transport of data in the platform 100 ( such as in the network 110) by using generic programming or other machine learning techniques to learn to configure network elements (such as configuring network transport paths, configuring network coding types and architectures, configuring network security elements), and the like.
[0338] Combination of inputs (including selection of what sensors or input sources to tum "on" or "off") may be performed under the control of machine-based intelligence,
such as using a local cognitive input selection system 4004, an optionally remote cognitive input selection system 4114, or a combination of the two. The cognitive input selection systems 4004, 4014 may use intelligence and machine learning capabilities described elsewhere in this disclosure, such as using detected conditions (such as conditions informed by the input sources 116 or sensors), state information (including state information determined by a machine state recognition system 4020 that may determine a state), such as relating to an operational state, an environmental state, a state within a known process or workflow, a state involving a fault or diagnostic condition, or many others. This may include optimization of input selection and configuration based on learning feedback from the learning feedback system 4012, which may include providing training data (such as from the host processing system 112 or from other data collection systems 102 either directly or from the host 112) and may include providing feedback metrics, such as success metrics calculated within the analytic system 4018 of the host processing system 112. For example, if a data stream consisting of a particular combination of sensors and inputs yields positive results in a given set of conditions (such as providing improved pattern recognize, improved prediction, improved diagnosis, improved yield, improved return on investment, improved efficiency, or the like), then metrics relating to such results from the analytic system 4018 can be provided via the learning feedback system 4012 to the cognitive input selection systems 4004, 4014 to help configure future data collection to select that combination in those conditions (allowing other input sources to be de-selected, such as by powering down the other sensors). In embodiments, selection and de-selection of sensor combinations, under control of one or more of the cognitive input selection systems 4004, may occur with automated variation, such as using genetic
programming techniques, based on learning feedback 4012, such as from the analytic system 4018, effective combinations for a given state or set of conditions are promoted, and less effective combinations are demoted, resulting in progressive
optimization and adaptation of the local data collection system to each unique environment. Thus, an automatically adapting, multi-sensor data collection system is
provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, energy production or usage, and the like), analytic metrics (such as success in recognizing
patterns, making predictions, classifying data, or the like), and local system metrics (such as bandwidth utilization, storage utilization, power consumption, and the like).
In embodiments, the analytic system 4018, the state system 4020 and the cognitive input selection system 4114 of a host may take data from multiple data collection systems 102, such that optimization (including of input selection) may be undertaken through coordinated operation of multiple systems 102. For example, the cognitive input selection system 4114 may understand that if one data collection system 102
is already collecting vibration data for an X-axis, the X-axis vibration sensor for the other data collection system might be turned off, in favor of getting Y-axis data from the other data collector 102. Thus, through coordinated collection by the host cognitive input selection system 4114, the activity of multiple collectors 102, across a host of different sensors, can provide for a rich data set for the host processing system 112, without wasting energy, bandwidth, storage space, or the like. As noted above, optimization may be based on overall system success metrics, analytic success metrics, and local system metrics, or a combination of the above.
[1177] In an embodiment, a system 11100 for data collection in an industrial environment may include a plurality of input sensors 11102 communicatively coupled to a controller 11106, a data collection circuit 11104 structured to collect output data 11108 from the input sensors 11102, and a machine learning data analysis circuit 11110 structured to receive the output data 11108 and learn received output data
patterns 11112 indicative of an outcome, wherein the machine learning data analysis circuit 11110 is structured to learn received output data patterns 11112 by being seeded with a model 11114 based on industry-specific feedback 11118. The model 11114 may be a physical model, an operational model, or a system model. The industry-specific feedback 11118 may be one or more of a utilization measure, an efficiency measure (e.g., power and/or financial), a measure of success in prediction or anticipation of states ( e.g., an avoidance and mitigation of faults), a productivity measure (e.g., a workflow), a yield measure, and a profit measure. The industry-specific feedback 11118 includes an amount of power generated by a machine about which the input sensors provide information during operation of the machine. The
industry-specific feedback 11118 includes a measure of the output of an assembly line about which the input sensors provide information. The industry-specific feedback 11118 includes a failure rate of units of product produced by a machine about which the input sensors provide information. The industry-specific feedback 11118 includes a fault rate of a machine about which the input sensors provide information. The industry-specific feedback 11118 includes the power utilization efficiency of a machine about which the input sensors provide information, wherein the machine is one of a turbine, a transformer, a generator, a compressor, one that stores energy, and one that includes power train components (e.g., the rate of extraction of a material by a machine about which the input sensors provide information, the rate of production of a gas by a machine about which the input sensors provide information, the rate of production of
a hydrocarbon product by a machine about which the input sensors provide information), and the rate of production of a chemical product by a machine about which the input sensors provide information. The machine learning data analysis circuit 11110 may be further structured to learn received output data patterns 11112 based on the outcome. The system 11100 may keep or modify operational parameters or equipment. The controller 11106 may adjust the weighting of the machine learning data analysis circuit 11110 based on the learned received output data patterns 11112 or the outcome, collect more/fewer data points from the input sensors based on the learned received output data patterns 11112 or the outcome, change a data storage technique
for the output data 11108 based on the learned received output data patterns 11112 or the outcome, change a data presentation mode or manner based on the learned received output data patterns 11112 or the outcome, and apply one or more filters (low pass, high pass, band pass, etc.) to the output data 11108. In embodiments, the system 11100 may remove/re-task under-utilized equipment based on one or more of the learned received output data patterns 11112 and the outcome. The machine learning data analysis circuit 11110 may include a neural network expert system.
The input sensors may measure vibration and noise data. The machine learning data analysis circuit 11110 may be structured to learn received output data patterns 11112
indicative of progress/alignment with one or more goals/guidelines ( e.g., which may be determined by a different subset of the input sensors). The machine learning data
analysis circuit 11110 may be structured to learn received output data patterns 11112 indicative of an unknown variable. The machine learning data analysis circuit 11110 may be structured to learn received output data patterns 11112 indicative of a preferred input among available inputs. The machine learning data analysis circuit 11110 may be structured to learn received output data patterns 11112 indicative
of a preferred input data collection band among available input data collection bands. The machine learning data analysis circuit 11110 may be disposed in part on a machine, on one or more data collectors, in network infrastructure, in the cloud, or any combination thereof. The system 11100 may be deployed on the data collection circuit 11104. The system 11100 may be distributed between the data collection circuit 11104 and a remote infrastructure. The data collection circuit 11104 may include a data collector.
As per claim 3, Cella discloses the collection of the operational data is based on the automatically adjusted one or more data collection policies [para 228, 271, 1177].
As per claim 4, Cella discloses the one or more data collection policies specify at least one of: data access rights, connection configurations, or data handling parameters for at least one of the plurality of industrial sensors [figure 181; par 1637, 1766].
As per claim 5, Cella discloses the cloud-based policy automation engine is further configured to store execution data related to a data collection policy of the one or more data collection policies in a distributed ledger [para 336, 341].
As per claim 6, Cella discloses the adjustment of the collection of operational data includes selectively enabling or disabling sensor inputs [para 633, 863].
As per claim 7, Cella discloses the one of the one or more data collection policies includes implementing sensor fusion across the multiple data collection systems [para 27, 223].
As per claim 8, Cella discloses the cloud-based policy automation engine is configured to receive feedback regarding a success of the machine learning model in predicting a condition of the industrial environment, and based on the received feedback, improve the machine learning model by at least one of: adjusting a weight of a sensor or adjusting a parameter of a sensor [para 336, 338].
As per claim 9, Cella disclose the cloud-based policy automation engine is configured to receive feedback regarding a success of the machine learning model in predicting a condition of the industrial environment, and based on the received feedback, improve the machine learning model by including or omitting at least one of the plurality of industrial sensors relative to the machine learning model [para 228, 271, 1177].
As to claims 17-20, claims 2-8 basically are the corresponding elements that are
carried out the method of operating step in claims 17-20. Accordingly, claims 17-20 are
rejected for the same reason as set forth in claims 2-8.
Allowable Subject Matter
10. Claims 10-17 allowed over prior art.
11. The following is a statement of reasons for the indication of allowable subject matter: in claim 10, the prior art of records do not teach a cognitive input selection system configured to: select a data collection policy from the one or more data collection policies based on the fault condition of the at least one industrial machine; and adjust collection of future operational data from the plurality of industrial sensors based on the selected data collection policy.
The office would like to preface this section with the following claims discussed have outstanding 35 U.S.C. 101 rejection and claim objection issues that must be resolved before the claims are considered allowable.
12. Examiner's note: Examiner has cited particular paragraphs and columns and line numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
13. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Cella et al, US publication no. 2019/0025813 teaches a method comprising: representing a plurality of components of an industrial machine from an industrial environment; selecting at least one representation of the plurality of components by a user via an expert graphical user interface; searching a database of industrial machine failure modes for at least one failure mode that corresponds to the user selection of the component of the plurality of components; and displaying the at least one corresponding failure mode to the user via the expert graphical user interface [para 1194].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUN CAO whose telephone number is (571)272-3664. The examiner can normally be reached on M-F 7:30 am-4:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached on 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHUN CAO/Primary Examiner, Art Unit 2115
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