DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-7, as filed 09/20/2023, are currently pending and have been considered below.
This application is a 371 of PCT/EP2022/056227 (03/10/2022).
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter 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.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A computer implemented method of deploying a network connected sensor for sensing characteristics of a plurality of infrastructure components in a transmission network for a utility service, the method comprising:
training a classifier to indicate a suitability of a location of an infrastructure component for deployment of the network connected sensor, the classifier operable on an input vector for the infrastructure component at the location including indications of: (additional element – generic ML model that amounts to merely applying the abstract idea with a computer)
a measure of an extent of coverage of infrastructure components in the transmission network by a sensor deployed at the location; a measure of relative performance of infrastructure components within an extent of coverage of a sensor deployed at the location; and an indication of suitability for network communication by a sensor deployed at the location, wherein the classifier is trained based on supervised training data; (mental process – a utility planner can perform this evaluation and judgement mentally or with pen and paper from a transmission network plan, equipment performance records, and a map. The human could spread a network plan and outage log on a desk, mark for each pole how many spans it overlooks, how fault-prone, they have been, and whether the pole has line-of-sight to a mast, and circle the best pole.)
receiving an input vector for each of a plurality of infrastructure components; (additional element – merely applying the abstract idea with a computer and/or data-gathering)
applying the classifier to the input vector for each of the plurality of infrastructure components (additional element – generic ML model that amounts to merely applying the abstract idea with a computer) to identify an infrastructure component being most suitable for situating the network connected sensor; and (mental process)
triggering deployment of the network connected sensor at a location of the identified infrastructure component. (insignificant extra-solution activity – mere data output. The Examiner notes that this triggering could be a signal indicating a notification)
Claims 2-5 recite additional considerations the planner could evaluate when performing the mental process and thus amount to further abstract ideas.
Claim 1 is presented as an exemplary claim but the same analysis applies to the other claims 6 and 7.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mental processes as described above but for the recitation of generic computer and machine learning technology. Nothing in the claims precludes the italicized portions from practically being performed in the mind.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application. Training a classifier based on supervised training data is an additional element that amount to generic invocation of machine learning technology. The classifier is recited as a black box, and the specification treats its implementation as conventional and interchangeable (“backpropagation training method or other suitable mechanism as will be apparent to those skilled in the art” spec. p. 4 lines 19-20). Inputs are encoded by “one-hot encoding as will be familiar to those skilled in the art” (p. 5 lines 20-21). Membership is determined “by reference to a classification score by the classifier 208, or using other suitable means as will be apparent to those skilled in the art” (p. 4 lines 16-17). Taken together, these machine learning techniques amount to “apply it” with a generic ML tool. See MPEP 2106.05(f). Similarly, the generic computer devices of claims 6 and 7 merely apply the abstract idea with a generic computer. “[R]eceiving an input vector” and “triggering deployment” add insignificant extra-solution activity to the abstract idea such as mere data gathering and output, see MPEP 2106.05(g))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv). Additionally, the ML techniques of the claims are well-understood, routine, and conventional as described in Step 2A Prong Two.
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Saha (US20210073692A1) in view of Ho (C. -Y. Ho, T. -E. Lee and C. -H. Lin, "Optimal Placement of Fault Indicators Using the Immune Algorithm," in IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 38-45, Feb. 2011, doi: 10.1109/TPWRS.2010.2048725), Graefe (US20190222652A1), and Sheth (US20180340787A1).
Regarding claim 1, Saha discloses: A computer implemented method of deploying a network connected sensor for sensing characteristics of a plurality of infrastructure components in a transmission network for a utility service (“this invention uses a stationary remote sensing sensor package that is attached/mounted on an electric transmission tower/pole,” [0012]; package scanning “for electric wires, transmission towers, power poles, terrain, vegetation and structures along the span of the electric lines,” [0052]; “The data transmission includes LAN/PAN/WAN/Wi-Fi/Cell/802.1/FTP/LoRaWAN/NB-IoT/Wi-SUN protocols,” [0056]), the method comprising:
--…a sensor deployed at the location… (“This sensor's viewing range and field of view (FOV) will cover an entire line span to the next tower after this host tower within the same span,” [0012]; “system with 100% coverage of all the power lines, structures, terrain and vegetation within a span,” [0058]);
--triggering deployment of the network connected sensor at a location of the identified infrastructure component (“One or more sensor packages are installed on one or more power pole(s)/tower(s) or structure(s) per span,” [0057]; “the UAS control module 1506 is generally configured to, in response to the determined condition, dispatch one or more dispatch and control one or more UAS (e.g., drones, etc.) to the location of the condition to surveil and/or treat the condition,” [0099]).
Saha’s disclosed installations cover every span (i.e., 100% coverage) and thus lack any techniques for selecting among candidate structures. Saha invites optimization of packages and installation locations in [0226] and generally describes using “machine learning classifiers”, but these techniques do not train the classifier to indicate the suitability of a location. Saha tells the POSITA to optimizes installation locations against structural an coverage variables but provides no techniques for doing so. Graefe teaches:
--a measure of an extent of coverage of infrastructure components in the transmission network by a sensor deployed at the location (“The first criterion is a number of previously uncovered cells (or non-observable cells) that a sensor 262 will cover (or observe) given the current orientation, and the second criterion is a number of cells that are in range of that sensor 262. An example of sensor ranking is described with respect to FIG. 5,” [0088])
--an indication of suitability for network communication by a sensor deployed at the location (“take into account already deployed access points, RAN nodes, gateway devices, etc,” [0055])
--receiving an input vector for each of a plurality of infrastructure components; applying the classifier to the input vector for each of the plurality of infrastructure components to identify an infrastructure component being most suitable for situating the network connected sensor; (“For the value assessment of an individual sensor 262, the sensors 262 are ranked using two criteria with staged priority. The first criterion is a number of previously uncovered cells (or non-observable cells) that a sensor 262 will cover (or observe) given the current orientation, and the second criterion is a number of cells that are in range of that sensor 262,” [0088]; “candidate sensor 562B can possibly cover region 2, which is larger than region 3 for candidate sensor 562A because region 2 includes more roadway segments than region 3. Therefore, candidate sensor 562B is favorable in comparison to candidate sensor 562A, and candidate sensor 562B will be ranked higher than candidate sensor 562A,” [0089])
A POSITA before the effective filing date would have been motivated to expand Saha’s utility monitoring to include Graefe’s sensor optimization techniques because it would “minimize the number of required sensors 262, while fully covering the coverage area” (Graefe [0086]).
Saha does not expressly disclose but Ho teaches:
--a measure of relative performance of infrastructure components within an extent of coverage of a sensor deployed at the location (“The node serving priority customers and frequently experiencing outage in service zones should be assigned an FI location,” p. 4).
A POSITA before the effective filing date would have been motivated to expand Saha and Graefe’s utility monitoring with sensor optimization to include Ho’s sensor placement consideration because this would “minimize the total cost of customer service outage and investment cost” (Ho Abstract).
Saha does not expressly disclose but Sheth teaches:
--training a classifier to indicate a suitability of a location of an infrastructure component for deployment of the network connected sensor, the classifier operable on an input vector for the infrastructure component at the location including indications of: (“the machine learning model 218 is a gradient boosted decision tree (GBDT) model, and is trained utilizing an algorithm such as XGboost,” [0040]; “Each GBDT classifies a corresponding candidate location,” [0010])
-- wherein the classifier is trained based on supervised training data (“The curated location serves as the reference for determining the correct authoritative candidate location in the training data, such that a loss function operating on the data can evaluate the selection of the machine learning model 218,” [0070]).
Saha and Graefe disclose using trained neural networks for this sensor analysis (e.g., Saha [0091]-[0093]; Graefe [0086]) but do not provide details of how the model is trained. Sheth provides these details and is thus reasonably pertinent to the problem faced by the inventor (i.e., how to train the machine learning model to choose among candidate locations).
A POSITA before the effective filing date would have been motivated to expand Saha, Graefe, and Ho’s utility monitoring with sensor optimization to include Sheth’s classifier training it “take[s] into account a variety of mapping features and metrics in a two part system for POI location selection.” Implementing Graefe’s per-candidate evaluation using a classifier trained on labelled examples is use of a known technique to improve a comparable method in the same with predictable results.
Additionally, it can be seen that each element is taught by Saha, Graefe, Ho, and Sheth. The training techniques of Sheth does not affect the normal functioning of the elements of the claim which are taught by Saha, Graefe, and Ho. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Saha, Graefe, Ho, and Sheth since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 2, Saha does not expressly disclose, but Graefe further teaches: wherein the measure of the extent of coverage by the sensor deployed at the location includes a measure of a number of infrastructure components that can be sensed by a sensor deployed at the location (“The first criterion is a number of previously uncovered cells (or non-observable cells) that a sensor 262 will cover (or observe) given the current orientation, and the second criterion is a number of cells that are in range of that sensor 262. An example of sensor ranking is described with respect to FIG. 5,” [0088]).
The motivation to combine is the same as in claim 1.
Regarding claim 3, Saha does not expressly disclose, but Ho further teaches wherein the measure of the relative performance of infrastructure components includes a normalized measure of one or more of: reliability; operability; faults; or uptime for the infrastructure components (“outage rate (failure per year/Km)” on p. 2).
The motivation to combine is the same as in claim 1.
Regarding claim 4, Saha does not expressly disclose, but Graefe further teaches: wherein the indication of suitability for network communication by the sensor deployed at the location is determined based on one or more of: a distance of the location to a nearest network access point; a record of obstacles located between the location and a nearest network access point; or a record of network access facilities at the location (“, the “configuration” and “reconfiguration” may include placement (deployment) of access points, RAN nodes, gateway devices, etc., or at least take into account already deployed access points, RAN nodes, gateway devices, etc. The (re)configuration includes, but is not limited to, the configuration subsystem 306 sending commands or instructions, with the assistance of sensor interface subsystem,” [0055]).
The motivation to combine is the same as in claim 1.
Regarding claim 5, Saha does not expressly disclose, but Sheth teaches: wherein the infrastructure component being most suitable for situating the network connected sensor is determined based on a measure of a degree of membership of the infrastructure component with a class of the classifier indicating suitability of the location of the infrastructure component (“If more than one candidate location satisfies the confidence threshold the candidate location with the greatest associate confidence value will be selected,” [0098]).
The motivation to combine is the same as in claim 1.
Claims 6 and 7 are substantially similar to claim 1 and are rejected with the same reasoning. The Examiner further notes that Saha teaches computer components in [0069] and [0056].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
De Souza (Efficient fuzzy approach for allocating fault indicators in power distribution lines (English) de Souza, D. M. B. S. / de Assis, A. F. / da Silva, I. N. / Usida, W. F. In: 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America; 1-6) teaches using fuzzy inference to optimize positioning of fault indicators (Abstract).
Falaghi (H. Falaghi et al., "FAULT INDICATORS EFFECTS ON DISTRIBUTION RELIABILITY INDICES " 18th International Conference on Electricity Distribution Turin, 6-9 June 2005) compares candidate fault indicator locations against reliability indices (Abstract).
Usida (Wesley Usida et al., "Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing" November 2012Power Systems, IEEE Transactions on 7(4):1841-1849 DOI:10.1109/TPWRS.2012.2190625) uses a genetic algorithm to optimize fault indicator placement in primary distribution feeders (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624
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