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 .
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.
Information Disclosure Statements
The Information Disclosure Statements (IDS) filed on 6/19/2025 has been acknowledged.
Title Objections
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.
Status of Application
Claims 1-22 are pending.
Claims 1, 9, and 16 are the independent claims.
Non-Final Office Action
CLAIM INTERPRETATION
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three prong test is used to determine the scope of a means-plus-function limitation in a claim:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that the claims do not contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
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 1-8, 9-15, and 16-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to an device (apparatus). Therefore, Claim 1 is within at least one of the four statutory categories.
Claim 9 is directed to an process (method). Therefore, Claim 9 is within at least one of the four statutory categories.
Claim 16 is directed to an medium (appartus). Therefore, Claim 16 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claims 1, 9 and 16 include limitations that recite an abstract idea (emphasized below) and Claim 1 will be used as a representative claim for the remainder of the 101 rejections.
Claim 1 recites: A computing device comprising: a memory device comprising a set of instructions; a processor configured to execute the set of instructions to:
receive one or more images of an area;
wherein the one or more images are representative of a vegetation in the area;
analyze the one or more images in the area based on a vegetation growth model;
identify one or more first locations of the vegetation in the area based on an analysis of the image, wherein the one or more first locations of the vegetation are in proximity to an electric utility;
predict one or more second locations of the one or more first locations based on the vegetation growth model and a type of vegetation in each of the one or more first locations,
wherein the one or more second locations correspond to future encroachment locations of the electric utility by the vegetation;
generate one or more tasks based on the prediction of the one or more second locations;
and rank the one or more tasks based at least on an infrastructure associated with the electric utility.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “receive, identify, analyze, generate, and then rank” steps encompass a user to make gather information, identify, analyze, and generate tasks, then rank said tasks. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “processor configured to”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “processor” is recited at a high level of generality and merely automates the receive, identify, analyze, generate, and then ranking steps, therefore acting as a generic processor to perform the abstract idea. Additionally, the prcoessor is claimed generically and are operating in their ordinary capacity and do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a processor. Furthermore, the examiner submits that the recitations of gathering data and determing tasks is a mere definition that does not necessarily impose any meaningful limits on performing the steps in the human mind, as it only compares data and does analysis where a user could in fact perform this mentally or using paper and pencil. In addition to that, the examiner submits that receiving data and using a processor for analysis, are insignificant extra-solution activities that merely use a processor to perform the process. In particular, the receiving steps are recited at a high level of generality (i.e. as a general means of gathering data for use in the determining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a processor or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent Claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the apparatus, the processor amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of receiving data and performing analysis, and finally generating and ranking tasks, the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of receiving the data, performing analysis, and generating and ranking tasks are well-understood, routine, and conventional activities because the background recites that the sensors from which the data is acquired/received are all conventional sensors. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, Claim 1 is not patent eligible.
Further Claims 9 and 16 are not patent eligible for the same reasons.
Dependent Claims 2-8, 10-15, and 17-22 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements, if any, in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with Claims 1, 9, and 16.
Office Note: In order to overcome this rejection, the Office suggests further defining the limitations of the independent claims, for example linking the claimed subject matter to a non-generic device and controlling a vehicle based on the tasks. Limitations such as these suggested above would further bring the claimed subject matter out of the realm of abstract idea and into the realm of a statutory category.
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.
Claims 1-22 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by Arya et al. (United States Patent Publication 2019/0102748).
With respect to Claim 1: Arya discloses “A computing device comprising” [Arya, ¶ 0022-0024 and 0037];
“a memory device comprising a set of instructions” [Arya, ¶ 0022-0024 and 0037];
“a processor configured to execute the set of instructions to” [Arya, ¶ 0022-0024 and 0037];
“receive one or more images of an area” [Arya, ¶ 0022-0024, 0027-0035 with Figure 3 (The prediction engine 204 may receive the location data from the vegetation fault localizer 202 via a data link 226. The prediction engine 204 may also receive, via the image source data input 218, image source data. The image source data may include, among other things, satellite imagery, street-view images, and images from unmanned aircraft)];
“wherein the one or more images are representative of a vegetation in the area” [Arya, ¶ 0022-0024, 0027-0035 with Figure 3 (The prediction engine 204 may receive the location data from the vegetation fault localizer 202 via a data link 226. The prediction engine 204 may also receive, via the image source data input 218, image source data. The image source data may include, among other things, satellite imagery, street-view images, and images from unmanned aircraft)];
“analyze the one or more images in the area based on a vegetation growth model” [Arya, ¶ 0022-0024, 0026-0035 with Figure 3 (The prediction engine 204 may receive the location data from the vegetation fault localizer 202 via a data link 226. The prediction engine 204 may also receive, via the image source data input 218, image source data. The image source data may include, among other things, satellite imagery, street-view images, and images from unmanned aircraft)];
“identify one or more first locations of the vegetation in the area based on an analysis of the image” [Arya, ¶ 0022-0035 with Figure 3 (The prediction engine 204 may use the received image source data and the vegetation data to determine a criticality score associated with each of the locations)];
“wherein the one or more first locations of the vegetation are in proximity to an electric utility” [Arya, ¶ 0022-0035 with Figure 3];
“predict one or more second locations of the one or more first locations based on the vegetation growth model and a type of vegetation in each of the one or more first locations” [Arya, ¶ 0022-0035 with Figure 3 (The tree growth estimator 210 may receive feedback from the utility crews after the field visits to determine the extent of vegetation growth. For example, during a field visit, a crew may note that vegetation grew by a certain amount or that a certain type of vegetation may be present. The growth of this vegetation may be calculated to determine when the location may need a future field visit. The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits)];
“wherein the one or more second locations correspond to future encroachment locations of the electric utility by the vegetation” [Arya, ¶ 0022-0035 with Figure 3 (The tree growth estimator 210 may receive feedback from the utility crews after the field visits to determine the extent of vegetation growth. For example, during a field visit, a crew may note that vegetation grew by a certain amount or that a certain type of vegetation may be present. The growth of this vegetation may be calculated to determine when the location may need a future field visit. The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits)];
“generate one or more tasks based on the prediction of the one or more second locations” [Arya, ¶ 0022-0035 with Claim 3 (determining, by the computer a time interval for a future field visit based on the received vegetation growth value; and scheduling, by the computer, the future field visit after the determined time interval)];
“and rank the one or more tasks based at least on an infrastructure associated with the electric utility” [Arya, ¶ 0022-0035 with Figure 3 (the criticality score may be used to determine one or more locations with a higher priority for a field visit)].
With respect to Claim 2: Arya discloses “The computing device of claim 1, wherein the one or more images of the area comprises at least one of a satellite imagery, unmanned aerial vehicle (UA V) imagery, drone inspection footage, mobile laser-derived imagery, and camera-derived photographs” [Arya, ¶ 0022-0035 with Figure 3 (The prediction engine 204 may receive the location data from the vegetation fault localizer 202 via a data link 226. The prediction engine 204 may also receive, via the image source data input 218, image source data. The image source data may include, among other things, satellite imagery, street-view images, and images from unmanned aircraft)].
With respect to Claim 3: Arya discloses “The computing device of claim 1, wherein the vegetation growth model analyzes the one or more images to at least identify the one or more first locations of the vegetation in the proximity of the electric utility and the type of vegetation at the one or more first locations of the electric utility” [Arya, ¶ 0022-0035 with Claim 5 (further comprising: receiving, by the computer, one or more images from one or more image sources; receiving, by the computer, vegetation growth data associated with a field visit; and calculate a vegetation growth value based on the received images)].
With respect to Claim 4: Arya discloses “The computing device of claim 1, wherein the vegetation growth model includes at least one regional model and 3D vegetation model” [Arya, ¶ 0022-0035 with Claim 5 (The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits)].
With respect to Claim 5: Arya discloses “The computing device of claim 1, wherein the vegetation growth model utilizes the one or more first locations of the vegetation and the type of the vegetation in each of the one or more first locations to predict a timing of a future encroachment of the electric utility by the vegetation” [Arya, ¶ 0022-0035 with Claim 5 (The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits)].
With respect to Claim 6: Arya discloses “The computing device of claim 1, wherein the vegetation at the one or more second locations are numerically scored based on the timing of the future encroachment of the electric utility by the vegetation” [Arya, ¶ 0022-0035 with Claim 5 (The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits) and (The planning engine 206 may receive the scores generated by the prediction engine 204 corresponding to the one or more locations via a data link 228. The planning engine 206 may also receive a list of open, active work orders from the database 112 (FIG. 1) via the active work order data input 220. The database of work orders may contain a large number of individual work orders. However, there may be at least some work orders in which a location does not have vegetation or in which the work is of a lower priority. Accordingly, it may be advantageous to determine only those work orders that correspond to locations identified as having faults caused by vegetation. Therefore, the planning engine 206 may compare the received locations and the received open work orders and determine a subset of locations to which a utility crew may be sent. The subset of locations may be chosen in a way to minimize field visits (“field visits”) by the utility company and maximize the work performed during each field visit. According to one embodiment, the subset may include a location determined by the prediction engine 204 to be the most critical and a number of active work orders received from the database 112 that correspond to locations within the vicinity of the determined critical location. According to an alternative embodiment, the subset of locations may include a location corresponding to a highest priority work order and a number of locations determined by the prediction engine 204 that are within the vicinity of the location of the highest priority work order. According to yet another embodiment, the subset of locations may include a plurality of locations from among the locations in the active work orders receive from the database 112 and determined by the prediction engine 204, either individually or in combination, corresponding to a greatest density of locations)].
With respect to Claim 7: Arya discloses “The computing device of claim 6, wherein the numerically scored one or more tasks are ranked in order of priority of performing maintenance around the one or more second locations” [Arya, ¶ 0022-0035 with Figure 3 (The tree growth estimator 210 may receive the information the scheduling engine 208 requested the utility crew to collect via the field visit data input 222. The tree growth estimator 210 may calculate an amount of vegetation growth since the last field visit to the location. Thus, the tree growth estimator 210 may determine a time at which the vegetation at the location may grow to a pre-determined amount based on the calculated rate of vegetation growth. The tree growth estimator 210 may then transmit this determined time to the prediction engine 204 via a data link 232 for use in determining locations and times for future field visits) and (The planning engine 206 may receive the scores generated by the prediction engine 204 corresponding to the one or more locations via a data link 228. The planning engine 206 may also receive a list of open, active work orders from the database 112 (FIG. 1) via the active work order data input 220. The database of work orders may contain a large number of individual work orders. However, there may be at least some work orders in which a location does not have vegetation or in which the work is of a lower priority. Accordingly, it may be advantageous to determine only those work orders that correspond to locations identified as having faults caused by vegetation. Therefore, the planning engine 206 may compare the received locations and the received open work orders and determine a subset of locations to which a utility crew may be sent. The subset of locations may be chosen in a way to minimize field visits (“field visits”) by the utility company and maximize the work performed during each field visit. According to one embodiment, the subset may include a location determined by the prediction engine 204 to be the most critical and a number of active work orders received from the database 112 that correspond to locations within the vicinity of the determined critical location. According to an alternative embodiment, the subset of locations may include a location corresponding to a highest priority work order and a number of locations determined by the prediction engine 204 that are within the vicinity of the location of the highest priority work order. According to yet another embodiment, the subset of locations may include a plurality of locations from among the locations in the active work orders receive from the database 112 and determined by the prediction engine 204, either individually or in combination, corresponding to a greatest density of locations)].
With respect to Claim 8: Arya discloses “The computing device of claim 1, wherein the one or more tasks at least comprises of pruning trees, cut underbrush and spray herbicides on the vegetation on the one or more second locations” [Arya, ¶ 0022-0035 and 0064 (Based on the new set of work orders with assigned dates, a utility crew may conduct a field visit to trim trees and vegetation, along with other operations)].
With respect to Claims 9-15: all limitations have been examined with respect to the apparatus in Claims 1-8. The method taught/disclosed in Claims 9-15 can clearly perform on the apparatus of Claims 1-8. Therefore Claims 9-15 are rejected under the same rationale.
With respect to Claims 16-22: all limitations have been examined with respect to the apparatus in Claims 1-8. The medium taught/disclosed in Claims 16-22 can clearly perform on the apparatus of Claims 1-8. Therefore Claims 16-22 are rejected under the same rationale.
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESS G WHITTINGTON whose telephone number is (571)272-7937. The examiner can normally be reached on 7-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Browne can be reached on (571)-270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c
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