DETAILED ACTION
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 .
The following is a Non-Final Office Action in response to communications filed November 13, 2025. Claims 1, 2, 4 are amended, claims 3, 5, 7, 15-20 are cancelled. Currently, claims 1, 2, 4, 6, 8-14 are pending.
Response to Amendment/Arguments
Applicant's arguments filed November 13, 2025 have been fully considered but they are not persuasive.
Applicant amended claim 1 to now recited “wherein the plurality of on-board truck sensors comprises at least a weighting sensor, a camera, and a spatial positioning sensor”, and states that Christensen’s sensor-based waste collection system does not teach the newly added limitation. The Examiner respectfully disagrees. Christensen discloses that “Operators’ vehicles can have weighing components to track the weight of the collected contents 40” [0116], and is able to wireless communicate from a scale on the vehicle [0186]. Christensen also discloses “camera on the operators vehicles… can record images” [0174]. Christensen further discloses “The server system 6 can monitor operators’ positions in real-time through communications from the navigation app. Fig. 23a illustrates a screenshot of a map tracking a location of a collection vehicle in real-time. The area map, starting point 126 (a flag), route already driven (a line), current location (truck icon)… are displayed.” [0169] Therefore, Christensen teaches “wherein the plurality of on-board truck sensors comprises at least a weighting sensor, a camera, and a spatial positioning sensor”.
Applicant amended claim 1 to now recited “and wherein the historical waste generation data comprises an indication of a nature of the waste, spatial position information pertaining to the plurality of waste bins within an urban environment and a temporal rate at which the waste bins are being filled with waste for each of the plurality of waste bins”, and states that Christensen’s sensor-based waste collection system does not teach the newly added limitation. The Examiner respectfully disagrees. Christensen discloses the following: [158] and Figure 8 teaching that the invention compiles a fill rate per day; see also [33] teaching measuring a fill level at least once per day; see further [162] teaching taking into account the day of the week when determining fill rates and collection routes; see also, e.g., at least [71], [105], [140], [155] teaching obtaining historical data; regarding spatial position information, see at least [155] teaching real time and historical maps of the sensor locations as well as [148], [159], [169], [171], [179], [180], et al. Furthermore, with respect to Figure 8, the associated description at [0107], and [0108] discloses “The sensors 2 can estimate the shapes of individual items within the contents 40…., for example to determine the materials of the contents 40. The content 40 of the containers 32 can be a number of different things, for example waste/trash including wrapped and unwrapped waste, liquid such as oil and water, sewer and slurry, clothing items, donations to charities either wrapped or unwrapped, recycling materials, human and animal food products, or industrial production material, or combinations thereof.” Therefore, Christensen teaches “and wherein the historical waste generation data comprises an indication of a nature of the waste, spatial position information pertaining to the plurality of waste bins within an urban environment and a temporal rate at which the waste bins are being filled with waste for each of the plurality of waste bins;”
Applicant’s arguments with respect to amended claim 1 that Christensen does not teach or disclose “and wherein the prediction model is fine-tuned based on an actual quantity of generated waste measured through the weighing sensor” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
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 6 and 12 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.
Regarding claims 6 and 12, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claims 12, the phrase "may" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 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, 2, 6, 8, 9, 12 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Christensen et al. (US 2020/0191580 A1, hereinafter “Christensen”) in view of U.S. Pub.No 2023/0260067 to Payette et al.
Claim 1. Christensen teaches: A smart waste collection system for monitoring and collecting waste of an area by forecasting waste generation and optimizing a dynamic waste collection route comprising:
a plurality of waste bins for receiving the waste of the area (see, e.g., Figures 3, 4A, 4B, 5, and 7 and at least [84] teaching container 32; see further, e.g., [88], [89], [151], [155], and [162] teaching a plurality of containers 32);
each of the plurality of waste bins is associated with a unique identification number (see, e.g., at least [147] and [183] teaching that each container 32 has an “identifying number or name” along with other information that can identify the bin);
a plurality of on-board truck sensor for acquiring a waste generation data of the plurality of waste bins, wherein the plurality of on-board truck sensors comprises at least a weighing sensor, a camera, and a spatial positioning sensor; (see, e.g., [174] teaching on-vehicle cameras that record images of the contents of the waste; see also [116], [186] teaching a scale on board the vehicle to weigh the contents inside the bins; see [169] teaching a spatial positioning sensor.);
a waste prediction module for analyzing actual and historical waste generation data, wherein local parameters are used for the plurality of waste bins to forecast a volume of daily waste generated in each of the plurality of waste bins, and wherein the historical waste generation data comprises an indication of a nature of the waste, spatial position information pertaining to the plurality of waste bins within an urban environment and a temporal rate at which the waste bins are being filled with waste for each of the plurality of waste bins; (see, e.g., Figure 21 collecting historic waste generation data for a plurality of bins in a specific geographic area; see also, e.g., [71], [140], [156], [158], and [184] teaching obtaining historic data trends to plan collection routes; see further [186] teaching a scale on the vehicle that obtains the weight of the contents; see also, e.g., [86] teaching substantially the same; Furthermore, [158] and Figure 8 teaching that the invention compiles a fill rate per day; see also [33] teaching measuring a fill level at least once per day; see further [162] teaching taking into account the day of the week when determining fill rates and collection routes; see also, e.g., at least [71], [105], [140], [155] teaching obtaining historical data; regarding spatial position information, see at least [155] teaching real time and historical maps of the sensor locations as well as [148], [159], [169], [171], [179], [180], et al. Furthermore, with respect to Figure 8, the associated description at [0107], and [0108].);
wherein the local parameters comprise types of households, demographic statistics, seasonal calendar, and other local data affecting waste generation patterns (this is addressed below);
an autonomous collection vehicle for collecting the waste from the plurality of waste bins therefrom (see at least, e.g., [13], [72], and [73] teaching that the waste collection vehicle can be a self-driving vehicle, i.e., autonomous); and
a cloud server for receiving one or more signals via a communication network to optimize the dynamic waste collection route (this is addressed below);
wherein the cloud server comprises:
a plurality of databases to store data of the plurality of waste bins, and a plurality of modules, characterized in that the waste prediction module is operably configured to forecast waste generation patterns by a prediction model for each of the plurality of waste bins (see, e.g., [76] teaching a plurality of databases for storing the waste bin sensor data; see further, e.g., at least [36], [39], [71], and [166] teaching predicting, i.e., forecasting a volume of daily waste generated in the bins based on actual and historical waste generation);
wherein the cloud server is configured to:
predict the volume of daily waste generated in each of the plurality of waste bins (see, e.g., at least [36], [39], [71], and [166] teaching predicting, i.e., forecasting a volume of daily waste generated in the bins based on actual and historical waste generation);
prioritize the waste bin from the plurality of waste bins to be serviced for collecting the waste (see, e.g., [34]-[36] and especially [155] and [164] teaching altering the scheduling to prioritize full waste bins);
feed the waste bin to be serviced along with real-time traffic data to a route optimization module for computing the dynamic waste collection route for collecting the waste (see, e.g., [163] teaching optimizing routes in real-time; see also [164] teaching optimizing route creation and predicted future routes with AI technology; see additionally [72] teaching the same but that the routing “can incorporate real-time traffic data;” see also [162] teaching dynamically creating the route including with predicted traffic conditions based on updated traffic data and [176] and [187] teaching the navigation delivering traffic-aware routing);
assign the waste bin to be serviced for collecting the waste to the autonomous collecting vehicle (see [36] teaching altering the schedule of garbage pickup for at least one bin because otherwise the “container will likely be overflowing before the next time the garbage truck is scheduled to pass the container;” see also [160] and [162] teaching determining whether particular bins need to be prioritized based on whether the containers are predicted to be above a specified fullness threshold and scheduling the route based on those determinations; see further [70] and [72] teaching assigning routes and specific bins to the vehicle, noting that [73] teaches that the vehicle can be self-driving); and
transmit the dynamic waste collection route with latitude and longitude coordinates of the waste bin (see, e.g., [142] teaching using GPS data to communicate positioning; see further [132] and [148] teaching using satellite-based radio navigation, e.g., GPS);
wherein a different dynamic waste collection route is optimized depending on the waste bins to be serviced (see [36] teaching altering the schedule of garbage pickup for at least one bin because otherwise the “container will likely be overflowing before the next time the garbage truck is scheduled to pass the container;” see also [160] and [162] teaching determining whether particular bins need to be prioritized based on whether the containers are predicted to be above a specified fullness threshold and scheduling the route based on those determinations).
Regarding a waste prediction module for analyzing actual and historical waste generation data, wherein local parameters are used for the plurality of waste bins to forecast a volume of daily waste generated in each of the plurality of waste bins, wherein the local parameters comprise types of households, demographic statistics, seasonal calendar, and other local data affecting waste generation patterns, Christensen fails to teach that the “historical” waste generation specifically includes types of households, demographic statistics, seasonal calendar, and other local data affecting waste generation patterns. However, Payette et al disclose a prediction based waste management system wherein waste collection routes are designed based on a plurality of data. Specifically, Payette et al disclose that the prediction module uses data such as seasonality, address, contact, location, type of business, and operation data. [0046], [0047] The two references are in the same field of endeavor as the claimed invention of waste collection route determination. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine the data used in waste prediction of Payette with the waste prediction system of Christensen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of prediction modules ingesting necessary data for prediction purposes.
Regarding a cloud server for receiving one or more signals via a communication network to optimize the dynamic waste collection route, Christensen does not expressly disclose a cloud server that receives the signals and optimizes the waste collection route. What Christensen teaches instead is a generic server system (see at least Figure 1 feature 6 and [76]) that communicates and retrieves data from one or more databases “such as a Postgres SQL database and/or a Cassandra database” (see [76]). However, Payette discloses the system for dynamically generating collections routes includes a backend system which comprises on or more servers with one or more processors and storage means. The prediction module, the simulation module, the user interface module, the control modules and the automated trigger module are all in communication with the backend system, allowing for exchanging information between the modules. In addition the backend system can be a local computer or server or any type of remove server and/or computing cloud. [0065], Fig 1. The two references are in the same field of endeavor as the claimed invention of waste collection route determination. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine the remote cloud server of the system for dynamically generating collection routes as disclose by Payette with the waste prediction system of Christensen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of various modules communicating with each other.
With regards to the limitation wherein the prediction model is fine-tunes based on an actual quantity of generated waste measured through the weighing sensor, Christensen teaches forecast waste generation patterns by a prediction model for each of the plurality of waste bins by disclosing fill level for a container can be tracked over time. By comparing the measured fill levels to threshold fill levels, the server system can predict when a container needs to be serviced [0039]. Christensen further disclose operators’ vehicles can have weighing components (scale on the vehicle) to track the weight of collected contents. The server system can use the real-time collection vehicle diagnostic information from mobile devices and other on-board diagnostics e.g., to determine the weight of currently gathered contents for reports. [0166], [0186]. Christensen does not expressly disclose that the prediction model for waste generation patterns is fine-tuned based on an actual quantity of generated waste measured through the weight sensor. Payette discloses a step of generating the predicted waste accumulation for each collection site with either historical accumulation data associated with each collection site or estimated accumulation data. Upon completing a route, the total weight of the waste collected along the route by the collection vehicle is measure. By calculating an average weight per unit, the actual accumulation of each site be determined. The actual accumulation data can subsequently be used by the method as historical accumulation data. Further a step of validating the accuracy of the prediction models used for predicting accumulation at the collection sites is evaluated by comparing predicted accumulation data with actual (or historical) accumulation data, if the accuracy falls outside of a desired range, the prediction model is changed. [0078] The two references are in the same field of endeavor as the claimed invention of waste collection route determination. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine the fine tuning of the prediction model based on an actual quantity of generated waste as disclose by Payette with the waste prediction system of Christensen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of predicting waste generation.
Claim 2. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen further teaches: The smart waste collection system as claimed in claim 1, wherein the cloud server further comprises
a bin selection module configured to prioritize the waste bin from the plurality of waste bins to be serviced for collecting waste by a bin selection algorithm based on the actual waste quantity and forecasted waste quantity (see, e.g., ¶s 34-36 and especially 155 and 164 teaching altering the scheduling to prioritize full waste bins);
the route optimization module configured to compute and optimize the dynamic waste collection route for the waste bin to be serviced and transmit an optimized waste collection route thereof (see, e.g., the abstract and ¶s 72, 74, and 163 teaching transmitting the optimized route to the vehicle to collect the waste from the waste bins);
an autonomous navigation module operably configured with the route optimization module to transmit the dynamic waste collection route for the waste bin to be serviced to the autonomous collection vehicle (see, e.g., the abstract and ¶s 72, 74, and 163 teaching transmitting the optimized route to the vehicle to collect the waste from the waste bins; see further ¶s 13 and 72-73 teaching that the waste collection vehicle can be a self-driving vehicle controlled directional routing, i.e., an autonomous vehicle);
wherein the communication network allows communication between the plurality of on-board truck sensors, the plurality of modules, the plurality of databases, the cloud server, and the autonomous collection vehicle, and wherein the smart waste collection system is an autonomous knowledge-based smart waste collection system (see, e.g., ¶ 103 teaching that the sensors on each bin can communicate with server system 6; see also ¶ 151 teaching a sensor network connection via a router to connect with the server; see also ¶s 73 and 171 teaching that the system can be set to operate autonomously with a self-driving vehicle and automatically collect the data that is used for prediction and optimized routing).
Claim 6. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen further teaches: The smart waste collection system as claimed in claim 1, wherein the prediction model constitutes different types of machine-learning algorithms, such as, classification, regression, neural networks, ensemble, or hybrid models, such as support vector machine, random forest, generalized linear model, and/or gradient boosted trees (see, e.g., ¶ 36 teaching using machine learning via a regression model).
Claim 8. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen further teaches: The smart waste collection system as claimed in claim 1, wherein a bin-selection algorithm selects one or more waste bin for service when the volume of daily waste of the waste bin predicted is larger than the capacity of the waste bin (see, e.g., ¶s 34-36 and especially 155 and 164 teaching altering the scheduling to prioritize full waste bins, noting that ¶ 36 teaches that without such altering the “container will likely be overflowing before the next time the garbage truck is scheduled to pass the container”).
Claim 9. The combination of Christensen and Payette teach the limitations of Claim 8. Christensen further teaches: The smart waste collection system as claimed in claim 8, wherein the waste bin that has reached its maximum capacity with a safety margin is prioritized for service (see, e.g., ¶s 34-36 and especially 155 and 164 teaching altering the scheduling to prioritize full waste bins, noting that ¶ 36 teaches that without such altering the “container will likely be overflowing before the next time the garbage truck is scheduled to pass the container”).
Claim 12. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen does not teach or disclose wherein the route optimization module may constitute individual and/or hybrid models based on exact optimization methods, such as integer programming and/or Dijkstra, and/or metaheuristic optimization methods, such as genetic algorithm, simulated annealing, particle swarm, and/or ant colony. However, Payette discloses the prediction models can include, without being limited to, mathematical models and machine-learning models, or various algorithms. For example, the prediction models can include an Autoregressive Integrated Moving Average (ARIMA) model or algorithm model, used when historical accumulation data is available, a Croston model, used when available historical accumulation data is limited. [0040] The two references are in the same field of endeavor as the claimed invention of waste collection route determination. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine the various models and algorithms for route determination as disclose by Payette with the waste prediction system of Christensen since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of generating routes for waste collection.
Claim 14. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen further teaches: The smart waste collection system as claimed in claim 1, wherein the autonomous collection vehicle is a GPS-navigated autonomous collection truck or an autonomous electric mobile robot (see, e.g., ¶s 13, 72, and 73 teaching controlling a self-driving vehicle along a route via a navigation system).
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over Christensen and Payette et al further in view of van’t Westeinde et al. (US 2023/0019662, hereinafter “van’t”).
Claim 4. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen teaches that the autonomous waste collection vehicles can have various sensors such as a level sensor by disclosing cameras on the operators’ vehicles can record images and use the images to identify contents and quantity [0174] whereby the quantity represents the fill level of the collection vehicles. Christensen does not expressly disclose the autonomous waste collection vehicles include a LiDAR sensor. van’t discloses an autonomous garbage collection vehicle with various sensors including LiDAR sensors. [0035], [0137] The references are in the same field of endeavor as the claimed invention of waste collection route determination. It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify/combine the LiDAR sensor within the autonomous waste collection vehicle as disclose by van’t with the waste prediction system of Christensen and Payette since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, with the predictable results of causing the motion of autonomous vehicle in response to control by autonomous driving logic as disclosed by van’t.
Claims 10 and 11 are rejected under 35 U.S.C. § 103 as being unpatentable over Christensen and Payette et al further in view of Kurani et al. (US 2021/0188541, hereinafter “Kurani”).
Claim 10. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen and Payette fail to teach, however, analogous reference Kurani teaches: The smart waste collection system as claimed in claim 1, wherein the route optimization module is further configured to optimize the shortest route for the waste bins to be serviced (see, e.g., ¶ 537 teaching using linear programming to generate the “shortest waste collection route” so that “drivers [can] save on fuel and time”).
Kurani is similar to Christensen and Payette and the instant application because it relates to managing sensed data from waste containers to create routing information for a fleet of waste vehicles (see at least Kurani Abstract). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using linear programming to create the routing information such as a shortest waste collection route (as disclosed by Kurani) to the known method and system of dynamically optimizing waste collection routes based on real time data (as disclosed by Christensen and Payette). One of ordinary skill in the art would have been motivated to apply the known technique of using linear programming to create the routing information such as a shortest waste collection route because this would save drivers both fuel and time (see Kurani ¶ 537).
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of using linear programming to create the routing information such as a shortest waste collection route (as disclosed by Kurani) to the known method and system of dynamically optimizing waste collection routes based on real time data (as disclosed by Christensen and Payette), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of using linear programming to create the routing information such as a shortest waste collection route to the known method and system of dynamically optimizing waste collection routes based on real time data, because predictably a known technique that can generate a shortest route can be used to create a shortest route for waste collection). See also MPEP § 2143(I)(D).
Claim 11. The combination of Christensen, Payette and Kurani teach the limitations of Claim 10. Christensen further teaches: The route optimization module as claimed in claim 10, wherein the real-time traffic data of a road network is used to avoid road closures, traffic accidents, road works, traffic congestion, and/or rush hours (see, e.g., Christensen ¶ 163 teaching optimizing routes in real-time; see also ¶ 164 teaching optimizing route creation and predicted future routes with AI technology; see additionally ¶ 72 teaching the same but that the routing “can incorporate real-time traffic data;” see also ¶ 162 teaching dynamically creating the route including with predicted traffic conditions based on updated traffic data and ¶s 176 and 187 teaching the navigation delivering traffic-aware routing).
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over Christensen and Payette and further in view of Ha (US 2021/0060786).
Claim 13. The combination of Christensen and Payette teach the limitations of Claim 1. Christensen and Payette fail to teach, however, analogous reference Ha teaches: The smart waste collection system as claimed in claim 1, wherein the smart waste collection system is operably configured with any smart transportation system and/or internet of things (IoT) systems in smart cities (see ¶ 2 teaching that the invention is meant to be adaptable to smart city applications in order to increase economic efficiency and to be more environmentally friendly).
Ha is similar to Christensen and Payette, and the instant application because it relates to using autonomous waste collection vehicles to collect waste utilizing artificial intelligence (see at least Ha Abstract). Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of configuring the waste collection with smart city applications (as disclosed by Ha) to the known method and system of dynamically optimizing waste collection routes based on real time data (as disclosed by Christensen and Payette). One of ordinary skill in the art would have been motivated to apply the known technique of configuring the waste collection with smart city applications because this would provide a fundamental replacement of existing infrastructure facilities with better quality ones that are more environmentally friendly and economically efficient (see Ha ¶ 2).
Furthermore, it would have been obvious to one of ordinary skill in the art as of the effective filing date to apply the known technique of configuring the waste collection with smart city applications (as disclosed by Ha) to the known method and system of dynamically optimizing waste collection routes based on real time data (as disclosed by Christensen and Payette), because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by applying the known technique of configuring the waste collection with smart city applications to the known method and system of dynamically optimizing waste collection routes based on real time data, because predictably autonomous vehicle collection operating via artificial intelligence can collect data from other automated sources). See also MPEP § 2143(I)(D).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rutao Wu whose telephone number is (571)272-6045. The examiner can normally be reached Mon-Fri 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tariq Hafiz can be reached at 571-272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623