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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 26 February 2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
Claim 1 has been amended.
Claims 2, 3, 5, and 7-9 have been cancelled.
Claims 1, 4, and 6 are currently pending and have been examined.
Response to Applicant's Remarks
35 U.S.C. § 101
Applicant’s remarks, see Page(s) 5-8, filed 26 February 2026, with respect to the 35 U.S.C. § 101 rejections, have been fully considered, but are not persuasive.
Applicant submits that the claims of the current application are not directed to an abstract idea and as a whole provide an improvement to this technological environment and also integrate any interpreted judicial exception into a clear practical application.
Examiner respectfully disagrees, as the claim limitations are not indicative of integration into a practical application, such as an improvement to the functioning of a computer or other technical field, as considered below in view of MPEP 2106. In particular, an improvement in the judicial exception itself is not an improvement in technology, such as, increasing the accuracy and the efficiency of the abstract idea. Applicant’s improvement in this case is not an improvement to the functioning of a computer, or to any other technology or technological field.
The following are examples of eligible subject matter based on technological improvements: see, e.g., McRO, 837 F.3d at 1315 ("The claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results: a sequence of synchronized, animated characters."); Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1304 (Fed. Cir. 2018) (finding patent eligible a claim drawn to a behavior-based virus scan that protects against viruses that have been "cosmetically modified to avoid detection by code-matching virus scans"); Enfish, 822 F.3d at 1330, 1333 (discussing patent eligible claims directed to "an innovative logical model for a computer database" that included a self-referential table allowing for greater flexibility in configuring databases, faster searching, and more effective storage); CardioNet, LLC v. InfoBionic, Inc., 955 F.3d 1358, 1368 (Fed. Cir. 2020) (explaining that the claims at issue focus on a specific means for improving cardiac monitoring technology; they are not "directed to a result or effect that itself is the abstract idea and merely invoke generic processes and machinery" (quoting McRO, 837 F.3d at 1314)).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (see MPEP 2106.05(a)).
Instead, the claims recite the additional element(s) of 'processing circuitry', ‘mobile terminals’, ‘a garbage amount prediction model’, ‘a garbage collection route determination model’. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than:
mere instructions to apply the exception using generic computer components (i.e., generic computer components performing generic computer functions) ('processing circuitry', ‘mobile terminals’). In their broadest reasonable interpretation, the additional element(s) comprise(s) only a processor, instructions in memory, a display, a receiver, and a transmitter, being used to implement the functions of the abstract idea. Accordingly, the claims do not amount to more than a recitation of the words "apply it" (or an equivalent) or more than mere instructions to implement an abstract idea or other exception in a generic computing environment (see MPEP 2106.05(f) Mere Instructions to Apply an Exception). Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim(s) is/are directed to the judicial exception.
generally linking the use of the judicial exception to a particular technological environment or field of use (‘a garbage amount prediction model’, ‘a garbage collection route determination model’).
Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim(s) is/are directed to the judicial exception.
Additionally, the claims recite “…by performing machine learning…” in regards to the model generations. Similarly to the case of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
35 U.S.C. § 103
Applicant’s remarks, see Page(s) 8-11, filed 26 February 2026, with respect to the 35 U.S.C. § 103 rejections, have been fully considered, but are persuasive, in view of the claim amendments. Therefore, the 35 U.S.C. § 103 rejections have been withdrawn.
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.
Claim(s) 1, 4, and 6 is/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.
Claim(s) 1 recite(s) a system and series of steps for predicting an amount of garbage based on historical data and population data and determining a garbage collection route, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind (i.e., observation, evaluation, judgment, opinion). These concepts are grouped as mental processes.
The limitation(s) of, ‘acquire population change data… and garbage amount record data…; ‘predict an amount of garbage…’; ‘determine and output…a garbage collection route for each of garbage trucks to be dispatched…’, as drafted, recite a process that, under broadest reasonable interpretation, is/are mental processes. Accordingly, the claim(s) recite(s) an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) the additional element(s) of 'processing circuitry', ‘mobile terminals’, ‘a garbage amount prediction model’, ‘a garbage collection route determination model’. These additional elements are recited at a high-level of generality such that in conjunction with the abstract limitations, they amount to no more than:
mere instructions to apply the exception using generic computer components (i.e., generic computer components performing generic computer functions) ('processing circuitry', ‘mobile terminals’). In their broadest reasonable interpretation, the additional element(s) comprise(s) only a processor, instructions in memory, a display, a receiver, and a transmitter, being used to implement the functions of the abstract idea. Accordingly, the claims do not amount to more than a recitation of the words "apply it" (or an equivalent) or more than mere instructions to implement an abstract idea or other exception in a generic computing environment (see MPEP 2106.05(f) Mere Instructions to Apply an Exception). Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim(s) is/are directed to the judicial exception.
generally linking the use of the judicial exception to a particular technological environment or field of use (‘a garbage amount prediction model’, ‘a garbage collection route determination model’).
Thus, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim(s) is/are directed to the judicial exception.
Additionally, the claims recite “…by performing machine learning…” in regards to the model generations. Similarly to the case of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Claim(s) 4 and 6 further recite(s) the system and series of steps for predicting an amount of garbage based on historical data and population data and determining a garbage collection route, which under broadest reasonable interpretation, is analogous to concepts performed in the human mind (i.e., observation, evaluation, judgment, opinion). These concepts are grouped as mental processes. Accordingly, the claim(s) recite(s) an abstract idea.
As analyzed above, the limitations as an ordered combination, are merely applying the abstract idea in a generic computing environment. In addition, the claims do not improve functionality of a computer or improve any other technology. Thus, claims 1, 4, and 6 are ineligible as the claims do not recite additional elements which result in significantly more than the abstract idea itself.
Novel/Non-Obvious Subject Matter
The subject matter of claims 1, 4, and 6 is not taught by the cited prior art and is considered novel/non-obvious. However, claims 1, 4, and 6 remain rejected under 35 U.S.C. 101 as described above.
The closest prior art of record is Kalinowski (U.S. Patent App. Pub. No. 20170154287), Swaroop (U.S. Patent App. Pub. No. 20210326658), Anderson (U.S. Patent App. Pub. No. 20220101280), Dalia (U.S. Patent App. Pub. No. 20170178261), Vengerov (U.S. Patent App. Pub. No. 20090319255), Kumar (U.S. Patent App. Pub. No. 20210103899), Kim (U.S. Patent App. Pub. No. 20220050593), and Likotiko (“Smart garbage bin: Garbage growth behavior prediction”, 2020).
The cited prior art, taken either individually or in combination, fails to teach or suggest generating a garbage amount prediction model,…by using, as an explanatory variable, population change data for each of the districts in a past predetermined period and, as an objective variable, garbage amount record data for each of the districts and each of the garbage types in the predetermined period…; and generating a garbage collection route determination model,…by using, as explanatory variables, the location information, the information on the collectable amount of each of the garbage trucks, and the predicted amount of garbage value for each of the districts when determining a collection route in past and, as an objective variable, the garbage collection route for each of the garbage trucks determined at the time of corresponding collection route determination, and determines a garbage collection route for each of the garbage trucks by inputting, to the generated garbage collection route determination model, the location information, the information on the collectable amount of each of the garbage trucks, and the predicted amount of garbage value for each of the districts at a current point in time.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE S MURRAY whose telephone number is (571)272-4306. The examiner can normally be reached M-F 8am-5pm.
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/Wayne S. Murray/Examiner, Art Unit 3628