DETAILED ACTION
This is in response to the applicant’s communication filed on 10/9/25 wherein:
Claims 1, 4-12, and 15-22 are currently pending.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-12, and 15-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention. Examiner has reviewed applicant’s disclosure and submits that these added limitations find no support in the specification as currently written, and is, therefore, directed to new matter.
Claim 1: “controlling, based on the garbage truck dispatching plan, the garbage truck to travel from a departure point along the garbage truck route to perform a garbage cleaning at the at least one garbage point to be treated” is not described in the specification as written. Examiner reviewed the entirety of the specification, particularly the cited portions of the specification ([0004][0058][0064]-[0073][0163]) and did not find the cited limitation. Examiner notes that the broadest reasonable interpretation of this limitation includes the interpretation that the garbage truck autonomously drives along the truck route and this is not covered in the Specification. There is no indication in the Specification that the management platform controls the garbage truck. Independent claim 11 includes a similar limitation. For the purposes of further examination, Examiner interprets the claims to include only that a human driver controls the truck based upon a dispatching plan provided to the driver.
The claims not specifically enumerated above are rejected as dependent upon one or more of the enumerated claims.
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, 4-12, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites a method and therefore, falls into a statutory category. Similar claims 11 and 20 recite a system and a computer readable medium, and therefore, also fall into a statutory category. Although claim 20 is dependent on claim 1, it is considered alongside claims 1 and 11 in the 101 rejection.
Step 2A – Prong 1 (Is a Judicial Exception Recited?): The underlined limitations of exemplary claim 1,
obtaining a garbage accumulation condition in a target area, wherein the garbage accumulation condition includes a historical garbage volume of each sub- area in the target area at a plurality of historical moments;
determining, based on the historical garbage volume and the garbage increment of the each sub-area, at least one sub-area in the target area as at least one garbage point to be treated; and
determining, based on the at least one garbage point to be treated, a garbage truck dispatching plan of the target area, wherein the garbage truck dispatching plan includes: a type of at least one garbage truck to be dispatched, a garbage truck route, and a departure time of the at least one garbage truck, and
are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting that the claim includes:
claim 1: no additional elements outside of the abstract idea
claim 11: a user platform, a service platform, a management platform, a sensor network platform, and an object platform,
claim 20: a non-transitory computer-readable storage medium storing computer instructions
nothing in the claim elements precludes the steps from practically being performed in the mind. These claim limitations encompass collecting information and using the information to perform evaluation, judgement, and opinion to make a determination. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim further recites:
predicting, based on the historical garbage volume of the each sub-area in the target area at the plurality of historical moments and a historical pedestrian volume sequence, garbage increment of the each sub-area at a future moment through a prediction model, wherein the historical pedestrian volume sequence is determined based on pedestrian volume corresponding to the each sub-area at the plurality of historical moments, the prediction model is a trained machine learning model, an input of the prediction model includes the garbage volume at the plurality of historical moments and one or more future moments, and a historical pedestrian volume sequence, an output of the prediction model is the garbage increment at the one or more future moments, and a training process of the prediction model includes:
obtaining first training samples and first lables, wherein each of the first training samples includes garbage volume at a plurality of sample historical moments and one or more sample future moments, and a sample historical pedestrian volume sequence, the garbage volume at the plurality of sample historical moments and the sample historical pedestrian volume sequence corresponds to a same time sequence, each of the first label is a garbage increment corresponding to the one or more sample future moments of a group;
inputting, by the management platform, the garbage volume at the plurality of sample historical moments and the one or more sample future moments, and the sample historical pedestrian volume sequence into the initial prediction model to output the garbage increment at the one or more future moments;
constructing a loss function based on the first labels and outputs of the initial prediction model, and
iteratively updating parameters of the initial prediction model based on the loss function until a first preset condition is satisfied to complete the training process, and obtaining the prediction model.
When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model by obtaining samples and labels, inputting the garbage volume, constructing a loss function, and iteratively updating parameters represents the creation of mathematical interrelationships between data. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
The claim limitation, controlling, based on the garbage truck dispatching plan, the garbage truck to travel from a departure point along the garbage truck route to perform a garbage cleaning at the at least one garbage point to be treated
is insignificant post-solution activity. The limitation of controlling a garbage truck along a route to perform garbage cleaning is well known and it does not impose meaningful limits on the claim.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
claim 1: a management platform, a trained machine learning model
claim 11: a user platform, a service platform, a management platform, a sensor network platform, and an object platform,
claim 20: a non-transitory computer-readable storage medium storing computer instructions.
The additional elements recited in claims 1, 11, and 20 are computer components, which are recited at a high-level of generality (i.e., as a generic processing device performing generic computer functions), such that they amount to no more than mere instructions to apply the exception using a generic computer component. Additionally, the obtaining (claims 1, 11, and 20) and transmitting (claim 11) limitations may be considered insignificant extra-solution activity (see MPEP 2106.05(g)). The claim limitation, controlling, based on the garbage truck dispatching plan, the garbage truck to travel from a departure point along the garbage truck route to perform a garbage cleaning at the at least one garbage point to be treated is also insignificant post-solution activity. The limitation of controlling a garbage truck along a route to perform garbage cleaning is well known and it does not impose meaningful limits on the claim. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claim is directed to an abstract idea.
The limitations reciting
predicting, based on the historical garbage volume of the each sub-area in the target area at the plurality of historical moments and a historical pedestrian volume sequence, garbage increment of the each sub-area at a future moment through a prediction model, wherein the historical pedestrian volume sequence is determined based on pedestrian volume corresponding to the each sub-area at the plurality of historical moments, the prediction model is a trained machine learning model, an input of the prediction model includes the garbage volume at the plurality of historical moments and one or more future moments, and a historical pedestrian volume sequence, an output of the prediction model is the garbage increment at the one or more future moments, and a training process of the prediction model includes:
obtaining first training samples and first lables, wherein each of the first training samples includes garbage volume at a plurality of sample historical moments and one or more sample future moments, and a sample historical pedestrian volume sequence, the garbage volume at the plurality of sample historical moments and the sample historical pedestrian volume sequence corresponds to a same time sequence, each of the first label is a garbage increment corresponding to the one or more sample future moments of a group;
inputting, by the management platform, the garbage volume at the plurality of sample historical moments and the one or more sample future moments, and the sample historical pedestrian volume sequence into the initial prediction model to output the garbage increment at the one or more future moments;
constructing a loss function based on the first labels and outputs of the initial prediction model, and
iteratively updating parameters of the initial prediction model based on the loss function until a first preset condition is satisfied to complete the training process, and obtaining the prediction model
provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the computers are invoked merely as a tool to perform existing processes. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the steps of the abstract idea amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the obtaining (claims 1, 11, and 20) and transmitting (claim 11) limitations) as WURC (see 2106.05(d), identifying receiving or transmitting data over a network as WURC, as recognized by Symantec). Moreover, the controlling function (which is interpreted as the garbage truck being controlled by a human) is considered an insignificant application, similar to In re Brown. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent claims 4-9 and 15-22 merely recite further additional embellishments to the abstract idea of independent claims 1 and 11 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claims 1 and 11; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits.
Claims 10 and 12 further recite the additional elements of a user platform, a service platform including several service sub-platforms, a sensor network platform including several sensor network sub-platforms, and an object platform, a management platform includes a general database of the management platform and several management sub-platforms, which are recited at a high-level of generality such that they amount no more than mere instructions to apply the exception using a generic computer component. Even in combination, this additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea.
Subject Matter Distinguished from Prior Art
The most closely applicable prior art of record is Lyman (US 20150307273). Lyman discloses a system for automated waste management (abstract).
Waitkus (US 7406402) is also closely related prior art of record. Waitkus discloses a similar system for scheduling the emptying or replacement of a waste container based on the fullness of the container or the usage of the container (abstract).
As to claim 1, the prior art of record neither anticipates not fairly and reasonable teach a method for managing a garbage treatment device in a smart city, executed based on a management platform of an Internet of Things (loT) system for managing the garbage treatment device in the smart city, comprising: obtaining a garbage accumulation condition in a target area, wherein the garbage accumulation condition includes a historical garbage volume of each sub- area in the target area at a plurality of historical moments; predicting, based on the historical garbage volume of the each sub-area in the target area at the plurality of historical moments and a historical pedestrian volume sequence, garbage increment of the each sub-area at a future moment through a prediction model, wherein the historical pedestrian volume sequence is determined based on pedestrian volume corresponding to the each sub-area at the plurality of historical moments, the prediction model is a trained machine learning model, an input of the prediction model includes the garbage volume at the plurality of historical moments and one or more future moments, and a historical pedestrian volume sequence, an output of the prediction model is the garbage increment at the one or more future moments, and a training process of the prediction model includes: obtaining first training samples and first lables, wherein each of the first training samples includes garbage volume at a plurality of sample historical moments and one or more sample future moments, and a sample historical pedestrian volume sequence, the garbage volume at the plurality of sample historical moments and the sample historical pedestrian volume sequence corresponds to a same time sequence, each of the first label is a garbage increment corresponding to the one or more sample future moments of a group; inputting, by the management platform, the garbage volume at the plurality of sample historical moments and the one or more sample future moments, and the sample historical pedestrian volume sequence into the initial prediction model to output the garbage increment at the one or more future moments; constructing a loss function based on the first labels and outputs of the initial prediction model, and iteratively updating parameters of the initial prediction model based on the loss function until a first preset condition is satisfied to complete the training process, and obtaining the prediction model; determining, based on the historical garbage volume and the garbage increment of the each sub-area, at least one sub-area in the target area as at least one garbage point to be treated; and determining, based on the at least one garbage point to be treated, a garbage truck dispatching plan of the target area, wherein the garbage truck dispatching plan includes: a type of at least one garbage truck to be dispatched, a garbage truck route, and a departure time of the at least one garbage truck; and controlling, based on the garbage truck dispatching plan, the garbage truck to travel from a departure point along the garbage truck route to perform a garbage cleaning at the at least one garbage point to be treated.
Claim 11 is similar to claim 1 and includes similar subject matter distinguishable from prior art.
Examiner notes that the underlined limitations above, in combination with the other limitations found within the independent claims are not found in the prior art.
Response to Arguments
I. Rejection under 35 USC 101
Step 2A, Prong Two: The Claimed Invention is Directed to An Abstract Idea
Applicant argues that the judicial exception is integrated into a practical application. Remarks 17. Applicant states that in the prior art, when there is an influx of people into a city, too much garbage may be generated, thus affecting the appearance of the city, breeding bacteria, and affecting people’s health; therefore, there is a technical problem regarding determining a garbage truck dispatching plan, so as to dispatch the trucks in a timely manner. Remarks 17-18. Examiner respectfully disagrees. The problem identified by Applicant is not a technical problem, but a business/management problem. The problems of dispatching garbage trucks have long been recognized by cities which may have festival or holiday celebrations.
Applicant then argues that the prediction of claim 1 is “bound to a specific technical implementation” because claim 1 requires the use of specific machines equipped with a trained machine learning model and by including pedestrian volume, the prediction is improved, thus improving the functionality of the computer. Remarks 18. Examiner respectfully disagrees. The claim limitations regarding the trained machine learning model provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the computers are invoked merely as a tool to perform existing processes. See MPEP 2106.05(f). As to the alleged improvement of the functionality of the computer, the computer itself is not being improved; at most, only the abstract idea is improved.
Applicant then refers to Example 39, stating that “the method of using special training rules to train a model is considered qualified because it improves computer technology” and that Example 39 is “integrated into practical applications” and therefore, claim 1 is also integrated into a practical application. Remarks 20. Examiner respectfully disagrees. Example 39 was found eligible because it did not recite any of the judicial exception groupings. This is not the case with claim 1.
Applicant further argues that claim 1 has “linked judicial exceptions” with the “technical field of smart city garbage treatment, and has also added a meaningful limitation” with regards to controlling the garbage truck. Remarks 21. Examiner respectfully disagrees. As is stated above, controlling a garbage truck is insignificant extra-solution activity. Controlling a garbage truck is well known and does not impose meaningful limits on the claim.
Applicant also argues that the garbage truck is indispensable to claim 1. Examiner respectfully disagrees. Control of the garbage truck is insignificant extra-solution activity as discussed above.
Applicant then argues that the limitations “enable timely and efficient scheduling of the garbage truck . . . solving technical problems such as resource waste and untimely cleaning caused by traditional fixed route/time scheduling methods, and thus improving existing garbage treatment technologies” and thus, provides a practical application. Remarks 21. Examiner respectfully disagrees. Timely and efficient scheduling, resource waste and untimely cleaning, are all business/management problems, not technical problems. Thus, there is no practical application.
Applicant argues that the feature regarding the garbage truck is similar to the feature in claim 2 of Example 46, “which links judicial exceptions with the technical field and improves existing technology by regulating physical devices based on the information generated by judicial exceptions.” Examiner respectfully disagrees. Limitation (d) of claim 2 adds a meaningful limitation where the feed dispenser is automatically (without human intervention) taking action based on identifying behavior patterns of the animals. This is different from claim 1, in which a human may be controlling the garbage truck according to the dispatching plan.
Step 2B. The Claims Do Not Provide Significantly More than the Abstract Idea
Applicant argues that the claimed invention recites limitations other than what is well-understood, routine, and conventional activity in the field and recites several limitations from claim 1. Remarks 25-27. Applicant specifically argues that “existing technology does not predict the garbage volume based on historical garbage volume and historical pedestrian volume sequence.” Examiner respectfully disagrees that this provides significantly more than the abstract idea. MPEP 2106.05(d) states, “[a]nother consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry.” The prediction of the garbage volume based on historical garbage volume and historical pedestrian volume sequence is not an additional element, but part of the abstract idea. Therefore, it cannot provide significantly more than the abstract idea.
Applicant then states that the controlling of the garbage truck results in physical actions, which reveals the practical application. Remarks 28. Examiner respectfully disagrees. Such a physical action does not provide significantly more than the abstract idea, for all the reasons explained above.
Applicant then argues that the “combination of the above features reflects a creative idea.” Remarks 28. A creative idea does not provide significantly more than the abstract idea.
Applicant argues that the Office has failed to show why the claims are “well-understood, routine, and conventional” as required by the Berkheimer Memo. Examiner respectfully disagrees. The Memo requires that only that evidence is provided when the Office Action addresses the consideration as to whether additional elements are well-understood, routine, and conventional. Other considerations may be used in place of this consideration. See MPEP 2106.05(d). Further, when this consideration is evaluated, the Office Action can provide case law in support of this consideration, as the Office has done above.
II. Rejection under 35 USC 102/103
The rejections under 102/103 are withdrawn, in light of Applicant’s amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARRIE S GILKEY/Primary Examiner, Art Unit 3626
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