DETAILED ACTION
This is in response to the amendment filed on December 4th 2025.
Response to Arguments
Applicant’s arguments, filed on 12/4/25, see pg. 10-12, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are persuasive. The argument that the art does not teach using a machine learning model to determine a traffic route is not persuasive. Applicant acknowledges Welin discloses determining a route and that Kulkarni teaches using a machine learning model to make decisions, yet neither teaches the limitation in whole. While this may be accurate, applicant is merely arguing the references individually. When the rejection is based on a combination of references, one cannot show non-obviousness by attaching the references individually. It is clear from the combination, that one of ordinary skill in the art would have understood that a machine learning model could be used to perform the decision of selecting a traffic route.
Furthermore, applicant’s remarks (pg. 12) that Welin only uses pre-allocated metrics such as bandwidth to perform route selection and does not determine a route based on a first emission amount is not persuasive because it factually disparages the references. The title of Welin is literally “Energy Efficient Routing and Switching”. Thus, it is obviously not limited to “bandwidth” as suggested by applicant. Welin not only teaches that “energy aware routing” is well-known in the prior art (paragraph 6), but discloses using additional energy metrics such as power consumption, cost and CO2 (paragraph 14, Fig. 4). Even assuming arguendo, Doyle was relied upon for the limitation regarding predicted power consumption and predicted emission. So applicant is again arguing the references individually. Therefore, this argument is not persuasive.
Although no remarks were specifically made regarding the amended feature of “emission is determined by the mitigation service using time-series carbon intensity values”, the previous rejection is withdrawn based on this amendment. However, upon further consideration, a new ground(s) of rejection is made in view of Mishra et al. US 2024/0427644 A1.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-2, 4-5, 8-12 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Amsterdam et al. US 2013/0247059 A1 in view of Welin et al. US 2013/0315257 A1 and J. Doyle, R. Shorten and D. O'Mahony, "Stratus: Load Balancing the Cloud for Carbon Emissions Control," in IEEE Transactions on Cloud Computing, vol. 1, no. 1, pp. 1-1, Jan.-June 2013, hereinafter “Doyle” and in further view of Mishra et al. US 2024/0427644 A1.
Regarding claim 1, Amsterdam discloses:
obtaining, on a mitigation service (carbon offsetting is mitigating – see paragraphs 5, 24), data indicating an overall amount of power consumption for a plurality of nodes in a service provider network, wherein each node of the plurality of nodes is located in a different geographical location, and wherein the service provider network provides service to a plurality of service provider network users (to calculate carbon offset a number of inputs are considered including “power consumption” – i.e. “overall amount of power” for the nodes in a network; also see Fig. 2 item 202 which discloses a power consumption meter; “geographic location” – nodes located in a different geographical location – see paragraph 22; system operates for plurality of users – paragraph 21; thus Amsterdam discloses obtaining data indicating an overall amount of power consumption via the power consumption meter, for nodes located in different geographical locations and wherein the network provides service to a plurality of users), the mitigation service being in communication with the plurality of nodes on a network (invention is implemented in a network environment – paragraph 31, Fig. 3);
determining, by the mitigation service, a fraction of network traffic that is handled by the plurality of nodes in the service provider network for serving a particular service provider network user (calculate user percentage – paragraph 22);
determining, by the mitigation service, a service provider network user power consumption for the particular service provider network user(determine user resource consumption –see paragraph 18 and Fig. 1; also see Fig. 2 item 202 which shows a power consumption/usage monitor, see paragraph 27 which discloses collecting energy usage using the power consumption monitor) based on the fraction of network traffic and the overall amount of power consumption (calculate user percentage by dividing execution time by total execution time – see paragraph 22 and claim 1);
determining, by the mitigation service, a first amount of emissions resulting from generating electrical power to support the service provider network user power consumption for the particular service provider network user (Amsterdam – calculate carbon offset required for user’s performance of network tasks, carbon offset is equivalent to emissions – see paragraphs 17-18).
Amsterdam does not explicitly disclose determining, by the mitigation service, a new traffic route for the network traffic of the particular service provider network user, wherein the new traffic route is determined based on the first amount of emissions resulting from generating the electrical power to support the service provider network user power consumption for the particular service provider network user. But this is taught by Welin as a method for energy efficient routing (abstract) that determines a new traffic route for a user wherein the new route is selected based on energy consumption metrics (paragraph 10, Fig. 4), wherein the node is configured to select the path with the lowest power consumption (paragraph 14).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Amsterdam with the energy efficient route selection taught by Welin for the purpose of conserving energy. Amsterdam itself is primarily concerned with power consumption and the route selection of Welin is an additional technique that enables a user to control their power consumption and thereby further conserve energy.
The combination of Amsterdam and Welin does not explicitly disclose the new route is determined based on a predicted power consumption and is further determined based on a predicted emission. However, this is taught by Doyle as calculating the power consumption and carbon emitted/emissions for network paths to service data center requests (Sections 1, 5.2 and 6).
It would have been obvious to one of ordinary skill in the art to modify the combination of Amsterdam and Welin with the predicted power consumption and predicted emissions as taught by Doyle for the purpose of determining a path/route. Doyle teaches this results in less carbon emissions (Sections 7 and 8).
The combination of Amsterdam, Welin and Doyle does not explicitly disclose wherein the first amount of emissions is determined by the mitigation service using time-series carbon intensity values associated with providing power to each node. But this is taught by Mishra as a workload management system that considers carbon intensity values associated with execution (abstract). Mishra explicitly discloses determining the network routing/transfer carbon intensity based on real-time carbon intensity information which is based on the operating emissions rate of power grids used by the data centers and network path (paragraph 49, Fig. 2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amsterdam, Welin and Doyle with the carbon intensity values taught by Mishra for the purpose of determining an amount of emissions. Mishra teaches that by using carbon intensity information, the total emissions (i.e. first amount of emissions) for a data transfer may be determined (paragraph 49). This allows users or organizations to track usage in order to achieve goals or financial rewards.
The combination of Amsterdam, Welin, Doyle and Mishra does not explicitly disclose “using a machine learning model” to determine a traffic route. But Welin discloses determining the routes based on route selection metrics (paragraphs 10-11, Figs. 1-2). Based on this teaching alone, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Amsterdam to use a machine learning model to determine a network route because Welin explicitly discloses taking inputs (route selection metrics) in order to output a selection (new route), which is the same functionality that a machine learning model provides. Furthermore, ML models are well-known in the art and yield predictable results (e.g. see pertinent art which is replete with teachings of “data models”, “predictive models”, “machine learning models”, etc.). Thus, this is merely the combination of an well-known technique according to its established function in order to yield a predictable result.
Regarding claims 2, 9 and 16, Amsterdam does not disclose wherein the new traffic route includes at least one different node compared to a current traffic route, and wherein the new traffic route [has] a second amount of emissions that is a lower amount of emissions than the first amount of emissions; transmitting, by the mitigation service, an instruction to cause the new traffic of the particular user to automatically follow the new traffic route when the new traffic route is associated with the lower emissions. But this is taught by Welin as a method for energy efficient routing (abstract) that determines a new traffic route for a user wherein the new route is selected based on energy consumption metrics (paragraph 10, Fig. 4). Welin further teaches this means the node is configured to select the path with the lowest power consumption (paragraph 14), and an instruction causes the user to use the new traffic route automatically (paragraph 44, Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Amsterdam with the energy efficient route selection taught by Welin for the purpose of conserving energy. Amsterdam itself is primarily concerned with power consumption and the route selection of Welin is an additional technique that enables a user to control their power consumption and thereby further conserve energy. Welin does not necessary disclose predicting emissions but this is taught by Doyle (see Sections 1, 5, 6 and 7 which disclose selecting network paths with the goal of lowering carbon emissions, which is done by predicting/calculating route emissions). The motivation to combine is the same as that given above.
Regarding claim 4, Amsterdam discloses the first amount of emissions is determined based on a product of the service provider network user power consumption at each node and a carbon intensity associated with providing the power to each node (Amsterdam teaches that carbon offsetting compensates for greenhouse gas emissions used in the performance of a particular computing task – paragraphs 6, 17; the specification indicates that “carbon intensity” may refer to an amount of carbon emissions to produce an amount of electricity (paragraph 26); thus the carbon offset of Amsterdam reads on the BRI of carbon intensity since both terms refer to an amount of emissions; Amsterdam teaches calculating carbon offset/power consumption by monitoring resource usage for users tasks – paragraphs 18, 22, 27; which results in the calculation of emissions generated in connection with a user’s task – paragraph 17; also see claim 1which explicitly discloses multiplying the energy cost by the usage). Amsterdam does not explicitly disclose the predicted emission is determined based on the first amount of emissions but this is taught Doyle (predict emissions based on amount – Sections 5-7). Doyle also explicitly discloses using “carbon intensity” associated with providing power (Sections 5-7). The motivation to combine is the same as that given above.
Regarding claim 5, Amsterdam discloses the carbon intensity associated with providing power to each node is determined according to a geographical location of each node. As discussed above, the BRI of “carbon intensity” include the emissions. Amsterdam explicitly disclose determining emissions so that a carbon offset can be calculated, and it considers geographic location (paragraph 22; and more explicitly, claim 6 recites “determining a geographic location wherein the computing task is being performed; and determining a form of power generation at the geographic location”).
Regarding claim 8, it is a system claim that corresponds to the method of claim 1; thus it is rejected for the same reasons and because Amsterdam discloses a processor and memory to perform the method (Fig. 3).
Regarding claims 10 and 17, the combination of Amsterdam, Welin and Doyle does not explicitly disclose wherein the time-series carbon intensity values fluctuate over time due to how power is generated but this is taught by Mishra (carbon intensity is determined based on “real-time” carbon intensity information – paragraph 49; also see Fig. 3 and paragraph 51 which disclose calculating average values because “Carbon intensity for locations may vary over time for locations, as various energy sources provide more or less energy (such as based on changing times of day or weather for solar or wind energy).”). The motivation to combine the teachings of Mishra is the same as that given above in the rejection of claim 1.
The combination of Amsterdam, Welin and Doyle and Mishra does not explicitly the machine learning model is one of LSTM, a CNN, and a RNN. But this would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. These are merely types of well-known, routine and conventional machine learning models (e.g. see Sidhu paragraphs 62-63, cited in the rejection of claim 6). Thus, this is merely the combination of a well-known technique (e.g. CNN/RNN) according to its established function in order to yield a predictable result.
Regarding claims 11-12, they correspond to claims 4-5 respectively so they are rejected for the same reasons.
Regarding claim 15, it is a non-transitory computer readable medium that corresponds to the method of claim 1; thus it is rejected for the same reasons.
Regarding claims 18-19, they correspond to claims 4-5 respectively so they are rejected for the same reasons.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Amsterdam, Welin and Doyle and Mishra in further view of Kulkarni et al. US 2023/0186217 A1.
Regarding claim 3, the combination of Amsterdam, Welin and Doyle does not explicitly disclose wherein the time-series carbon intensity values fluctuate over time due to how power is generated but this is taught by Mishra (carbon intensity is determined based on “real-time” carbon intensity information – paragraph 49; also see Fig. 3 and paragraph 51 which disclose calculating average values because “Carbon intensity for locations may vary over time for locations, as various energy sources provide more or less energy (such as based on changing times of day or weather for solar or wind energy).”). The motivation to combine the teachings of Mishra is the same as that given above in the rejection of claim 1.
The combination of Amsterdam, Welin, Doyle and Mishra does not explicitly disclose the model included in the mitigation service is trained using historical data including historical power consumption data and historical carbon intensity data. But this is taught by Kulkarni a machine learning model that is trained using carbon emissions data (abstract). Indeed, Kulkarni teaches that the training data explicitly includes “historical” carbon emission data (paragraphs 38 , Fig. 2; also see paragraph 51 and Fig. 7 steps 702-704).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Amsterdam, Welin and Mishra with the model trained using historic carbon emissions taught by Kulkarni for the purpose of mitigating emissions. Kulkarni teaches this allows enterprises to reduce their carbon footprint (paragraphs 1-2).
Claim(s) 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Amsterdam, Welin and Doyle and Mishra in further view of Sidhu et al. US 2023/0059038 A1.
Regarding claims 6, 13 and 20, Amsterdam does not explicitly disclose transmitting an instruction to cause the network traffic of the particular user to be throttled to reduce the first amount of emissions by a particular amount. But this is taught by Sidhu as streaming media in according with a carbon footprint (abstract). Sidhu does not explicitly disclose “throttle” but it teaches optimizing carbon emissions by controlling stream quality (paragraph 32) and that quality directly relates to carbon footprint (see paragraphs 70, 73, Fig. 2). Finally, Sidhu discloses auto adjusting the quality (paragraph 74), this reads on “throttled” under the BRI because by lowering quality, a user’s network traffic is reduced which is equivalent to “throttled”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Amsterdam with the traffic throttling technique taught by Sidhu for the purpose of improving power consumption. Sidhu teaches that by reducing quality, a user’s carbon footprint can be controlled (paragraphs 3-4).
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Amsterdam, Welin and Doyle and Mishra in further view of Amend et al. US 2022/0377673 A1.
Regarding claims 7 and 14, Amsterdam discloses the first amount of emissions is determined … based on an amount of power consumption at each node and based on measured … values for the particular network user at each node (measure resource usage and power consumption – paragraphs 7, 18, 26-27 and Fig. 2).
Amsterdam does not explicitly disclose “hop-by-hop” but it teaches monitoring the use of computing resources (paragraphs 17-18) and these resource metrics determine the amount of emissions/carbon offset (paragraph 18). Thus, Amsterdam reads on “hop-by-hop” because it accounts for all different resources.
Amsterdam does not explicitly disclose measured traffic values but this is taught by Amend as measuring the energy consumption of network flows (paragraph 25) including user traffic (paragraph 28). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Amsterdam to consider network traffic when determining power consumption as taught by Amend for the purpose of calculating carbon offset. Amend teaches network flows have energy consumption and measuring flows allows for reducing energy consumption of mobile devices which provides a benefit to the user (paragraphs 16, 25).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ramchurn et al. US 2025/0258208 A1 discloses tracking real-time energy usage by using carbon intensity score (abstract, Figs. 2-3) and taking response actions in response to carbon thresholds (paragraph 123).
Shi US 2020/0372588 A1 discloses a system for machine-learning carbon emission prediction (abstract, Fig. 4) that uses carbon intensity scores (paragraph 46, Fig. 1).
Hossain MM, Georges JP, Rondeau E, Divoux T. Energy, Carbon and Renewable Energy: Candidate Metrics for Green-aware Routing? Sensors (Basel). 2019 Jun 30;19(13):2901. doi: 10.3390/s19132901. PMID: 31262056; PMCID: PMC6651093; discloses a pollution-aware routing algorithm to reduce CO2 emissions (abstract).
A. Chambon, A. Rachedi, A. Sahli and A. Mebarki, "When Carbon Footprint Meets Data Transportation in IoT Networks," 2023 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Istanbul, Turkiye, 2023, pp. 1-6; discloses considering end-to-end energy consumption model as well as carbon emissions for data transportation across networks (abstract and Sections I-III).
Krishnamurthy et al. US 2011/0145603 A1 discloses green routing protocol which selects links with lower power consumption (paragraph 28).
Sampath et al. US 2017/0237649 A1 discloses determining path cost values using dynamic network parameters (abstract, Fig. 2), the dynamic network parameters include predicted power consumption (see paragraphs 23, 28, 30 and claim 5).
Ramchurn et al. US 2025/0258208 A1 discloses using carbon intensity scores to measure how green a user’s energy consumption is (abstract, paragraph 123, Figs. 5-6).
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.
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/JASON D RECEK/Primary Examiner, Art Unit 2458