DETAILED ACTION
The following Non-Final office action is in response to application 18/881,941 filed on 1/7/2025. Examiner notes priority claim to application PCT/CN2023/106771 filed 7/11/2023 and CN202210836035.3 filed 7/15/2022. IDS filed 6/26/2025 has been considered.
Status of Claims
Claims 1-7 and 10-22 are currently pending and have been rejected as follows.
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 1-7, 10 and 12-17 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system).
Claims 11 and 18-22 the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because it is directed to a signal per se. Examiner recommends to amend, and for purposes of compact prosecution interprets, the claim to recite a “non-transitory” element.
Claims 1-7 and 10-22 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 integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-7 are directed toward the statutory category of a process (reciting a “method”). Claims 10 and 12-17 are directed toward the statutory category of a machine (reciting a “device”). Claims 11 and 18-22 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable storage medium”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 10, and 11 are directed to an abstract idea by reciting receiving a service request; wherein the service request carries service requirement; generating a resource provider list of resource providers that meet the service requirement based on the service requirement and status of the resource providers; selecting a target resource provider in the resource provider list based on the resource provider list and first information; wherein the first information is used to indicate a global optimization goal; and calling the target resource provider to provide service (Example Claim 1).
The claims are considered abstract because these steps recite certain methods of organizing human activity like commercial interactions and managing interactions between people; and mental processes. The claims recite receiving a request with a service requirement, identifying a list of resource providers, selecting a target resource provider and calling the target service provider to provide service. It is understood that the claimed steps aim to achieve global performance optimization instead of single-time optimization for service requests (Applicant’s Specification, [0003]-[0004]). By this evidence, the claims recite a type of certain methods of organizing human activity like commercial interactions and managing interactions between people; and mental processes common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., a method for resource scheduling).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as a computing device, comprising: a processor and a memory storing a computer program) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Dependent claims 2-7 and 12-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps of “receiving” a service request; “generating” a resource provider list, “selecting” a target resource provider, and “calling” the target service provider (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-7 and 12-22 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Claim Rejections - 35 USC § 103
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.
Claims 1, 5-7, 10, 11, 15-17, 21, and 22 are rejected under 35 USC 103 as being unpatentable over the teachings of
Jackson, US 20110016214 A1, hereinafter Jackson, in view of
Ozog, US 20110231028 A1, hereinafter Ozog. As per,
Claims 1, 10, 11
Jackson teaches
A resource scheduling method, comprising: /
A computing device, comprising: a processor and a memory storing a computer program; wherein the computer program, when executed by the processor, causes the processor to perform: /
A computer-readable storage medium, comprising instructions stored thereon; wherein the instructions, when executed on a computer, causes the computer to perform: (Jackson fig. 1 noting the computing device 100; processor; memory; see associated text in [0024])
receiving a service request; wherein the service request carries service requirement; (Jackson [0011] “receiving a request for compute services at the brokering system, the request for compute resources being associated with a service level agreement” note the request for services and associated with a service level agreement)
generating a resource provider list of resource providers that meet the service requirement based on the service requirement and status of the resource providers; (Jackson [0009] “periodically polling a group of separately administered compute environments to identify resource capabilities and/or other data associated with the environment such as availability, cost, reliability;” [0011] “based on the identified resource information across the group of compute resource environments, selecting compute resources in one or more of compute resource environments” note the polling of multiple resource providers for status information and using that information for selecting a resource provider)
selecting a target resource provider in the resource provider list based on the resource provider list and first information; (Jackson [0046] “as it receives requests for processing workload, it can analyze all of the clouds using the various principles set forth above and disclosed herein to select the appropriate resources in one or more clouds for processing the workload;” [0048] “the broker 310 selects the appropriate resources (based on factors disclosed herein) and then route the received workload to those selected resources” note the selection of a target resource provider from a set of candidate providers)
[…];
calling the target resource provider to provide service. (Jackson [0011] “The method next can include receiving workload associated with the request and communicating the workload to the selected resources in the group of compute resource environments for processing;” [0048] “Requesters 312, 314, 316 and 318 provide workload to the broker 310 which then communicates the workload to the selected resources in one or more of the compute environment” note the workload sent to the selected resources for processing)
Jackson does not explicitly teach, Ozog however in the analogous art of resource optimization teaches
wherein the first information is used to indicate a global optimization goal; and (Ozog [0016] “The system may also optimize energy distribution, energy use, the cost of service, or avoided cost … Interaction between the calculating, forecasting and optimizing may allow management and dispatch of end-uses and energy supply at a micro level in near real-time;” [0027] “the optimizing may include at least one of: maximizing revenue of the energy provider, minimizing customer discomfort, maximizing avoided costs, minimizing incentive costs, minimizing cost to provide and deliver power, and combinations thereof, while achieving a required total demand reduction or total energy reduction;” [0340] “processing the data and forecasted inputs to produce instructions for energy distribution and energy use based upon one or more desired goals” note the optimization goals)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Jackson’s resource scheduling system to include global optimization information in view of Ozog in an effort to improve optimizations by enabling more accurate information for inputs (see Ozog ¶ [0170]-[0171] & MPEP 2143G).
Claims 5, 15, 21
Jackson does not explicitly teach, Ozog however in the analogous art of resource optimization teaches
wherein the selecting a target resource provider in the resource provider list based on the resource provider list and first information, includes: in the resource provider list, selecting resource providers that report a predicted value of the global optimization goal to form a target resource provider list; (Ozog [0197] “The forecasting system 104 may forecast end-use and total premise loads, energy costs, energy prices, avoided costs and renewable resource generation … supplying those outputs to the optimizations 105;” [0198] “The results of the forecasting system 104 may be stored ... As new information continually arrives, the forecasts may be continually updated;” [0340] “Optimizing … may include taking data and forecasted inputs, processing the data and forecasted inputs to produce instructions for energy distribution and energy use based upon one or more desired goals” note the use of forecasted optimization values as inputs to optimization decisions)
The motivation/rationale to combine Jackson with Ozog persists.
Jackson teaches
in the target resource provider list, selecting a resource provider that meets the service requirement as the target resource provider. (Jackson [0011] “based on the identified resource information across the group of compute resource environments, selecting compute resources in one or more of compute resource environments … The selection of compute resources ensures that the processing complies with the service level agreement” note the selection of resources that satisfy the request’s service requirement)
Claims 6, 16, 22
Jackson does not explicitly teach, Ozog however in the analogous art of resource optimization teaches
wherein the predicted value of the global optimization goal is calculated by the resource provider based on the service requirement and historical resource target data of the resource provider. (Ozog [0019] “The optimizing may also consider data selected from the group consisting of: … end-use usage history, … historical individualized demand, … customer responses to prior program offerings … and combinations thereof;” [0198] “The forecasting system 104 may use regression-based modeling procedures that include weather conditions, time of day, and day of week variables to forecast the end-use for each customer until the end of the current month or season” note the calculating of predicted values for optimization using historical usage data and current input data)
The motivation/rationale to combine Jackson with Ozog persists.
Claims 7, 17
Jackson does not explicitly teach, Ozog however in the analogous art of resource optimization teaches
wherein the global optimization goal includes at least one of the following: a first global optimization goal corresponding to a global transaction resource volume indicator; a second global optimization goal corresponding to a global total resource transaction price indicator; a third global optimization goal corresponding to a global energy consumption indicator; a fourth global optimization goal corresponding to a global service stability indicator; and a fifth global optimization goal corresponding to a global carbon emission indicator. (Ozog [0340] “produce instructions for energy distribution and energy use based upon one or more desired goals” note the one or more optimization goals;” [0197] “The following may be forecast: … capacity values … The amount of resource extractable today versus waiting until the end of a specified peak pricing period may also be forecast” note the capacity/extractable resource amount corresponding to a resource volume indicator)
The motivation/rationale to combine Jackson with Ozog persists.
Claims 2, 3, 12, 13, 18, and 19 are rejected under 35 USC 103 as being unpatentable over the teachings of
Jackson in view of Ozog in view of
Bharti et al., US 20180356776 A1, hereinafter Bharti. As per,
Claims 2, 12, 18
Jackson teaches
wherein after calling the target resource provider to provide service, the method further comprises: (Jackson [0011] “communicating the workload to the selected resources … for processing”)
Jackson / Ozog do not explicitly teach, Bharti however in the analogous art of resource optimization teaches
sending second information to a first resource party in a case where the service meets the global optimization goal; (Bharti fig. 1; [0008] “The file server is for receiving specified energy consumption data from customers. The business intelligence analytical engine … determines a reward for the one customer, based on the defined rewards program, for maintaining the one customer's energy utilization, … in a given range compared to said baseline of energy consumption;” [0032] “The chart of FIG. 1 shows the flow of a process to award points to the consumer” noting the determining and awarding reward information to the user when the user’s energy utilization satisfies the target range corresponding to the sending of second information to a first resource party when the service meets the optimization goal)
wherein the first resource party includes a resource user and/or a resource provider, and (Bharti [0009] “motivate customers to increase energy savings and to collaborate with energy distribution utilities” note the resource user and the resource provider)
the second information is used to indicate an incentive for incentivizing the first resource party to adapt to the global optimization goal. (Bharti [0010] “motivate customers to save energy by awarding points;” [0023] “a pragmatic and comprehensive model for motivating consumers to continuously optimize energy consumption with a quantitative reward mechanism” note the reward points used to incentivize users to adapt to the optimization goal)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Jackson’s resource scheduling system and Ozog’s global optimization information to include resource users and providers and incentivization information in view of Bharti in an effort to motivate users to improve resource efficiency (see Bharti ¶ [0071] & MPEP 2143G).
Claims 3, 13, 19
Jackson / Ozog do not explicitly teach, Bharti however in the analogous art of resource optimization teaches
wherein the incentive indicated in the second information is determined according to the global optimization goal. (Bharti [0004] “determining a reward for the customer … for maintaining the customer's energy utilization … in a given range compared to said baseline” note the reward determined according to whether the user’s usage satisfies the optimization target)
The motivations/rationales to combine Jackson / Ozog with Bharti persists.
Claims 4, 14, and 20 are rejected under 35 USC 103 as being unpatentable over the teachings of
Jackson in view of Ozog in view of Bharti in further view of
Vaswani et al., US 20100228601 A1, hereinafter Vaswani. As per,
Claims 4, 14, 20
Jackson / Bharti do not explicitly teach, Ozog however in the analogous art of resource optimization teaches
wherein when the global optimization goal includes a first global optimization goal corresponding to a global transaction resource volume indicator, the incentive is positively correlated with a transaction resource volume of a current service; (Ozog [0197] “The following may be forecast: energy costs, energy prices, avoided load; avoided costs; capacity values;” [0339] “the system may protect users and utility decision makers from over supplying demand response into a market, by valuing the marginal capacity costs as a function of the end use and microgrid resource dispatching resource's magnitude, relative to the system and the hours of resource availability” noting the optimization and valuation as a function of the magnitude/amount of dispatched resource and capacity contribution)
when the global optimization goal includes a second global optimization goal corresponding to a global total resource transaction price indicator, the incentive is positively correlated with a total resource transaction price of a current service; (Ozog [0197] “The following may be forecast: energy costs, energy prices;” [0027] “optimizing may include … maximizing revenue of the energy provider, … minimizing cost to provide and deliver power” noting the price based optimization)
The motivations/rationales to combine Jackson / Bharti with Ozog persists.
Jackson / Ozog do not explicitly teach, Bharti however in the analogous art of resource optimization teaches
when the global optimization goal includes a third global optimization goal corresponding to a global energy consumption indicator, the incentive is negatively related to an energy consumption of a current service; (Bharti [0004] “determining a reward for the customer … for maintaining the customer's energy utilization … in a given range compared to said baseline;” [0010] “motivate customers to save energy by awarding points for reducing their energy consumption” note as energy consumption is reduced, the reward is earned)
when the global optimization goal includes a fourth global optimization goal corresponding to a global service stability indicator, the incentive is positively correlated with stability of a current service; (Bharti [0009] “motivate customers to increase energy savings and to collaborate with energy distribution utilities to help achieve a more stable and reliable power distribution” noting the stability optimization goal)
The motivations/rationales to combine Jackson / Ozog with Bharti persists.
Jackson / Bharti / Ozog do not explicitly teach, Vaswani however in the analogous art of resource optimization teaches
when the global optimization goal includes a fifth global optimization goal corresponding to a global carbon emission indicator, the incentive is negatively related to the carbon emission indicator of a current service. (Vaswani [0005] “Electrical energy generation carbon impact information is retrieved … The electrical energy generation carbon impact information indicates carbon released ... A carbon credit is calculated according to the retrieved electrical energy generation carbon impact information … The calculated carbon credit is then used to update a display of carbon credit related information” note the incentive determined from emissions information)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Jackson’s resource scheduling system, Ozog’s global optimization information, and Bharti’s incentivization information to include carbon emissions information tied to incentivization information in view of Vaswani in an effort to incentivize users to reduce emissions (see Vaswani ¶ [0070] & MPEP 2143G).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 8589549 B1: A method and system for customer incentive-based management of computing resource utilization. According to one embodiment, a method may include provisioning a computing resource according to a given level of resource utilization, and dynamically predicting utilization of the computing resource that is expected to occur during a given interval of time. In response to dynamically predicting that utilization of the computing resource will be less than the given level of resource utilization during the given interval of time, the method may further include offering an incentive to a customer to utilize the computing resource during at least a portion of the given interval of time;
WO 2007/120663 A2: Virtual service switching includes contracting to provide a requestor with a unit of computing capacity to a service within a specific time period (500), creating one or more plans based at least in part on the contracting (505), scheduling execution of the one or more plans using the computing capacity (510), and if a bid for at least part of the unit of computing capacity is received prior to the end of the specific time period (515), allocating at least part of the computing capacity based at least in part on the bid (520). Each of the one or more plans comprises software code and associated data;
Nguyen et al., Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey, 2016: This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA MUNSON can be reached on (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
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