The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Notice to Applicant
In response to the communication received on 12/31/2025, the following is a Final Office Action for Application No. 18370262.
Status of Claims
Claims 1-20 are pending.
Claims 21-23 are new.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The processor and/or memory medium is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, processor and/or memory medium to inter alia perform the function of formulate an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of formulate an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: processor and/or memory medium. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, processor and/or memory medium to inter alia perform the function of formulate an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
In an effort to further expedite prosecution, see: Appendix 1 to the October 2019 Update: Subject Matter Eligibility, Life Sciences & Data Processing Examples, October 2019 30, Example 46. Livestock Management. Per claim 1 of Example 46, the memory, display and processor are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. As an exemplary direction for similar claim limitations to be eligible, see claims 2-4 of Example 46.
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 1-23 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. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
A computer-implemented method of leveraging artificial intelligence (Al) for forward looking scheduling, the computer-implemented method comprising: receiving, at one or more processors, a set of demand data and a set of schedules data corresponding to a first period for a service provider; formulating, by the one or more processors, an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data using at least one of: (i) a linear programming technique, (ii) an integer programming technique, or (iii) a constraint programming technique, the optimization model being configured to generate optimal schedules based on sets of demand data and sets of schedules data; inputting, by the one or more processors, the set of demand data and the set of schedules data into an optimization model; generating, by the one or more processors executing the optimization model, an optimal schedule for the first period based on the set of demand data and the set of schedules data; and outputting, by the one or more processors, the optimal schedule for display to a user associated with the service provider.
[or]
A system leveraging artificial intelligence (AI) for forward looking scheduling, comprising:a memory storing a set of computer-readable instructions including an optimization model; and one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to: receive a set of demand data and a set of schedules data corresponding to a first period for a service provider, formulate an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data using at least one of: (i) a linear programming technique, (ii) an integer programming technique, or (iii) a constraint programming technique, the optimization model being configured to generate optimal schedules based on sets of demand data and sets of schedules data, input the set of demand data and the set of schedules data into an optimization model, generate, by executing the optimization model, an optimal schedule for the first period based on the set of demand data and the set of schedules data, and output the optimal schedule for display to a user associated with the service provider.
[or]
A non-transitory computer-readable storage medium configured to store instructions executable by one or more processors, the instructions comprising: instructions for receiving a set of demand data and a set of schedules data corresponding to a first period for a service provider; instructions for inputting the set of demand data and the set of schedules data into an optimization model; instructions for formulating an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data using at least one of: (i) a linear programming technique, (ii) an integer programming technique, or (iii) a constraint programming technique, the optimization model being configured to generate optimal schedules based on sets of demand data and sets of schedules data; instructions for generating, by executing the optimization model, an optimal schedule for the first period based on the set of demand data and the set of schedules data; and instructions for outputting the optimal schedule for display to a user associated with the service provider.
The claim(s) recite(s) the following summarization of the abstract idea which includes generating, by executing the optimization model, an optimal schedule for the first period based on the set of demand data and the set of schedules data executed by the additional element(s) of non-transitory computer readable storage medium, and/or processor. This falls into at least the Abstract Idea Grouping of Mental Processes since the information can be analyzed by an abstract evaluation judgment process. Thus, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity since the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion). Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory medium and/or processor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic memory medium and/or processor limitation is no more than mere instructions to apply the exception using a generic computer component. Further, output the optimal schedule for display by a memory medium and/or processor is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory medium and processor. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, output the optimal schedule for display by a memory medium and/or processor is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0019 wherein “a non-transitory computer-readable storage medium configured to store instructions executable by one or more processors is disclosed.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Namboothiri et al. (US 20180107965 A1) hereinafter referred to as Namboothiri in view of Albert et al. (US 20150332294 A1) hereinafter referred to as Albert.
Namboothiri teaches:
Claim 1. A computer-implemented method of leveraging artificial intelligence (Al) for forward looking scheduling, the computer-implemented method comprising:
receiving, at one or more processors, a set of demand data and a set of schedules data corresponding to a first period for a service provider (¶0030 the present invention may be implemented by using, individually or in combination, a variety of different types of optimization approaches. These optimization approaches include, but not limited to, linear programming, quadratic programming, mixed integer non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. According to the present system, the objective function and constraints may be updated continuously so that changes may be handled and incorporated in real time into new allocation schedules that are generated upon a triggering event or defined period. The optimizer 48 then maybe used to recompute the allocation schedule over the strategic planning cycle or operational planning cycle, whatever the case may be. The optimizer 48 may repeat this process for each optimization cycle, thereby, constantly maintaining optimal allocation scheduling as unforeseen outages and other events occur.);
formulating, by the one or more processors, an optimization model for an individual scheduling problem associated with the set of demand data and the set of schedules data using at least one of: (i) a linear programming technique, (ii) an integer programming technique, or (iii) a constraint programming technique, the optimization model being configured to generate optimal schedules based on sets of demand data and sets of schedules data (¶0020 In order to meet the maintenance needs of the many customer power plants it serves, the field services company typically employs many types of field engineers so that a wide and complete range of services may be offered. The field services company may employ many field engineers having similar skills so that a redundancy of skills accords with potential customer demand. As mentioned, such service companies may include a resource manager who is responsible for managing, mobilizing, scheduling and assigning the field engineers to perform the maintenance tasks that cover planned and unplanned outages of customer power plants located within his defined territory ¶0028-0029 calculating an optimized field engineer allocation schedule 49 and communicating it to the resource manager via the user device 45. The user device 45 also may be used by the resource manager to define or choose the objectives according to which the optimization is completed within the optimizer 48 of the processing unit 44. The optimizer 48 may be used to optimize a defined objective function. As used herein, the objective function includes multiple variables or, as used herein, “decision variables”, and is subject to a set of defined constraints. As will be appreciated, the decision variables of the objective function may represent “cost variables”, such as labor or travel costs, which the optimizer 48 will generally seek to minimize, or may represent “benefit variables”, such as revenue or profit, which the optimizer will generally seek to maximize. Thus, as used herein, the objective function is a mathematical representation of how, for example, a generated allocation schedule performs relative to the defined decision variables ¶0030 These optimization approaches include, but not limited to, linear programming, quadratic programming, mixed integer non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. According to the present system, the objective function and constraints may be updated continuously so that changes may be handled and incorporated in real time into new allocation schedules that are generated upon a triggering event or defined period. The optimizer 48 then maybe used to recompute the allocation schedule over the strategic planning cycle or operational planning cycle, whatever the case may be. The optimizer 48 may repeat this process for each optimization cycle, thereby, constantly maintaining optimal allocation scheduling as unforeseen outages and other events occur);
inputting, by the one or more processors, the set of demand data and the set of schedules data into an optimization model trained to generate optimal schedules based on a set of training demand data and a set of training schedules data (¶0027 the field engineering module 42 may connect to a human resources database related to the field services company for determining information related to vacation time for each of the field engineers; a learning and training database for determining the upcoming training schedule for each of the field engineers; an immigration database for visa and travel restriction information for each of the field engineers; and a performance database for determining customer preferences relating to each of the field engineers. ¶0030 the present invention may be implemented by using, individually or in combination, a variety of different types of optimization approaches. These optimization approaches include, but not limited to, linear programming, quadratic programming, mixed integer non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. According to the present system, the objective function and constraints may be updated continuously so that changes may be handled and incorporated in real time into new allocation schedules that are generated upon a triggering event or defined period. The optimizer 48 then maybe used to recompute the allocation schedule over the strategic planning cycle or operational planning cycle, whatever the case may be. The optimizer 48 may repeat this process for each optimization cycle, thereby, constantly maintaining optimal allocation scheduling as unforeseen outages and other events occur.);
generating, by the one or more processors executing the optimization model, an optimal schedule for the first period based on the set of demand data and the set of schedules data (¶0028-0029 calculating an optimized field engineer allocation schedule 49 and communicating it to the resource manager via the user device 45. The user device 45 also may be used by the resource manager to define or choose the objectives according to which the optimization is completed within the optimizer 48 of the processing unit 44. The optimizer 48 may be used to optimize a defined objective function. As used herein, the objective function includes multiple variables or, as used herein, “decision variables”, and is subject to a set of defined constraints. As will be appreciated, the decision variables of the objective function may represent “cost variables”, such as labor or travel costs, which the optimizer 48 will generally seek to minimize, or may represent “benefit variables”, such as revenue or profit, which the optimizer will generally seek to maximize. Thus, as used herein, the objective function is a mathematical representation of how, for example, a generated allocation schedule performs relative to the defined decision variables ¶0030 the optimizer 48 of the present invention may optimize the objective function according to conventional systems and methods. As will be appreciated, one common method for optimizing an objective function, for example, is known as “gradient descent optimization.” Gradient descent is an optimization algorithm that approaches a local minimum of the objective function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. It should be understood that a number of different optimization techniques may be used depending on the form of the model and the decision variables and constraints); and
outputting, by the one or more processors, the optimal schedule for display to a user associated with the service provider (¶0048 an optimized operational schedule in accordance with those and other decision variables may be generated operationally to handle unforeseen day-to-day happenings. In doing this, the objective function may include a decision variable that reflects the level of disruption that proposed changes would make to a previously generated long-term strategic schedule that is already in place. In such cases, the optimization of the present invention may generally operate to find solutions that minimize such disruption. As will be appreciated, these decision variables may be used in combination so that the optimized solutions found by the present invention are ones that best balance all of the different considerations represented by each of the included decision variables. ¶0057 These and other input devices are often connected to the processing unit 114 through a serial port interface 152 that is coupled to the system bus 118, but may be connected by other interfaces, such as a parallel port, a game port, a universal serial bus (“USB”), an IR interface, etc. A monitor 154, or other type of display device, is also connected to the system bus 118 via an interface, such as a video adapter 156. In addition to the monitor, a computer typically includes other peripheral output devices (not shown), such as speakers, printers etc.).
Although not explicitly taught by Namboothiri, Albert teaches in the analogous art of system for profiling and scheduling of thermal residential energy use for demand-side management programs:
inputting, by the one or more processors, the set of demand data and the set of schedules data into an optimization model … the optimization model being configured to generate optimal schedules based on sets of demand data and sets of schedules data (¶0080 A typical estimation method for such models is the Baum-Welch algorithm, which is an Expectation Maximization (EM) type algorithm. However, the model formulation used in embodiments of the present invention includes constraints on the emission parameters b.sub.k (which have to be positive). This makes using EM cumbersome and less computationally efficient. Instead embodiments of the present invention use a direct likelihood maximization procedure: θ*=max.sub.θL.sub.T(x;θ) (9) For a given customer, the quantity θ≡(a.sub.k,b.sub.k,σ.sub.k.sup.2,c.sub.k,d.sub.k|k=1, . . . , K) is estimated by defining the likelihood as a non-linear function of θ and applying a numeric optimization algorithm. In contrast to the EM algorithm, which only results in point estimates of the parameters, this procedure allows to compute confidence intervals. The analytic engine computes the most likely sequence of states {s.sub.t} that fits a given observation sequence {x.sub.t} (the decoding problem) using the standard Viterbi algorithm. This results in the recovery of the sequence of hidden decisions that gave rise to the observed consumption {x.sub.t}. The number K of states (the model size) is generally not known in real applications. A suitable trade off between model complexity and expressive power may be found by selecting the simplest model (smallest K) that can account for at least R % out-of-sample variance.¶0133 In the simplest case it is assumed that the customer will comply fully with the requests for control. This is appropriate for the case of automated controls done via a smart thermostat that has a two-way communications channel. If this is not the case, but compliance depends, e.g., on occupancy (whether the customer is at home or not) or other unobservable factors, the effort schedule and the constraints in the optimization problem above become probabilistic ¶0110 The analytic engine computes the benchmarks introduced above for the two customers, and present the temperature-dependent stationary probability distribution over thermal occupancy regimes in FIGS. 7A-C. For Alice (FIG. 7A) the probability distribution P(a(T)) captures the prevalence of the heating state 3 for low temperatures, the low cooling regime 1 for intermediate temperatures, and low cooling regime for intermediate and high temperatures higher than 75° F. Similarly, for Bob (FIG. 7B) the model identifies the temporally-consistent cooling state 2 as dominant for high temperatures (above 95° F.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Namboothiri ¶0005 teaches that there is an ever-growing demand for tools for automatically scheduling, optimizing, and/or improving the allocation process of mobile assets or field engineering resources in the power generating industry;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Namboothiri Abstract teaches a system for allocating field engineers to perform maintenance tasks according to a generated allocation schedule, and Albert Abstract teaches a methodology is provided for informing targeted Demand-Response (DR) and marketing programs that focus on the temperature-sensitive part of residential electricity demand; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Namboothiri at least the above cited paragraphs, and Albert at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Namboothiri teaches:
Claim 2. The computer-implemented method of claim 1, further comprising: receiving, at the one or more processors, a set of volume data and a set of average handle time data corresponding to a historical period for the service provider; and generating, by the one or more processors executing a demand model, the set of demand data based on the set of volume data and the set of average handle time data, wherein the set of demand data defines minimum supply requirements for the service provider during the first period (¶0048 the objective function may include a decision variable that reflects the level of disruption that proposed changes would make to a previously generated long-term strategic schedule that is already in place. In such cases, the optimization of the present invention may generally operate to find solutions that minimize such disruption. As will be appreciated, these decision variables may be used in combination so that the optimized solutions found by the present invention are ones that best balance all of the different considerations represented by each of the included decision variables. In such cases, the decision variables may be weighted by user defined coefficients in accordance with user preferences so that the optimized solutions may be made to reflect current or evolving attitudes of the user. This type of functionality also may be used by users to produce “what if” scenarios that, as discussed below, may be used to advise the planning process. ¶0015 Market, operating, and ambient data each may include historical records, present condition data, and/or data relating to forecasts. For example, data resources 26 may include present and forecast meteorological/climate information, present and forecast market conditions, usage and performance history records about the operation of the power plant, maintenance data, and/or measured parameters regarding the operation of other power plants having similar components and/or configurations, as well as other data as may be appropriate and/or desired.).
Namboothiri teaches:
Claim 3. The computer-implemented method of claim 2, further comprising: filtering, by the one or more processors executing the demand model, the set of demand data by:(i) adding a shrinkage value to the set of demand data, (ii) applying a smoothing technique to peaks in the set of demand data exceeding a threshold value, (iii) redistributing demand volume based on a service level agreement (SLA), or (iv) loosening demand constraints at one or more intervals of the first period (¶0046 At a step 63, a determination may be made as to all of the applicable task constraints given the maintenance tasks that were determined to occur within the planning cycle. This may include importing all of the data related to the relevant task constraints as maintained with the database, as provided above in relation to FIG. 2. Concurrently, at a step 64, a determination may be made as to all of the applicable field engineer constraints given the field engineers that were determined available within the planning cycle. This may include importing all of the field engineer constraints collected and stored within the database, as provided club in relation to FIG. 2. ¶0030 the optimizer 48 of the present invention may optimize the objective function according to conventional systems and methods. As will be appreciated, one common method for optimizing an objective function, for example, is known as “gradient descent optimization.” Gradient descent is an optimization algorithm that approaches a local minimum of the objective function by taking steps proportional to the negative of the gradient (or the approximate gradient) of the function at the current point. It should be understood that a number of different optimization techniques may be used depending on the form of the model and the decision variables and constraints).
Namboothiri teaches:
Claim 4. The computer-implemented method of claim 1, further comprising: receiving, at the one or more processors, a set of period constraints corresponding to the first period; and generating, by the one or more processors executing a scheduling model, one or more schedule templates based on the set of period constraints, wherein the set of schedules data comprises the one or more schedule templates (¶0039 Another possible reserve enabled by the present invention relates to the skill level of the field engineer being assigned to the maintenance tasks. In this case, for example, the reserve capacity is one defined within the required minimum skill, experience or performance levels for qualifying field engineers with regard to work for certain customers or assignment types. The present invention further may enable defining a maximum work schedule by a field engineer, which, for example, may be expressed as a maximum work hours within a defined period, such as a week or month. Reserve field engineering resources may be maintained by skill and/or country, with visa and travel restrictions processed in advance so that field engineers are ready for travel should unforeseen contingencies require it.).
Namboothiri teaches:
Claim 5. The computer-implemented method of claim 4, wherein the set of period constraints comprises: (i) an allowable working days value, (ii) a shifts per week value, (iii) a permissible start times value, (iv) a start time allowed variance value, (v) a maximum unique start time value, (vi) a shift length value, (vii) a maximum unique shift length value, (viii) a non- permissible working hours value, (ix) a minimum weekly hours value, (x) a maximum weekly hours value, (xi) a maximum over time value, (xii) a maximum continuous days off value, or (xiii) a break time per shift value (¶0039 Another possible reserve enabled by the present invention relates to the skill level of the field engineer being assigned to the maintenance tasks. In this case, for example, the reserve capacity is one defined within the required minimum skill, experience or performance levels for qualifying field engineers with regard to work for certain customers or assignment types. The present invention further may enable defining a maximum work schedule by a field engineer, which, for example, may be expressed as a maximum work hours within a defined period, such as a week or month. Reserve field engineering resources may be maintained by skill and/or country, with visa and travel restrictions processed in advance so that field engineers are ready for travel should unforeseen contingencies require it.).
Namboothiri teaches:
Claim 6. The computer-implemented method of claim 1, wherein generating the optimal schedule further comprises: generating, by the one or more processors, a head count vector based on the set of demand data; generating, by the one or more processors, a schedules matrix based on the set of schedules data; and generating, by the one or more processors, the optimal schedule by multiplying the schedules matrix with the head count vector (¶0040 the present invention may be employed to determine, for example, the minimum number of field engineers (or “headcount”) required to perform all of the forecasted maintenance tasks within the strategic planning cycle. The term “forecasted maintenance tasks”, as used herein, represents the total number and type of maintenance tasks forecasted or otherwise predicted to take place during the strategic planning cycle within the power plants of the territory. The forecasted maintenance tasks may include both forecasted planned outages and forecasted unplanned outages. Such headcount analysis may assist the field services company plan hiring and/or moving field engineering resources according to skill and/or country.).
Although not explicitly taught by Namboothiri, Albert teaches in the analogous art of system for profiling and scheduling of thermal residential energy use for demand-side management programs:
generating, by the one or more processors, the optimal schedule by multiplying the schedules matrix with the head count vector (¶0081 For a given customer, the quantity θ≡(a.sub.k,b.sub.k,σ.sub.k.sup.2,c.sub.k,d.sub.k|k=1, . . . , K) is estimated by defining the likelihood as a non-linear function of θ and applying a numeric optimization algorithm. In contrast to the EM algorithm, which only results in point estimates of the parameters, this procedure allows to compute confidence intervals. The analytic engine computes the most likely sequence of states {s.sub.t} that fits a given observation sequence {x.sub.t} (the decoding problem) using the standard Viterbi algorithm. This results in the recovery of the sequence of hidden decisions that gave rise to the observed consumption {x.sub.t}. ¶0125 The utility operator issues requests for effort schedules u.sub.i(t) to control the HVAC usage for certain customers i=1, . . . , N, with N the total number of customers. The quantity u.sub.i(t) is the requested number of degrees (Fahrenheit) that the thermostat setpoint be modified at time t by customer i (see below). Note that a zero schedule u.sub.i=(0, . . . , 0) is equivalent to not requesting participation from customer I As a result of the request u.sub.i(t) the utility receives the energy reductions δ.sub.i=A.sub.iu.sub.i from user i at time t, where A.sub.i=diag(a.sub.i), and a.sub.i is the thermal response of customer i. ¶0129 the cost of purchasing additional generation is in general quadratic in the amount desired, as the marginal costs of generation increase approximately linearly with the generation needed. As such, the utility's cost may be expressed via a quadratic form. where Q=diag(q) and the matrix H is given by[00004]Hi,j={A_i2.Math.Q+QWiif.Math..Math.i=jA_iT.Math.Q.Math.A_jif.Math..Math.i≠j).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Namboothiri ¶0005 teaches that there is an ever-growing demand for tools for automatically scheduling, optimizing, and/or improving the allocation process of mobile assets or field engineering resources in the power generating industry;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Namboothiri Abstract teaches a system for allocating field engineers to perform maintenance tasks according to a generated allocation schedule, and Albert Abstract teaches a methodology is provided for informing targeted Demand-Response (DR) and marketing programs that focus on the temperature-sensitive part of residential electricity demand; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Namboothiri at least the above cited paragraphs, and Albert at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Namboothiri teaches:
Claim 7. The computer-implemented method of claim 1, wherein the optimal schedule comprises a plurality of optimal schedules, and the computer-implemented method further comprises: generating, by the one or more processors executing the optimization model, a set of personnel assignments for each schedule of the plurality of optimal schedules (¶0038 cost data may be used by the optimizer 48 so that economic optimization may be achieved in instances where borrowing a field engineer from outside the territory is a consideration. Another such additional constraint includes travel rules and regulations. As the laws and regulations affecting international travel change with some frequency, the present invention anticipates updating the database 41 frequently so to reflect the most current requirements, thus ensuring the relevance of the generated optimized version of the allocation schedules. ¶0039 Another possible reserve enabled by the present invention relates to the skill level of the field engineer being assigned to the maintenance tasks. In this case, for example, the reserve capacity is one defined within the required minimum skill, experience or performance levels for qualifying field engineers with regard to work for certain customers or assignment types.).
Namboothiri teaches:
Claim 8. The computer-implemented method of claim 1, further comprising: generating, by the one or more processors executing the optimization model, a cost value corresponding to the optimal schedule (¶0029 The optimizer 48 may be used to optimize a defined objective function. As used herein, the objective function includes multiple variables or, as used herein, “decision variables”, and is subject to a set of defined constraints. As will be appreciated, the decision variables of the objective function may represent “cost variables”, such as labor or travel costs, which the optimizer 48 will generally seek to minimize, or may represent “benefit variables”, such as revenue or profit, which the optimizer will generally seek to maximize. Thus, as used herein, the objective function is a mathematical representation of how, for example, a generated allocation schedule performs relative to the defined decision variables.).
As per claims 9-15,22 and 16-20,23 and , the system and manufacture tracks the method of claims 1-7,21 and 1,2,3,4&5,6,21, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7,21 and 1,2,3,4&5,6,21 are applied to claims 9-15,22 and 16-20,23, respectively. Namboothiri discloses that the embodiment may be found as a system and manufacture (Fig. 4 and ¶0055).
Namboothiri teaches:
Claim 21. The computer-implemented method of claim 1, wherein the optimization model comprises at least a first individual model corresponding to a first individual associated with the service provider, and the computer-implemented method further comprises: receiving, at the one or more processors, an outcome corresponding to the optimal schedule, wherein the outcome indicates whether the first individual complied with the optimal schedule; and updating, by the one or more processors, the first individual model based on the outcome (¶0020 In order to meet the maintenance needs of the many customer power plants it serves, the field services company typically employs many types of field engineers so that a wide and complete range of services may be offered. The field services company may employ many field engineers having similar skills so that a redundancy of skills accords with potential customer demand. As mentioned, such service companies may include a resource manager who is responsible for managing, mobilizing, scheduling and assigning the field engineers to perform the maintenance tasks that cover planned and unplanned outages of customer power plants located within his defined territory ¶0028-0029 calculating an optimized field engineer allocation schedule 49 and communicating it to the resource manager via the user device 45. The user device 45 also may be used by the resource manager to define or choose the objectives according to which the optimization is completed within the optimizer 48 of the processing unit 44. The optimizer 48 may be used to optimize a defined objective function. As used herein, the objective function includes multiple variables or, as used herein, “decision variables”, and is subject to a set of defined constraints. As will be appreciated, the decision variables of the objective function may represent “cost variables”, such as labor or travel costs, which the optimizer 48 will generally seek to minimize, or may represent “benefit variables”, such as revenue or profit, which the optimizer will generally seek to maximize. Thus, as used herein, the objective function is a mathematical representation of how, for example, a generated allocation schedule performs relative to the defined decision variables ¶0030 These optimization approaches include, but not limited to, linear programming, quadratic programming, mixed integer non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. According to the present system, the objective function and constraints may be updated continuously so that changes may be handled and incorporated in real time into new allocation schedules that are generated upon a triggering event or defined period. The optimizer 48 then maybe used to recompute the allocation schedule over the strategic planning cycle or operational planning cycle, whatever the case may be. The optimizer 48 may repeat this process for each optimization cycle, thereby, constantly maintaining optimal allocation scheduling as unforeseen outages and other events occur).
Although not explicitly taught by Namboothiri, Albert teaches in the analogous art of system for profiling and scheduling of thermal residential energy use for demand-side management programs:
wherein the outcome indicates whether the first individual complied with the optimal schedule; and updating, by the one or more processors, the first individual model based on the outcome (¶0133 In the simplest case it is assumed that the customer will comply fully with the requests for control. This is appropriate for the case of automated controls done via a smart thermostat that has a two-way communications channel. If this is not the case, but compliance depends, e.g., on occupancy (whether the customer is at home or not) or other unobservable factors, the effort schedule and the constraints in the optimization problem above become probabilistic ¶0110 The analytic engine computes the benchmarks introduced above for the two customers, and present the temperature-dependent stationary probability distribution over thermal occupancy regimes in FIGS. 7A-C. For Alice (FIG. 7A) the probability distribution P(a(T)) captures the prevalence of the heating state 3 for low temperatures, the low cooling regime 1 for intermediate temperatures, and low cooling regime for intermediate and high temperatures higher than 75° F. Similarly, for Bob (FIG. 7B) the model identifies the temporally-consistent cooling state 2 as dominant for high temperatures (above 95° F.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Namboothiri ¶0005 teaches that there is an ever-growing demand for tools for automatically scheduling, optimizing, and/or improving the allocation process of mobile assets or field engineering resources in the power generating industry;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Namboothiri Abstract teaches a system for allocating field engineers to perform maintenance tasks according to a generated allocation schedule, and Albert Abstract teaches a methodology is provided for informing targeted Demand-Response (DR) and marketing programs that focus on the temperature-sensitive part of residential electricity demand; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Namboothiri at least the above cited paragraphs, and Albert at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system for profiling and scheduling of thermal residential energy use for demand-side management programs of Albert with the system for allocating field engineering resources for power plant maintenance of Namboothiri. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
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 extension fee 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 date of this final action.
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/KURTIS GILLS/Primary Examiner, Art Unit 3624