DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following NON-FINAL Office Action is in response to Applicant’s submission filed 07/07/2022 regarding Application 17/791,525. The following is the first action on the merits.
Priority Acknowledgment
Examiner acknowledges Applicant’s claim to Foreign Application KR10-2020-0013835 and PCT/KR2021/000965, with filing dates 02/05/2022, and 01/25/2021 respectively.
Status of Claim(s)
Claim(s) 1-10 is/are currently pending and are rejected as follows.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 9, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
For the sake of compact prosecution, the claim will be interpreted with the phrase “For example” being omitted and will be further analyzed below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is/are directed towards a judicial exception (i.e. law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1-10 are directed towards an invention for a plurality of resources for providing an information technology-related service and creating and transmitting status information for each resource every time the services are drive, receiving the status information from the plurality of resources and processing the status information in to analysis data, calculating a capacity and usage of each of investment resources required for driving the service based on the analysis data and create and store operation information including capacity and usage of the investment resources, creating an optimization algorithm by teaching interrelation between capacity and usage of the investment resources to present big data analysis or neural network model on the basis of the accumulated and stored operation information and to create optimization information in which at least one of capacity and usage of the investment resources, when driving a service by applying at least some operation information for a present service operation status using the created optimization algorithm, and perform economic feasibility through a preset economic feasibility analysis algorithm by comparing the operation information created through the first calculation with the optimization information created in accordance with contract information for a predetermined contract of each of the investment resources and to created one or more different items of prediction information for a cost reduction specification according to a service driving environment where at least one of capacity and usage has been changed in the operation information thereby providing decision-making support for the service optimization on the basis of the prediction information. These actions fall under a subject matter grouping which the courts have considered ineligible (Organizing Human Activity and Math)These claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under Step 1 of the Alice/Mayo framework it must be considered whether the claims are directed to one or more statutory categories of invention. Claim(s) 1-9 are directed towards an apparatus. Claim(s) 10 are directed towards a method comprising at least one step. Accordingly, the claims fall within the four statutory categories of invention, (apparatus and method) and will be further analyzed under Step 2 of the Alice/Mayo framework.
Under Step 2, Prong One, of the Alice/Mayo framework it must be considered whether the claims recite any abstract ideas.
Independent claims 1 and 10 recite an invention for a plurality of resources for providing an information technology-related service and creating and transmitting status information for each resource every time the services are drive, receiving the status information from the plurality of resources and processing the status information in to analysis data, calculating a capacity and usage of each of investment resources required for driving the service based on the analysis data and create and store operation information including capacity and usage of the investment resources, creating an optimization algorithm by teaching interrelation between capacity and usage of the investment resources to present big data analysis or neural network model on the basis of the accumulated and stored operation information and to create optimization information in which at least one of capacity and usage of the investment resources, when driving a service by applying at least some operation information for a present service operation status using the created optimization algorithm, and perform economic feasibility through a preset economic feasibility analysis algorithm by comparing the operation information created through the first calculation with the optimization information created in accordance with contract information for a predetermined contract of each of the investment resources and to created one or more different items of prediction information for a cost reduction specification according to a service driving environment where at least one of capacity and usage has been changed in the operation information thereby providing decision-making support for the service optimization on the basis of the prediction information which recite the ideas of Organizing Human Activity and Math in the following limitations:
creating and transmitting status information for each resource, which is used every time the services are driven by…a plurality of resources for providing an information technology-related service;
receiving the status information…and processing the status information into analysis data…;
calculating capacity and usage of each of investment resources required for driving the service on the basis of the analysis data received from the data management unit, and creating and storing operation information including the capacity and usage of each of investment resources…;
creating an optimization algorithm by teaching the interrelation between the capacity and usage of each investment resource to preset big data analysis or neural network model on the basis of the accumulated and stored operation information, and creating optimization information, in which at least one of capacity and usage of each investment resource, when driving a service by applying at least some of operation information for a present service operation status using the created optimization algorithm…;
and performing economic feasibility through a preset economic feasibility analysis algorithm by comparing the operation information created through the first…calculation…with the optimization information created…in accordance with contract information for a predetermined contract of each of investment resources, and creating, in accordance with variation, one or more different items of prediction information for a cost reduction specification according to a service driving environment, in which at least one of capacity and usage has been changed in the operation information for investment resources selected in accordance with the economic feasibility analysis, and variation of at least one of capacity and usage of the selected investment resources, by means of a second…calculation…, thereby providing decision-making support for the service optimization on the basis of the prediction information.
Dependent claim(s) 2-9 merely further limit the abstract idea and are thus subject to the same rationale as expressed above.
Under Step 2A, Prong Two, any additional elements are recited:
Independent claims 1 and 10 recite:
a terminal unit
a data management unit
a first integration calculation unit
an intellectual unit
a second integration calculation unit
a neural network model
Dependent claim 3 recites:
a display unit
These additional elements, considered both individually and as an ordered pair do no more than represent mere instructions to implement the abstract idea ("apply it" compute (See MPEP 2106.05(f)). Additionally, the claims represent insignificant extra solution activity (See MPEP 2106.05(g)). These elements are recited with a high degree of generality, and the specification sets forth the general purpose nature of the technologies required to implement the invention (emphasis added).
Support for this determination can be found on Page 63 line 9 – Page 64 line 1 of Applicant’s specification.
Under Step 2B eligibility analysis evaluates whether the claims as a whole amounts to significantly more than the recited exception, i.e. whether any additional element, or combination of elements, adds an inventive concept to the claims (MPEP 2106.05). As explained with respect to Step 2A, Prong Two, there are several additional elements. The terminal unit, a data management unit, a first integration calculation unit, an intellectual unit, a second integration calculation unit, display unit, a neural network model are all, at best, the equivalent of merely adding the words "apply it" to the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (See MPEP 2106.05(f). Further, the termina unit and data management unit represent insignificant extra solution activity (See MPEP 2106.05(g)), specifically that of mere data gathering which is known to be well-understood, routine, or conventional within the art (See MPEP 2106.05(d)(II)). Insignificant extra solution activity, especially that which is well-understood, routine, or conventional in the art does not provide an inventive concept. Even when considered in combination, these additional elements to are not deemed to be sufficient enough to provide an inventive concept onto the abstract idea, therefore, they are not eligible. (Alice Corp., 134 S. Ct. at 2358 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984 (warning against a §101 that turns on "the draftsman's art")).
Dependent claim(s) 2, and 4-9 do not recite any further additional elements and are thus rejected for the same reasons enumerated above.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 7-8, and 10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun (US 2016/0171410 A1).
Claim(s) 1 and 10 –
Sun discloses the following limitations:
creating and transmitting status information for each resource, which is used every time the services are driven by means of a terminal unit composed of a plurality of resources for providing an information technology-related service; (Sun: Paragraph 21, “Forecast of the capacity demands may be accomplished using predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events (such as future capacity demands).”; Paragraph 27, “The purchased equipment will not last forever. At certain point of time, some of the equipment will be broken. This is called the depreciation. Typically, the lifetime of equipment is positively related to the MTBF (Mean Time between Failures), which is typically provided by the manufacturer. Once this parameter is known, an estimation of available inventory can be calculated after a certain period of time according to current inventory. Herein, this is denoted by function I.sub.t.sup.m=I.sup.m(I.sub.0.sup.m,t), where m denotes the model of the equipment. If there are multiple models of equipment, then I.sub.t={I.sub.t.sup.m} is used to denote the array of inventory of each type of equipment. Once the inventory of each model of equipment is known, a future capacity prediction is calculated. That is, the future total capacity provided by all the equipment basing on current capacity. Herein, capacity (C, t) indicates this future capacity prediction.”; Paragraph 46, “With the discussion of process 200, presume that there is a time window (t.sub.0, . . . , t.sub.n) in which costs are minimized to cover input capacity demands (D.sub.t.sub.0, . . . , D.sub.t.sub.n), like that provided by the future capacity demands table 212. There is a known or calculated initial capacity C.sub.t.sub.0. The overall optimization solves this problem C.sub.t.sub.i+R.sub.t.sub.i≧D.sub.t.sub.i for ∀t.sub.iε[t.sub.0, t.sub.n]. R.sub.t.sub.i is the gap between C.sub.t.sub.i=C(I.sub.t.sub.i) and D.sub.t.sub.i (where I.sub.t.sub.i is the inventory of purchased equipment at particular time point).”; Paragraph 51, “For each capacity level C if C>=D.sub.t.sub.n, DP[t.sub.n][I]=0. Because the inventory equipment can cover the capacity demands, there is no further cost. If C<D.sub.t.sub.n, then DP[t.sub.n][C]=rental_cost(D.sub.t.sub.n−C). This rental_cost( ) is just an approximation. The average rental price can be used to calculate this approximate cost. These results are stored in the approximate-rental-cost table 232.”; Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”)
receiving the status information from the terminal unit and processing the status information into analysis data by means of a data management unit; (Sun: Paragraph 21, “Forecast of the capacity demands may be accomplished using predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events (such as future capacity demands).”; Paragraph 27, “The purchased equipment will not last forever. At certain point of time, some of the equipment will be broken. This is called the depreciation. Typically, the lifetime of equipment is positively related to the MTBF (Mean Time between Failures), which is typically provided by the manufacturer. Once this parameter is known, an estimation of available inventory can be calculated after a certain period of time according to current inventory. Herein, this is denoted by function I.sub.t.sup.m=I.sup.m(I.sub.0.sup.m,t), where m denotes the model of the equipment. If there are multiple models of equipment, then I.sub.t={I.sub.t.sup.m} is used to denote the array of inventory of each type of equipment. Once the inventory of each model of equipment is known, a future capacity prediction is calculated. That is, the future total capacity provided by all the equipment basing on current capacity. Herein, capacity (C, t) indicates this future capacity prediction.”; Paragraph 46, “With the discussion of process 200, presume that there is a time window (t.sub.0, . . . , t.sub.n) in which costs are minimized to cover input capacity demands (D.sub.t.sub.0, . . . , D.sub.t.sub.n), like that provided by the future capacity demands table 212. There is a known or calculated initial capacity C.sub.t.sub.0. The overall optimization solves this problem C.sub.t.sub.i+R.sub.t.sub.i≧D.sub.t.sub.i for ∀t.sub.iε[t.sub.0, t.sub.n]. R.sub.t.sub.i is the gap between C.sub.t.sub.i=C(I.sub.t.sub.i) and D.sub.t.sub.i (where I.sub.t.sub.i is the inventory of purchased equipment at particular time point).”; Paragraph 51, “For each capacity level C if C>=D.sub.t.sub.n, DP[t.sub.n][I]=0. Because the inventory equipment can cover the capacity demands, there is no further cost. If C<D.sub.t.sub.n, then DP[t.sub.n][C]=rental_cost(D.sub.t.sub.n−C). This rental_cost( ) is just an approximation. The average rental price can be used to calculate this approximate cost. These results are stored in the approximate-rental-cost table 232.”; Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”)
calculating capacity and usage of each of investment resources required for driving the service on the basis of the analysis data received from the data management unit, and creating and storing operation information including the capacity and usage of each of investment resources by means of a first integration calculation unit; (Sun: Paragraph 21, “Forecast of the capacity demands may be accomplished using predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events (such as future capacity demands).”; Paragraph 27, “The purchased equipment will not last forever. At certain point of time, some of the equipment will be broken. This is called the depreciation. Typically, the lifetime of equipment is positively related to the MTBF (Mean Time between Failures), which is typically provided by the manufacturer. Once this parameter is known, an estimation of available inventory can be calculated after a certain period of time according to current inventory. Herein, this is denoted by function I.sub.t.sup.m=I.sup.m(I.sub.0.sup.m,t), where m denotes the model of the equipment. If there are multiple models of equipment, then I.sub.t={I.sub.t.sup.m} is used to denote the array of inventory of each type of equipment. Once the inventory of each model of equipment is known, a future capacity prediction is calculated. That is, the future total capacity provided by all the equipment basing on current capacity. Herein, capacity (C, t) indicates this future capacity prediction.”; Paragraph 46, “With the discussion of process 200, presume that there is a time window (t.sub.0, . . . , t.sub.n) in which costs are minimized to cover input capacity demands (D.sub.t.sub.0, . . . , D.sub.t.sub.n), like that provided by the future capacity demands table 212. There is a known or calculated initial capacity C.sub.t.sub.0. The overall optimization solves this problem C.sub.t.sub.i+R.sub.t.sub.i≧D.sub.t.sub.i for ∀t.sub.iε[t.sub.0, t.sub.n]. R.sub.t.sub.i is the gap between C.sub.t.sub.i=C(I.sub.t.sub.i) and D.sub.t.sub.i (where I.sub.t.sub.i is the inventory of purchased equipment at particular time point).”; Paragraph 51, “For each capacity level C if C>=D.sub.t.sub.n, DP[t.sub.n][I]=0. Because the inventory equipment can cover the capacity demands, there is no further cost. If C<D.sub.t.sub.n, then DP[t.sub.n][C]=rental_cost(D.sub.t.sub.n−C). This rental_cost( ) is just an approximation. The average rental price can be used to calculate this approximate cost. These results are stored in the approximate-rental-cost table 232.”; Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”; Paragraph 58, “With the 2D DP optimization action 252, the sub-problem is calculated backwards. The base case of sub-problem is DP [t.sub.n][C], which is the minimal cost of the last time point with different initial inventories. The initialization process will calculate DP[t.sub.n][C] for all possible initial inventories, where C ranges from min(D.sub.t.sub.i) to max(D.sub.t.sub.1) for all i in [0, n]. In real world there is a minimal number of equipment in a purchase order, thus a step length for C can be defined.”; Paragraph 73, “One or more implementations of the technology discussed herein may also be described as a process (or system performing that process or a computer-readable media with instructions) that does at least the following actions: forecasting capacity demands over a defined time period, wherein the capacity demands are for services provided by equipment, the equipment having a defined service capacity, wherein the forecasting of the capacity demands utilizes predictive analytics to determine future capacity demands over the time period; forecasting pricing data over the defined time period, the forecasted pricing data includes future equipment price dataset and future rental price dataset, the future equipment price dataset include estimated future prices for purchase of additional equipment over the defined time period and the future rental prices dataset include estimated future prices for rental orders of additional equipment over the defined time period; generating an optimized equipment purchase plan that includes indicators to purchase equipment having sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period, wherein the generating of the optimized equipment purchase plan includes performing a two-dimensional dynamic programming function based, at least in part, upon the forecasted capacity demands and the forecasted pricing data; determining the remainder capacity demands that is not fulfilled by the equipment purchase in accordance with the optimized equipment purchase plan; generating an optimized rental order plan that includes indicators to order equipment rentals having sufficient capacity to at least fulfill the remainder capacity demands over the defined time period, wherein the generating of the optimized rental order plan includes performing a one-dimensional dynamic programming function based, at least in part, upon the remainder capacity demands and the forecasted pricing data; producing an equipment-acquisition plan by combining the optimized equipment purchase plan and the optimized rental order plan; reporting the produced equipment-acquisition plan.”)
creating an optimization algorithm by teaching the interrelation between the capacity and usage of each investment resource to preset big data analysis or neural network model on the basis of the accumulated and stored operation information, and creating optimization information, in which at least one of capacity and usage of each investment resource, when driving a service by applying at least some of operation information for a present service operation status using the created optimization algorithm by means of an intellectual unit; (Sun: Paragraph 21, “Forecast of the capacity demands may be accomplished using predictive analytics. Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events (such as future capacity demands).”; Paragraph 27, “The purchased equipment will not last forever. At certain point of time, some of the equipment will be broken. This is called the depreciation. Typically, the lifetime of equipment is positively related to the MTBF (Mean Time between Failures), which is typically provided by the manufacturer. Once this parameter is known, an estimation of available inventory can be calculated after a certain period of time according to current inventory. Herein, this is denoted by function I.sub.t.sup.m=I.sup.m(I.sub.0.sup.m,t), where m denotes the model of the equipment. If there are multiple models of equipment, then I.sub.t={I.sub.t.sup.m} is used to denote the array of inventory of each type of equipment. Once the inventory of each model of equipment is known, a future capacity prediction is calculated. That is, the future total capacity provided by all the equipment basing on current capacity. Herein, capacity (C, t) indicates this future capacity prediction.”; Paragraph 46, “With the discussion of process 200, presume that there is a time window (t.sub.0, . . . , t.sub.n) in which costs are minimized to cover input capacity demands (D.sub.t.sub.0, . . . , D.sub.t.sub.n), like that provided by the future capacity demands table 212. There is a known or calculated initial capacity C.sub.t.sub.0. The overall optimization solves this problem C.sub.t.sub.i+R.sub.t.sub.i≧D.sub.t.sub.i for ∀t.sub.iε[t.sub.0, t.sub.n]. R.sub.t.sub.i is the gap between C.sub.t.sub.i=C(I.sub.t.sub.i) and D.sub.t.sub.i (where I.sub.t.sub.i is the inventory of purchased equipment at particular time point).”; Paragraph 51, “For each capacity level C if C>=D.sub.t.sub.n, DP[t.sub.n][I]=0. Because the inventory equipment can cover the capacity demands, there is no further cost. If C<D.sub.t.sub.n, then DP[t.sub.n][C]=rental_cost(D.sub.t.sub.n−C). This rental_cost( ) is just an approximation. The average rental price can be used to calculate this approximate cost. These results are stored in the approximate-rental-cost table 232.”; Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”; Paragraph 58, “With the 2D DP optimization action 252, the sub-problem is calculated backwards. The base case of sub-problem is DP [t.sub.n][C], which is the minimal cost of the last time point with different initial inventories. The initialization process will calculate DP[t.sub.n][C] for all possible initial inventories, where C ranges from min(D.sub.t.sub.i) to max(D.sub.t.sub.1) for all i in [0, n]. In real world there is a minimal number of equipment in a purchase order, thus a step length for C can be defined.”; Paragraph 73, “One or more implementations of the technology discussed herein may also be described as a process (or system performing that process or a computer-readable media with instructions) that does at least the following actions: forecasting capacity demands over a defined time period, wherein the capacity demands are for services provided by equipment, the equipment having a defined service capacity, wherein the forecasting of the capacity demands utilizes predictive analytics to determine future capacity demands over the time period; forecasting pricing data over the defined time period, the forecasted pricing data includes future equipment price dataset and future rental price dataset, the future equipment price dataset include estimated future prices for purchase of additional equipment over the defined time period and the future rental prices dataset include estimated future prices for rental orders of additional equipment over the defined time period; generating an optimized equipment purchase plan that includes indicators to purchase equipment having sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period, wherein the generating of the optimized equipment purchase plan includes performing a two-dimensional dynamic programming function based, at least in part, upon the forecasted capacity demands and the forecasted pricing data; determining the remainder capacity demands that is not fulfilled by the equipment purchase in accordance with the optimized equipment purchase plan; generating an optimized rental order plan that includes indicators to order equipment rentals having sufficient capacity to at least fulfill the remainder capacity demands over the defined time period, wherein the generating of the optimized rental order plan includes performing a one-dimensional dynamic programming function based, at least in part, upon the remainder capacity demands and the forecasted pricing data; producing an equipment-acquisition plan by combining the optimized equipment purchase plan and the optimized rental order plan; reporting the produced equipment-acquisition plan.”; Paragraph 64, “While calculating the DP table, the inner minimizing function will choose a capacity level at t.sub.j for a given capacity level at t.sub.i-1 In this way, multiple lists of capacities can be built for each capacity level at t.sub.i-1. The list starting with C.sub.0 at t.sub.0 is chosen as the source of capacity curve mentioned above as part of the equipment inventory curve. The equipment purchase plan 258 is derived directly from the equipment inventory curve.”)
and performing economic feasibility through a preset economic feasibility analysis algorithm by comparing the operation information created through the first integration calculation unit with the optimization information created through the intellectual unit in accordance with contract information for a predetermined contract of each of investment resources, and creating, in accordance with variation, one or more different items of prediction information for a cost reduction specification according to a service driving environment, in which at least one of capacity and usage has been changed in the operation information for investment resources selected in accordance with the economic feasibility analysis, and variation of at least one of capacity and usage of the selected investment resources, by means of a second integration calculation unit, thereby providing decision-making support for the service optimization on the basis of the prediction information. (Sun: Paragraph 17, “With forecasted capacity demands (given or calculated), the technology described herein generates a business-equipment acquisition plan by first optimizing an equipment purchase plan and then optimizing an equipment rental plan, where the rental plan covers the capacity demands not satisfied by the purchase plan. The rental plan leverages discounts offered by rental service providers to provide capacity flexibility as needed.”; Paragraph 45, “The generation of the optimized equipment purchase plan and rental plans (of actions 104 and 105) are part of the infrastructure management block 240. The action 104 of generating the optimized equipment purchase plan is shown in FIG. 2 as purchase plan optimization 250, two-dimensional (2D) dynamic programming (DP) optimization 252, time dataset 254, capacity level dataset 256, capacity demands fulfilled by rent dataset 242, and equipment purchase plan 258. The action 106 of generating the optimized equipment rental plan is shown in FIG. 2 as rental order optimization 260, one-dimensional (1D) dynamic programming (DP) optimization 262, merge-orders dataset 264, and rental order plan 266. The combination of the equipment purchase plan 258 and the rental order plan 266 forms the equipment-acquisition plan produced by action 108 and reported by action 110.”; Paragraph 53, “The purchase plan optimization 250 includes the two-dimensional (2D) dynamic programming (DP) optimization action 252 that takes the future capacity demands table 212, the future equipment price table 222, and the approximate rental cost table 232 as inputs. Based on capacity levels 256 at defined time points 254, the 2D DP optimization action 252 generates the equipment purchase plan 258, which represents the optimal plan for purchasing sufficient equipment so that the total inventory of purchased equipment may meet the forecasted capacity demands. The 2D DP Optimization action 252 also generates the capacity-demands-fulfilled-by-rent-dataset 242, which represents the difference between the forecasted capacity demands and the capacity provided by the total inventory of purchased equipment (in accordance with the equipment purchase plan).”; Paragraph 65, “The rental order optimization 260 includes the one-dimensional D) dynamic programming (DP) optimization action 262 that takes the capacity-demands-fulfilled-by-rent dataset 242 and the future rental price table 234 as inputs. Because optimized rental order plan 266 is created to fill in the gaps not covered by the inventoried (purchased and already owned) equipment, the rental order optimization 260 is performed after the purchase plan optimization.”; Paragraph 71, “One or more implementations of the technology discussed herein may also be described as a process for system performing that process) that does at least the following actions: forecasting capacity demands over a defined time period, wherein the capacity demands are for services provided by equipment, the equipment having a defined service capacity; forecasting pricing data over the defined time period, the forecasted pricing data includes future equipment price dataset and future rental price dataset, the future equipment price dataset include estimated future prices for purchase of additional equipment over the defined time period and the future rental prices dataset include estimated future prices for rental orders of additional equipment over the defined time period; generating an optimized equipment purchase plan that includes indicators to purchase equipment having sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; determining the remainder capacity demands that is not fulfilled by the equipment purchase in accordance with the optimized equipment purchase plan; generating an optimized rental order plan that includes indicators to order equipment rentals having sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; producing an equipment-acquisition plan by combining the optimized equipment purchase plan and the optimized rental order plan; reporting the produced equipment-acquisition plan.”)
Claim(s) 2 –
Sun discloses the limitations of claim 1
Sun further discloses the following:
wherein the plurality of resources includes at least one of a sensor, a physical security device, an application, software, hardware, a line, a building, electricity, a machine, a server, a rack, a utility including electricity, and manpower, and (Sun: Paragraph 2, “Nearly all businesses require some form of business-related equipment to perform business functions. For example, a retail business may need point-of-sale registers, marketing displays, shelving and racks, cleaning equipment, and the like. An information technology business may need racks of computers (with storage), software, diagnostic equipment, and the like. A lawn-care business needs lawnmowers, edgers, string trimmers, leaf blowers, and the like. A manufacturing business needs various and several pieces of machinery for manufacturing, packaging, transportation, and delivery of raw materials and manufactured goods.”)
the operation information includes at least one of the kinds of resources, intensity of light according to an environment, a frame speed, image precision, magnitude of noise, storage capacity, capacity for processing users, the number of users, search performance, performance between a storage device and a cooperation device, a CPU speed, a packet process speed, a communication speed, a round trip time (RTT), a disc speed, cooling performance, a power consumption amount, the number of invested people, and work time. (Sun: Paragraph 15, “In such a scenario, a business requires some minimum amount of equipment to satisfy its business needs. In the real world, there so many more conditions and/or constraints that should be considered than those that factored. into the basic ski-rental problem. Firstly, rather than facing choices between rent and buy, a typically business must consider the customer capacity demands for its products and/or services and how such capacity demands can be fulfilled by purchased or rented business equipment. Secondly, the customer capacity demands (and thus, the business equipment needs) vary over time. Thirdly, the equipment will depreciate over time. Fourthly, the price of equipment will change over time. Lastly, there are also many detailed conditions to consider in order to achieve an optimal solution.”; Paragraph 35, “The technology generates an equipment inventory curve. That is, a series of capacity determinations over time. This may be accomplished by plotting at, for example, regular intervals the quantitative capacity at a sequence of points in time.”)
Claim(s) 3 –
Sun discloses the limitations of claim 1
Sun further discloses the following:
further comprising a user interface unit configured to display the prediction information through a display unit, to receive user input and inform a user of prediction information satisfying a preset condition in accordance with the user input, and to change setting of specific resources that are used for the service in accordance with user input or a preset operation condition. (Sun: Paragraph 42, “At 108, the computing device produces an equipment-acquisition plan that is optimized for both equipment purchases and rentals to meet the anticipated capacity demands over time.”; Paragraph 43, “At 110, the computing device reports the optimized equipment-acquisition plan. A report may be generated via an output device and/or user interface. The report may be generated via screen, printed material, and the like. In addition, an automated order may be generated to purchase and/or rent the appropriate equipment in accordance with the optimized plan.”; Paragraph 72, “Other implementations may have one or more of these features: the forecasting of the capacity demands utilizes predictive analytics to determine future capacity demands over the time period; the defined capacity of purchased and rented equipment varies over the time period; forecasting an approximate rental cost over the time period based upon an average price for renting equipment; the generating of the optimized equipment purchase plan includes performing a two-dimensional dynamic programming function based, at least in part, on the forecasted capacity demands and the forecasted pricing data; the generating of the optimized rental order plan includes performing a one-dimensional dynamic programming function based, at least in part, on the remainder capacity demands and the forecasted pricing data; the optimized equipment purchase plan lists when to purchase equipment with sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; the optimized rental order plan lists when to order rental equipment with sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; the optimized rental order plan specifies duration of rental orders; the reporting includes generating of a user-interface to communicate to a user the produced equipment-acquisition plan.”)
Claim(s) 5 –
Sun discloses the limitations of claim 1
Sun further discloses the following:
wherein the terminal unit creates and transmits status information of each resource that is used every time driving a service according to a specific function for each of a plurality of different functions set in advance in relation to an information communication service. (Sun: Paragraph 27, “The purchased equipment will not last forever. At certain point of time, some of the equipment will be broken. This is called the depreciation. Typically, the lifetime of equipment is positively related to the MTBF (Mean Time between Failures), which is typically provided by the manufacturer. Once this parameter is known, an estimation of available inventory can be calculated after a certain period of time according to current inventory. Herein, this is denoted by function I.sub.t.sup.m=I.sup.m(I.sub.0.sup.m,t), where m denotes the model of the equipment. If there are multiple models of equipment, then I.sub.t={I.sub.t.sup.m} is used to denote the array of inventory of each type of equipment. Once the inventory of each model of equipment is known, a future capacity prediction is calculated. That is, the future total capacity provided by all the equipment basing on current capacity. Herein, capacity (C, t) indicates this future capacity prediction.”; Paragraph 46, With the discussion of process 200, presume that there is a time window (t.sub.0, . . . , t.sub.n) in which costs are minimized to cover input capacity demands (D.sub.t.sub.0, . . . , D.sub.t.sub.n), like that provided by the future capacity demands table 212. There is a known or calculated initial capacity C.sub.t.sub.0. The overall optimization solves this problem C.sub.t.sub.i+R.sub.t.sub.i≧D.sub.t.sub.i for ∀t.sub.iε[t.sub.0, t.sub.n]. R.sub.t.sub.i is the gap between C.sub.t.sub.i=C(I.sub.t.sub.i) and D.sub.t.sub.i (where I.sub.t.sub.i is the inventory of purchased equipment at particular time point).”; Paragraph 58, “With the 2D DP optimization action 252, the sub-problem is calculated backwards. The base case of sub-problem is DP [t.sub.n][C], which is the minimal cost of the last time point with different initial inventories. The initialization process will calculate DP[t.sub.n][C] for all possible initial inventories, where C ranges from min(D.sub.t.sub.i) to max(D.sub.t.sub.1) for all i in [0, n]. In real world there is a minimal number of equipment in a purchase order, thus a step length for C can be defined.”; Paragraph 60, “To achieve this, a function cost(t.sub.i, t.sub.j, C) is defined to calculate the cost from t.sub.i to t.sub.j with initial inventory I providing capacity C at t.sub.i, and from t.sub.i to t.sub.j, there are no new equipment purchased. Then if C.sub.t<D.sub.t for any t ε[t.sub.i, . . . t.sub.j], the rest capacity demands will be fulfilled with rental orders. Depreciation is considered. Because of that, C.sub.t is changing even when no new equipment are purchased. The capacity function discussed above (with regard to the equipment inventory and capacity) may be used to calculate capacity provided by already-owned equipment for [t.sub.i, . . . , t.sub.i].”)
Claim(s) 7 –
Sun discloses the limitations of claim 1
Sun further discloses the following:
wherein when the terminal unit includes a plurality of terminals units and the plurality of terminal units are connected to each other, the data management unit has connection information set in advance about a data cooperation relationship between the plurality of terminal units and processes status information provided from each of the plurality of terminal units in accordance with the connection information into the analysis data in accordance with the data cooperation relationship. (Sun: Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”; Paragraph 71, “One or more implementations of the technology discussed herein may also be described as a process for system performing that process) that does at least the following actions: forecasting capacity demands over a defined time period, wherein the capacity demands are for services provided by equipment, the equipment having a defined service capacity; forecasting pricing data over the defined time period, the forecasted pricing data includes future equipment price dataset and future rental price dataset, the future equipment price dataset include estimated future prices for purchase of additional equipment over the defined time period and the future rental prices dataset include estimated future prices for rental orders of additional equipment over the defined time period; generating an optimized equipment purchase plan that includes indicators to purchase equipment having sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; determining the remainder capacity demands that is not fulfilled by the equipment purchase in accordance with the optimized equipment purchase plan; generating an optimized rental order plan that includes indicators to order equipment rentals having sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; producing an equipment-acquisition plan by combining the optimized equipment purchase plan and the optimized rental order plan; reporting the produced equipment-acquisition plan.”; Paragraph 72, “Other implementations may have one or more of these features: the forecasting of the capacity demands utilizes predictive analytics to determine future capacity demands over the time period; the defined capacity of purchased and rented equipment varies over the time period; forecasting an approximate rental cost over the time period based upon an average price for renting equipment; the generating of the optimized equipment purchase plan includes performing a two-dimensional dynamic programming function based, at least in part, on the forecasted capacity demands and the forecasted pricing data; the generating of the optimized rental order plan includes performing a one-dimensional dynamic programming function based, at least in part, on the remainder capacity demands and the forecasted pricing data; the optimized equipment purchase plan lists when to purchase equipment with sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; the optimized rental order plan lists when to order rental equipment with sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; the optimized rental order plan specifies duration of rental orders; the reporting includes generating of a user-interface to communicate to a user the produced equipment-acquisition plan.”)
Claim(s) 8 –
Sun discloses the limitations of claim 1
Sun further discloses the following:
wherein the second integration calculation unit applies at least one of costs of resources, a rental, a rental feel, a labor cost, a depreciation expense, an electricity charge, and a fee according to the contract information to the economic feasibility analysis algorithm to create the prediction information. (Sun: Paragraph 57, “The DP sub-problem of the 2D DP optimization action 252 may be described in this fashion. DP[t][C] denotes the minimal cost from t to the end of the time window with an initial inventory I providing capacity C at t. Therefore, the solution to the entire DP problem should be DP[t.sub.0][C.sub.t0]. The solutions to the sub-problems are stored in a 2D-DP table DP[t][C].”; Paragraph 71, “One or more implementations of the technology discussed herein may also be described as a process for system performing that process) that does at least the following actions: forecasting capacity demands over a defined time period, wherein the capacity demands are for services provided by equipment, the equipment having a defined service capacity; forecasting pricing data over the defined time period, the forecasted pricing data includes future equipment price dataset and future rental price dataset, the future equipment price dataset include estimated future prices for purchase of additional equipment over the defined time period and the future rental prices dataset include estimated future prices for rental orders of additional equipment over the defined time period; generating an optimized equipment purchase plan that includes indicators to purchase equipment having sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; determining the remainder capacity demands that is not fulfilled by the equipment purchase in accordance with the optimized equipment purchase plan; generating an optimized rental order plan that includes indicators to order equipment rentals having sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; producing an equipment-acquisition plan by combining the optimized equipment purchase plan and the optimized rental order plan; reporting the produced equipment-acquisition plan.”; Paragraph 72, “Other implementations may have one or more of these features: the forecasting of the capacity demands utilizes predictive analytics to determine future capacity demands over the time period; the defined capacity of purchased and rented equipment varies over the time period; forecasting an approximate rental cost over the time period based upon an average price for renting equipment; the generating of the optimized equipment purchase plan includes performing a two-dimensional dynamic programming function based, at least in part, on the forecasted capacity demands and the forecasted pricing data; the generating of the optimized rental order plan includes performing a one-dimensional dynamic programming function based, at least in part, on the remainder capacity demands and the forecasted pricing data; the optimized equipment purchase plan lists when to purchase equipment with sufficient capacity to fulfill but not exceed the forecasted capacity demands over the defined time period; the optimized rental order plan lists when to order rental equipment with sufficient capacity to at least fulfill the remainder capacity demands over the defined time period; the optimized rental order plan specifies duration of rental orders; the reporting includes generating of a user-interface to communicate to a user the produced equipment-acquisition plan.”)
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2016/0171410 A1) in view of Brown (US 8,478,879 B2)
Claim(s) 4 –
Sun discloses the limitations of claims 1 and 3
Sun does not explicitly disclose the following, however, in analogous art of investment planning, Brown discloses the following:
wherein the intellectual unit receives setting change-related setting information about at least one of one or more resources, which are used for the service in accordance with user input through the user interface unit, and creates optimization information corresponding to the setting information by reflecting the setting information to the optimization algorithm; and (Brown: Column 5 lines 8 – 21, “With reference now to FIG. 2, exemplary customer-weighted attributes of an information technology (IT) infrastructure are depicted on a graph 202. Graph 202 presents an overview of reasons for a customer to configure/reconfigure/deploy an IT infrastructure. For example, a customer may desire to deploy a new IT infrastructure in order to provide new capabilities to an enterprise. These new capabilities may enable new business innovations and/or better tie IT to business processes, thus generally improving the position of the business for the future. These new capabilities provide improved business capabilities (e.g., a reduction in management interfaces), improved IT capabilities (e.g., enable increased IT integration), etc.”; Column 6 line 62 – Column 7 line 24, “Referring then to FIG. 5, a mapping of questions from a customer questionnaire to attributes of an IT infrastructure is presented. A questionnaire 502 includes multiple statements about various attributes of a component of a candidate IT infrastructure. For example, assume that the component is a platform performance manager (PPM) used to dynamically adjust system resources to help ensure that multi-architecture workloads meet service level agreement (SLA) goals within an enterprise priority policy. The customer is then asked, on a scale of 0-4, what his level of agreement is regarding various attributes of the PPM. For example, question/assertion A may ask the customer to rate, on a scale of 0-4, how strongly he agrees with the assertion that the PPM would enable increased IT integration by allowing IT to focus on end-to-end workload performance goals rather than the individual parts. In the example shown in column 506, the customer strongly agrees with this attribute assertion by giving the statement a "4". Note that this question/assertion A is mapped to the "New Capabilities" of the proposed/candidate IT infrastructure, as shown in column 504. As depicted in FIG. 5, the customer's responses to the various assertions (i.e., how much he agrees/disagrees with the assertions) are then summed up for each attribute category. These ratings are then used to create a customer-weighted attribute graph 508 by mapping both the technology weights (created in the process shown above in FIG. 4) with the customer responses to the questionnaire 502 shown in FIG. 5. This combined input results in a graph such as graph 602 shown in FIG. 6.”)
the second integration calculation unit creates one or more items of prediction information for supporting decision- making for service optimization for a service operation environment corresponding to the setting information by applying optimization information corresponding to the operation information and the setting information to the economic feasibility algorithm. (Brown: Column 7 lines 24 – 67, “Referring now to FIG. 6, a radar graph 602, generated by comparing customer expectations with system capabilities across multiple dimensions, is depicted. Radar graph 602 shows how well a candidate IT infrastructure configuration will meet the needs of a specific workload, based on both the initial analysis (e.g., the technology weights derived in the process shown in FIG. 4) as well as the customer's opinions (derived in the process shown in FIG. 5). Thus, the IT consultant who is proposing the IT infrastructure and the customer both have a "say" in how well the candidate IT infrastructure will function in executing a specific workload. As depicted by region 604 of radar graph 602, the IT consultant and the customer both agree that the candidate IT infrastructure will provide excellent increased efficiency. Similarly, the time to value attribute is reasonably well met (region 606). However, new capabilities provided by the candidate IT infrastructure are poor (region 608), despite the fact that the customer thought that the PPM described above would have a very positive effect on the new capabilities of the candidate IT infrastructure. Region 608 can be lacking for various reasons. In one embodiment, the IT consultant may "know" (based on mappings described above and other technology rule sets) that the PPM described above will not have a beneficial effect on the new capabilities of the candidate IT infrastructure, despite what the customer may think. Thus, since the IT consultant's information and the customer's assessment are combined (e.g., by weighted multiplications of one against the other), the candidate IT infrastructure will not provide much in the way of new capabilities. A decision can then be made to proceed with deploying the candidate IT infrastructure (e.g., if the customer really doesn't care about this attribute), or else a new candidate IT infrastructure can be proposed/configured. Referring now to FIG. 7, a high level flow chart of one or more exemplary steps taken by a processor to configure and/or optimize an IT system is presented. After initiator block 702, a technology rule set is established (block 704). This technology rule set defines technology weights of an IT infrastructure by mapping capabilities of IT infrastructure components to IT infrastructure attributes needed to execute a specific workload (e.g., as described in FIG. 4). A candidate IT infrastructure is then configured, using mapped components from the process depicted in block 704, for a specific workload (block 706).”; Column 8 lines 37 – 43, “In one embodiment, the candidate IT infrastructure is configured by discarding existing IT resources from an existing IT infrastructure that do not meet the IT infrastructure attributes needed to execute the specific workload, thus resulting in an optimized and simpler IT infrastructure.”)
Sun discloses a method for investment planning and determinations based on current and forecasted requirements and capabilities. Brown discloses a method for optimizing infrastructure configuration with the aid of user input. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Sun with the teachings of Brown in order to enhance optimization of resources as disclosed by Brown (Brown: Column 1 lines 28 – 31, “In response to the processor determining that the candidate IT infrastructure fails to meet the customer's expectations, the candidate IT infrastructure is reconfigured until the customer's expectations are met.”)
Claim(s) 9 –
Sun discloses the limitations of claim 1
Sun does not explicitly disclose the following, however, in analogous art of investment planning, Brown discloses the following:
the second integration calculation unit calculates one or more service driving environments, in which the operation information has been changed, such that the difference from at least one of the usage and capacity of each investment resource, which is generated between the operation information and the optimization information, through the economic feasibility analysis algorithm, as candidate service driving environments; (Brown: Column 7 lines 24 – 67, “Referring now to FIG. 6, a radar graph 602, generated by comparing customer expectations with system capabilities across multiple dimensions, is depicted. Radar graph 602 shows how well a candidate IT infrastructure configuration will meet the needs of a specific workload, based on both the initial analysis (e.g., the technology weights derived in the process shown in FIG. 4) as well as the customer's opinions (derived in the process shown in FIG. 5). Thus, the IT consultant who is proposing the IT infrastructure and the customer both have a "say" in how well the candidate IT infrastructure will function in executing a specific workload. As depicted by region 604 of radar graph 602, the IT consultant and the customer both agree that the candidate IT infrastructure will provide excellent increased efficiency. Similarly, the time to value attribute is reasonably well met (region 606). However, new capabilities provided by the candidate IT infrastructure are poor (region 608), despite the fact that the customer thought that the PPM described above would have a very positive effect on the new capabilities of the candidate IT infrastructure. Region 608 can be lacking for various reasons. In one embodiment, the IT consultant may "know" (based on mappings described above and other technology rule sets) that the PPM described above will not have a beneficial effect on the new capabilities of the candidate IT infrastructure, despite what the customer may think. Thus, since the IT consultant's information and the customer's assessment are combined (e.g., by weighted multiplications of one against the other), the candidate IT infrastructure will not provide much in the way of new capabilities. A decision can then be made to proceed with deploying the candidate IT infrastructure (e.g., if the customer really doesn't care about this attribute), or else a new candidate IT infrastructure can be proposed/configured. Referring now to FIG. 7, a high level flow chart of one or more exemplary steps taken by a processor to configure and/or optimize an IT system is presented. After initiator block 702, a technology rule set is established (block 704). This technology rule set defines technology weights of an IT infrastructure by mapping capabilities of IT infrastructure components to IT infrastructure attributes needed to execute a specific workload (e.g., as described in FIG. 4). A candidate IT infrastructure is then configured, using mapped components from the process depicted in block 704, for a specific workload (block 706).”; Column 8 lines 37 – 43, “In one embodiment, the candidate IT infrastructure is configured by discarding existing IT resources from an existing IT infrastructure that do not meet the IT infrastructure attributes needed to execute the specific workload, thus resulting in an optimized and simpler IT infrastructure.”)
calculates the final profit-loss by calculating and adding up profit-loss in accordance with each of investment resources, which need at least one of enlargement, a personnel increase, reduction, personnel reduction, performance optimization, service structure improvement, infrastructure optimization, utilization optimization, distribution, and bottleneck improvement according to the difference between the present service driving environment and the candidate service driving environments for each of the one or more candidate service driving environments; and creates the prediction information for each candidate service driving environment in which the final profit-loss satisfies a predetermined profit-loss condition. (Brown: Column 5 lines 21 – 47, “A customer may desire to improve time to value, in which the new technology decreases the time for IT-enabled business services to begin providing a positive impact on revenue and/or profit. This results in faster capability deployment (e.g., reduced test cycle); improved quality of deployment (e.g., more accurate installation); improved ability to change dynamically (e.g., reduced complexity of change), etc. A customer may desire to deploy new technology in order to increase efficiency. Thus, deploying the new technology may drive technological, organizational and process improvements that ultimately reduce costs, either due to a lowered requirement for capital and personnel time or by doing more work with the same resources. This increased efficiency results in improved technology efficiency (e.g., increased server utilization); improved organization efficiency (e.g., reduced/eliminated tasks); improved process efficiency (e.g., reduced error rates), etc. The customer may desire to deploy new technology in order to improve an enterprise's quality of service. Thus, deploying the new technology may improve some aspect of the non-functional requirements (e.g. ability to increase availability, performance and customer satisfaction) of business services. This may result in improved availability of IT resources (e.g., fewer outages); increased performance (e.g., improved response time); improved customer satisfaction (e.g., more consistent service levels), etc.”; Column 6 line 62 – Column 7 line 24, “Referring then to FIG. 5, a mapping of questions from a customer questionnaire to attributes of an IT infrastructure is presented. A questionnaire 502 includes multiple statements about various attributes of a component of a candidate IT infrastructure. For example, assume that the component is a platform performance manager (PPM) used to dynamically adjust system resources to help ensure that multi-architecture workloads meet service level agreement (SLA) goals within an enterprise priority policy. The customer is then asked, on a scale of 0-4, what his level of agreement is regarding various attributes of the PPM. For example, question/assertion A may ask the customer to rate, on a scale of 0-4, how strongly he agrees with the assertion that the PPM would enable increased IT integration by allowing IT to focus on end-to-end workload performance goals rather than the individual parts. In the example shown in column 506, the customer strongly agrees with this attribute assertion by giving the statement a "4". Note that this question/assertion A is mapped to the "New Capabilities" of the proposed/candidate IT infrastructure, as shown in column 504. As depicted in FIG. 5, the customer's responses to the various assertions (i.e., how much he agrees/disagrees with the assertions) are then summed up for each attribute category. These ratings are then used to create a customer-weighted attribute graph 508 by mapping both the technology weights (created in the process shown above in FIG. 4) with the customer responses to the questionnaire 502 shown in FIG. 5. This combined input results in a graph such as graph 602 shown in FIG. 6.”; Column 7 lines 57 – 57, “Referring now to FIG. 7, a high level flow chart of one or more exemplary steps taken by a processor to configure and/or optimize an IT system is presented. After initiator block 702, a technology rule set is established (block 704). This technology rule set defines technology weights of an IT infrastructure by mapping capabilities of IT infrastructure components to IT infrastructure attributes needed to execute a specific workload (e.g., as described in FIG. 4). A candidate IT infrastructure is then configured, using mapped components from the process depicted in block 704, for a specific workload (block 706).”)
Sun discloses a method for investment planning and determinations based on current and forecasted requirements and capabilities. Brown discloses a method for optimizing infrastructure configuration with the aid of user input. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Sun with the teachings of Brown in order to enhance optimization of resources as disclosed by Brown (Brown: Column 1 lines 28 – 31, “In response to the processor determining that the candidate IT infrastructure fails to meet the customer's expectations, the candidate IT infrastructure is reconfigured until the customer's expectations are met.”)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2016/0171410 A1) in view of Dufresne (US 2013/0218628 A1)
Claim(s) 6 –
Sun discloses the limitations of claim 1
Sun does not explicitly disclose the following, however, in analogous art of investment planning, Dufresne discloses the following:
further comprising a contract unit configured to create and providing contract information including a contract of purchase and use of each resource and a labor contract, wherein the first integration calculation unit sets contract information received from the contract unit into the second integration calculation unit. (Dufresne: Paragraph 14, “While to this point the prioritization of performing certain actions has been primarily discussed, another concern for organizations is the cost involved with performing certain actions to determine which actions can be accomplished within, for example, a yearly budget. The cost estimation system includes a database of information that includes cost information associated with the performance of discrete actions and/or groups of actions. In one embodiment, the cost estimation system allows for multiple cost estimation sources to provide cost information, which may include labor, materials and equipment costs adjusted for the location of the facility. These multiple cost estimation sources could include separate costs listing to be viewed by a user, and could further include a cost estimate that is averaged across the multiple cost estimation sources. An example of multiple cost estimation sources could include, for example, RSMeans (a cost estimation database for the U.S.) and Building Cost Information Service (BCIS) of the Royal Institute of Chartered Surveyors (RICS) (a cost estimation database for the U.K.), which could provide cost estimates on a countrywide basis. The user could select which cost estimation source they desire based on the location of the project(s).”; Paragraph 15, “It is further understood that discrete cost line items could be identified by, for example, and item code, which could be searchable by a user to enhance searching capabilities. Still further, it is understood that costing information can be navigated by a user in various ways, including, for example, via an expandable cost line item navigation "tree" (e.g., a visual interface indicating which costs are associated with which cost categories) that allows a user could click on to view individual line item costs depicted on the "tree." In one embodiment, the "tree" could comprise a hierarchical listing of costing information. This hierarchical listing could be provided in, for example, a first frame on a web page and when a particular line item is selected (clicked on by a user) line item cost information could then be presented in a second frame”; Paragraph 66, “As can be seen in FIGS. 5 and 10, the Budget Scenarios 104 tab has been selected now that the Ranking Strategies 102 has been determined. Here the user is provided wide latitude for allocating a budget for a project. For example, a user may allocate a specific amount of funding per year for a project, or may provide a percent of the total project cost per year, or extrapolate a percent annual increase. This allows the user to see various funding scenarios so as to be able to get an accurate picture of project costs and to allocate and schedule appropriate funds.”; Paragraph 83, “The example illustrated in FIG. 18 is utilizing the RSMeans database to provide cost information for the estimation of costs for an action item. For example, a first tier of information is provided in the first frame labeled "Cost Categories." In the example provided, the category "Assemblies" has been selected and the user has drilled down in the "Assemblies" category via "Services", then "Conveying", then "Elevators and Lifts", then "Hydraulic." As can be seen from FIG. 18, additional information relating to the selection "Hydraulic" in the first frame is shown in a second frame showing both an ID number and a description for each hydraulic elevator. Also shown in FIG. 18, the first hydraulic elevator in the second frame has been selected and line item cost information/details is shown in a third frame providing among other information, a price breakdown for the selected hydraulic passenger elevator. In this manner, a user can quickly and easily drill down in a database to see cost information and even line item cost details associated with an action item or a system in the facility.”)
Sun discloses a method for investment planning and determinations based on current and forecasted requirements and capabilities. Dufresne discloses a method for cost budgeting and estimation for various investments. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Sun with the teachings of Dufresne in order to increase the efficiency of resource allocation as disclosed by Dufresne (Dufresne: Paragraph 7, “What is desired therefore is a system and method that generates a capital budget plan allowing for various projects to be identified, quantified and ranked relative to each other based on objective criteria.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Phadke (US 2017/0039506 A1) discloses a method for IT services integrated work management
Ulwick (US 2017/0024672 A1) discloses a method for creating a market growth strategy
Adler (US 2002/0169658 A1) discloses a method for modeling and analyzing strategic business decisions
Ettl (US 2009/0164262 A1) discloses a method for risk-based resource planning
Duffy (US 2010/0131317 A1) discloses a method for organization assessment and representation
Ciccone (US 2016/0292621 A1) discloses a method for automatically identifying a project’s staffing risk
Mancuso (US 2015/0242781 A1) discloses a method for utilizing enhanced manpower forecasting
Hadar (US 9,189,203 B1) discloses a method for a solution modeling and analysis toolset
Pemberton (WO 0229697 A1) discloses a method for reporting and managing contract performance
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624