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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system comprising: a database; a processor communicatively coupled to the database…”. A system of the described configuration is within one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers an abstract idea but for recitation of generic computer components:
“generate a predicted function for the digitally-represented space, wherein the predicted function represents a relationship between the at least one optimization parameter and the at least one independent variable” (Generating a “function” that represents a relationship between numbers (parameters and variables) is a mathematical process. A mathematical process is an abstract idea (MPEP 2106).)
“generate an optimal allocation of a subset of the set of elements in the digitally-represented space, wherein the optimal allocation maximizes the at least one optimization parameter” (This is a mental process. A person can mentally evaluate a subset of the set of elements and make a judgement to optimally allocate them in a way that maximizes at least one parameter. A mental process is an abstract idea (MPEP 2106).)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as an abstract idea but for the recitation of generic computer components, then accordingly, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has
determined that the following additional elements do not integrate this judicial exception into a
practical application:
“A system, comprising: a database; a processor communicatively coupled to the database, wherein the processor is configured to read a set of instructions to…” (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
“receive a request to optimize a digitally-represented space, wherein the request includes a data structure storing the digitally-represented space and at least one optimization parameter” (Mere instructions to apply the judicial exception (MPEP 2106.05(f)).)
“obtain, from the database, a set of elements for insertion into the digitally-represented space, wherein each element in the set of elements includes at least one independent variable” (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)).)
“wherein the predicted function is generated by a scaled neural multiplicative model (SNMM) prediction model” (Mere instructions to apply the judicial exception (MPEP 2106.05(f)).)
“update the data structure storing the digitally-represented space to include the optimal allocation of the subset of the set of elements, wherein the updated data structure is stored in the database” (Adding insignificant extra-solution activity (mere data storage) to the judicial exception (MPEP 2106.05(g)).)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element (iii) recites use of a computer as a tool to perform the abstract idea, which is not indicative of significantly more. Additional elements (iv) & (vi) recite mere instructions to apply the judicial exception, which is not indicative of significantly more. Additional elements (v) & (vii) recite insignificant extra-solution activities. Further, these elements recite steps that stores and retrieves information in memory which has been determined by the courts to recite a well-understood, routine, and conventional activity which is not indicative of significantly more (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites “wherein the SNMM prediction model includes at least two linear layers each having a scaling weight and a bias” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 2, and thereby incorporates the limitations of, and corresponding analysis applied to claim 2. Further, claim 3 recites the following additional abstract idea:
“wherein the SNMM prediction model generates the prediction function according to one or more subdivisions of the digitally-represented space” (Generating a function according to subdivisions of data is a mathematical process. A mathematical process recites an abstract idea (MPEP 2106).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 4 recites the following additional abstract idea:
“wherein the optimal allocation is generated by an optimization process including an index of feature pairs” (A person can mentally evaluate a subset of the set of elements in regard to a predefined process and an index of feature pairs, and make a judgement to optimally allocate that subset of elements based on that (MPEP 2106).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 5 recites “wherein the digitally-represented space is representative of a retail space, and wherein the optimization parameter includes expected sales, the at least one independent parameter includes a fixture count, and wherein the SNMM model is configured to determine space optimization of a particular category within the retail space for a particular brand within a particular department.” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 6 recites the following additional abstract idea:
“wherein the optimal allocation is defined by the at least one independent variable, wherein the independent variable includes a set of features, and wherein the optimal allocation is constrained by an upper bound and a lower bound of the at least one independent variable” (A person can mentally evaluate a subset of a set of elements and at least one independent variable including a set of features and make a judgement to allocate that subset of elements within constraints as defined by that variable (MPEP 2106).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites the following additional abstract idea:
“wherein the optimal allocation of the subset of the set of elements is generated by applying a power cone formulation” (The application of a power cone formulation is a mathematical process, which recites an abstract idea (MPEP 2106).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 8 recites “wherein the processor reads the set of instructions to generate an interface including the updated data structure storing the digitally-represented space and the optimal allocation of the subset of the set of elements” (In step2A, prong 2, this recites mere instructions to apply the judicial exception (MPEP 2106.05(f).) In step 2B, mere instructions to apply the judicial exception is not indicative of significantly more.)
Regarding claim 9, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer-implemented method…”. A method is within one of the four statutory categories of invention.
Further, claim 9 recites similar limitations as claim 1, and is rejected under the same rationale, with the following addition: “A computer-implemented method…” (In step2A, prong 2, this recites using a computer as a tool to perform an abstract idea (MPEP 2106.05(f).) In step 2B, using a computer as a tool to perform an abstract idea is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claims 10-16, they are dependent upon claim 9, and thereby incorporate the limitations of, and corresponding analysis applied to claim 9. Further, claims 10-16 comprise similar additional limitations as claims 2-8, respectively, and are rejected under the same rationale.
Regarding claim 17, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A non-transitory computer-readable medium…”. A non-transitory medium is within one of the four statutory categories of invention.
Further, claim 17 recites similar limitations as claim 1, and is rejected under the same rationale, with the following addition: “A non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:…” (In step2A, prong 2, this recites using a computer as a tool to perform an abstract idea (MPEP 2106.05(f).) In step 2B, using a computer as a tool to perform an abstract idea is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 18, it is dependent upon claim 17, and thereby incorporates the limitations of, and corresponding analysis applied to claim 17. Further, claim 18 recites similar additional limitations as claim 6, and is rejected under the same rationale.
Regarding claim 19, it is dependent upon claim 18, and thereby incorporates the limitations of, and corresponding analysis applied to claim 18. Further, claim 19 recites similar additional limitations as claims 2 & 3, and is rejected under the same rationale.
Regarding claim 20, it is dependent upon claim 18, and thereby incorporates the limitations of, and corresponding analysis applied to claim 18. Further, claim 20 recites similar additional limitations as claim 5, and is rejected under the same rationale.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-14, & 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mihic et al., US Patent No. US 8,930,235 B2 (hereafter, MIHIC), and further in view of Zhao, L. et al. “PSO-based single multiplicative neuron
model for time series prediction.” Available in March of 2009 (hereafter, ZHAO)
Regarding claim 1, MIHIC teaches “A system, comprising: a database; a processor communicatively coupled to the database, wherein the processor is configured to read a set of instructions”:
([Figure 1] The figure is a block diagram of a computer system 10, which explicitly shows the processor 22 to be a part of it, which is shown to be communicatively coupled to the ERP Data 17 (a database).)
And further:
([Col. 2, Lines 43-45] “System 10 further includes a memory 14 for storing information and instructions to be executed by processor 22.”)
Further, MIHIC teaches “receive a request to optimize a digitally-represented space, wherein the request includes a data structure storing the digitally-represented space and at least one optimization parameter”:
([Col. 1, Lines 36-39] “The shelf space optimization problem can be considered a… problem of creating a planogram (“POG”) for a given retail category within given physical space (a digitally-represented space).”)
And further:
([Col. 1, Lines 46-50] “The system receives decision variables and constraints (receiving a request to optimize the digitally represented space), and executes a Randomized Search (“RS) using the decision variables and constraints until an RS solution is below a pre-determined improvement threshold).”)
And further:
([Col. 2, Lines 29-35] “One embodiment optimizes shelf space placement… to determine, for a retail item, which shelf the item should be assigned and the number of its facings. The determination maximizes at least one of a key performance indicator (“KPI) (at least one optimization parameter), such as revenue, profit or sales volume.”)
Further, MIHIC teaches “obtain, from the database, a set of elements for insertion into the digitally-represented space”:
([Col. 2, Lines 29-33] “One embodiment optimizes shelf space placement… to determine, for a retail item, (an element in the database) which shelf the item should be assigned and the number of its facings.”)
And further:
([Col. 1, Lines 36-39] “The shelf space optimization problem can be considered a… problem of creating a planogram (“POG”) for a given retail category (a set of elements in a database to insert into the digitally represented space) within given physical space).”)
Further, MIHIC teaches “wherein each element in the set of elements includes at least one independent variable”:
([Col. 4, Lines 10-17] “In one embodiment, the following constraints are considered as inputs to the optimization problem:
Usable Shelf Capacity
This is a hard constraint based on the shelf length. Attribute-Based Blocking and General Visual Guidance
This constraint forces items that share the same attribute value (e.g., brand or size) (elements including at least one independent variable.) to be placed together in the block defined by vertical or horizontal boundaries.”)
Further, MIHIC teaches “generate a… function for the digitally-represented space, wherein the… function represents a relationship between the at least one optimization parameter and the at least one independent variable”:
([Col. 3, Lines 13-22] “In one embodiment, system 10 receives a set of merchandise items (i.e., a planogram (“POG”) category) in a selected store area (e.g., an aisle or department) defined by the area fixtures, item attributes (independent variables) and demand as a function of the number of facings and location (generation of a function for the digitally represented space). Given the item set positioned in the specific area of a particular store, the main objective is to determine location and the number of facings for each item that would maximize certain KPI parameters (optimization parameters for each item, and thus the relationship between the independent variables of the items and the optimization parameter), such as total revenue, profit, or sales Volume subject to the total shelf capacity, and certain item placement and assortment rules.”)
Further, MIHIC teaches “generate an optimal allocation of a subset of the set of elements in the digitally-represented space, wherein the optimal allocation maximizes the at least one optimization parameter”:
([Abstract] “The system… solves a Mixed-Integer Linear Program (“MILP”) problem using the decision variables and constraints… to generate a MILP solution. The system repeats the RS executing and MILP solving as long as the MILP solution is not within a predetermined accuracy or does not exceed a predetermined time duration. The system then, based on the final MILP solution, outputs a shelf position and a number of facings....”) The optimal allocation is output based on the KPI optimization parameters as cited above at [Col. 3, Lines 13-22].
Further, MIHIC teaches “update the data structure storing the digitally-represented space to include the optimal allocation of the subset of the set of elements, wherein the updated data structure is stored in the database”:
([Col. 4, Lines 31-34] “In one embodiment, input data in addition to the constraints is received by system 10 of FIG. 1 in order to determine the optimized shelf space product placement. The data may be stored in database 17 of FIG. 1…”) This citation explicitly shows the storage of the optimized planogram (digitally represented space) being stored within the database 17 cited earlier in figure 1.
MIHIC fails to explicitly teach the function being a “…predicted function…” and “…wherein the predicted function is generated by a scaled neural multiplicative model (SNMM) prediction model”. However, analogous art, ZHAO, does teach these limitations:
([Abstract] “Single multiplicative neuron model is a novel neural network model introduced recently, which has been used for time series prediction and function approximation. The model is based on a polynomial architecture that is the product of linear functions in different dimensions of the space. Particle swarm optimization (PSO), a global optimization method, is proposed to train the single neuron model in this paper...”) This clearly illustrates that a predicted function is generated by a neural multiplicative model prediction model where the architecture is the product of linear functions in different dimensions of the space.
And further:
([Page 2806, Equation (1)] The cited equation explicitly shows that the multiplicative neuron model involves weighted/scaled linear terms, which naturally results in scaling weights, meaning that the model is a scaled neural multiplicative model.)
It would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to combine the base reference of MIHIC with the teachings of ZHAO because ZHAO directly ties multiplicative neural models to prediction, function approximation, and optimization/training, while MIHIC describes techniques and systems for optimization.
One of ordinary skill in the art would be motivated to do so because, as pointed out by the final sentence of the abstract of ZHAO, “The experimental results demonstrate the superiority of CRPSO-based neuron model in efficiency and robustness over… other… algorithms.”
Regarding claim 2, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, X teaches “wherein the SNMM prediction model includes at least two linear layers each having a scaling weight and a bias”:
([Page 2806, Figure 1] “The figure explicitly shows the structure of the model having at least two linear layers (x1, x2, xn, etc.).”)
And further:
([Page 2806, Equation (1)] The cited equation explicitly shows that the multiplicative neuron model involves weighted/scaled linear terms including a scaling weight (wi) and bias (bi), which naturally results in scaling weights, meaning that the model is a scaled neural multiplicative model.)
Regarding claim 3, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, claim 3 recites “wherein the SNMM prediction model generates the prediction function according to one or more subdivisions of the digitally-represented space”.
MIHIC teaches of systems for functions which optimize “multiple shelves” (one or more subdivisions) of a “retail store” (a digitally-represented space) at ([Col. 1, Lines 36-50]), and ZHAO teaches that a predicted function is generated by an SNMM model for the optimization, at ([Abstract]).
Regarding claim 4, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, X teaches “wherein the optimal allocation is generated by an optimization process including an index of feature pairs”:
([Col. 4, Lines 10-17] “In one embodiment, the following constraints are considered as inputs to the optimization problem:
Usable Shelf Capacity
This is a hard constraint based on the shelf length. Attribute-Based Blocking and General Visual Guidance
This constraint forces items that share the same attribute value (e.g., brand or size) to be placed together in the block defined by vertical or horizontal boundaries.”) In other words, items that share attribute values (features) are paired together (feature pairs) and since all the items are in an indexed database, it is an index of feature pairs.
Regarding claim 5, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, MIHIC teaches “wherein the digitally-represented space is representative of a retail space”:
([Col. 1, Lines 13-15] “One embodiment is directed generally to a computer system, and in particular to a computer system that optimizes retail shelf space product placement.”)
Further, MIHIC teaches “wherein the optimization parameter includes expected sales”:
([Col. 1, Lines 28-33] “A retail shelf space optimization problem in general is the problem of finding the optimal placement of merchandise items on the shelves to maximize one of many potential key performance indicators (“KPI') (optimization parameter), such as revenue, profit or sales volume (expected sales) …”)
Further, MIHIC teaches “the at least one independent parameter includes a fixture count”:
([Col. 3. Lines 13-17] “In one embodiment, system 10 receives a set of merchandise items (i.e., a planogram (“POG”) category) in a selected store area (e.g., an aisle or department) defined by the area fixtures, item attributes and demand as a function of the number of facings and location.”)
Further, MIHIC teaches “determine space optimization of a particular category within the retail space for a particular brand within a particular department”:
([Col. 4, Lines 10-17] “In one embodiment, the following constraints are considered as inputs to the optimization problem:
Usable Shelf Capacity
This is a hard constraint based on the shelf length. Attribute-Based Blocking and General Visual Guidance
This constraint forces items that share the same attribute value (e.g., brand or size) to be placed together in the block defined by vertical or horizontal boundaries.”)
Further, ZHAO teaches that “…the SNMM model is configured to…” perform the operations:
([Abstract] As cited above in claim 1.)
Regarding claim 6, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, MIHIC teaches “wherein the optimal allocation is defined by the at least one independent variable”:
([Col. 3, Lines 13-22] “In one embodiment, system 10 receives a set of merchandise items (i.e., a planogram (“POG”) category) in a selected store area (e.g., an aisle or department) defined by the area fixtures, item attributes (independent variables) and demand as a function of the number of facings and location (generation of a function for the digitally represented space). Given the item set positioned in the specific area of a particular store, the main objective is to determine location and the number of facings for each item that would maximize certain KPI parameters (optimization parameters for each item, and thus the relationship between the independent variables of the items and the optimization parameter), such as total revenue, profit, or sales Volume subject to the total shelf capacity, and certain item placement and assortment rules.”)
Further, MIHIC teaches “wherein the optimal allocation is constrained by an upper bound and a lower bound of the at least one independent variable”:
([Col. 3, Lines 36-42] “Further, in one embodiment each item may have one or two attributes determining its placement rules. One attribute (e.g., the brand of the item) is referred to as the “vertical blocking attribute” and determines the vertical boundaries between groups of items with the same attribute value (i.e., Vertical blocking between different brands). These boundaries are defined within some given tolerance (upper and lower bounds of an independent variable (vertical blocking)) …”)
Further, X teaches “wherein the independent variable includes a set of features”:
([Col. 4, Lines 10-17] “In one embodiment, the following constraints are considered as inputs to the optimization problem:
Usable Shelf Capacity
This is a hard constraint based on the shelf length. Attribute-Based Blocking and General Visual Guidance
This constraint forces items that share the same attribute value (e.g., brand or size) (a set of features) to be placed together in the block defined by vertical or horizontal boundaries.”)
Regarding claim 8, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, MIHIC teaches “wherein the processor reads the set of instructions to generate an interface including the updated data structure storing the digitally-represented space and the optimal allocation of the subset of the set of elements”:
([Col. 2, Lines 61-65] “Processor 22 is further coupled via bus 12 to a display 24, such as a Liquid Crystal Display (LCD), for displaying information to a user. A keyboard 26 and a cursor control device 28, Such as a computer mouse, is further coupled to bus 12 to enable a user to interface with system 10.”)
Regarding claim 9, it comprises similar limitations as claim 1, and is rejected under the same rationale, with the following addition:
MIHIC teaches “a computer-implemented method”
([Figure 1] The figure explicitly shows that the method is executed on a computer.)
Regarding claims 10-14 & 16, MIHIC in view of ZHAO teaches the limitations of claim 9. Further, claims 10-14 & 16 comprise similar additional limitations as claims 2-6 & 8, respectively, and are rejected under the same rationale.
Regarding claim 17, it comprises similar limitations as claim 1, and is rejected under the same rationale, with the following addition:
MIHIC teaches “A non-transitory computer-readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations”
([Figure 1] The figure explicitly shows that the method is executed on a computer with a processor, which is a non-transitory computer-readable medium.)
Regarding claims 18-20, MIHIC in view of ZHAO teaches the limitations of claim 17. Further, claims 18-20 comprise similar additional limitations as claims 6, 3, & 5, respectively, and are rejected under the same rationale.
Claims 7 & 15 are rejected under 35 U.S.C. 10] as being unpatentable over MIHIC & ZHAO, as applied to claims above, and further in view of MOSEK, “MOSEK Modeling Cookbook Release 3.3.0.” Available on 13 September 2022 (hereafter, MOSEK)
Regarding claim 7, MIHIC in view of ZHAO teaches the limitations of claim 1. Further, MIHIC in view of ZHAO fails to explicitly teach “wherein the optimal allocation of the subset of the set of elements is generated by applying a power cone formulation”. However, analogous art, MOSEK, does teach the application of a power cone formulation for optimization:
([Preface] “This cookbook is about model building using convex optimization… In this manual we have chosen a different route, where we instead show the different sets and functions that can be modeled using convex optimization, which can subsequently be combined into realistic examples and applications. In other words, we present simple convex building blocks, which can then be combined into more elaborate convex models… With the advent of more expressive and sophisticated tools like conic optimization, we feel that this approach is better suited”) This citation shows that the book is about optimization methods, which can be used for a variety of models.
And further:
([Page 20, Paragraph 1] “This chapter extends the notion of linear optimization with quadratic cones. Conic quadratic optimization, also known as second-order cone optimization, is a straightforward generalization of linear optimization, in the sense that we optimize a linear function under linear (in)equalities with some variables belonging to one or more (rotated) quadratic cones. We discuss the basic concept of quadratic cones, and demonstrate the surprisingly large flexibility of conic quadratic modeling.”)
And further:
([Page 30, paragraph 1] “In this part we expand the quadratic and rotated quadratic cone family with power cones, which provide a convenient language to express models involving powers other than 2.”) This citation alongside the ones above, show that the use of power cones to optimize model algorithms is known in the art.
It would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to combine the base reference of MIHIC in view of ZHAO with the teachings of MOSEK because both references teach optimization methods of models.
One of ordinary skill in the art would be motivated to do so because, as pointed out by MOSEK at (Page 20, Paragraph 1), “We discuss the basic concept of quadratic cones, and demonstrate the surprisingly large flexibility of conic quadratic modeling” and further, at (Page 30, paragraph 1), “we expand the quadratic and rotated quadratic cone family with power cones, which provide a convenient language to express models involving powers other than 2.”
Regarding claim 15, MIHIC in view of ZHAO teaches the limitations of claim 9. Further, claim 15 comprises similar additional limitations as claim 7, and is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW LEE LEWIS whose telephone number is (571)272-1906. The examiner can normally be reached Monday: 12:00PM - 4:00PM and Tuesday - Friday: 12:00PM - 9PM.
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/Matthew Lee Lewis/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights