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 .
DETAILED ACTION
Status of the Claims
Independent claims 1, 7 and 13 have been amended to clarify the invention.
Claims 1,4,7,10,13,16 are pending.
The rejection under 35 USC 101 is maintained.
Response to Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed technique improves floor space optimization using machine generated output and cannot fall under an abstract idea of organizing human activity. (Applicant’s 2/12/26 remarks, p.25; p.29, Applicant's claimed system produces results in the form of generates a set of floorplans and planograms which are in directly consumable format, based on the patterns for remodeling of stores, the remodeling is achieved with reduced implementation issues and reduced number of space changes and reduced magnitude of space changes. Therefore, the claimed steps are completely non-abstract in nature.”) The examiner respectfully disagrees.
The examiner notes that Applicant’s invention accepts data input, processes data and outputs floor planning information. This appears to be abstract in nature. Apart from execution on a generic computer, the invention does not appear to directly affect or result in any practical or tangible change to any process or activity. Although it is intimated that the space changes are implemented in stores, it is not clear how this is accomplished and may also be an insignificant application of an abstract idea. (MPEP 2106.05g, “Insignificant application:
i. Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential);”)
Applicant also argues that the invention is a practical application of an abstract idea. (Applicant’s 2/12/26 remarks, p.31, “In response, Applicant humbly states that the claimed features provide practical application
in terms of 'multi level iterative architecture with dvnamic optimization rules that operate
in real time and cannot be meaningfully performed without specialized computing
resources.'”). The examiner respectfully disagrees.
The examiner acknowledges Applicant’s extensive calculations employing iterations, category selection, discriminant/pattern analysis. However, just the fact that the calculations require real time computational resources does not preclude them from being an abstract idea. Similarly, Applicant has not articulated under what consideration the invention should be considered a practical application. (MPEP 2106.05d, “I. RELEVANT CONSIDERATIONS FOR EVALUATING WHETHER ADDITIONAL ELEMENTS INTEGRATE A JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION
The Supreme Court and Federal Circuit have identified a number of considerations as relevant to the evaluation of whether the claimed additional elements demonstrate that a claim is directed to patent-eligible subject matter. The list of considerations here is not intended to be exclusive or limiting. Additional elements can often be analyzed based on more than one type of consideration and the type of consideration is of no import to the eligibility analysis. Additional discussion of these considerations, and how they were applied in particular judicial decisions, is provided in MPEP § 2106.05(a) through (c) and MPEP § 2106.05(e) through (h).”)
Applicant also argues that the invention is patent eligible citing the Desjardins decision. (Applicant’s 2/12/26 remarks, p.35, “Importantly, the ARP evaluated the claims as a whole in discerning at least the
limitation "adjust the first values of the plurality of parameters to optimize performance of the
machine learning model on the second machine learning task while protecting performance of the
machine learning model on the first machine learning task" reflected the improvement disclosed
in the specification. Accordingly, the claims as a whole integrated what would otherwise be a
judicial exception instead into a practical application at Step 2A Prong Two, and therefore the
claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).
Similarly, Applicant's claimed technique also discloses a method and system for space
planning providing techniques to create floor plans and planograms.
The present disclosure provides practical application by providing a self learning mechanism.
The claimed method is providing technical advancement by providing a method and system
for space planning providing techniques to create floor plans and planograms.”) The examiner respectfully disagrees.
Desjardins is distinguishable at least because the invention at issue in Desjardins represented an improvement to the technology of machine learning itself. Applicant’s invention by contrast is an application of machine learning to improve space planning, a fundamental economic practice. Applicant’s invention, although it may improve space planning, does not technically improve machine learning or improve the performance of machine learning generally.
Applicant also argues that the invention is a technical improvement to decision making. (Applicant’s 2/16/25 remarks, p.36). Even if it the invention describes an advancement in planogram decision making, the examiner respectfully suggests that the advancement is in the use of the claimed mathematical algorithms to improve an economic practice. Further, it may be true that the iterative approach claimed by Applicant may be faster and save memory compared to other approaches. However, this is also an improvement to the algorithm itself and how the computer runs the particular algorithm. The improvement to speed and resource consumption is not an improvement to the general computer performance per se.
Applicant also points out the “Example 39” of the USPTO’s eligibility examples does not recite a judicial exception. (Applicant’s 2/16/26 remarks, p.37). The examiner notes that Example 39 recites at a high level training machine learning for a facial recognition task. Accordingly, the example does not specify a human mental process or fundamental economic practice. It also is claimed at such a high level it does not recite mathematics. However, this is distinguishable from Applicant’s claimed invention, which is directed to space planning, a fundamental economic practice, and is also replete with mathematical concepts.
Regarding the rejection under 35 USC 101, Applicant argues that the iterative nature of the claimed invention cannot be performed in the human mind. (Applicant’s 10/14/25 remarks, p.29).
The examiner does not dispute that given enough complexity or data points, any algorithm could potentially overwhelm the processing capability of a human being. Accordingly, this aspect of the rejection under 35 USC 101 is withdrawn. However, even if the algorithm claimed cannot be performed by a human mind, the abstract idea could still fall under different abstract idea groupings under 35 USC 101. Formulating a planogram would fall within a fundamental economic activity under methods of organizing human activity. The claimed invention also makes extensive use of matrix and vector calculations, which would fall under patent ineligible mathematical formulas. These groupings are distinct from “mental processes” and do not necessarily require performance by a human.
Applicant also argues that the claimed invention is a practical application of an abstract idea. (Applicant’s 10/14/25 remarks, p.32, “Therefore, from the above, it can be clearly inferred that the proposed approaches are not mere abstract ideas. The present disclosure provides a practical application by providing a self- learning mechanism. The claimed method is providing technical advancement by providing a method and system for space planning providing techniques to create floor plans and planograms. The method identifies underlying patterns that reside in space recommendations across stores and creation of an optimal number of floor plans and planograms in accordance with the identified set of patterns unlike large number of floor plans or planograms generated by state of the art space planning systems.”) The examiner respectfully disagrees.
Even if it the invention describes an advancement in planogram decision making, the examiner respectfully suggests that the advancement is in the use of the claimed mathematical algorithms to improve an economic practice. Further, it may be true that the iterative approach claimed by Applicant may be faster and save memory compared to other approaches. However, this is also an improvement to the algorithm itself. The improvement to speed and resource consumption is not an improvement to the general computer performance per se.
Applicant also argues that the invention is not a fundamental economic practice. (Applicant’s 10/14/25 remarks, p.35,”" The claimed features are not directed towards a fundamental economic practice.
No features are directed towards transactions, profit/loss, or any other economic related activities.
The invention is providing technical advancement by providing a solution in terms of space planning and space optimization.
Particularly, Applicant’s claimed feature “performing, via the one or more hardware processors three level iterations comprising:…cannot be considered as fundamental economic activity.”) The examiner respectfully disagrees.
Applicant points out that that space planning is a sales activity performed in retail stores. (Applicant’s 6/4/25 remarks, p.30, “Floor planning happens at the corporate level for each retail store guided by space planner, analyst, category managers and store managers. Floor planning is the process of arranging the categories within the retail store floor based on space allocated to the respective categories. As floor planning execution cannot be altered frequently and involves huge cost impact in terms of labor, procurement of infrastructure, fixtures, product inventory and other store remodel aspects, it is given utmost importance and considered as one of the primary functions of retail merchandise planning.”) Although Applicant’s algorithm may indeed be novel, the examiner respectfully suggests that it is one of the enumerated groupings of abstract ideas, because it relates to sales activity that retail stores are known to engage in. (fundamental economic activity).
Applicant also argues that the invention presents a patent eligible technical improvement to space planning. (Applicant’s 10/14/25 remarks, p.37, “Applicant humbly states that the Proposed disclosure teach about ‘hidden behavior’ or pattern
identification among the space recommendations which were already generated and to enable
technical advancement, ie compliance to space planning.
Proposed disclosure performs PCA for pattern generation by proposing suitable format to use
already generated space recommendations.
Identification of reasons for patterns using multivariate techniques through suitable format to
improve compliance by store managers and also used for practical applications such as new store
planogram generation, etc.,.
The claimed invention discloses integration of different systems/applications to improve
space planning compliance.”) The examiner respectfully disagrees.
A process that starts with data, applies an algorithm to the data, and then ends with a new form of data is abstract. (Recognicorp, LLC v. Nintendo Co, Ltd., 855 F.3d 1322 (Fed. Cir. 2017)) Applicant’s claims recite remarkable detail as to the particular steps of the algorithm used (Applicant’s 10/14/25 remarks, pp.33-50). Despite processing data from multiple stores and reporting the results to interested parties, the invention is nevertheless a patent ineligible algorithm. (SAP vs. Investpic, Fed Cir, 2018, “The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations (in the plot of a probability distribution function). No matter how much of an advance in the finance field the claims recite, the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the nonabstract application realm. An advance of that nature is ineligible for patenting.”)
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 1-4,8-10,13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding independent claims 1,7,13 the claimed invention recites an abstract idea without significantly more. The claims recite the abstract idea of generating a planogram which is a mental process. Other than reciting a processor/tools nothing in the claims precludes the steps from being performed mentally. But for the processor, the limitations on:
acquiring, … store-data over a predefined time span from a plurality of stores; obtaining, via the one or more hardware processors, sales, space, and demographic information from the store-data on a plurality of categories in the plurality of stores; processing, the sales, space, and demographic information, via a space optimization tool…, to perform space optimization in accordance with a set of predefined optimization rules comprising category level optimization rules and in aisle optimization rules, to generate:
a) aplurality of final space allocations for each of the plurality of categories of each of the plurality of stores, wherein the plurality of categories are various types of group of products having attribute values alternative to each other;
b) a plurality of delta space allocations for each of the plurality of categories of each of the plurality of stores, wherein a delta space is defined as a difference between an initial space occupied by a category amongst the plurality of categories and a final space … for the category; and
c) details on a space allocation order for the category for each incremental foot during each iteration of space optimization, … captured in a plurality of log files for a plurality of stores, wherein a) the final space is measured in the plurality of formats comprising, (i) linear feet, (11) Square feet (111) weighted visible space and (iv) cognitive visible space,
b) the delta space is measured in the plurality of formats comprising, (i) the delta space in square feet,(1i) the delta space in percentage, calculated by proportion of the delta space as compared to old space occupied by each of the plurality of categories and (111) the delta space in percentage, calculated by proportion of delta space as compared to available space for each of the plurality of categories of each of the plurality of stores in which the available space is decided by the optimization rules comprising minimum space and maximum space, and c) the order of space occupation is represented by the plurality of formats comprising (i) incremental square feet and (ii) cumulative square feet. wherein based on need, macro space optimization is carried out with one or more objectives including expansion of space, reduction of space and constant space of the store at individual level, wherein an ideal range of space change for each category for each store is decided by considering based on historical number of space changes and magnitude of space changes of the categories of the stores, wherein the optimization rules are used by the space optimization tool to address implementation strategies and to avoid implementation issues; creating, via the one or more hardware processors, a plurality of vectors comprising:
a) afinal space vector capturing a final space allocation corresponding to each of the plurality of categories of a store among the plurality of stores, wherein the final space allocation corresponding to each of the plurality of stores is processed in a single row vector format as the final space vector;
b) a delta space vector capturing a delta space allocation for each of the plurality of categories for the store, wherein the delta space allocation corresponding to each of the plurality of stores is processed in a single row vector format as the delta space vector; and
c) an allocation order vector capturing order of priority in space allocation from the space allocation order across the plurality of categories for the store, wherein a log file for a store from amongst the plurality of log files is processed into a plurality of row vectors with same length noted as ‘basic
allocation order vectors’, wherein each row vector represents an iteration of optimization, and number of row vectors 1s equal to number of iterations and length of each row vector is equal to number of the plurality of categories of corresponding store, each cell of the row represents a category among the plurality of categories, and value of each cell of the row represents a fixed incremental space allocation for the category for an incremental foot during the iteration, processing of basic allocation order vectors is carried out by calculating cumulative totals for each iteration and termed as ‘cumulative total allocation order vectors’ and further processing is carried out by adding basic allocation order vectors or cumulative total allocation order vectors one by one to form a single row vector representing the allocation order vector in two formats namely basic allocation order vector and cumulative allocation order vector, wherein a set of priority categories specific to stores for different applications associated with remodeling of stores are identified by locating the categories that are gaining incremental space during initial iterations and identification of corresponding stores from the set of patterns derived from allocation matrix and from cumulative total order vectors of corresponding stores; generating, via the one or more hardware processors, a plurality of space matrices wherein each column represents the store and number of columns are equivalent to number of the plurality of stores, and wherein each row represents a category and number of rows equivalent to a number of the plurality of categories, the plurality of space matrices comprising:
a) afinal space matrix, generated from the final space vector corresponding to each of the plurality of stores, by converting the format of the final space vector from a single row vector to a single column vector, and considering the plurality of the stores and arranging a plurality of final space vectors of the plurality of stores into a matrix format, wherein value of each element of the column vector represents final space allocated to each of the plurality of categories;
b) a delta space matrix, generated from the delta space vector corresponding to each of the plurality of stores, by converting format of the delta space vector from a single row vector to a single column vector, and considering the plurality of the stores and arranging a plurality of delta space vectors of the plurality of stores into a matrix format; and
c) aspace allocation order matrix, generated from the allocation order vector corresponding to each of the plurality of stores, by converting the allocation order vector format to a column vector format and considering the plurality of stores and arranging a plurality of allocation order vectors of the plurality of stores into a matrix format;
processing, … the final space matrix, the delta space matrix and the space allocation order matrix by using a plurality of formats of measurements of final space, delta space, and the space allocation order and applying standardization, a covariance matrix creation, and principal component analysis (PCA) to identify a set of patterns for each of the final space matrix, the delta space matrix and the space allocation order matrix based on eigen vectors and eigen values generated during the (PCA);
generating, …a store level mismatch score by comparing existing floor plans with the set of patterns derived from each of the final Space matrix, the delta space matrix and the space allocation order matrix, and processing the set of patterns for each of the final space matrix , the delta space matrix and the space allocation order matrix under the plurality of formats of measurements of the final space, the delta space, and the space allocation order to determine quality of the set of patterns from variance contribution of the set of patterns of PCA;
performing, …three level iterations comprising:
a) a first level of iterations to select top set of patterns from the set of patterns formed from each of (i) the final space matrix (ii) the delta space matrix, and (i11) the allocation order, under different formats of final space, delta space, and allocation order and selecting a top set of pattern for a suitable format among one of the final space, the delta space, and the space allocation order, wherein the quality of set of patterns is maximum for the top set of pattern, wherein improving the quality of patterns indicates increase in the number of stores following a pattern;
b) asecond level of iterations to apply to set of patterns received from the first level iterations in which outcome of each iteration within second level of iterations is used to locate a mismatch store using the mismatch score and recalculating any one of the (i) the final space matrix (11) the delta space matrix, and (iii) the space allocation order for only located mismatching store using identified optimization rules based on outcome of previous iteration within second level of iterations,
wherein when the new iteration
within second level iterations is started, new sub ranges are calculated based
on the mismatch score of previous iteration within second level iterations,
wherein the sub ranges are calculated for specific categories and specific
stores identified in the previous iteration within second level iterations based
on mismatch score and the sub ranges are decided based on narrowing down
the mismatch score and to reduce time for aisle fitment
and generating the set of patterns by adding new outcome received from the mismatching store for one of the recalculated (i) the final space matrix (ii) the delta space matrix, and (111) the space allocation order, wherein iteration continues until no store is deviating from the pattern in terms of aisle fitment;
wherein when
aisle are fitted for optimized space recommendation for the store, aisle
mismatch score is calculated based on extent of underfilling or overfilling of
the categories, wherein aisle are fitted in mods of aisle with optimized space
recommendation, wherein the mod is section of an aisle where a category of
goods is on display, wherein store level aisle mismatch score is a ratio of
number of mods resulting in underfilled or overfilled aisles to the total
number of mods across all the aisles within store, wherein the pattern is
formed at each iteration by combining new outcome received from
mismatch store and old outcomes present for remaining stores, wherein the
old outcome refers to the outcome present before present iteration resulting
only a part of input is modified at each iteration and enabling reduction of
running time for each iteration;
and
c) athird level of iterations applied to set of patterns received from the second level iterations in which outcome of each iteration within the third level of iterations is used to locate the store deviating from the set of patterns using multivariate distance, and recalculating any one of the (1) the final space matrix (ii) the delta space matrix, and (111) the allocation order for only those deviating store using identified optimization rules based on outcome of previous iteration within third level of iterations, and generating set of patterns by adding new outcome received from the deviating store for one of the recalculated (1) the final space matrix (ii) the delta space matrix, and (iii) the space allocation, wherein iteration continues until no store is deviating from the pattern, wherein the set of patterns received from the third level of iterations are accompanied with a set of reasons that lead to the formation of set of patterns based on one or more sales drivers associated with one or more stores, wherein the one or more sales drivers comprises demography, weather, and competition;
dynamically optimizing the optimization rules based on the outcome of each iteration and adjusting of the optimization rules is done based on the latest outcome of the iteration,
wherein dynamic optimization comprises identifying deviating
category for the deviating store to apply dynamic optimization rules,
wherein the first level iterations use predefined optimization rules, the second level iterations and the third level iterations use dynamic optimization rules in real time, wherein the identification of deviating stores and deviating categories enable to choose the stores and categories which require fine tuning to improve quality of patterns;
wherein each pattern comprises a value for each category, the value of each
category of the pattern is used to decide the new sub range of space change for the
category, wherein the value of each category of the pattern becomes a center of new
sub range and the center value is compared with upper bound and lower bound of
sub range of space change of a category of previous iteration and nearest one is
chosen as new sub range of space change for the category leads to minimization of
difference between recommended space of the category for the store and
recommended space of the category for the store as per the pattern indicating
improving quality of patterns as the store behaves exactly similar to the pattern;
and generating, via the one or more hardware processors, a set of floorplans and planograms in accordance with the set of patterns received from third level iterations and
assigning the set of floorplans and planograms to the plurality of stores based on the
patterns for remodeling of stores, the remodeling is achieved with reduced implementation
issues and reduced number of space changes and reduced magnitude of space changes,
wherein the floorplans and planograms are assigned to each of the plurality of stores in accordance with set of patterns received from third level iterations,
wherein the set of patterns has information of prioritization of categories and number of stores adhering to those patterns, wherein formation of the set of patterns received from the third level of iterations are identified based on the sales drivers associated with one or more stores, and a canonical discriminant analysis is carried out between patterns tagged to each store and store sales drivers, wherein the store sales drivers are factors which are captured at store level and influence sales of the items of the store, wherein the canonical discriminant analysis is a multivariate technique used for determination of a linear combination of sales drivers such that differentiation between the patterns is maximum, wherein the canonical discriminant analysis mines all possible linear combinations of sales drivers and provides top combinations in a form of canonical variable and canonical discriminant analysis enables to predict an expected pattern used for space optimization; and
selecting the number of top categories for remodeling of stores by utilizing the set of patterns, thereby improving the overall implementation efficiency by enabling simultaneous selection of categories for remodeling and implementations for multiple stores.
is a process that under its broadest reasonable interpretation could be considered a “Method of Organizing Human Activity” relating to the managing human behavior and interactions (fundamental economic practice). In addition, the claims appear to recite an ineligible mathematical process using vector and matrix manipulation to generate a result. Thus, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process, method of organizing human activity or mathematical formula grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element processor/tools amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Collecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”).] The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the planogram generating steps. The subject matter of claims 2/8/14 (multiple space formats), 3/9/15 (patterns are based on sales drivers), 4/10/16 (visualization of space allocation using order vectors) appear to add additional steps to the abstract idea, implemented by generic computers. These claims neither introduce a new abstract idea nor additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLEN C CHEIN whose telephone number is (571)270-7985. The examiner can normally be reached Monday-Friday 8am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian Zeender can be reached at (571) 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627