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 .
Introduction
The following is a final Office action in response to Applicant’s submission filed on 3/5/2026. Currently claims 1-22 are pending and claims 1 and 12 are independent. Claims 1 and 12 have been amended from the original claim set dated 11/11/2022. No claims have been added or cancelled.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-22, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-11) are directed to a statutory category, namely a method. Claim 12 and its dependent claims (claims 13-22) are directed to a statutory category, namely a system/machine.
Step 2A (Prong 1): Claims 1, 12, which are substantially similar claims to one another, are directed to the abstract idea of “Mental processes”, or more particularly, “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system to analyze facilities/locations and determine analogous locations which can provide substitute data. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that finance/inventory analyst might perform for analyzing a potential new location/facility. The abstract elements of claims 1, 12, recite in part “Store data…Initiate forecast…Determine sufficiency…Obtain identifier…Select set…Obtain configuration…Generate similarity indicator…Select candidate facility…Substitute data…Utilize time series…”. Dependent claims 2-11 and 13-22, add to the abstract idea the following limitations which recite in part “Obtain proximity parameter…Select candidate…Initiate forecasting…Determine time series…Determine ranks…Combine ranks…Define order of execution…Determine if dataset included…Generate rank…Discard candidate facility…Determine whether dataset includes…Determine distance…Compare time series…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 12.
Step 2A (Prong 2): Independent claims 1, 12, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Memory…Processor…Datasets…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-11, 13-22 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 12, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Memory…Processor…Datasets…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (analyze facilities/locations and determine analogous locations which can provide substitute data) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0061] – “It will be appreciated that some embodiments may be comprised of one or more specialized processors (or "processing devices") such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein.”
Additionally, dependent claims 2-11, 13-22 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-22 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Brooks et al. (US 20180197130 A1) in view of Zenor (US 20140278768 A1) further in view of Yamada et al. (US 20200250691 A1)
Regarding claims 1 and 12, Brooks discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores), obtaining an identifier of a target facility among the facilities (Brooks ¶40 - At block 202, demographic and/or geographic data are collected and stored for a new store {i.e. target facility}); and substituting the historical time series of the selected candidate facility for the historical time series of the target facility in a forecasting mechanism (Brooks ¶14 - the system 100 identifies one or more primary sister stores that have similar demographic data in a similar geographic region (which can be in a completely different part of the country). The historic sales for the primary sister store(s) may be used as an input in determining forecasted sales... The system 100 evaluates these inputs in forecasting sales at the new store 101, and uses the forecasted sales to identify an initial inventory and quantities of that inventory for the new store 101, as well as the first set of replenishment shipments).
Brooks lacks storing, for a plurality of facilities, respective facility datasets including (i) facility attributes, and (ii) historical time series of values for a plurality of performance metrics; selecting a set of candidate facilities from the plurality of the facilities; obtaining a similarity evaluation stack configuration; for each candidate facility, generating a similarity indicator based on (i) the respective facility attributes, (ii) the respective historical time series, and (iii) the similarity evaluation stack configuration; selecting, based on the similarity indicators, one of the candidate facilities.
Zenor, from the same field of endeavor, teaches storing, for a plurality of facilities, respective facility datasets including (i) facility attributes, and (ii) historical time series of values for a plurality of performance metrics (Zenor ¶33 - The program 200 of FIG. 2 begins at block 202 where the example physical characteristics manager 110 assembles existing store data to identify corresponding store attributes (e.g., non-outcome descriptors) and corresponding outcome data (e.g., annual sales figures). In some examples, the client store descriptor database 104 is managed by a client having any number of stores throughout the region of interest (e.g., a nation)); selecting a set of candidate facilities from the plurality of the facilities (Zenor ¶34 - FIG. 3A is an example table 300 that merges client store data with physical characteristics information. In the illustrated example of FIG. 3A, the client data 302 includes store numbers 304 and corresponding store names 306 {i.e. set of candidate facilities}); obtaining a similarity evaluation stack configuration; for each candidate facility, generating a similarity indicator based on (i) the respective facility attributes, (ii) the respective historical time series, and (iii) the similarity evaluation stack configuration (Zenor ¶37 - the example principal components engine 114 generates principal components factors for each existing store location (analogs) using non-outcome descriptors (block 206)…FIG. 3C builds upon the example table 300 of FIGS. 3A and 3B described above. In the illustrated example of FIG. 3C, the principal components engine 114 generates principal components factors 350 for each store 304 based on the non-outcome data 308. While the illustrated example of FIG. 3C includes eight (8) example principal components factors, any number and/or types of additional and/or alternate factor(s) may be used); selecting, based on the similarity indicators, one of the candidate facilities (Zenor ¶48 - FIG. 8B illustrates an example table 850 in view of another candidate store location. In the illustrated example of FIG. 8B, the example table 850 shows a comparison between candidate store number 6611 in Mukwonago, Wis. (852), which is proximate to the previously discussed candidate location in Delafield, as described in connection with FIG. 8A. As a result of similarity calculations, the example table 850 includes a rank order of all available existing stores. In the illustrated example of FIG. 8B, an existing store in Portland, Oreg. (854) is associated with the highest relative similarity score to the candidate store in Mukwonago (i.e., similarity score value 0.5819) 852. In this example, the forecast suggests sales will be over $97 million (856) in the event the new store is constructed on the candidate site location in Mukwonago, Wis).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Brooks further lacks determining whether a historical time series of a target facility is sufficient for the forecasting mechanism, the target facility being an existing facility among the plurality of facilities and responsive to determining the historical time series of the target facility is sufficient for the forecasting mechanism, utilizing the historical time series of the target facility in the forecasting mechanism.
Yamada, from the same field of endeavor, teaches determining whether a historical time series of a target facility is sufficient for the forecasting mechanism, the target facility being an existing facility among the plurality of facilities and responsive to determining the historical time series of the target facility is sufficient for the forecasting mechanism, utilizing the historical time series of the target facility in the forecasting mechanism (Yamada Figs. 16 and 17).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the information analysis techniques of Yamada because Yamada discloses “since an appropriate prediction model is selected from among the plurality of prediction models by using the performance data, it is possible to select a relatively high accurate prediction model (Yamada ¶156)”. Additionally, Brooks further details that “As addressed further below, the system 100 provides an approach for determining an accurate estimate of merchandise and inventory demands at newly opened stores (Brooks ¶13)” so it would be obvious to consider including the additional information analysis techniques that Yamada discloses because it would help identify a more accurate model for Brooks to use within its analysis.
Regarding claims 2 and 13, Brooks in view of Zenor further in view of Yamada discloses obtaining a proximity parameter; and selecting the set of candidate facilities based on the proximity parameter (Brooks ¶14 - The system 100 uses demographic information of the area around the new store 101 and the geographic region where the new store 101 is. Based on this information, the system 100 identifies one or more primary sister stores that have similar demographic data in a similar geographic region).
Regarding claims 3 and 14, Brooks in view of Zenor further in view of Yamada discloses prior to selecting the set of candidate facilities, initiating the forecasting mechanism; and determining that the historical time series for the target facility does not satisfy a forecasting condition (Brooks ¶4 - This challenge of estimating inventory demands is even more difficult for newly opened stores. These stores do not have a historical record of sales that they can use to estimate inventory demands {i.e. condition not met}).
Regarding claims 4 and 15, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches wherein the similarity evaluation stack configuration includes a set of evaluation mechanisms; and wherein generating the similarity indicator for each candidate facility includes: determining respective ranks of the candidate facility relative to the other candidate facilities for each evaluation mechanism; and combining the ranks to generate the similarity indicator (Zenor ¶48 - FIG. 8B illustrates an example table 850 in view of another candidate store location. In the illustrated example of FIG. 8B, the example table 850 shows a comparison between candidate store number 6611 in Mukwonago, Wis. (852), which is proximate to the previously discussed candidate location in Delafield, as described in connection with FIG. 8A. As a result of similarity calculations, the example table 850 includes a rank order of all available existing stores. In the illustrated example of FIG. 8B, an existing store in Portland, Oreg. (854) is associated with the highest relative similarity score to the candidate store in Mukwonago (i.e., similarity score value 0.5819) 852. In this example, the forecast suggests sales will be over $97 million (856) in the event the new store is constructed on the candidate site location in Mukwonago, Wis).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 5 and 16, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches the similarity evaluation stack configuration defines an order of execution for the evaluation mechanisms; and wherein determining the respective ranks for each candidate facility is performed according to the order of execution (Zenor ¶37 - To reduce (e.g., minimize) the quantity of variables to be used when predicting performance related data associated with one or more candidate store locations, the example principal components engine 114 generates principal components factors for each existing store location (analogs) using non-outcome descriptors (block 206). As described above, principal components analysis on a data set reduces a relatively large number of data variables into a smaller number of data variables {i.e. order of execution}, in which the reduced number of data variables (i.e., the principal component variables) are uncorrelated with each other).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 6 and 17, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches the set of evaluation mechanisms includes an availability mechanism; and wherein generating the rank for each candidate facility for the availability mechanism includes determining whether the dataset of the candidate facility includes a historical time series for a first performance metric specified in the similarity evaluation stack configuration (Zenor ¶43 - Turning briefly to FIG. 6, an example table 600 showing calculation of the dissimilarity value 602 and the similarity value 604 between a candidate site 606 and an existing store 608 is shown. In the illustrated example of FIG. 6, principal components factors 610 associated with the candidate store 606 and principal components factors 612 associated with the existing store 608 are applied to example Equation 1 to generate dissimilarity values 614 associated with each factor {i.e. performance metrics} - Zenor ¶46 - FIG. 8A includes an example table 800 showing a comparison between candidate store number 6610 for the example candidate store location named "Big Box" in Delafield, Wis. 802. As a result of similarity calculations described above, the example table 800 includes a rank order of all available existing stores, which includes an existing store number 804, an existing store location 806, an existing store similarity score 808 in view of the candidate store of interest 802, an empirical outcome variable value 810 (e.g., prior year sales), and a weighted outcome variable value 812).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 7 and 18, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches generating the rank for each candidate facility for the availability mechanism further includes: generating the rank based on (i) the presence of the historical time series for the first performance metric, and (ii) a count of missing values for the first performance metric in the historical time series (Zenor ¶43 - Turning briefly to FIG. 6, an example table 600 showing calculation of the dissimilarity value 602 and the similarity value 604 between a candidate site 606 and an existing store 608 is shown. In the illustrated example of FIG. 6, principal components factors 610 associated with the candidate store 606 and principal components factors 612 associated with the existing store 608 are applied to example Equation 1 to generate dissimilarity values 614 associated with each factor {i.e. performance metrics} - Zenor ¶46 - FIG. 8A includes an example table 800 showing a comparison between candidate store number 6610 for the example candidate store location named "Big Box" in Delafield, Wis. 802. As a result of similarity calculations described above, the example table 800 includes a rank order of all available existing stores, which includes an existing store number 804, an existing store location 806, an existing store similarity score 808 in view of the candidate store of interest 802, an empirical outcome variable value 810 (e.g., prior year sales), and a weighted outcome variable value 812).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 8 and 19, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches when the candidate facility does not include a historical time series for the first performance metric, discarding the candidate facility prior to executing a subsequent one of the evaluation mechanisms (Zenor ¶43 - Turning briefly to FIG. 6, an example table 600 showing calculation of the dissimilarity value 602 and the similarity value 604 between a candidate site 606 and an existing store 608 is shown. In the illustrated example of FIG. 6, principal components factors 610 associated with the candidate store 606 and principal components factors 612 associated with the existing store 608 are applied to example Equation 1 to generate dissimilarity values 614 associated with each factor - Zenor ¶46 - FIG. 8A includes an example table 800 showing a comparison between candidate store number 6610 for the example candidate store location named "Big Box" in Delafield, Wis. 802. As a result of similarity calculations described above, the example table 800 includes a rank order of all available existing stores, which includes an existing store number 804, an existing store location 806, an existing store similarity score 808 in view of the candidate store of interest 802, an empirical outcome variable value 810 (e.g., prior year sales), and a weighted outcome variable value 812 - FIG. 8B (6609 Schaumburg) {i.e. 0 similarity so discard}.
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 9 and 20, Brooks in view of Zenor further in view of Yamada discloses a method (Brooks ¶2 - This invention relates generally to estimating inventory demands, and more particularly, to estimating inventory demands of merchandise items at newly opened stores).
Zenor further teaches the set of evaluation mechanisms includes an attribute matching mechanism; and wherein generating the rank for each candidate facility includes determining whether the dataset of the candidate facility includes a first facility attribute matching a corresponding facility attribute of the target facility (Zenor Fig. 3A - Zenor ¶33 - The program 200 of FIG. 2 begins at block 202 where the example physical characteristics manager 110 assembles existing store data to identify corresponding store attributes (e.g., non-outcome descriptors)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the new store analysis methodology/system of Brooks by including the site selection techniques of Zenor because Zenor discloses “Example methods, apparatus, systems and/or articles of manufacture disclosed herein employ one or more similarity functions with existing physical stores (sometimes referred to herein as "analogs") to identify which existing stores are the most similar to a candidate site (Zenor ¶14)”. Additionally, Brooks further details that “It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101 (Brooks ¶22)” so it would be obvious to consider including the additional site selection techniques that Zenor discloses because it would help identify the most similar existing store for subsequent analysis.
Regarding claims 10 and 21, Brooks in view of Zenor further in view of Yamada discloses the set of evaluation mechanisms includes a proximity mechanism; and wherein generating the rank for each candidate facility includes determining a geographic distance between the candidate facility and the target facility (Brooks ¶22 - As should be evident, the existing store database 112 could be searched on the basis of any desired demographic/geographic criteria (or combination of criteria) to match existing stores to the newly opened store 101. A number of different algorithms may be used that assign varying weights to different criteria to try to determine one or more existing stores that are the closest match to the newly opening store 101. It is generally contemplated that existing stores (or sister stores) with similar geographic/demographic characteristics {i.e. distance} as a newly opening store 101 will serve as good predictors of the inventory demands for the newly opening store 101).
Regarding claims 11 and 22, Brooks in view of Zenor further in view of Yamada discloses the set of evaluation mechanisms includes a historical matching mechanism; and wherein generating the rank for each candidate facility includes comparing at least one historical time series of the candidate facility to a corresponding historical time series of the target facility (Brooks ¶47 - At block 218, a second estimate of inventory demand is calculated based on the real time inventory data at the new store. This recalculation is intended to allow the consideration of real time feedback immediately after the new store opens. So, for example, if the sales of a certain type of merchandise item in the first few days is twice as much as was anticipated {i.e. compare time series}, the second estimate might be recalculated as twice as much as the first estimate).
Response to Arguments
Applicant's arguments filed 3/5/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to USPTO guidance for 35 USC § 101 rejections contained within MPEP 2106, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application.
Regarding the 35 USC § 103 rejections on the previous Office Action, Applicant amended the independent claims to further limit the claims with respect determining if there is enough data for a prediction. In light of this amendment, Examiner agrees that the original references did not teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Yamada as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
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 Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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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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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
Prosecution Insights