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 .
This application is a 371 of PCT/IB2022/061093 11/17/2022
This application has PRO 63/381,214 10/27/2022
This application has PRO 63/264,299 11/19/2021
Claim Interpretation
Although the present claims appear to recite analyzing investment in stock portfolio, but the examples in the specification and FIG. 4 and FIG. 5 suggest the term “portfolio” actually means inventory and “portfolio components” corresponds to items in inventory.
Objection to Abstract
Applicant did not file a proper abstract. The abstract dated 05/10/2024 shows the front page of WO 2023/089525. Applicant must submit a proper abstract.
Claim Objection
Claim 5 is objected for the following informality – “…based on the external index mapping a material or material or material group that matches the candidate portfolio” appears to contain typo.
Claim Rejection – 35 U.S.C. 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 1-13 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below. In the instant case, the claims are directed towards generating pricing information in the form purchase price index using company internal spend data, company proprietary price index, and public available price index. The concept is clearly related to generating company transaction data to manage company transaction activities, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Moreover, the claimed procedure can be performed mentally and the result can be presented on paper, thus the present claims also fall within the Mental Processes grouping. The claims do not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that the limitations, in the instant claims, are done by the generically recited computer device. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claims 1-13 and 15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-13 and 15 are directed to a process, machine, manufacture, or composition matter.
In Alice Corp. Pty. Ltd. v. CLS Bank Intern., 134 S. Ct. 2347 (2014), the Supreme Court applied a two-step test for determining whether a claim recites patentable subject matter. First, we determine whether the claims at issue are directed to one or more patent-ineligible concepts, i.e., laws of nature, natural phenomenon, and abstract ideas. Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–96 (2012)). If so, we then consider whether the elements of each claim, both individually and as an ordered combination, transform the nature of the claim into a patent-eligible application to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.
Claims 1-12 are directed to a machine (i.e., device/system claims).
Claims 13 is directed to a process (i.e., method claims).
Claims 15 is directed to a manufacture (i.e., machine-readable medium claims).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards generating pricing information in the form purchase price index using company internal spend data, company proprietary price index, and public available price index. The concept comprises storing purchasing transaction data for an organization, one or more portfolio components associated with the purchasing transaction data, and one or more external indices, selecting one or more of the portfolio components for form a candidate portfolio, determining a reference quantity and a reference spend associated with the candidate portfolio, generating a monthly adjusted portfolio spend, normalizing the monthly adjusted portfolio spend to form an internal index, storing the internal index, and outputting comparative data between the internal index and a corresponding external index. The concept is clearly related to generating company transaction data to manage company transaction activities, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Examiner also points out that the present claims, similar to the ineligible claims in Electric Power Group v. Alstom, recite obtaining data, analyzing data, and presenting result of the analysis. The claimed concept can be performed in the human mind and the result can be presenting on paper. As such, the present claims also fall within the Mental Processes grouping. The performance of the claim limitations using generic computer components (i.e., a processing circuitry and a memory and an interface) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping or mental processes grouping. Accordingly, the present claims recite an abstract idea.
Prong Two
Independent claims 1, 13, and 15 recite a processing circuitry and a memory and an interface as additional elements. Applicant’s specification states that the claimed invention can be implemented “in hardware, software, firmware or any combination thereof” (see paragraph 0076). The claimed invention clearly does not require any particular hardware or any hardware at all. Therefore, the additional elements are considered generic computer components. The additional elements are claimed to perform basic computer functions, such as storing data, selecting portfolio components, determining a reference quantity and a reference spend, generating a monthly adjusted portfolio spend (i.e., processing data and performing calculations), normalizing adjusted portfolio spend (i.e., processing data and performing calculations), storing data, and outputting data. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. The present claims do not solve a problem specifically arising in the realm of computer networks. The present claims do not recite limitation that improve the functioning of computer, effect a physical transformation, or apply the abstract concept in some other meaningful way beyond generally linking the use of the abstract concept to a particular technological environment. As such, the present claims fail to integrate into a practical application.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, Independent claims 1, 13, and 15 recite a processing circuitry and a memory and an interface as additional elements. Applicant’s specification states that the claimed invention can be implemented “in hardware, software, firmware or any combination thereof” (see paragraph 0076). The claimed invention clearly does not require any particular hardware or any hardware at all. Therefore, the additional elements are considered generic computer components. The additional elements are claimed to perform basic computer functions, such as storing data, selecting portfolio components, determining a reference quantity and a reference spend, generating a monthly adjusted portfolio spend (i.e., processing data and performing calculations), normalizing adjusted portfolio spend (i.e., processing data and performing calculations), storing data, and outputting data. According to MPEP 2106.05(d), “performing repetitive calculations”, “receiving, processing, and storing data”, “electronically scanning or extracting data from a physical document”, “electronic recordkeeping”, “storing and retrieving information in memory”, and “receiving or transmitting data over a network, e.g., using the Internet to gather data” are considered well-understood, routine, and conventional functions of computer. The present claims do not improve the functioning of computer. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Claim Rejection – 35 U.S.C. 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.
Claim(s) 1, 2, 5, 6, 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blank et al. (Patent No.: US 8,666,847), in view of LIFOPro (Lee Richardson, Problems that Can Arise When Using Internal Index LIFO to Calculate Inflation, May 27 2015, https://lifopro.com/problems-that-can-arise-when-using-internal-index-lifo-to-calculate-inflation/).
As per claim 1, Blank teaches a system comprising:
a memory configured to store purchasing transaction data for an organization, one or more portfolio components associated with the purchasing transaction data, and one or more external indices (see col 4 line 64 through col 5 line 5, prior art discloses receiving past data of purchased inventory items, this implies past data was stored in memory for retrieval, and the inventory items correspond to portfolio components; see col 5 line 25-32, the past price trend correspond to external indices; see col 13 line 57 through col 14 line 34, “components of a computing device 1100 that may utilized to execute embodiments and that includes a memory 1110, replenishment status program instructions 1112, a processor or controller 1120” for memory); and
processing circuitry communicatively coupled to the memory, the processing circuitry being configured to (see col 13 line 57 through col 14 line 34, “components of a computing device 1100 that may utilized to execute embodiments and that includes a memory 1110, replenishment status program instructions 1112, a processor or controller 1120”):
select one or more of the portfolio components stored to the memory to form a candidate portfolio (see col 5 line 17-32, “The replenishment status program analyzes prices for the inventory items that were listed by multiple merchants in the past in order to determine the price trend”; The “items that were listed” correspond to the candidate portfolio);
determine a reference quantity (see col 9 line 24-38, “At the start 305 of the method, intermediate computer 250 receives from electronic sources 215a-b via networks 240a-b electronic item data 216a-b indicating a number of specific inventory items 212s purchased by user 230 from merchants 210a-b in the past 310”; the reference quantity is the number purchased) and a reference spend (see col 9 24-38, “Intermediate computer 250 also receives from electronic sources 215a-b via networks 240a-b electronic item data 216a-b indicating a number of specific inventory items 212s sold by user 230 to merchants 210a-b in the past 315”; the reference spend is the number sold) associated with the candidate portfolio based on portions of the purchasing transaction data stored to the memory that are associated with the one or more portfolio components included in the candidate portfolio (see col 8 line 41 through col 9 line 3);
generate a monthly adjusted portfolio spend for the candidate portfolio (see col 12 line 48-59 and FIG. 8, inventory decreases as the items are sold over time, thus the portfolio spend is always being adjusted);
normalize the monthly adjusted portfolio spend to form an internal index
associated with the candidate portfolio (see col 13 line 11-34, the average rate is used to calculate (i.e., normalize to) an expected sales rate, which correspond to an internal index); and
store the internal index to the memory (see col 13 line 11-34); and
an interface configured to output number of portfolio components to purchase/replenish for profit maximization (see col 13 line 11-34).
Examiner notes however, Blank does not explicitly an interface configured to output comparative data between the internal index and a corresponding external index selected from the one or more external indices stored to the memory.
LIFOPro teaches an interface configured to output comparative data between the internal index and a corresponding external index selected from the one or more external indices stored to the memory (see page 1, “External indexes – The IRS Regs. were amended in 1982 to permit the use of the Inventory Price Index Computation (IPIC) method for which Producer Price Indexes (PPI) or Consumer Price Indexes (CPI) are used”, “Internal indexes – Thi is a comparison of the current & prior or base year prices for all items”; see page 4, “we have made numerous comparisons of internal vs. external index inflation…”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from LIFOPro to include an interface configured to output comparative data between the internal index and a corresponding external index selected from the one or more external indices stored to the memory. The modification would have been obvious, because it is merely applying a known technique (i.e., outputting comparative data between internal index and external index related to inventory items cost) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., allow user to quickly compare internal pricing to external pricing).
As per claim 2, Blank does not explicitly teach wherein the internal index is a purchase price index associated with the candidate portfolio as procured by the organization, and wherein each of the one or more external indices represent publicly available pricing information associated with the candidate portfolio.
LIFOPro teaches the internal index is a purchase price index associated with the candidate portfolio as procured by the organization, and wherein each of the one or more external indices represent publicly available pricing information associated with the candidate portfolio (see page 1, “External indexes – The IRS Regs. were amended in 1982 to permit the use of the Inventory Price Index Computation (IPIC) method for which Producer Price Indexes (PPI) or Consumer Price Indexes (CPI) are used”, “Internal indexes – This is a comparison of the current & prior or base year prices for all items”; see page 4, “we have made numerous comparisons of internal vs. external index inflation…”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from LIFOPro to include the internal index is a purchase price index associated with the candidate portfolio as procured by the organization, and wherein each of the one or more external indices represent publicly available pricing information associated with the candidate portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., outputting comparative data between internal index and external index related to inventory items cost) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., allow user to quickly compare internal pricing to external pricing).
As per claim 5, Blank teaches wherein the processing circuitry is configured to select the external index based on the external index mapping a material or material or material group that matches the candidate portfolio (see col 5 line 17-32, “The replenishment status program analyzes prices for the inventory items that were listed by multiple merchants in the past in order to determine the price trend”; also see col 1 line 51-61, “the price trend for the inventory items is determined based at least in part upon past prices of raw materials used to manufacture the inventory items, past retail prices of the inventory items, and/or past wholesale prices of the inventory items”).
As per claim 6, Blank teaches wherein the purchasing transaction data comprises historical purchasing transactions (see col 1 line 26-36, “receiving, at the computer, electronic data indicating prior sales of the inventory items, and determining, with the computer, a number of the invention items on a current data based on the number of inventory items purchased by the user in the past and the prior sales of the inventory items”).
Claim 13 and claim 15 are rejected for the same reasons as claim 1.
Claim(s) 3, 4, and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blank et al. (Patent No.: US 8,666,847), in view of LIFOPro (Lee Richardson, Problems that Can Arise When Using Internal Index LIFO to Calculate Inflation, May 27 2015, https://lifopro.com/problems-that-can-arise-when-using-internal-index-lifo-to-calculate-inflation/), and further in view of Official Notice.
As per claim 3, Blank does not teach wherein to normalize the monthly adjusted portfolio spend, the processing circuitry is configured to divide a monthly adjusted spend time series associated with the candidate portfolio by a sum of reference spend
values associated with the candidate portfolio.
Official Notice is taken that a general-purpose computer can divide a monthly adjusted spend time series associated with the candidate portfolio by a sum of reference spend values associated with the candidate portfolio. This limitation is merely a mathematical manipulation, not related to technology innovation. Therefore, this limitation does not carry patentable weight.
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Official Notice to include divide a monthly adjusted spend time series associated with the candidate portfolio by a sum of reference spend values associated with the candidate portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., normalize monthly adjusted portfolio spend) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., manipulate data to a better form for further analysis).
As per claim 4, Blank does not teach wherein to normalize the monthly adjusted portfolio spend, the processing circuitry is configured to divide a monthly adjusted spend time series associated with the candidate portfolio by an adjusted spend associated with the candidate portfolio in a predetermined reference month.
Official Notice is taken that a general-purpose computer can divide a monthly adjusted spend time series associated with the candidate portfolio by an adjusted spend associated with the candidate portfolio in a predetermined reference month. This limitation is merely a mathematical manipulation, not related to technology innovation. Therefore, this limitation does not carry patentable weight.
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Official Notice to include divide a monthly adjusted spend time series associated with the candidate portfolio by an adjusted spend associated with the candidate portfolio in a predetermined reference month. The modification would have been obvious, because it is merely applying a known technique (i.e., normalize monthly adjusted portfolio spend) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., manipulate data to a better form for further analysis).
As per claim 7, Blank does not explicitly teach wherein the processing circuitry is further configured to identify an inflection point in the comparative data based on detecting a temporal intersection between an increase in the internal index and an unchanging trend in the external index.
Official Notice is taken that identifying an inflection point in the comparative data based on detecting a temporal intersection between an increase in the internal index and an unchanging trend in the external index is an old and well-known data analytic technique. It is also not related to technology innovation, thus this feature does not carry patentable weight.
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Official Notice to include identifying an inflection point in the comparative data based on detecting a temporal intersection between an increase in the internal index and an unchanging trend in the external index. The modification would have been obvious, because it is merely applying a known technique (i.e., identifying inflection point) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., detect anomaly of inventory data).
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blank et al. (Patent No.: US 8,666,847), in view of LIFOPro (Lee Richardson, Problems that Can Arise When Using Internal Index LIFO to Calculate Inflation, May 27 2015, https://lifopro.com/problems-that-can-arise-when-using-internal-index-lifo-to-calculate-inflation/), and further in view of Tong (CN 112396468 A).
As per claim 8, Blank does not teach wherein the processing circuitry is further configured to remove one or more quantity-based anomalies and/or one or more price-based anomalies from the candidate portfolio to form an anomaly-corrected portfolio, and wherein to generate the monthly adjusted portfolio spend for the candidate portfolio, the processing circuitry is configured to generate the monthly adjusted portfolio spend based on the anomaly-corrected portfolio.
Tong teaches remove one or more quantity-based anomalies and/or one or more price-based anomalies from the candidate portfolio to form an anomaly-corrected portfolio, and wherein to generate the monthly adjusted portfolio spend for the candidate portfolio, the processing circuitry is configured to generate the monthly adjusted portfolio spend based on the anomaly-corrected portfolio (see page 3, “establishing k-means clustering algorithm, processing the to-be-processed quantity price data according to the k-means clustering algorithm…removing the unclassified to-be-processed quantity price data as abnormal transaction data”; see page 6, “firstly clustering by the k-means clustering algorithm, and eliminating the abnormal data”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Tong to include remove one or more quantity-based anomalies and/or one or more price-based anomalies from the candidate portfolio to form an anomaly-corrected portfolio, and wherein to generate the monthly adjusted portfolio spend for the candidate portfolio, the processing circuitry is configured to generate the monthly adjusted portfolio spend based on the anomaly-corrected portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., removing anomaly to generated adjusted portfolio spend) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., prevent anomalies from skewing the data).
As per claim 9, Blank does not teach wherein to remove the one or more quantity-based anomalies and/or one or more price-based anomalies, the processing circuitry is configured to: implement K-means clustering to form a standard transaction cluster and a nonstandard transaction cluster from the portions of the purchasing transaction data stored to the memory that are associated with the one or more portfolio components included in the candidate portfolio; and remove all transactions of the nonstandard transaction cluster from the candidate portfolio to form the anomaly-corrected portfolio.
Tong teaches wherein to remove the one or more quantity-based anomalies and/or one or more price-based anomalies, the processing circuitry is configured to: implement K-means clustering to form a standard transaction cluster and a nonstandard transaction cluster from the portions of the purchasing transaction data stored to the memory that are associated with the one or more portfolio components included in the candidate portfolio; and remove all transactions of the nonstandard transaction cluster from the candidate portfolio to form the anomaly-corrected portfolio (see page 3, “establishing k-means clustering algorithm, processing the to-be-processed quantity price data according to the k-means clustering algorithm…removing the unclassified to-be-processed quantity price data as abnormal transaction data”; see page 6, “firstly clustering by the k-means clustering algorithm, and eliminating the abnormal data”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Tong to include wherein to remove the one or more quantity-based anomalies and/or one or more price-based anomalies, the processing circuitry is configured to: implement K-means clustering to form a standard transaction cluster and a nonstandard transaction cluster from the portions of the purchasing transaction data stored to the memory that are associated with the one or more portfolio components included in the candidate portfolio; and remove all transactions of the nonstandard transaction cluster from the candidate portfolio to form the anomaly-corrected portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., removing anomaly to generated adjusted portfolio spend) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., prevent anomalies from skewing the data).
As per claim 10, Blank does not teach wherein the K-means clustering is a two-cluster K-means clustering, and wherein to implement the K-means clustering to form the standard transaction cluster and the nonstandard transaction cluster, the processing circuitry is configured to determine that the standard cluster has a first centroid position that is nearer to a zero point of a plane of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio as compared to a second centroid position of the nonstandard cluster.
Tong teaches the K-means clustering is a two-cluster K-means clustering, and wherein to implement the K-means clustering to form the standard transaction cluster and the nonstandard transaction cluster, the processing circuitry is configured to determine that the standard cluster has a first centroid position that is nearer to a zero point of a plane of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio as compared to a second centroid position of the nonstandard cluster (see page 6, “firstly clustering by the k-means clustering algorithm, and eliminating the abnormal data, clustering is the data object into a plurality of classes or clusters, the object in the same cluster has a higher similarity”, plurality of clusters include two-cluster).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Tong to include the K-means clustering is a two-cluster K-means clustering, and wherein to implement the K-means clustering to form the standard transaction cluster and the nonstandard transaction cluster, the processing circuitry is configured to determine that the standard cluster has a first centroid position that is nearer to a zero point of a plane of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio as compared to a second centroid position of the nonstandard cluster. The modification would have been obvious, because it is merely applying a known technique (i.e., using two cluster K-means clustering) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., using existing clustering technic to classify transaction data).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Blank et al. (Patent No.: US 8,666,847), in view of LIFOPro (Lee Richardson, Problems that Can Arise When Using Internal Index LIFO to Calculate Inflation, May 27 2015, https://lifopro.com/problems-that-can-arise-when-using-internal-index-lifo-to-calculate-inflation/), and further in view of Tong (CN 112396468 A) and Bria (Patent No.: US 8,190,534).
As per claim 11, Blank does not teach wherein to remove the one or more price-based anomalies, the processing circuitry is configured to: fit a piece-wise linear function to unit price history included in the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio using one or more forecasting tools; and remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit.
Bria teaches fit a piece-wise linear function to unit price history included in the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio using one or more forecasting tools; and remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit (see col 4 line 20-56, “The pricing module 124 may then institute a second-level filter to identify and exclude any price and demand deviations that are greater than two standard deviations away from a static mean of all respective deviations”, “A fit of a linear function may be negatively affected if the price elasticity determined using the suspected data produces price recommendations which are completely outside the bounds of historical data”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Bria to include fit a piece-wise linear function to unit price history included in the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio using one or more forecasting tools; and remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit. The modification would have been obvious, because it is merely applying a known technique (i.e., fitting linear function to historical price data) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., using existing linear function technic to analyze transaction data).
As per claim 12, Blank does not teach wherein the processing circuitry is further configured to remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit based on a determination that the one or more transactions form less than a predetermined percentage of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio.
Bria teaches remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit based on a determination that the one or more transactions form less than a predetermined percentage of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio (see col 4 line 20-56, “The pricing module 124 may then institute a second-level filter to identify and exclude any price and demand deviations that are greater than two standard deviations away from a static mean of all respective deviations”, “A fit of a linear function may be negatively affected if the price elasticity determined using the suspected data produces price recommendations which are completely outside the bounds of historical data”).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the present application to modify Blank with teaching from Bria to include remove one or more transactions of the portions of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio that deviate from an acceptance interval around the fit based on a determination that the one or more transactions form less than a predetermined percentage of the purchasing transaction data associated with the one or more portfolio components included in the candidate portfolio. The modification would have been obvious, because it is merely applying a known technique (i.e., removing anomaly to generated adjusted portfolio spend) to a known system (i.e., inventory data analytic system) ready to provide predictable result (i.e., prevent anomalies from skewing the data).
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
JAN-2026