DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/12/2026 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 5/12/2026.
Claims 1, 13, and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are eligible. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner notes that the Applicant’s claims are merely implementing the abstract idea on a general purpose computer. The Execution of the abstract idea using generic cloud computing technology does not make the claims eligible (See MPEP 2106.05 and Cloud Computing). The Applicant merely amends in generic cloud usage for implementing the abstract idea, where Applicant did not invent cloud computing as shown by the prior art. Applicant’s arguments are not persuasive.
The Examiner further notes that the additional comparison amended into the claims make the abstract idea narrower, where a narrow abstract idea is still an abstract idea. Applicant does not properly identify the additional elements. Applicant’s arguments are not persuasive.
The Examiner again asserts that the interactive element is a generic display or GUI, where the displaying of data even if it is anonymized is not an improvement to the technology. For example, the Applicant’s claims generically recite that the data is anonymized, without reciting any technical details of how the data is anonymized. The Examiner refers to the rejection below for more information. Applicant’s arguments are not persuasive.
Applicant’s claims do not recite any sort of machine learning or training related to the Desjardins decision. Providing real time alerts for a human to make decisions does not improve the functioning of the technology and is directed towards the abstract idea as shown by the Examiner. Applicant again does not properly identify the additional elements, where merely implementing the abstract idea on a general purpose computer does not improve the technology (See MPEP 2106.05). Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
Applicant argues that the cited prior art does not disclose an actual recorded mean distribution, but Mulukutla et al. clearly discloses analyzing trends using data stored in a database throughout the reference for past present and future analysis as shown in Figures 4A-4E. The Examiner also specifically asserts that Figures 4A-4E specifically show different drop downs and input values for a human to manipulate to achieve the desired output, which are interactive elements. The Examiner strongly recommends amending in actual limitations of what was invented because generically reciting an interactive element is extremely broad and Applicant’s originally filed specification does not contain any figures of any sort of display or interactive element. Applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-18 are directed toward a process and claims 19-20 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), Independent claim 1 is directed toward a method, comprising: executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising: collecting transaction data from a plurality of stores, wherein the transaction data is continuously gathered and updated in real-time as transactions are processed at the stores; aggregating the collected transaction data to form anonymized aggregated data; analyzing the anonymized aggregated data to detect an anomaly in item sales for a monitored item by (i) comparing actual current item sales for a given store against a mean distribution of the monitored item sales for the given store to identify deviations beyond a threshold range and (ii) comparing actual current item sales for the given store against an actual recorded mean distribution of item sales continuously maintained for a monitored group as a whole to identify deviations beyond the threshold range, wherein the anomaly is detected within small increments of time of five minutes or less; generating a real-time alert based on the detected anomaly within the small increments of time to enable immediate item adjustments by stores to address current market conditions; and providing the real-time alert to a user via a user interface (UI) to enable immediate pricing, promotion, or inventory strategy adjustment with respect to the monitored item, wherein the real- time alert is provided as an interactive element that visually depicts movement or velocity of sales for the monitored item within a monitored group and wherein the interactive element is anonymized such that no identifying information for any store of the monitored group is displayed to the user (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing collected transaction data to detect anomalies in item sales to provide alerts regarding pricing, promotion, or inventory strategy to a human to make decisions by monitoring current sales throughout the stores, which is managing how humans interact for commercial purposes.
Independent claim 13 is directed toward a method, comprising: executing operations on a cloud-based analytics platform provided as a software-as-a-service to multiple retail stores, the cloud-based analytics platform operating external to and independently of processing environments of each of the multiple retail stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retail stores simultaneously without being constrained by any single retail store's infrastructure, the operations comprising: receiving transaction data from multiple retail stores in real time, wherein the transaction data is continuously received and updated from transaction managers of terminals for in-store transactions and from transaction systems for online transactions as the transactions are processed; anonymizing the transaction data to ensure confidentiality; analyzing the anonymized data by (i) processing a mean distribution analysis on item sales for each retail store, (ii) comparing actual current item sales for each retail store against an actual recorded mean distribution of item sales as a whole, and (iii) comparing actual current item sales for each retail store against the actual recorded mean distribution of item sales continuously maintained for the multiple retail stores as a whole using a standard deviation analysis to identify deviations, wherein the analyzing occurs in small increments of time of five minutes or less; generating a customized alert based on the analyzing within small increments of time, wherein the alerts provide information necessary for a particular retail store to make a real-time pricing, promotion, and inventory decision with respect to a monitored item; and presenting the customized alert within a user interface for interactive user engagement with the information provided in the customized alert, wherein the customized alert is presented as an interactive element that visually depicts movement or velocity of sales for the monitored item, and wherein the interactive element is anonymized such that no identifying information for any store is displayed to the user (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing collected transaction data to detect anomalies in item sales to provide alerts regarding pricing, promotion, or inventory strategy to a human to make decisions by monitoring current sales throughout the stores, which is managing how humans interact for commercial purposes.
Independent claim 19 is directed toward a system, comprising: at least one processor of a cloud-based server operating external to and independently of processing environments of each of multiple retailers, provided as a software-as-a-service to the multiple retailers, wherein the cloud-based server comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the at least one processor configured to execute instructions from a non-transitory computer- readable storage medium; and the instructions when executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least processor to perform operations comprising: collecting transaction data in real time from a plurality of terminals and transaction systems associated with multiple retailers wherein the transaction data is continuously gathered and updated as transactions are processed at the plurality of terminals and transaction systems; aggregating the transaction data into monitored groups of the multiple retailers based on applying user-defined filters; anonymizing the aggregated transaction data of each monitored group to prevent corresponding transaction data from being associated with any particular store; processing statistical analysis on the transaction data of each monitored group by (i) maintaining an accrual recorded mean distribution of item sales for each retailer in a monitored group, (ii) maintaining an actual recorded mean distribution of item sales for the monitored group as a whole, (iii) comparing actual current item sales for each retailer against the actual recorded mean distribution of item sales maintained for the monitored group as a whole to detect anomalies in sales of a monitored item by identifying deviations beyond a threshold range, wherein the anomalies are detected within small increments of time of five minutes or less; and providing an alert to a particular retailer associated with a particular monitored group based on a particular detected anomaly within the small increments of time as an interactive element that visually depicts movement or velocity of sales for the monitored item, wherein the interactive element is anonymized such that no identifying information for any retailer of the monitored group is displayed to a user, to enable the particular retailer to implement immediate item adjustments to address current market conditions (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing collected transaction data to detect anomalies in item sales to provide alerts regarding pricing, promotion, or inventory strategy to a human to make decisions by monitoring current sales throughout the stores, which is managing how humans interact for commercial purposes.
Dependent claims 2-12, 14-18, and 20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising: to a user via a user interface (UI) (claim 1)”; “executing operations on a cloud-based analytics platform provided as a software-as-a-service to multiple retail stores, the cloud-based analytics platform operating external to and independently of processing environments of each of the multiple retail stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retail stores simultaneously without being constrained by any single retail store's infrastructure, the operations comprising: from transaction managers of terminals; from transaction systems; within a user interface (claim 13)”; “a system, comprising: at least one processor of a cloud-based server operating external to and independently of processing environments of each of multiple retailers, provided as a software-as-a-service to the multiple retailers, wherein the cloud-based server comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the at least one processor configured to execute instructions from a non-transitory computer- readable storage medium; and the instructions when executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least processor to perform operations comprising: from a plurality of terminals and transaction systems associated with multiple retailers (claim 19)” which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-12, 14-18, and 20 further narrow the abstract idea and dependent claims 11-12 and 17 additionally recite “application programming interfaces (claim 11)”; “a multi-tenant database; a platform (claim 12)”; “an interactive user interface (claim 17)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; and System Independent claims 1, 13, and 19 recite “executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising: to a user via a user interface (UI) (claim 1)”; “executing operations on a cloud-based analytics platform provided as a software-as-a-service to multiple retail stores, the cloud-based analytics platform operating external to and independently of processing environments of each of the multiple retail stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retail stores simultaneously without being constrained by any single retail store's infrastructure, the operations comprising: from transaction managers of terminals; from transaction systems; within a user interface (claim 13)”; “a system, comprising: at least one processor of a cloud-based server operating external to and independently of processing environments of each of multiple retailers, provided as a software-as-a-service to the multiple retailers, wherein the cloud-based server comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the at least one processor configured to execute instructions from a non-transitory computer- readable storage medium; and the instructions when executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least processor to perform operations comprising: from a plurality of terminals and transaction systems associated with multiple retailers (claim 19)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0012-0016 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, dependent claims 2-12, 14-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, dependent claims 11-12 and 17 additionally recite “application programming interfaces (claim 11)”; “a multi-tenant database; a platform (claim 12)”; “an interactive user interface (claim 17)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mulukutla et al. (US 2011/0238461 A1) in view of Howe et al. (US 2022/0230164 A1) and further in view of Cloud Computing (Archived webpaged from december 22, 2023, https://web.archive.org/web/20231222001231/https://en.wikipedia.org/wiki/Cloud_computing).
Regarding Claim 1: Mulukutla et al. teach a method, comprising (See Abstract):
collecting transaction data from a plurality of stores (See Figure 1A, Figure 1C, Paragraph 0017 – “Information relevant to forecasting, including, without limitation, sales information, product information, store information and geographic information is periodically received by the present invention”, and Paragraph 0019);
wherein the transaction data is continuously gathered and updated in real-time as transactions are processed at the stores (See Paragraph 0017 – “the information may be received in near real-time as the sales occur, hourly, daily, weekly or in any other predefined or configurable frequency”, Paragraph 0029 – “statistically generated product-store-day sales forecasts and actual sales are analyzed in at least near real-time as the sales occur”, and claim 9);
aggregating the collected transaction data (See Table 6 – “aggregated”, Paragraphs 0021-0024 – “grouping of products, grouping of geographical areas, grouping of distribution channels”, Paragraph 0028, and Paragraph 0094);
analyzing the aggregated data to detect an anomaly in item sales for a monitored item (See Paragraphs 0028-0029 – “a product-store-day forecast is adjusted by analyzing forecasting anomalies across a cluster as well as the patterns of anomalies at a store within a configurable period of time”, Paragraph 0036, and claim 1);
by (i) comparing actual current item sales for a given store against a mean distribution of the monitored item sales for the given store to identify deviations beyond a threshold range and (ii) comparing actual current item sales for the given store against an actual recorded mean distribution of item sales continuously maintained for a monitored group as a whole to identify deviations beyond the threshold range, wherein the anomaly is detected within small increments of time of five minutes or less (See Paragraph 0017, Paragraphs 0028-0029 – “a product-store-day forecast is adjusted by analyzing forecasting anomalies across a cluster as well as the patterns of anomalies at a store within a configurable period of time”, Table 6, Paragraph 0035 – “difference between advancer and decliner counts, minimum sales thresholds, the number of trend reversals in a period, and any trend persistence”, Paragraph 0036, Paragraphs 0041-0048 – “retail alerts having an alert type equal to "Store Daily Forecast Vs Sales Deviation" from the start of the week to the current day … store weekly demand forecasts for the current week (distribution center level forecasts as well, overrides will get propagated bottom-up to distribution center level) … daily split ratios”, Paragraph 0050 – “sales for a product are less than the WSMinSalesThreshold then it is not considered for trend analysis”, Paragraph 0079 – “consistently deviating within the store by a certain threshold number of days from the forecast within a configurable period”, Paragraph 0084, Paragraph 0090, claim 1, and claim 9);
generating a real-time alert based on the detected anomaly within the small increments of time to enable immediate item adjustments by stores to address current market conditions (See Figures 4A-4E, Paragraph 0017, Paragraph 0029, Paragraphs 0032-0034, Paragraph 0042 – “a product-store-day forecast is adjusted by analyzing forecasting anomalies across a cluster as well as the patterns of anomalies at a store within a configurable period of time”, Paragraph 0069, Paragraph 0090 – “The "alert generation engine" looks at data anomalies and creates alerts”, and Paragraph 0091 – “promotions”);
and providing the real-time alert to a user via a user interface (UI) to enable immediate pricing, promotion, or inventory strategy adjustment with respect to the monitored item (See Figures 4A-4E, Paragraphs 0032-0034, Paragraph 0042 – “a product-store-day forecast is adjusted by analyzing forecasting anomalies across a cluster as well as the patterns of anomalies at a store within a configurable period of time”, Paragraph 0069, Paragraph 0090 – “The "alert generation engine" looks at data anomalies and creates alerts”, and Paragraph 0091 – “promotions”);
wherein the real- time alert is provided as an interactive element that visually depicts movement or velocity of sales for the monitored item within a monitored group (See Figure 4A, Figure 4B, Figure 4C, Figure 4D, Figure 4E, Paragraph 0031, Paragraph 0134 – “line 814 represents actual sales … line 822 represents the forecast without continuous forecasting, line 824 represents actual sales, and line 826 represents an adjusted forecast based on continuous forecasting”).
Mulukutla et al. do not specifically disclose executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising; to form anonymized aggregated data and wherein the interactive element is anonymized such that no identifying information for any store of the monitored group is displayed to the user. However, Howe et al. further teach to form anonymized aggregated data and wherein the interactive element is anonymized such that no identifying information for any store of the monitored group is displayed to the user (See Figures 3A, Figure 3B, Abstract, Paragraph 0013 – “anonymize consumer transaction data”, and Paragraphs 0019-0020).
The teachings of Mulukutla et al. and Howe et al. are related because both are analyzing consumer transactions/sales to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the anomaly in sales determination system of Mulukutla et al. to incorporate the anonymity of Howe et al. in order to ensure privacy is protected.
Mulukutla et al. in view of Howe et al. do not specifically disclose executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising. However, Cloud Computing further teaches executing operations on a cloud-based analytics platform provided as a software-as-a-service operating external to and independently of processing environments of each of a plurality of stores, wherein the cloud-based analytics platform comprises multiple servers cooperating as a single logical server and is scaled to serve multiple competing retailers simultaneously without being constrained by any single retailer's infrastructure, the operations comprising (See entire document as Applicant recites generic use of cloud computing).
The teachings of Mulukutla et al. and Howe et al. are related because both are analyzing consumer transactions/sales to make determinations. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the anomaly in sales determination system of Mulukutla et al. in view of Howe et al. to incorporate the cloud based technology of Cloud Computing in order to ensure the latest general purpose technology can be used to implement the abstract idea.
Regarding Claim 2: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 1. Mulukutla et al. do not specifically disclose the following. However, Howe et al. further teach wherein aggregating further includes ensuring that any store-specific identifying data from each store represented in the collected transaction data is masked to remain confidential and anonymous within the anonymized aggregated data (See Figures 3A, Figure 3B, Abstract, Paragraph 0013 – “anonymize consumer transaction data”, and Paragraphs 0019-0020).
The teachings of Mulukutla et al. and Howe et al. are related because both are analyzing consumer transactions/sales to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the anomaly in sales determination system of Mulukutla et al. to incorporate the anonymity of Howe et al. in order to ensure privacy is protected.
Regarding Claim 3: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 1. Mulukutla et al. further teach wherein aggregating further includes applying customizable filters to the aggregated data to form one or more monitored groups of stores (See Paragraphs 0021-0024 – “grouping of products, grouping of geographical areas, grouping of distribution channels” and Paragraph 0056).
Mulukutla et al. do not specifically disclose anonymized aggregated data. However, Howe et al. further teach anonymized aggregated data (See Figures 3A, Figure 3B, Abstract, Paragraph 0013 – “anonymize consumer transaction data”, and Paragraphs 0019-0020).
The teachings of Mulukutla et al. and Howe et al. are related because both are analyzing consumer transactions/sales to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the anomaly in sales determination system of Mulukutla et al. to incorporate the anonymity of Howe et al. in order to ensure privacy is protected.
Regarding Claim 4: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 3. Mulukutla et al. further teach wherein applying further includes applying customized filters to form the one or more monitored groups based on filters associated with regional trends, store formats or types, and cultural consumer behaviors (See Paragraphs 0021-0024 – “grouping of products, grouping of geographical areas, grouping of distribution channels”, Paragraphs 0030-0031 – “Retail sales trends may be, for example, a reaction to a particular marketing event or a weather related event in the area”, Paragraph 0056, and claim 1).
Regarding Claim 5: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 1. Mulukutla et al. further teach wherein analyzing further includes calculating and maintaining a mean distribution of item sales for the monitored item by store and by a monitored group as a whole (See Paragraph 0082, Paragraph 0084 – “smoothing_index = average_cumulative_sales (from the start of the period to the current day for all stores in the distribution center deviating in the same direction) / average_cumulative_forecast (for all stores in the distribution center deviating in the same direction (i.e., the main result-trend direction)); for (i=today; i < period end; i++) { projected_daily_forecast[i] = (smoothing_index) * daily_forecast[i]; }”, Paragraph 0086, and Paragraph 0089).
Regarding Claim 6: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 5. Mulukutla et al. further teach wherein analyzing further includes obtaining forecasted item sales for the monitored item by store from a forecasting model associated with each store of the monitored group (See Paragraph 0075 – “The forecasting engine is run on Tuesday. It identifies all alerts having an alert type of "Store Daily Forecast Vs Sales Deviation" for Sunday, Monday, Tuesday, which have a positive deviation from the forecast, and all alerts which have negative deviation. Assume that there are: (a) 100 stores within the distribution center (DC) or cluster”, Paragraph 0079, Paragraph 0082, Paragraph 0084, and Paragraph 0086,).
Regarding Claim 7: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 6. Mulukutla et al. further teach wherein obtaining further includes obtaining first thresholds for each store relevant to the mean distribution of item sales for a corresponding store (See Figure 1C, Table 6 – “threshold of inventory”, Paragraph 0050 – “WSMinSalesThreshold: The minimum threshold of sales to be considered for WS trend analysis”, Paragraph 0075, Paragraph 0079, Paragraph 0080, and Paragraph 0105 – “WSTrendConfirmationRatio, minTrendReversalsForNoiseDetection, SignalNoise Ratio, localforecastTrendDetectionThreshold, minSalesPerWeek ForNoise, LocalSignal NoiseRatio, minLocalTrendDurationThreshold, and LocalTrendConfirmationCount”).
Regarding Claim 8: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 7. Mulukutla et al. further teach wherein obtaining further includes obtaining second thresholds for each store relevant to the mean distribution of item sales for the monitored group as a whole (See Figure 1C, Table 6 – “threshold of inventory”, Paragraph 0050 – “WSMinSalesThreshold: The minimum threshold of sales to be considered for WS trend analysis”, Paragraph 0075, Paragraph 0079, Paragraph 0080, and Paragraph 0105 – “WSTrendConfirmationRatio, minTrendReversalsForNoiseDetection, SignalNoise Ratio, localforecastTrendDetectionThreshold, minSalesPerWeek ForNoise, LocalSignal NoiseRatio, minLocalTrendDurationThreshold, and LocalTrendConfirmationCount”).
Regarding Claim 9: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 8. Mulukutla et al. further teach wherein generating further includes identifying the detected anomaly by evaluating actual and current item sales by store against a corresponding forecasted item sales of a corresponding store for deviations above or below a corresponding first threshold (See Paragraph 0075, Paragraph 0079, Paragraph 0080, and Paragraph 0105).
Regarding Claim 10: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 9. Mulukutla et al. further teach wherein generating further includes identifying an additional detected anomaly by evaluating an updated mean distribution of item sales for the monitored group as a whole that accounts for the actual and current item sales of the monitored group as a whole against each mean distribution of item sales for each of the stores for additional deviations above or below corresponding a corresponding second threshold (See Figure 1C, Table 6, Paragraph 0050, Paragraph 0075, Paragraph 0079, Paragraph 0080, Paragraph 0082, Paragraph 0084, Paragraph 0086, Paragraph 0089, and Paragraph 0105).
Regarding Claim 11: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 1. Mulukutla et al. further teach further comprising integrating the method through application programming interfaces into existing services or existing systems of a retailer for an automated pricing, promotion, or inventory operation with respect to the monitored item (See Figures 4A-4E, Paragraphs 0032-0034, Paragraph 0042 – “a product-store-day forecast is adjusted by analyzing forecasting anomalies across a cluster as well as the patterns of anomalies at a store within a configurable period of time”, Paragraph 0069, Paragraph 0090 – “The "alert generation engine" looks at data anomalies and creates alerts”, Paragraph 0091 – “promotions”, and Paragraph 0134).
Regarding Claim 12: Mulukutla et al. in view of Howe et al. and further in view of Cloud Computing teach the limitations of claim 11. Mulukutla et al. further teach further comprising maintaining a multi-tenant database that allows multiple users from different retail stores to access a platform simultaneous (See Figure 1A, Paragraph 0018, Paragraph 0063, and Paragraph 0091).
Mulukutla et al. do not specifically disclose while maintaining data anonymity among the multiple users. However, Howe et al. further teach while maintaining data anonymity among the multiple users (See Figures 3A, Figure 3B, Abstract, Paragraph 0013 – “anonymize consumer transaction data”, and Paragraphs 0019-0020).
The teachings of Mulukutla et al. and Howe et al. are related because both are analyzing consumer transactions/sales to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the anomaly in sales determination system of Mulukutla et al. to incorporate the anonymity of Howe et al. in order to ensure privacy is protected.
Regarding Claims 13-20: Claims 13-20 recite limitations already addressed by the rejections of claims 1-12 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
Prosecution Insights