DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claims 1-20 are currently pending and have been addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 8, and 15 recite the limitation “training a machine-learning model using weighted averages.” The specification on Paragraphs 0072 & 0085 discloses training data sets. However, the specification when examined as a whole does not disclose wherein the training data sets include weighted averages of historical data over defined time periods.
Claims 2-7, 9-14, and 16-20 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 8, and 15.
Response to Arguments
Applicant's arguments filed on 05/26/2026 (related to the 103 Rejection) have been fully considered but they are not persuasive.
Applicant states, on pages 7-8, that Balasubramanian does not teach, suggest, or motivate "enriching the performance data, wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a machine-learning model using the historical data; and generating, using the trained machine-learning model, a target performance goal prediction."
Examiner respectfully disagrees with Applicant. Applicant’s specification states that a target performance goal prediction is merely using a machine learning to predict whether a store will meet its target performance goals based on historical data (see Applicant’s specification, Paragraphs 0073 & 0135). Balasubramanian et al. discloses wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a machine-learning model using the historical data; and generating, using the trained machine-learning model, a target performance goal prediction (Paragraph 0036, influencing factors that drive the performance metric; Paragraph 0044, training data includes historic sales from multiple retail stores; Paragraph 0077, Some embodiments include a forecast system that applies a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals). Based on broadest reasonable interpretation in light of the specification, Balasubramanian et al. discloses generating a target performance goal prediction since it predicts whether a store will meet its target performance goals based on historical data (e.g., predicted trend vs. goals). Therefore, Balasubramanian et al. improves upon Alspach-Goss et al. by further specifying wherein the one or more patterns, trends, or predictions related to sales are analyzed using a machine learning model.
Applicant's arguments filed on 05/26/2026 (related to the 101 Rejection) have been fully considered but are not persuasive.
Applicant states, on pages 6-7, that taken as a whole, the amended limitations of claim 1 amount to a non-conventional and non-generic arrangement of computer processor and functions, and other technical limitations, because the instant Application does not contain any information to suggest that the elements and/or the combination thereof are conventional, no other evidence has been provided indicating that such elements and/or their combination is conventional, and Applicant does not admit that the elements and/or their combination are conventional.
Examiner respectfully disagrees with Applicant. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In this case, tracking transaction data of a retailer to generate a target performance goal prediction is a sales behavior activity (e.g., predictions related to sales by the retailer or group of retailers). If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The mere nominal recitation of generic computer components does not take the claim out of the methods of organizing human activity grouping. The machine learning model is merely used to predict whether a store will meet its target performance goals based on historical data, which may be any which shown to have an effect on the transaction data, such as historical weather data., historical time of year data, historical holiday data, and/or historical store size data (Paragraph 0135). The machine learning is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element (MPEP 2106.05f). In this case, the machine learning model includes inputs (e.g., transaction data weighted averages) and outputs (e.g., target performance goal prediction). However, the claim does not provide any details about how the machine learning model operates (e.g., how the predictions are generated). See 2024 AI Guidance, Example 47. Thus, the training step is a black box, which is merely claiming the idea of a solution or outcome (MPEP 2106.05(a)).
Also, the step of “automatically receiving performance data in response to linking at least one retailer” is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)).
Lastly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or 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.
Independent claims 8 and 15 recite similar features and therefore are rejected for the same reasons as independent claim 2. Claims 2-7, 9-14, and 16-20 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claims 1, 8, and 15.
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 a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to an apparatus which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A system for creating and managing a retailer, the system to perform steps including: receiving an identifier for a retailer; linking at least one retailer to the identifier; in response to the linking the identifier to the at least one retailer, automatically receiving performance data for the at least one retailer, the performance data including at least one of new member enrollment data or transaction data; normalizing and processing the performance data, wherein normalizing and processing the performance data identifies one or more patterns, trends, or predictions related to sales by the retailer; and enriching the performance data, wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a model using weighted averages of historical data over defined time periods; and generating, using the trained model, a target performance goal prediction. These claim elements are considered to be abstract ideas because they are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In this case, tracking transaction data of a retailer to generate a target performance goal prediction is a sales behavior activity (e.g., predictions related to sales by the retailer or group of retailers). If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: a processor; a memory; a retailer network; a remote computing device; and a machine learning model.
The processor is merely used to: receive an identifier; link the at least one retailer to the identifier; receive performance data for the at least one retailer; and display the performance data (Paragraph 0008). The memory is merely used to store instructions (Paragraph 0069). The retailer network is merely used to enable accessibility and management of the data by various parties associated with the retailer (Paragraph 0002). The remote computing device is merely used to receive performance data for the at least one retailer (Paragraph 0006). The machine learning model is merely used to predict whether a store will meet its target performance goals based on historical data, which may be any which shown to have an effect on the transaction data, such as historical weather data., historical time of year data, historical holiday data, and/or historical store size data (Paragraph 0135). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f). These elements of “processor,” “memory,” “retailer network,” “remote computing device,” and “machine learning” are recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. The network and computing device are considered “field of use” since they are just used to receive and provide information for a performance analysis, but the technology is not improved (MPEP 2106.05h). Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of managing and analyzing data for a plurality of retailers. The specification shows that the processor is merely used to: receive an identifier; link the at least one retailer to the identifier; receive performance data for the at least one retailer; and display the performance data (Paragraph 0008). The memory is merely used to store instructions (Paragraph 0069). The retailer network is merely used to enable accessibility and management of the data by various parties associated with the retailer (Paragraph 0002). The remote computing device is merely used to receive performance data for the at least one retailer (Paragraph 0006). The machine learning model is merely used to predict whether a store will meet its target performance goals based on historical data, which may be any which shown to have an effect on the transaction data, such as historical weather data., historical time of year data, historical holiday data, and/or historical store size data (Paragraph 0135). In this case, the machine learning model includes inputs (e.g., weighted averages transaction data) and outputs (e.g., target performance goal prediction). However, the claim does not provide any details about how the machine learning model operates (MPEP 2106.05f). Also, the step of “automatically receiving performance data in response to linking at least one retailer” is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 8 is directed to a method at step 1, which is a statutory category. Claim 8 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 8 further recites: a user interface – which is merely used to display a visual representation of the performance data. Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Independent claim 15 is directed to an article of manufacture at step 1, which is a statutory category. Claim 15 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Claim 15 further recites: a non-transitory computer-readable medium; and a user interface. The non-transitory computer-readable medium is merely used to store a computer program (Paragraph 0053). The user interface is merely used to display a visual representation of the performance data (Paragraph 0007). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claim 2 is directed to an additional element such as: a user interface. The user interface is merely used to display a visual representation of the performance data (Paragraph 0007). Merely stating that the step is performed by a computer component results in “apply it” on a computer (MPEP 2106.05f) being applicable at both Step 2A, Prong 2 and Step 2B. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, instructions to display and/or arrange information in a graphical user interface may not be sufficient to show an improvement in computer-functionality (MPEP 2106.05a). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 3, 9, and 16 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of the abstract idea mentioned above - such as wherein the at least one retailer comprises a plurality of retailers. These processes are similar to the abstract idea noted in the independent claim because they further the limitations of the independent claim which are directed to “certain methods of organizing human activity” which include “commercial or legal interactions.” In addition, there are no additional elements to consider at Step 2A Prong 2 and Step 2B. Therefore, the claims still recite an abstract idea that can be grouped into “certain methods of organizing human activity.”
Dependent claims 4, 6, 10, 12, 17, and 19 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as wherein the processors is programmed to: rank the plurality of retailers based on the processed performance data; display to the retailer a ranking of the plurality of retailers; receive, in real-time updated performance data, normalize and process the performance data; and display a visual representation of the updated performance data. In this case, the main functions are merely used to: collect data (e.g., transaction data for a plurality of retailer); analyze the data (e.g., rank the plurality of retailers); and display certain results of the collection and analysis (e.g., display to the retailer a ranking of the plurality of retailers and updated performance). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the step of “receiving updated performance data” is considered a well-understood, routing, and conventional function since it’s just “receiving or transmitting data over a network” and “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 5, 11, and 18 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as wherein linking the at least one retailer to the identifier occurs in response to a user input. At Step 2A, Prong 2 - the user input is considered “field of use” since it’s just used to receive a retailer identifier, but the technology is not improved (MPEP 2106.05(h)). At Step 2B – the linking is considered a conventional computer function of “receiving and transmitting over a network” (MPEP 2106.05d). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 7 and 14 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as wherein the at least one processor is further programmed to receive performance goal data and compare the processed performance data to the performance goal data. In this case, the main functions are merely used to: collect data (e.g., performance data and performance goal data) and analyze the data (e.g., compare performance data to the performance goal data). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Dependent claims 13 and 20 are not directed to any additional claim elements. Rather, these claims offer further descriptive functions of elements found in the independent claims and addressed above - such as wherein normalizing and processing the performance data comprises applying one or more algorithms to the performance data to identify one or more patterns, trends, or predictions. In this case, the main functions are merely used to: collect data (e.g., performance data) and analyze the data (e.g., identify one or more patterns, trends, or predictions). Those are functions that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
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 1-3, 6-9, 12-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alspach-Goss et al. (US 2006/0053056 A1), in view of Balasubramanian et al. (US 2025/0307852 A1), in further view of Megahed et al. (US 2018/0005253 A1).
Regarding claim 1 (Currently Amended), Alspach-Goss et al. discloses a computing system for creating and managing a retailer network, the computing system comprising at least one processor and a memory device, the at least one processor programmed to perform steps including (Paragraph 0030, The present invention is described below with reference to block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products according to various embodiments of the invention; Paragraph 0031, These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks; Paragraph 0099, In an exemplary embodiment, retailer system 104 comprises a retailer terminal 108 and a retailer processor 110 in communication with database 111. Retailer terminal 108 comprises any input device capable of identifying a consumer ID or a supplementary member ID; Paragraph 0108, In this context, network-wide purchases include any purchases of items corresponding to retailers and/or manufacturers participating in system 100; In this case, Examiner notes that only the participating retailers are included in the retailer network):
receiving an identifier for a retailer network (Paragraph 0064, "Retailer ID", as used herein, comprises any symbol, indicia, code, number, or other identifier that may be associated with a retailer of any type of goods and/or services offered to a consumer, supplementary member, or other end-user. A retailer ID may also include or be associated with a "store ID", which designates the location of a particular store);
linking at least one retailer to the identifier (Paragraph 0135, In an exemplary embodiment, the UPC datafield may be linked by one or more additional pointers to other key fields, such as a consumer ID or supplementary member ID, a retailer ID, a manufacturer ID, and/or a third-party ID. These additional pointers may be used as means for compiling data which may be useful in any of the various data analyses performed by the rewards server 120);
in response to the linking the identifier to the at least one retailer, automatically receiving, from at least one remote computing device, performance data for the at least one retailer, the performance data including at least one of new member enrollment data or transaction data (Paragraph 0063, "Purchase data", as used herein, comprises data relating to the offer of any item to a consumer, supplementary member, or other user of goods and/or services. Purchase data may include data regarding any or all of the following: an item purchased; an item price; a number of items purchased; a total transaction price; a payment vehicle (e.g., cash, credit card, debit card, check, etc.); a time, date, and/or day of the week associated with a purchase; a store identifier; an employee identifier; a retailer item identifier; a loyalty identifier; a retailer loyalty identifier; a consumer's use of (which includes a reference to) a marketing offer (e.g., a coupon, a bonus offering, reward points, etc.); whether a purchase transaction takes place online or offline; and/or the like; Paragraph 0115, The online retailer may then communicate with the central rewards mechanism 102 to transmit and process a consumer ID or supplementary member ID, purchase data, etc., as described above with reference to retailer 104 of FIG. 1. In one embodiment, consumer terminal 118 operates in real-time, as described above with respect to rewards terminal 116; It can be noted that the claim language is written in alternative form. The limitation taught by Alspach-Goss et al. is based on “transaction/purchasing data");
normalizing and processing the performance data, wherein normalizing and processing the performance data identifies one or more patterns, trends, or predictions related to sales by the retailer (Paragraph 0051, "Consumer enrollment data" may comprise any of the following: name; address; date of birth; social security number; email address; gender; the names of any household members; a credit card number for charging any fees that may be associated with participation in the system; survey data; interests; educational level; spending trends; and/or any preferred brand names; Paragraph 0068, "Data analysis", as used herein, shall be understood to comprise quantitative and qualitative research, statistical modeling, regression analyses, market segmentation analyses, econometrics, and/or the like. Such analyses may be used to characterize a consumer, predict a consumer's behavior, and/or correlate any of the following: a consumer profile, a part of a consumer profile, a supplementary member profile, a part of a supplementary member profile, consumer enrollment data, purchase data, retailer data, manufacturer data, product or service data, and/or the like; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Examiner interprets “standardizing the data” as “normalizing the data” since the data is formatted/edited to permit mapping of multiple retailers, which is then used to analyze spending behaviors or patterns for at least one retailer);
and enriching the performance data, … (Paragraph 0051, "Consumer enrollment data" may comprise any of the following: name; address; date of birth; social security number; email address; gender; the names of any household members; a credit card number for charging any fees that may be associated with participation in the system; survey data; interests; educational level; spending trends; and/or any preferred brand names; Paragraph 0068, "Data analysis", as used herein, shall be understood to comprise quantitative and qualitative research, statistical modeling, regression analyses, market segmentation analyses, econometrics, and/or the like. Such analyses may be used to characterize a consumer, predict a consumer's behavior, and/or correlate any of the following: a consumer profile, a part of a consumer profile, a supplementary member profile, a part of a supplementary member profile, consumer enrollment data, purchase data, retailer data, manufacturer data, product or service data, and/or the like; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example; As specified in Paragraph 0134 of Applicant’s specification, data enrichment applies one or more analytical tools and/or algorithms to the transformed data to extract meaningful insights, patterns and trends. Based on broadest reasonable interpretation in light of the specification, Alspach-Goss et al. discloses enrichment the performance data since it can use the normalized data to provide consumer spending behaviors or patterns).
Although Alspach-Goss et al. discloses receiving and analyzing historical data to identify one more one or more patterns, trends, or predictions related to sales by the retailer (Paragraph 0051, spending trends; Paragraph 0068, predict a consumer’s behavior; Paragraph 0072, consumer spending behaviors or patterns), Alspach-Goss et al. does not specifically disclose generating, using the trained machine-learning model, a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data).
However, Balasubramanian et al. discloses and enriching the performance data, wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a machine-learning model using … historical data over defined time periods; and generating, using the trained machine-learning model, a target performance goal prediction (Paragraph 0030, In some embodiments, the anomaly detection system 102 applies a series of machine learning anomaly detection models to business metrics data, such as but not limited to product data, inventory data, sales data and the like, relative to products being sold through a retailer, and/or other relevant information to identify one or more anomalies. For example, a series of anomaly detection models can be applied to sales data relative to products being sold through a retailer to identify an anomaly relative to a threshold variation in the respective one or more business metrics (e.g., sales) over time of a particular category of products and/or an individual product; Paragraph 0036, The identification of cause-effect attributions includes the evaluation of multiple influencing factors that drive the metric performance. For example, embodiments typically consider time based factors that help in explaining and/or distinguishing whether the metric deviations are because of seasonal patterns, specific events, and other such time based considerations; Paragraph 0044, The training data database stores and updates relevant training data. The training data includes historic data of recipients and their association with known companies, predefined profiles of types of recipients, predefined profiles of known preferences of information, predefined associations of responsibilities to types of recipients and other such information. Further, the training data includes historic business metrics data, such as historic sales data (e.g., quantities of products sold, pricing, pricing adjustments, etc.), typically for one or more years, in association with historic inventory information, historic marketing information, and other such information. Some embodiments further include historic anomaly detected events in relation to known historic causes of those historic anomaly events. The training data additionally includes historic information about different information supplied to and/or accessed by different users corresponding to thousands or more products from hundreds of different suppliers and/or manufactures and sold from multiple different retail stores distributed over multiple different geographic areas; Paragraph 0077, Some embodiments include a forecast system that applies a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals. The forecasting can provide granular level of forecasts, including at multiple different retail sales channel levels (e.g., in-store, order and pickup, order and ship, e-commerce, etc.). Further, the forecasting, in some embodiments can provide exceptions or deviations from forecasts as against goals or thresholds. For example, trends and patterns can be mapped against goals).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers of the invention of Alspach-Goss et al. to further incorporate generating a target performance goal prediction of the invention of Balasubramanian et al. because doing so would allow the system to apply a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals (see Balasubramanian et al., Paragraph 0077). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Alspach-Goss et al. and Balasubramanian et al. discloses using a machine learning for generating a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data). Although Balasubramanian further discloses training the machine learning by identifying multiple influencing factors that drive the metric performance (see Balasubramanian, Paragraph 0036), the combination of Alspach-Goss et al. and Balasubramanian et al. does not specifically disclose wherein the machine learning is trained using the weighted averages of historical data over defined time periods.
However, Megahed et al. discloses training a machine-learning model using the weighted averages of historical data over defined time periods (Paragraph 0062, The problem of predicting sales stage conversion factor, and growth factor for opportunities in each of different sales stages may be defined as a machine learning problem over the historical sales data in the sales pipelines with corresponding sales stages. In particular, the sales stage conversation factor may be defined over the historical data of the same sales stage as a weighted average function. In such a function, two set of variables exist: (i) how many historical time periods should be used in computing the weighted average function, and (ii) how to identify the weights in this function to yield in the best prediction accuracy; Paragraph 0086, Then, the problem of sales predicting may be casted as a machine learning problem for learning the weights of the predicting function from the historical data. The weight learning may be approached as an optimization problem with the objective of minimizing the average error rates of sales prediction for opportunities in any of the sales stages pipelines based on all historical data available before the given target time period).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers (e.g., using a machine learning to predict whether a store will meet its target performance goals based on historical data) of the invention of Alspach-Goss et al. and Balasubramanian et al. to further specify wherein the machine learning is trained using the weighted averages of historical data over defined time periods generating a target performance goal prediction of the invention of Megahed et al. because doing so would allow the system to define a sales stage conversion factor over the historical data of the same sales stage as a weighted average function (see Megahed et al., Paragraph 0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8 (Currently Amended), Alspach-Goss et al. discloses a computer-implemented method for creating and managing a retailer network, the method comprising (Paragraph 0030, The present invention is described below with reference to block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products according to various embodiments of the invention; Paragraph 0099, In an exemplary embodiment, retailer system 104 comprises a retailer terminal 108 and a retailer processor 110 in communication with database 111. Retailer terminal 108 comprises any input device capable of identifying a consumer ID or a supplementary member ID; Paragraph 0108, In this context, network-wide purchases include any purchases of items corresponding to retailers and/or manufacturers participating in system 100; In this case, Examiner notes that only the participating retailers are included in the retailer network):
receiving an identifier for a retailer network (Paragraph 0064, "Retailer ID", as used herein, comprises any symbol, indicia, code, number, or other identifier that may be associated with a retailer of any type of goods and/or services offered to a consumer, supplementary member, or other end-user. A retailer ID may also include or be associated with a "store ID", which designates the location of a particular store);
linking at least one retailer to the identifier (Paragraph 0135, In an exemplary embodiment, the UPC datafield may be linked by one or more additional pointers to other key fields, such as a consumer ID or supplementary member ID, a retailer ID, a manufacturer ID, and/or a third-party ID. These additional pointers may be used as means for compiling data which may be useful in any of the various data analyses performed by the rewards server 120);
in response to the linking the identifier to the at least one retailer, automatically receiving, from at least one remote computing device, performance data for the at least one retailer, the performance data including at least one of new member enrollment data or transaction data (Paragraph 0063, "Purchase data", as used herein, comprises data relating to the offer of any item to a consumer, supplementary member, or other user of goods and/or services. Purchase data may include data regarding any or all of the following: an item purchased; an item price; a number of items purchased; a total transaction price; a payment vehicle (e.g., cash, credit card, debit card, check, etc.); a time, date, and/or day of the week associated with a purchase; a store identifier; an employee identifier; a retailer item identifier; a loyalty identifier; a retailer loyalty identifier; a consumer's use of (which includes a reference to) a marketing offer (e.g., a coupon, a bonus offering, reward points, etc.); whether a purchase transaction takes place online or offline; and/or the like; Paragraph 0115, The online retailer may then communicate with the central rewards mechanism 102 to transmit and process a consumer ID or supplementary member ID, purchase data, etc., as described above with reference to retailer 104 of FIG. 1. In one embodiment, consumer terminal 118 operates in real-time, as described above with respect to rewards terminal 116; It can be noted that the claim language is written in alternative form. The limitation taught by Alspach-Goss et al. is based on “transaction/purchasing data");
normalizing and processing the performance data (Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Examiner interprets “standardizing the data” as “normalizing the data” since the data is formatted/edited to permit mapping of multiple retailers, which is then used to analyze spending behaviors or patterns for at least one retailer);
and enriching the performance data, … (Paragraph 0051, "Consumer enrollment data" may comprise any of the following: name; address; date of birth; social security number; email address; gender; the names of any household members; a credit card number for charging any fees that may be associated with participation in the system; survey data; interests; educational level; spending trends; and/or any preferred brand names; Paragraph 0068, "Data analysis", as used herein, shall be understood to comprise quantitative and qualitative research, statistical modeling, regression analyses, market segmentation analyses, econometrics, and/or the like. Such analyses may be used to characterize a consumer, predict a consumer's behavior, and/or correlate any of the following: a consumer profile, a part of a consumer profile, a supplementary member profile, a part of a supplementary member profile, consumer enrollment data, purchase data, retailer data, manufacturer data, product or service data, and/or the like; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example; As specified in Paragraph 0134 of Applicant’s specification, data enrichment applies one or more analytical tools and/or algorithms to the transformed data to extract meaningful insights, patterns and trends. Based on broadest reasonable interpretation in light of the specification, Alspach-Goss et al. discloses enrichment the performance data since it can use the normalized data to provide consumer spending behaviors or patterns);
and causing to be displayed, on a user interface of a user computing device, a visual representation of the processed and enriched performance data (Paragraph 0026, Moreover, the computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by participants; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Paragraph 0165, Analytics module 916 generates purchaser profiles by accessing and using the data in detail database 918 (step 1130). As will be appreciated, any known methods for performing data analysis, analytics, econometrics, modeling, data mining, marketing analyses, etc., may make use of the combined consumer enrollment data and purchase data stored in detail database 918. Analytics module 916 may generate purchaser profiles in the form of reports, summary data sheets, spread sheets, graphical output, combinations of these, and/or the like. The purchaser profiles may be stored by detail database 918, viewed on a display screen (e.g., display device 930), printed, transmitted to an end-user 924, and/or the like).
Although Alspach-Goss et al. discloses receiving and analyzing historical data to identify one more one or more patterns, trends, or predictions related to sales by the retailer (Paragraph 0051, spending trends; Paragraph 0068, predict a consumer’s behavior; Paragraph 0072, consumer spending behaviors or patterns), Alspach-Goss et al. does not specifically disclose generating, using the trained machine-learning model, a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data).
However, Balasubramanian et al. discloses and enriching the performance data, wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a machine-learning model using … historical data over defined time periods; and generating, using the trained machine-learning model, a target performance goal prediction (Paragraph 0030, In some embodiments, the anomaly detection system 102 applies a series of machine learning anomaly detection models to business metrics data, such as but not limited to product data, inventory data, sales data and the like, relative to products being sold through a retailer, and/or other relevant information to identify one or more anomalies. For example, a series of anomaly detection models can be applied to sales data relative to products being sold through a retailer to identify an anomaly relative to a threshold variation in the respective one or more business metrics (e.g., sales) over time of a particular category of products and/or an individual product; Paragraph 0036, The identification of cause-effect attributions includes the evaluation of multiple influencing factors that drive the metric performance. For example, embodiments typically consider time based factors that help in explaining and/or distinguishing whether the metric deviations are because of seasonal patterns, specific events, and other such time based considerations; Paragraph 0044, The training data database stores and updates relevant training data. The training data includes historic data of recipients and their association with known companies, predefined profiles of types of recipients, predefined profiles of known preferences of information, predefined associations of responsibilities to types of recipients and other such information. Further, the training data includes historic business metrics data, such as historic sales data (e.g., quantities of products sold, pricing, pricing adjustments, etc.), typically for one or more years, in association with historic inventory information, historic marketing information, and other such information. Some embodiments further include historic anomaly detected events in relation to known historic causes of those historic anomaly events. The training data additionally includes historic information about different information supplied to and/or accessed by different users corresponding to thousands or more products from hundreds of different suppliers and/or manufactures and sold from multiple different retail stores distributed over multiple different geographic areas; Paragraph 0077, Some embodiments include a forecast system that applies a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals. The forecasting can provide granular level of forecasts, including at multiple different retail sales channel levels (e.g., in-store, order and pickup, order and ship, e-commerce, etc.). Further, the forecasting, in some embodiments can provide exceptions or deviations from forecasts as against goals or thresholds. For example, trends and patterns can be mapped against goals).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers of the invention of Alspach-Goss et al. to further incorporate generating a target performance goal prediction of the invention of Balasubramanian et al. because doing so would allow the system to apply a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals (see Balasubramanian et al., Paragraph 0077). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Alspach-Goss et al. and Balasubramanian et al. discloses using a machine learning for generating a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data). Although Balasubramanian further discloses training the machine learning by identifying multiple influencing factors that drive the metric performance (see Balasubramanian, Paragraph 0036), the combination of Alspach-Goss et al. and Balasubramanian et al. does not specifically disclose wherein the machine learning is trained using the weighted averages of historical data over defined time periods.
However, Megahed et al. discloses training a machine-learning model using the weighted averages of historical data over defined time periods (Paragraph 0062, The problem of predicting sales stage conversion factor, and growth factor for opportunities in each of different sales stages may be defined as a machine learning problem over the historical sales data in the sales pipelines with corresponding sales stages. In particular, the sales stage conversation factor may be defined over the historical data of the same sales stage as a weighted average function. In such a function, two set of variables exist: (i) how many historical time periods should be used in computing the weighted average function, and (ii) how to identify the weights in this function to yield in the best prediction accuracy; Paragraph 0086, Then, the problem of sales predicting may be casted as a machine learning problem for learning the weights of the predicting function from the historical data. The weight learning may be approached as an optimization problem with the objective of minimizing the average error rates of sales prediction for opportunities in any of the sales stages pipelines based on all historical data available before the given target time period).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers (e.g., using a machine learning to predict whether a store will meet its target performance goals based on historical data) of the invention of Alspach-Goss et al. and Balasubramanian et al. to further specify wherein the machine learning is trained using the weighted averages of historical data over defined time periods generating a target performance goal prediction of the invention of Megahed et al. because doing so would allow the system to define a sales stage conversion factor over the historical data of the same sales stage as a weighted average function (see Megahed et al., Paragraph 0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15 (Currently Amended), Alspach-Goss et al. discloses at least one non-transitory computer-readable medium comprising instructions stored thereon for creating and managing a retailer network, the instructions executable by at least one processor to cause the at least one processor to perform steps including (Paragraph 0029, he present invention may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium; Paragraph 0030, These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executing on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks; Paragraph 0099, In an exemplary embodiment, retailer system 104 comprises a retailer terminal 108 and a retailer processor 110 in communication with database 111. Retailer terminal 108 comprises any input device capable of identifying a consumer ID or a supplementary member ID; Paragraph 0108, In this context, network-wide purchases include any purchases of items corresponding to retailers and/or manufacturers participating in system 100; In this case, Examiner notes that only the participating retailers are included in the retailer network):
receiving an identifier for a retailer network (Paragraph 0064, "Retailer ID", as used herein, comprises any symbol, indicia, code, number, or other identifier that may be associated with a retailer of any type of goods and/or services offered to a consumer, supplementary member, or other end-user. A retailer ID may also include or be associated with a "store ID", which designates the location of a particular store);
linking at least one retailer to the identifier (Paragraph 0135, In an exemplary embodiment, the UPC datafield may be linked by one or more additional pointers to other key fields, such as a consumer ID or supplementary member ID, a retailer ID, a manufacturer ID, and/or a third-party ID. These additional pointers may be used as means for compiling data which may be useful in any of the various data analyses performed by the rewards server 120);
in response to the linking the identifier to the at least one retailer, automatically receiving, from at least one remote computing device, performance data for the at least one retailer, the performance data including at least one of new member enrollment data or transaction data (Paragraph 0063, "Purchase data", as used herein, comprises data relating to the offer of any item to a consumer, supplementary member, or other user of goods and/or services. Purchase data may include data regarding any or all of the following: an item purchased; an item price; a number of items purchased; a total transaction price; a payment vehicle (e.g., cash, credit card, debit card, check, etc.); a time, date, and/or day of the week associated with a purchase; a store identifier; an employee identifier; a retailer item identifier; a loyalty identifier; a retailer loyalty identifier; a consumer's use of (which includes a reference to) a marketing offer (e.g., a coupon, a bonus offering, reward points, etc.); whether a purchase transaction takes place online or offline; and/or the like; Paragraph 0115, The online retailer may then communicate with the central rewards mechanism 102 to transmit and process a consumer ID or supplementary member ID, purchase data, etc., as described above with reference to retailer 104 of FIG. 1. In one embodiment, consumer terminal 118 operates in real-time, as described above with respect to rewards terminal 116; It can be noted that the claim language is written in alternative form. The limitation taught by Alspach-Goss et al. is based on “transaction/purchasing data");
normalizing and processing the performance data (Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Examiner interprets “standardizing the data” as “normalizing the data” since the data is formatted/edited to permit mapping of multiple retailers, which is then used to analyze spending behaviors or patterns for at least one retailer;
enriching the performance data, … (Paragraph 0051, "Consumer enrollment data" may comprise any of the following: name; address; date of birth; social security number; email address; gender; the names of any household members; a credit card number for charging any fees that may be associated with participation in the system; survey data; interests; educational level; spending trends; and/or any preferred brand names; Paragraph 0068, "Data analysis", as used herein, shall be understood to comprise quantitative and qualitative research, statistical modeling, regression analyses, market segmentation analyses, econometrics, and/or the like. Such analyses may be used to characterize a consumer, predict a consumer's behavior, and/or correlate any of the following: a consumer profile, a part of a consumer profile, a supplementary member profile, a part of a supplementary member profile, consumer enrollment data, purchase data, retailer data, manufacturer data, product or service data, and/or the like; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example; As specified in Paragraph 0134 of Applicant’s specification, data enrichment applies one or more analytical tools and/or algorithms to the transformed data to extract meaningful insights, patterns and trends. Based on broadest reasonable interpretation in light of the specification, Alspach-Goss et al. discloses enrichment the performance data since it can use the normalized data to provide consumer spending behaviors or patterns);
and causing to be displayed, on a user interface of a user computing device, a visual representation of the processed and enriched performance data (Paragraph 0026, Moreover, the computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by participants; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Paragraph 0165, Analytics module 916 generates purchaser profiles by accessing and using the data in detail database 918 (step 1130). As will be appreciated, any known methods for performing data analysis, analytics, econometrics, modeling, data mining, marketing analyses, etc., may make use of the combined consumer enrollment data and purchase data stored in detail database 918. Analytics module 916 may generate purchaser profiles in the form of reports, summary data sheets, spread sheets, graphical output, combinations of these, and/or the like. The purchaser profiles may be stored by detail database 918, viewed on a display screen (e.g., display device 930), printed, transmitted to an end-user 924, and/or the like).
Although Alspach-Goss et al. discloses receiving and analyzing historical data to identify one more one or more patterns, trends, or predictions related to sales by the retailer (Paragraph 0051, spending trends; Paragraph 0068, predict a consumer’s behavior; Paragraph 0072, consumer spending behaviors or patterns), Alspach-Goss et al. does not specifically disclose generating, using the trained machine-learning model, a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data).
However, Balasubramanian et al. discloses enriching the performance data, wherein enriching the performance data comprises generating a target performance goal prediction, wherein generating the target performance goal prediction comprises: receiving historical data, wherein the historical data is data that influences transaction data; training a machine-learning model using … historical data over defined time periods; and generating, using the trained machine-learning model, a target performance goal prediction (Paragraph 0030, In some embodiments, the anomaly detection system 102 applies a series of machine learning anomaly detection models to business metrics data, such as but not limited to product data, inventory data, sales data and the like, relative to products being sold through a retailer, and/or other relevant information to identify one or more anomalies. For example, a series of anomaly detection models can be applied to sales data relative to products being sold through a retailer to identify an anomaly relative to a threshold variation in the respective one or more business metrics (e.g., sales) over time of a particular category of products and/or an individual product; Paragraph 0036, The identification of cause-effect attributions includes the evaluation of multiple influencing factors that drive the metric performance. For example, embodiments typically consider time based factors that help in explaining and/or distinguishing whether the metric deviations are because of seasonal patterns, specific events, and other such time based considerations; Paragraph 0044, The training data database stores and updates relevant training data. The training data includes historic data of recipients and their association with known companies, predefined profiles of types of recipients, predefined profiles of known preferences of information, predefined associations of responsibilities to types of recipients and other such information. Further, the training data includes historic business metrics data, such as historic sales data (e.g., quantities of products sold, pricing, pricing adjustments, etc.), typically for one or more years, in association with historic inventory information, historic marketing information, and other such information. Some embodiments further include historic anomaly detected events in relation to known historic causes of those historic anomaly events. The training data additionally includes historic information about different information supplied to and/or accessed by different users corresponding to thousands or more products from hundreds of different suppliers and/or manufactures and sold from multiple different retail stores distributed over multiple different geographic areas; Paragraph 0077, Some embodiments include a forecast system that applies a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals. The forecasting can provide granular level of forecasts, including at multiple different retail sales channel levels (e.g., in-store, order and pickup, order and ship, e-commerce, etc.). Further, the forecasting, in some embodiments can provide exceptions or deviations from forecasts as against goals or thresholds. For example, trends and patterns can be mapped against goals).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers of the invention of Alspach-Goss et al. to further incorporate generating a target performance goal prediction of the invention of Balasubramanian et al. because doing so would allow the system to apply a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals (see Balasubramanian et al., Paragraph 0077). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Alspach-Goss et al. and Balasubramanian et al. discloses using a machine learning for generating a target performance goal prediction (see Applicant’s specification, Paragraphs 0073 & 0135, machine learning to predict whether a store will meet its target performance goals based on historical data). Although Balasubramanian further discloses training the machine learning by identifying multiple influencing factors that drive the metric performance (see Balasubramanian, Paragraph 0036), the combination of Alspach-Goss et al. and Balasubramanian et al. does not specifically disclose wherein the machine learning is trained using the weighted averages of historical data over defined time periods.
However, Megahed et al. discloses training a machine-learning model using the weighted averages of historical data over defined time periods (Paragraph 0062, The problem of predicting sales stage conversion factor, and growth factor for opportunities in each of different sales stages may be defined as a machine learning problem over the historical sales data in the sales pipelines with corresponding sales stages. In particular, the sales stage conversation factor may be defined over the historical data of the same sales stage as a weighted average function. In such a function, two set of variables exist: (i) how many historical time periods should be used in computing the weighted average function, and (ii) how to identify the weights in this function to yield in the best prediction accuracy; Paragraph 0086, Then, the problem of sales predicting may be casted as a machine learning problem for learning the weights of the predicting function from the historical data. The weight learning may be approached as an optimization problem with the objective of minimizing the average error rates of sales prediction for opportunities in any of the sales stages pipelines based on all historical data available before the given target time period).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers (e.g., using a machine learning to predict whether a store will meet its target performance goals based on historical data) of the invention of Alspach-Goss et al. and Balasubramanian et al. to further specify wherein the machine learning is trained using the weighted averages of historical data over defined time periods generating a target performance goal prediction of the invention of Megahed et al. because doing so would allow the system to define a sales stage conversion factor over the historical data of the same sales stage as a weighted average function (see Megahed et al., Paragraph 0062). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2 (Currently Amended), which is dependent of claim 1, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claim 1. Alspach-Goss et al. further discloses wherein the at least one processor is further programmed to cause to be displayed, on a user interface of a user computing device, a visual representation of the processed and enriched performance data (Paragraph 0026, Moreover, the computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by participants; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Paragraph 0165, Analytics module 916 generates purchaser profiles by accessing and using the data in detail database 918 (step 1130). As will be appreciated, any known methods for performing data analysis, analytics, econometrics, modeling, data mining, marketing analyses, etc., may make use of the combined consumer enrollment data and purchase data stored in detail database 918. Analytics module 916 may generate purchaser profiles in the form of reports, summary data sheets, spread sheets, graphical output, combinations of these, and/or the like. The purchaser profiles may be stored by detail database 918, viewed on a display screen (e.g., display device 930), printed, transmitted to an end-user 924, and/or the like; As specified in Paragraph 0134 of Applicant’s specification, data enrichment applies one or more analytical tools and/or algorithms to the transformed data to extract meaningful insights, patterns and trends. Based on broadest reasonable interpretation in light of the specification, Alspach-Goss et al. discloses enrichment the performance data since it can use the normalized data to provide consumer spending behaviors or patterns).
Regarding claims 3, 9, and 16 (Original), which are dependent of claims 1, 8, and 15, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 1, 8, and 15. Alspach-Goss et al. further discloses wherein the at least on retailer comprises a plurality of retailers (see Figure 1 and related text in Paragraph 0108, In this context, network-wide purchases include any purchases of items corresponding to retailers and/or manufacturers participating in system 100).
Regarding claim 6 (Original), which is dependent of claim 1, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claim 1. Alspach-Goss et al. further discloses wherein the at least one processor is further programmed to receive, in real-time from the at least one remote computing device, updated performance data, normalize and process the performance data (Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Paragraph 0115, In one embodiment, consumer terminal 118 operates in real-time, as described above with respect to rewards terminal 116; Paragraph 0162, If the consumer associated with the transaction file already has an established record in detail database 918, as determined by a search of detail database 918 by data conditioner 914 for the consumer ID contained in the transaction file, then data conditioner 914 writes the newly obtained transaction file to the established record in detail database 918 (step 1122), thereby updating the consumer's record to reflect the additional purchases. This updated record may then be used by analytics module 916 to generate a purchaser profile, as described in greater detail below (step 1130)).
Regarding claims 7 and 14 (Original), which is dependent of claims 1 and 18, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 1 and 8. Although Alspach-Goss et al. discloses analyzing performance data for a plurality of retailers (Paragraph 0072, spending behaviors or patterns for multiple retailers), Alspach-Goss et al. does not specifically disclose wherein the at least one processor is further programmed to receive performance goal data and compare the processed performance data to the performance goal data.
However, Balasubramanian et al. further discloses wherein the at least one processor is further programmed to receive performance goal data and compare the processed performance data to the performance goal data ((Paragraph 0030, In some embodiments, the anomaly detection system 102 applies a series of machine learning anomaly detection models to business metrics data, such as but not limited to product data, inventory data, sales data and the like, relative to products being sold through a retailer, and/or other relevant information to identify one or more anomalies. For example, a series of anomaly detection models can be applied to sales data relative to products being sold through a retailer to identify an anomaly relative to a threshold variation in the respective one or more business metrics (e.g., sales) over time of a particular category of products and/or an individual product; Paragraph 0044, he training data database stores and updates relevant training data. The training data includes historic data of recipients and their association with known companies, predefined profiles of types of recipients, predefined profiles of known preferences of information, predefined associations of responsibilities to types of recipients and other such information. Further, the training data includes historic business metrics data, such as historic sales data (e.g., quantities of products sold, pricing, pricing adjustments, etc.), typically for one or more years, in association with historic inventory information, historic marketing information, and other such information. Some embodiments further include historic anomaly detected events in relation to known historic causes of those historic anomaly events. The training data additionally includes historic information about different information supplied to and/or accessed by different users corresponding to thousands or more products from hundreds of different suppliers and/or manufactures and sold from multiple different retail stores distributed over multiple different geographic areas; Paragraph 0077, Some embodiments include a forecast system that applies a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals. The forecasting can provide granular level of forecasts, including at multiple different retail sales channel levels (e.g., in-store, order and pickup, order and ship, e-commerce, etc.). Further, the forecasting, in some embodiments can provide exceptions or deviations from forecasts as against goals or thresholds. For example, trends and patterns can be mapped against goals).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers of the invention of Alspach-Goss et al. to further incorporate generating a target performance goal prediction of the invention of Balasubramanian et al. because doing so would allow the system to apply a set of machine learning forecast models to identify deviations between predicted trends relative to intended goals (see Balasubramanian et al., Paragraph 0077). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 12 and 19 (Original), which are dependent of claims 8 and 15, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 8 and 15. Alspach-Goss et al. further discloses wherein the at least one processor is further programmed to receive, in real-time from the at least one remote computing device, updated performance data, normalize and process the performance data (Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Paragraph 0115, In one embodiment, consumer terminal 118 operates in real-time, as described above with respect to rewards terminal 116; Paragraph 0162, If the consumer associated with the transaction file already has an established record in detail database 918, as determined by a search of detail database 918 by data conditioner 914 for the consumer ID contained in the transaction file, then data conditioner 914 writes the newly obtained transaction file to the established record in detail database 918 (step 1122), thereby updating the consumer's record to reflect the additional purchases. This updated record may then be used by analytics module 916 to generate a purchaser profile, as described in greater detail below (step 1130)), and cause to be displayed, on the user interface of the user computing device, a visual representation of the updated processed performance data (Paragraph 0165, Analytics module 916 generates purchaser profiles by accessing and using the data in detail database 918 (step 1130). As will be appreciated, any known methods for performing data analysis, analytics, econometrics, modeling, data mining, marketing analyses, etc., may make use of the combined consumer enrollment data and purchase data stored in detail database 918. Analytics module 916 may generate purchaser profiles in the form of reports, summary data sheets, spread sheets, graphical output, combinations of these, and/or the like. The purchaser profiles may be stored by detail database 918, viewed on a display screen (e.g., display device 930), printed, transmitted to an end-user 924, and/or the like).
Regarding claims 13 and 20 (Original), which are dependent of claims 8 and 15, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 8 and 15. Alspach-Goss et al. further discloses wherein normalizing and processing the performance data comprises applying one or more algorithms to the performance data to identify one or more patterns, trends, or predictions (Paragraph 0051, "Consumer enrollment data" may comprise any of the following: name; address; date of birth; social security number; email address; gender; the names of any household members; a credit card number for charging any fees that may be associated with participation in the system; survey data; interests; educational level; spending trends; and/or any preferred brand names; Paragraph 0068, "Data analysis", as used herein, shall be understood to comprise quantitative and qualitative research, statistical modeling, regression analyses, market segmentation analyses, econometrics, and/or the like. Such analyses may be used to characterize a consumer, predict a consumer's behavior, and/or correlate any of the following: a consumer profile, a part of a consumer profile, a supplementary member profile, a part of a supplementary member profile, consumer enrollment data, purchase data, retailer data, manufacturer data, product or service data, and/or the like; Paragraph 0072, The system of the present invention associates or maps manufacturer UPC data and retailer SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyses and/or analyses relating to consumer spending behaviors or patterns for example. By matching or associating the retailer SKU and the manufacturer's UPC, the system permits the standardization of goods and/or services codes at the network level. This standardization not only permits a record of both the specific item purchased and its manufacturer, regardless of the particular retailer involved in the transaction, but it permits the mapping of multiple consumers, multiple goods and/or services, multiple retailers, and/or multiple manufacturers to advantageously cross-market goods and services to consumers; Examiner interprets “standardizing the data” as “normalizing the data” since the data is formatted/edited to permit mapping of multiple retailers, which is then used to analyze spending behaviors or patterns for at least one retailer).
Claims 4, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Alspach-Goss et al. (US 2006/0053056 A1), in view of Balasubramanian et al. (US 2025/0307852 A1), in further view of Megahed et al. (US 2018/0005253 A1) and Gao (CN 115311014 A).
Regarding claims 4, 10, and 17 (Original), which are dependent of claims 3, 9, and 16, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 3, 9, and 16. Although Alspach-Goss et al. discloses analyzing performance data for a plurality of retailers (Paragraph 0072, spending behaviors or patterns for multiple retailers), Alspach-Goss et al. does not specifically disclose ranking the plurality of retailers based on the processed performance data.
However, Gao discloses wherein the at least one processor is further programmed to rank the plurality of retailers based on the processed performance data and causing to be displayed to the retailer a ranking of the plurality of retailers (Page 7, The merchant ranking information is "merchant TOP5" in FIG. 7, showing a single amount and/or sales ranking first five merchants, such as merchant B2-B6 in the drawing. at the same time, displaying the single amount and/or sales of each merchant through the manner of strip-shaped picture and digital. Taking the merchant B2 as an example to illustrate, the single amount is 112 pen, sales is 12000h).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers of the invention of Alspach-Goss et al. to further specify wherein the analysis includes ranking the plurality of retailers based on the performance data of the invention of Gao because doing so would allow the system to display the top 5 merchants based on sales of each merchant (see Gao, Page 7). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 5, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Alspach-Goss et al. (US 2006/0053056 A1), in view of Balasubramanian et al. (US 2025/0307852 A1), in further view of Megahed et al. (US 2018/0005253 A1) and Olives et al. (US 2009/0048884 A1).
Regarding claims 5, 11, and 18 (Original), which are dependent of claims 1, 8, and 15, the combination of Alspach-Goss et al., Balasubramanian et al., and Megahed et al. discloses all the limitations in claims 1, 8, and 15. Although Alspach-Goss et al. discloses linking at least one retailer to the identifier (Paragraph 0135, datafield may be linked by one or more additional pointers to other key fields, such as a retailer ID), Alspach-Goss et al. does not specifically disclose wherein linking the at least one retailer to the identifier occurs in response to a user input.
However, Olives et al. discloses wherein linking the at least one retailer to the identifier occurs in response to a user input (Paragraph 0018, Referring to FIG. 1, a merchant benchmarking tool provider (MBTP) 106 provides multiple merchants M(1 to J) 102 access to the MBT, an output of the MBT, or a combination thereof via a network 104. As seen in FIG. 1, each merchant M(j) 102 of multiple merchants M(1 to J) 102 is represented by the M(j) 102, where j can be a value from 1 to J.). The computer may be communicatively linked to a database 108 storing the transaction data of multiple merchants such as the merchants M(1 to J) 102; Paragraph 0019, The MBT may have an interactive user interface allowing for data entry such as information about the merchant M(j) 102. The MBT can then utilize the inputted information along with other information to which the MBT has access, such as payment processing industry information or consumer transaction histories in the database 108 to create the reports (e.g., sales volume, new customer, existing customer reports); Paragraph 0025, The merchant M(j) 102 may be a group of stores organized as a single franchise of retail clothing business entity (e.g., Saks Fifth Avenue) or a single store that may be a franchisee of the franchisor (e.g., a Saks Fifth Avenue.RTM. store 9999 located in Los Angeles) having the corresponding merchant identifier (ID) "RETCL4456.").
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the system used for analyzing performance data for a plurality of retailers, wherein the plurality of retailers are linked using an identifier of the invention of Alspach-Goss et al. to further specify wherein linking the at least one retailer to the identifier occurs in response to a user input of the invention of Olives et al. because doing so would allow the system to access consumer transaction histories in the database for a specific merchant (see Olives et al., Paragraph 0019). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Yates (US 2023/0076398 A1) – discloses unique affiliate links with unique URLs linking to e-commerce sites of enrolled merchants. Enrolled merchants or an affiliate network handling such linking can then track purchases made by enrolled cardholders who are linked to e-commerce cites by unique URLs (see at least Paragraph 0049).
Bhasin et al. (US 2020/0104865 A1) – discloses a purchase prediction module to continuously upload the merchant transaction data to the purchase prediction system to receive immediate prediction data for a user's second purchase based on the user's first purchase, or to periodically upload the data and receive delayed or periodic prediction data (see at least Paragraph 0023).
Kim (KR 102482869 B1) - discloses sales information received from each of the plurality of store owner terminal devices 120, among a plurality of franchise affiliated stores corresponding to the plurality of store owner terminal devices 120, A sales ranking of the first franchise member store corresponding to the first store owner terminal device 120a may be determined. For example, the processor 111 may determine the sales ranking of the first franchise member store by arranging the plurality of franchise member stores in order of highest sales (see at least Page 8).
Laserson et al. (US 2022/0383339 A1) – discloses retailers and the entities can access data store 115 and perform queries and generate reports via interface 135 of retailer servers 130 and via interface 143 of entity servers 140. t is to be noted that data store 115 can be organized in any number of manners, such as via tables by retailer, tables by store of retailer, tables by entity, tables by type of item, etc. When integration service 114 processes the workflows, the appropriate tables needed that span multiple different retailers/entities or that are associated with just one retailer/entity or store of the retailer can be searched selectively. In this manner, transaction identifiers are unique for a given retailer or a given store within a given table, but the integration service 114 can search across multiple different tables associated with multiple different retailers/entities when conditions defined in a retailer/entity workflow dictate (see at least Paragraphs 0049-0050).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARJORIE PUJOLS-CRUZ whose telephone number is (571)272-4668. The examiner can normally be reached Mon-Thru 7:30 AM - 5:00 PM.
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/MARJORIE PUJOLS-CRUZ/ Examiner, Art Unit 3624
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