DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
This office action is in response to the most recent filings filed by applicant on 07/27/26. This office action is a final.
Claims 1 and 8 are amended
Claims 3 and 10 are cancelled
No claims are added
Claims 1-2, 4-9 and 11-14 are pending
Note:
In the amended independent claims 1 and 8, the claim limitations discussed are broad and the specification does not provide enough detailed support to show to one of ordinary skill in the art what certain terms in the claim limitations mean.
For instance, the amended claim limitations in claim 8 recite: “reducing dimensionality of the received consumer data by using factor analysis of the collected consumer data, wherein the factor analysis comprises constructing a correlation matrix of variables from the received consumer data, extracting factors from the correlation matrix, rotating the factors, and identifying consumer segments based on attitudes, functional needs, and emotional needs represented by the extracted factors” is recited so broadly and at a high level of generality and applicants haven’t pointed to the specification to show where the amended claims are recited in the specification to help one of ordinary skill in the art better understand the scope of the claims.
The specification is pretty short and does not contain a whole lot of detail on how the above steps are carried out. For instance, in [0033]: The data received through the data analysis module 104 is high dimensional data. High-dimensional data refers to datasets with a large number of variables, or "features," per observation. In high-dimensional spaces, the distance between data points increases, making it harder for predictive models to distinguish between relevant and irrelevant patterns. This leads to poor generalization and overfitting, where the model learns noise rather than useful patterns. As the number of dimensions increases, the volume of the space grows exponentially, and the data points become sparse. Predictive models rely on finding relationships between data points, but in high-dimensional spaces, it becomes more challenging to identify meaningful patterns as data points are spread thinly across the space. This results in poor generalization capabilities of the model. High-dimensional datasets require models to perform significantly more computations, slowing down the training and prediction processes. Each additional dimension adds complexity to the model, requiring more computational power and time, which reduces the model's practicality, especially when applied to real-time decision-making. With many features or dimensions, predictive models can easily "overfit" the training data, capturing noise or irrelevant patterns rather than the true underlying relationships. This overfitting means that the model will perform well on the training data but fail to generalize to new, unseen data. Further, higher dimensions mean that more data is needed to create a robust model. For each additional dimension, exponentially more data points are required to maintain the same level of accuracy. Without enough data, predictive models trained on high-dimensional datasets are likely to perform poorly due to insufficient coverage of the data space. [0034]: In an aspect of the present invention, the processor 102 includes an analysis module 106 configured to perform factor analysis on the received consumer data. The factor analysis may be used to identify different consumer segments based on attitudes, functional needs, and emotional needs. The factor analysis may involve identifying latent variables or factors that explain a large part of the variation observed in the data. These latent variables or factors may be used to define different consumer segments…. [0039]: This approach reduces dimensionality by identifying latent variables that capture the complex relationships between consumer attitudes, functional needs, and emotional needs. By clustering consumer segments based on these latent factors, the system offers more accurate insights and predictions than traditional methods. This technical challenge of multi-dimensional consumer data is solved by transforming raw, high-dimensional data into a manageable format for use in predictive models, leading to higher accuracy in consumer segmentation and market growth predictions. It advances existing AI and ML technologies by automating and refining this data preparation process. Here, there are no details provided how these processes are being carried out.
Additionally, the steps in the claim are not tied together. For instance, the limitation “estimating market size for each identified consumer segment based on current consumption” is so broad. How are you doing this? How are you estimating market size based on current consumption? How are you gathering current consumption information? Is this something you are calculating or is it just being gathered or received?
Similarly, the limitation “creating sub-groups based on demographics” is also very broad. How did you gather or calculate demographics? How are you now sub-grouping based on that? Also, how does this connect to the previous steps?
Similarly, the limitation “determining growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics using multinomial regression” is also very broad and again disconnected from previous steps. How are you determining consumption behavior? How does this relate to the previous steps? Also are demographics and consumer demographics the same? Similarly, is the consumption behavior in any way related to current consumption or are they two separate things?
Similarly, the limitation “predicting potential market size based on model weights and predicted probability of switching between consumer classes” is also very broad and again disconnected from previous steps. Is potential market size and estimated market size related? Where are you getting model weights from? Where are you getting consumer classes? How are you switching between consumer classes?
The above are just a few examples of the ambiguity in understanding the claims. In light of these notes, the amended claims do not overcome previously presented rejections under 101 and 103. As is discussed below. This note is intended as a conversation starter to help applicants understand the examiner’s perspective.
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 1-2, 4-9 and 11-14 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-2 and 4-7 is/are directed to a system which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 8-9 and 11-14 is/are directed to a method which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1 and 8, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method Claim 8 is directed to an abstract idea, as evidenced by claim limitations “receiving, consumer data; reducing dimensionality of the received consumer data by using factor analysis of the collected consumer data and identifying consumer segments, wherein the factor analysis comprises constructing a correlation matrix of variables from the received consumer data, extracting factors from the correlation matrix, rotating the factors, and identifying consumer segments based on attitudes, functional needs, and emotional needs represented by the extracted factors; estimating market size for each identified consumer segment based on current consumption; creating sub-groups based on demographics; determining growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics; and predicting potential market size based on model weights and predicted probability of switching between consumer classes.”
The specification discusses [0007]: According to an aspect of the present disclosure, a system for quantifying drivers of growth of niche categories through a Market Emergence Model is provided. The system includes a data collection module configured to receive consumer data collected using a carefully designed survey. The system also includes an analysis module configured to identify consumer segments using factor analysis of the received consumer data. The system further includes a market size estimation module configured to estimate market size for each identified consumer segment based on current consumption. Additionally, the system includes a demographic categorization module configured to create sub-groups based on demographics. The system also includes a growth potential determination module configured to determine growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics using multinomial regression. Finally, the system includes a market size prediction module configured to predict potential market size based on model weights or coefficients and predicted probability of switching from one consumer class to another.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing consumer segments using consumer data to estimate market size for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 8 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A method for quantifying drivers of growth of niche categories, comprising: by a processor, using multinomial regression;. A system for quantifying drivers of growth of niche categories, comprising: a data collection module configured to: an analysis module configured to: a market size estimation module configured to: a market size prediction module configured to:” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, 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. The claims are directed to an abstract idea with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1 and 8 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
The additional elements of a “using multinomial regression”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “using multinomial regression” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2, 4-7, 9 and 11-14 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 9 recite “wherein the consumer data is collected using a survey that collects data on demographics, socio-economic status, consumption behavior, category perception, general psychographics, and functional and emotional needs related to the category”. Dependent claims 11 recite “wherein creating sub-groups based on demographics comprises creating sub-groups based on a combination of age, city, and gender”. Dependent claims 13 recite “further comprising performing data cleaning and reliability checks to handle potential outliers or anomalies in the consumer data”. Dependent claims 14 recite “wherein performing data cleaning and reliability checks comprises removing data from consumers who give the same rating to all questions in the survey”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 12 recites “wherein the multinomial regression uses the identified consumer segments and demographics as independent variables and consumption behavior as the dependent variable”. In the claim limitations “multinomial regression” is an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2, 4-7, 9 and 11-14 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A method for quantifying drivers of growth of niche categories, comprising: by a processor, using multinomial regression; A system for quantifying drivers of growth of niche categories, comprising: a data collection module configured to: an analysis module configured to: a market size estimation module configured to: a market size prediction module configured to:” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0042]-[0043], [0051]-[0052], [0055], [0059]-[0060], [0075]-[0079]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
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 1 is/are recite substantially similar limitations to independent claim 8 and is/are rejected under 2B for similar reasons to claim 8 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 8 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2, 4-7, 9 and 11-14 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1 and 8. As a result, Examiner asserts that dependent claims, such as dependent claims 2, 4-7, 9 and 11-14 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-2, 4-9 and 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2021/0142346 A1) Swartz et al., and further in view of (US 10469275 B1) Broomall et al.
As per claims 1 and 8: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
A system for quantifying drivers of growth of niche categories, comprising (Reference Swartz shows in abstract: Purchasing data from a variety of sources such as consumer panels, loyalty cards, and retailer point of sale data, are fused to estimate purchasing behavior by loyalty card users. As a significant advantage, the techniques described herein permit accurate imputation, on a household level, of unreported purchases in loyalty card data, and can thus facilitate accurate estimates of total spending behavior by loyalty card households beyond those venues for which loyalty program data is collected. [0013] Described herein are devices, systems, computer program products, and methods for modeling and estimating household spending on consumer products. In this manner, the present disclosure includes techniques for processing purchasing data from a variety of sources to estimate household-level purchasing activity and the like. Specifically, a technique according to the present disclosure may include the use of point-of-sale data, consumer panel data, and loyalty card data, where each may be derived from a different source, and where each may have different attributes and coverage. Advantageously, the purchasing data (e.g., the shopper loyalty data) may be demographically weighted to make it representative of a national population. In this manner, total spend for a household can be estimated at a product-by-product level, and the household spend on each product may be estimated for each outlet. Thus, the present teachings may include a demographic weighting technique, a total spend estimation technique, and a share allocation technique, as well as validation for any and all of the above.):
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
a data collection module configured to ([0022] One or more of the first source 110, second source 120, and third source 130 of data may be aggregated by an analytics provider and stored within a single data store in certain implementations—e.g., where this data is collected by a single entity and stored in a database 150 or the like. In general, the technical challenges to integrating these sources of information do not lie in the area of aggregating data from different sources. Rather, the challenges arise because the relationship between purchases from each source are largely unknown. That is, each individual consumer transaction reflected in one of the data sets 112, 122, 132 may or may not be reflected in other ones of the data sets 112, 122, 132. For example, a loyalty card user may purchase goods in a transaction that is recorded in the loyalty card data 132, however, it may not be known whether this loyalty card user is also a consumer panel member that records transactions in the consumer panel data 122. Similarly, although a retailer may know precisely how much of a product is purchased at each store and across all stores over a given time period, the retailer may not be able to determine the composition of purchases of that product, and/or whether a particular consumer also purchased that product over a given time period at other retailers. Thus in general, it is generally unknown whether one of the first plurality of consumers 114 overlaps with the second plurality of consumers 124 and/or the third plurality of consumers 134, and to the extent that an overlap might be inferred, the degree of such overlap will generally be unknown.):
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
an analysis module configured to
(Reference Swartz shows in [0022] One or more of the first source 110, second source 120, and third source 130 of data may be aggregated by an analytics provider and stored within a single data store in certain implementations—e.g., where this data is collected by a single entity and stored in a database 150 or the like. [0049]: This information may in turn be used to offer promotions, adjust marketing strategy or advertising spend, or otherwise take steps to increase consumer awareness of products or increase consumer purchase of such products. In another aspect, a retailer may evaluate what percentage of product purchases its loyalty card purchasers are making in-store, or at competitor's stores. More generally, the techniques described herein may permit retailers, product manufacturers, and others to draw inferences from loyalty card data that describe consumers' shopping behavior outside of loyalty-card purchases, which can facilitate a wide range of analyses, as well as deployment of responsive manufacturing, marketing, and advertising resources or strategies. Even though Reference Swartz shows an analytics provider, Reference Swartz does not explicitly show “module” in the claim above.
Reference Broomall shows the above limitations at least in (col. 7, lines 7-25): Grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
a market size estimation module configured to:
(Reference Swartz shows in [0022] One or more of the first source 110, second source 120, and third source 130 of data may be aggregated by an analytics provider and stored within a single data store in certain implementations—e.g., where this data is collected by a single entity and stored in a database 150 or the like. [0049]: This information may in turn be used to offer promotions, adjust marketing strategy or advertising spend, or otherwise take steps to increase consumer awareness of products or increase consumer purchase of such products. In another aspect, a retailer may evaluate what percentage of product purchases its loyalty card purchasers are making in-store, or at competitor's stores. More generally, the techniques described herein may permit retailers, product manufacturers, and others to draw inferences from loyalty card data that describe consumers' shopping behavior outside of loyalty-card purchases, which can facilitate a wide range of analyses, as well as deployment of responsive manufacturing, marketing, and advertising resources or strategies. Even though Reference Swartz shows an analytics provider, Reference Swartz does not explicitly show “module” in the claim above.
Reference Broomall shows the above limitations at least in (col. 7, lines 7-25): Grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
a market size prediction module configured to:
(Reference Swartz shows in [0022] One or more of the first source 110, second source 120, and third source 130 of data may be aggregated by an analytics provider and stored within a single data store in certain implementations—e.g., where this data is collected by a single entity and stored in a database 150 or the like. [0049]: This information may in turn be used to offer promotions, adjust marketing strategy or advertising spend, or otherwise take steps to increase consumer awareness of products or increase consumer purchase of such products. In another aspect, a retailer may evaluate what percentage of product purchases its loyalty card purchasers are making in-store, or at competitor's stores. More generally, the techniques described herein may permit retailers, product manufacturers, and others to draw inferences from loyalty card data that describe consumers' shopping behavior outside of loyalty-card purchases, which can facilitate a wide range of analyses, as well as deployment of responsive manufacturing, marketing, and advertising resources or strategies. Even though Reference Swartz shows an analytics provider, Reference Swartz does not explicitly show “module” in the claim above.
Reference Broomall shows the above limitations at least in (col. 7, lines 7-25): Grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
A method for quantifying drivers of growth of niche categories, comprising: (Reference Swartz shows in abstract: Purchasing data from a variety of sources such as consumer panels, loyalty cards, and retailer point of sale data, are fused to estimate purchasing behavior by loyalty card users. As a significant advantage, the techniques described herein permit accurate imputation, on a household level, of unreported purchases in loyalty card data, and can thus facilitate accurate estimates of total spending behavior by loyalty card households beyond those venues for which loyalty program data is collected. [0013] Described herein are devices, systems, computer program products, and methods for modeling and estimating household spending on consumer products. In this manner, the present disclosure includes techniques for processing purchasing data from a variety of sources to estimate household-level purchasing activity and the like. Specifically, a technique according to the present disclosure may include the use of point-of-sale data, consumer panel data, and loyalty card data, where each may be derived from a different source, and where each may have different attributes and coverage. Advantageously, the purchasing data (e.g., the shopper loyalty data) may be demographically weighted to make it representative of a national population. In this manner, total spend for a household can be estimated at a product-by-product level, and the household spend on each product may be estimated for each outlet. Thus, the present teachings may include a demographic weighting technique, a total spend estimation technique, and a share allocation technique, as well as validation for any and all of the above.):
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
receiving, by a processor, consumer data (Swartz: [0026] The data processing engine 140 may also or instead include data storage, a network interface, and/or other processing circuitry. In the following description, where the functions or configuration of a data processing engine 140 are described, this is intended to include corresponding functions or configuration (e.g., by programming) of a processor 142 of the data processing engine 140, or in communication with the data processing engine 140. In general, the data processing engine 140 (or processors 142 thereof or in communication therewith) may perform a variety of processing tasks related to the secure processing of data as discussed herein. For example, the data processing engine 140 may manage information received from one or more of the data sources, and provide related supporting functions such as generating, receiving, and/or transmitting data, communicating with other resources 160, storing data, and the like. The data processing engine 140 may also or instead include backend algorithms that react to actions performed by a user of the system 100. The backend algorithms may also or instead be located elsewhere in the system 100.);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
reducing dimensionality of the received consumer data by using factor analysis of the collected consumer data, wherein the factor analysis comprises constructing a correlation matrix of variables from the received consumer data, extracting factors from the correlation matrix, rotating the factors, and identifying consumer segments based on attitudes, functional needs, and emotional needs represented by the extracted factors;
Reference Swartz shows [0013] Described herein are devices, systems, computer program products, and methods for modeling and estimating household spending on consumer products. In this manner, the present disclosure includes techniques for processing purchasing data from a variety of sources to estimate household-level purchasing activity and the like. Specifically, a technique according to the present disclosure may include the use of point-of-sale data, consumer panel data, and loyalty card data, where each may be derived from a different source, and where each may have different attributes and coverage. Advantageously, the purchasing data (e.g., the shopper loyalty data) may be demographically weighted to make it representative of a national population. In this manner, total spend for a household can be estimated at a product-by-product level, and the household spend on each product may be estimated for each outlet. Thus, the present teachings may include a demographic weighting technique, a total spend estimation technique, and a share allocation technique, as well as validation for any and all of the above. [0020] The second source 120 of data may be a marketing company that acquires and provides consumer panel data 122 related to a second plurality of consumers 124. The consumer panel data 122 may include self-reported purchases for a group of consumers or the like, or any other data gathered for a group of volunteers or similar panel participants. The consumer panel data 122 may help with insights into consumer behavior and shifting trends and may contain information relating to shopping patterns among different retailers, purchasing behaviors, consumer demographic information, and so forth. The consumer panel data 122 may thus yield insights into the impact of marketing initiatives on consumers' attitudes, behavior, and purchases. However, the consumer panel data 122 may include sample sizes that are too small relative to a general population, and that accordingly limit the granularity of insights derived therefrom.
Reference Swartz does not explicitly show “dimensionality”. However, Broomall shows (col. 4, lines 41-45) FIG. 3 is a block diagram depicting an illustrative operating environment 300 for creating discussion groups and assigning users to discussion groups based on a multi-dimensional user profile analysis.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
estimating market size for each identified consumer segment based on current consumption;
Reference Swartz shows 0024] In certain aspects, the memory 144 stores code executable by the processor 142 to acquire data for processing—e.g., the point of sale data 112, the consumer panel data 122, and the loyalty card data 132. The memory 144 may also or instead store code executable by the processor 142 to demographically weight the loyalty card data 132 to represent a national market by weighting each of a number of purchasing segments as a ratio of national households in that purchasing segment to loyalty card households in that purchasing segment. Further, the memory 144 may store code executable by the processor 142 to estimate a first household spend by the third plurality of consumers 134 for a number of products on a per-product basis using a probabilistic distribution to adjust for underreporting of product purchases within the loyalty card purchases by the third plurality of consumers 134 based on distributions of the loyalty card data 132, the consumer panel data 122, and the point of sale data 112. The memory 144 may also or instead store code executable by the processor 142 to train a model 146 (e.g., a machine learning model) with the consumer panel data 122 to estimate a proportion of shopping trips made by a group of consumers to a second plurality of outlets not included in the first plurality of outlets, and to estimate a second household spend by the third plurality of consumers 134 on one of the number of products on a per outlet basis by applying the model 146 to identify unreported purchases of the product in the loyalty card data 132 by the third plurality of consumers 134. More generally, the memory 144 may store compute executable code that configures the processor 142 to perform any of the steps in the methods herein. [0032] As shown in step 202, the method 200 may include acquiring point of sale data for a first plurality of consumers. This may, for example, include point of sale data from retail locations operated by one or more retailers, including, e.g., per-store and per-product sales volumes on a periodic basis, e.g., daily, weekly, monthly, quarterly, or annually. In general, stores may be categorized using any suitable attributes such as size and geographic market. Products may be organized by any information associated with individual point-of-sale stockkeeping units (or other labels or identifiers including) including without limitation product name, product type, brand, size, packaging, and so forth. A retailer's point of sale data can provide a highly accurate measurement of market sales for that retailer in a manner that is generally uncorrelated to particular consumers or households. However, it will be understood that, while a particular retailer may track point of sale data on a location-by-location basis, the data may also or instead be aggregated or otherwise processed for third-party use. [0036] As shown in step 208, the method 200 may include demographically weighting the loyalty card data to represent a national market. Demographically weighting the loyalty card data may include weighting each of a number of purchasing segments as a ratio of national households in that purchasing segment to loyalty card households in that purchasing segment. For example, one technique for weighting includes use of the following formula:
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
creating sub-groups based on demographics;
Reference Swartz does not explicitly show the above limitation. Reference Broomall shows the above limitation at least in col 3, lines 62-67 – col. 4, lines 1-16: (16) While the user interface illustrated in FIG. 1 is focused on presenting recommendations of discussion groups for a user, it will be appreciated that options to join a discussion group may be provided within a wide variety of different user interfaces or pages associated with a retail service or other service. For example, in one embodiment, a page or user interface that presents item information regarding an item available for purchase (such as a specific movie) may include an option that a user may select in order to join a discussion group associated with that item, a discussion group associated with that category of item (or genre of movie, for example), and/or other recommended discussion group based at least in part on the given item. As another example, a user viewing a page associated with a given interest or topic (such as hiking) may be presented with an option to join a discussion group associated with that topic (such as a discussion group including other hiking enthusiasts in the same geographic region as the user). The specific group or sub-group that the user may be invited to join in either example may be selected for the user based on aspects of the present disclosure described further below.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
determining growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics using multinomial regression; and
Reference Swartz does not explicitly show the above limitation. Reference Broomall shows the above limitation at least in col 9, lines 15-31: (33) Depending on the embodiment, the machine learning model(s) may include models based on supervised or unsupervised learning approaches. In the supervised model context, the model may have been previously trained based on existing discussion group data and user profiles from which the model determined the user profile similarities, differences, or associations between users in a group that correlate with the group being successful. A successful group may be defined by one or more different metrics, depending on the embodiment. For example, the machine learning model(s) may be configured to assign a population of users to subgroups in order to maximize average user engagement, as further described herein. In some embodiments, the machine learning model(s) may employ various known techniques or model types, including but not limited multinomial logistic regression, an artificial neural network, supervised learning, unsupervised learning, reinforcement learning, and/or clustering algorithms.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
predicting potential market size based on model weights and predicted probability of switching between consumer classes.
Swartz shows [0024] In certain aspects, the memory 144 stores code executable by the processor 142 to acquire data for processing—e.g., the point of sale data 112, the consumer panel data 122, and the loyalty card data 132. The memory 144 may also or instead store code executable by the processor 142 to demographically weight the loyalty card data 132 to represent a national market by weighting each of a number of purchasing segments as a ratio of national households in that purchasing segment to loyalty card households in that purchasing segment. Further, the memory 144 may store code executable by the processor 142 to estimate a first household spend by the third plurality of consumers 134 for a number of products on a per-product basis using a probabilistic distribution to adjust for underreporting of product purchases within the loyalty card purchases by the third plurality of consumers 134 based on distributions of the loyalty card data 132, the consumer panel data 122, and the point of sale data 112. The memory 144 may also or instead store code executable by the processor 142 to train a model 146 (e.g., a machine learning model) with the consumer panel data 122 to estimate a proportion of shopping trips made by a group of consumers to a second plurality of outlets not included in the first plurality of outlets, and to estimate a second household spend by the third plurality of consumers 134 on one of the number of products on a per outlet basis by applying the model 146 to identify unreported purchases of the product in the loyalty card data 132 by the third plurality of consumers 134. More generally, the memory 144 may store compute executable code that configures the processor 142 to perform any of the steps in the methods herein. [0038] As shown in step 210, the method 200 may include estimating a first household spend, more specifically, the total household spend, by the third plurality of consumers for a number of products on a per-product basis. While a number of naive assumptions may be applied to estimate the total household spend, the techniques described herein may advantageously employ a statistically accurate, data-driven model of underreporting to more accurately extrapolate total consumer spend, at the household level, based on loyalty card spend. This may be accomplished, for example, using a probabilistic distribution to adjust for underreporting of product purchases within the loyalty card purchases by the third plurality of consumers based on distributions of the loyalty card data, the consumer panel data, and the point-of-sale data. Stated otherwise, the probabilistic distribution may be used to fit existing data and/or model the underreporting of transactions.
As per claims 2 and 9: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
wherein the consumer data is collected using a survey that collects data on demographics, socio-economic status, consumption behavior, category perception, general psychographics, and functional and emotional needs related to the category.
Swartz shows [0013] Described herein are devices, systems, computer program products, and methods for modeling and estimating household spending on consumer products. In this manner, the present disclosure includes techniques for processing purchasing data from a variety of sources to estimate household-level purchasing activity and the like. Specifically, a technique according to the present disclosure may include the use of point-of-sale data, consumer panel data, and loyalty card data, where each may be derived from a different source, and where each may have different attributes and coverage. Advantageously, the purchasing data (e.g., the shopper loyalty data) may be demographically weighted to make it representative of a national population. In this manner, total spend for a household can be estimated at a product-by-product level, and the household spend on each product may be estimated for each outlet. Thus, the present teachings may include a demographic weighting technique, a total spend estimation technique, and a share allocation technique, as well as validation for any and all of the above. [0020] The second source 120 of data may be a marketing company that acquires and provides consumer panel data 122 related to a second plurality of consumers 124. The consumer panel data 122 may include self-reported purchases for a group of consumers or the like, or any other data gathered for a group of volunteers or similar panel participants. The consumer panel data 122 may help with insights into consumer behavior and shifting trends and may contain information relating to shopping patterns among different retailers, purchasing behaviors, consumer demographic information, and so forth. The consumer panel data 122 may thus yield insights into the impact of marketing initiatives on consumers' attitudes, behavior, and purchases. However, the consumer panel data 122 may include sample sizes that are too small relative to a general population, and that accordingly limit the granularity of insights derived therefrom.
As per claims 4 and 11: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
wherein creating sub-groups based on demographics comprises creating sub-groups based on a combination of age, city, and gender.
Swartz shows [0013] Described herein are devices, systems, computer program products, and methods for modeling and estimating household spending on consumer products. In this manner, the present disclosure includes techniques for processing purchasing data from a variety of sources to estimate household-level purchasing activity and the like. Specifically, a technique according to the present disclosure may include the use of point-of-sale data, consumer panel data, and loyalty card data, where each may be derived from a different source, and where each may have different attributes and coverage. Advantageously, the purchasing data (e.g., the shopper loyalty data) may be demographically weighted to make it representative of a national population. In this manner, total spend for a household can be estimated at a product-by-product level, and the household spend on each product may be estimated for each outlet. Thus, the present teachings may include a demographic weighting technique, a total spend estimation technique, and a share allocation technique, as well as validation for any and all of the above. [0020] The second source 120 of data may be a marketing company that acquires and provides consumer panel data 122 related to a second plurality of consumers 124. The consumer panel data 122 may include self-reported purchases for a group of consumers or the like, or any other data gathered for a group of volunteers or similar panel participants. The consumer panel data 122 may help with insights into consumer behavior and shifting trends and may contain information relating to shopping patterns among different retailers, purchasing behaviors, consumer demographic information, and so forth. The consumer panel data 122 may thus yield insights into the impact of marketing initiatives on consumers' attitudes, behavior, and purchases. However, the consumer panel data 122 may include sample sizes that are too small relative to a general population, and that accordingly limit the granularity of insights derived therefrom.
As per claims 5 and 12: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
wherein the multinomial regression uses the identified consumer segments and demographics as independent variables and consumption behavior as the dependent variable.
Reference Swartz does not explicitly show the above limitation. Reference Broomall shows the above limitation at least in col 9, lines 15-31: (33) Depending on the embodiment, the machine learning model(s) may include models based on supervised or unsupervised learning approaches. In the supervised model context, the model may have been previously trained based on existing discussion group data and user profiles from which the model determined the user profile similarities, differences, or associations between users in a group that correlate with the group being successful. A successful group may be defined by one or more different metrics, depending on the embodiment. For example, the machine learning model(s) may be configured to assign a population of users to subgroups in order to maximize average user engagement, as further described herein. In some embodiments, the machine learning model(s) may employ various known techniques or model types, including but not limited multinomial logistic regression, an artificial neural network, supervised learning, unsupervised learning, reinforcement learning, and/or clustering algorithms.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A).
As per claims 6 and 13: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
further comprising performing data cleaning and reliability checks to handle potential outliers or anomalies in the consumer data.
Swartz shows in [0011] Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated herein, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately” or the like, when accompanying a numerical value, are to be construed as indicating a deviation as would be appreciated by one of ordinary skill in the art to operate satisfactorily for an intended purpose. Similarly, words of approximation such as “approximately” or “substantially” when used in reference to physical characteristics, should be understood to contemplate a range of deviations that would be appreciated by one of ordinary skill in the art to operate satisfactorily for a corresponding use, function, purpose, or the like. Ranges of values and/or numeric values are provided herein as examples only, and do not constitute a limitation on the scope of the described embodiments. Where ranges of values are provided, they are also intended to include each value within the range as if set forth individually, unless expressly stated to the contrary. The use of any and all examples, or exemplary language (“e.g.,” “such as,” or the like) provided herein, is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the embodiments. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments.
As per claims 7 and 14: Regarding the claim limitations below, Reference Swartz in view of Reference Broomall shows:
wherein performing data cleaning and reliability checks comprises removing data from consumers who give the same rating to all questions in the survey.
Reference Swartz does not explicitly show the above limitation. Reference Broomall shows the above limitation at least in col 5, lines 5-25: (19) Retail server 304 may be connected to and/or in communication with an item data store 312 that stores item information regarding a number of items, such as items listed in an electronic catalog as available for browsing and/or purchasing via the retail server 304. Item data stored in item data store 312 may include any information related to each item. For example, item data may include, but is not limited to, price, availability, title, item identifier, item images, item description, item attributes, item text, item reviews, etc. The item data store 312 may additionally store digital content items (e.g., audiobooks, electronic books, music, movies, multimedia works, etc.). The retail server 304 may also be connected to or in communication with user data store 334, which may store user data associated with users of retail server 304. The stored user data may include account information, purchase history, browsing history, item reviews and ratings, personal information, location information, billing information, messages exchanged with other users, messages posted to discussion groups, etc.
Reference Swartz and Reference Broomall are analogous prior art to the claimed invention because the references generally relate to field of consumer data analysis. Further, said references are part of the same classification, i.e., G06N. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Broomall, particularly the module in (col. 7, lines 7-25), in the disclosure of Reference Swartz, particularly in the analytics provider in ([0022], [0049]), in order to provide for a system that has the ability to provide grouping module 311 may analyze user information to assign or recommend users to specific discussion groups or subgroups based on various factors, as described herein. Discussion module 476 may be responsible for maintaining discussion groups and enabling members of the groups to electronically communicate within the discussion groups using known techniques. While grouping module 311 and discussion module 476 are shown in FIG. 4 as part of the discussion service 310, in other embodiments, all or a portion of the grouping module 311 and/or discussion module 476 may be implemented by the retail server 304 and/or another computing device. In some embodiments, the retail server 304 may include several components that operate similarly to the components illustrated as part of the discussion service 310, including a user interface module, grouping module, discussion module, processing unit, computer readable medium drive, etc. In some such embodiments, a separate discussion service 310 may not be needed as taught by Reference Broomall (see at least in col. 7, lines 7-25), where upon the execution of the method and system of Reference Broomall for discussion profiles may be generated for individual users based on behavioral data, linguistic analysis, and demographic data so that the process of managing consumer data analysis can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar consumer data analysis field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Swartz in view of Reference Broomall, the results of the combination were predictable (MPEP 2143 A).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Please see Note above, applicants’ claims are ambiguous and as such applicants’ arguments will need further search and consideration once applicants help clarify the scope of the claims to one of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
J. P. Shim and R. Taylor, "Purchase-Based Analytics and Big Data for Actionable Insights," in IT Professional, vol. 21, no. 5, pp. 48-56, 1 Sept.-Oct. 2019, doi: 10.1109/MITP.2019.2897658.
Foreign Reference:
(CN 112163876 A) Zhang et al. The invention claims a modelling and prioritizing method of dynamic demand response item in power market, belonging to the technical field of energy internet. The purpose of the invention is based on benefit function and elastic requirement of the client, establishing dynamic economic model combined emergency demand response plan and time-sharing electric price; and using the ordering technology of weighting method and ideal solution similarity to model and prioritizing the dynamic demand response item in the power market with priority ordering for the developed demand response items. The steps of the present invention are as follows: establishing response load demand curve and demand elasticity model; establishing a response load dynamic economic model; The developed demand response items are prioritized by means of a weighting method and an ordering technique of the ideal solution similarity. The invention develops different demand response item solution, and the multi-attribute decision as an effective decision method, providing the decision maker according to the viewpoint selecting the highest priority scheme of the chance.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.N.P/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
