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 Claims
This is a final office action in response to the amendment filed 22 May 2026. Claims 1, 10-11, and 20 have been amended. Claims 1-20 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 1, 10-11, and 20 has been entered.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the claims are not directed to a judicial exception because the amended claims are directed to “a technical improvement to a computer processing system involving the architecture of the system including contents of the memories, dedicated computing devices, and in-store computing devices” that is “not directed to the management of human behavior” but to “a specific approach of determining clean data using a dedicated computing system and operating in-store computing devices using this clean data resulting in the improved operation of these in-store devices” and solving the technical problem of “how and where to process extremely large scale, dimensional, and fragmented customer data in an automated environment … using constraint based statistical comparisons, iterative normalization and defragmentation, and distributed computing” in a manner that does not recite an abstract idea. Examiner respectfully disagrees.
While the amended independent claims recite “a dedicated computing device” to “obtain constraint data from at lease one data source, and store the constraint data in memory for computational access” … “implement operations that automatically generate a clean dataset by performing defragmentation operations that normalize constraint-specific scores … implement a verification process that computes a divergence metric between cumulative high-dimensional data of the clean dataset and the global distributions and iteratively regenerates the clean dataset with the divergence metric exceeds a predetermined margin of error” and “transmits the clean dataset to a plurality of computing systems,” the newly added limitations detail how the data is dimensionally reduced and filtered for data comparison, evaluation, and validation for improving a business process for determining consumer behavior related to “purchase trends, purchase patters, purchase motivations, channel specific spending behavior, future public interest” using customer profile data of the customer and constraint data to “determine and shape business strategies” See Specification at [0002-0004]. Gathering and analyzing customer profile data to “determine and shape business strategies” for digital content generation relates to commercial interactions in the form of sales activities or behaviors, and business relations with customers and falls withing the fundamental economic practices grouping of abstract concepts. See MPEP 2106.04(a)(2)(II)(B). Further, the steps related to data gathering, analysis, and output are analogous to those of Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (characterizing collecting information, analyzing information by steps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas). See MPEP 2106.04(a)(2)(III)(A). Therefore, under Step 2A Prong One, the claims recite an abstract idea.
Applicant further asserts that even if the claims recite an exception, this exception is integrated into a practical application because “the dedicated computing device centralizes the heavy data-cleaning operations (e.g. extraction, scoring, normalizing, sampling) so that channel-specific computing systems do not have to perform these tasks,” preventing resource waste, eliminating duplication of complex data-processing across multiple systems, and guaranteeing that every channel computing system receives a consistently high-quality dataset, which can be trusted for analytics at remote devices, in a manner that “is an improvement to distributed computer system operation … and analogous to the self-referential table of Enfish.” Examiner respectfully disagrees.
While the method for pre-processing the data to achieve a certain output is improved, the improvement is to the recited abstract idea. Using a computer to pre-process data for storage, transmission, and further data processing amounts to a general link to the underlying data processing and transmission techniques and technologies, and not to an improvement thereof. The claimed self-referential table in Enfish was a specific type of data structure designed to improve the way a computer stores and retrieves data in memory. Enfish LLC v. Microsoft Corporation, 822 F.3d 1327 at 1339 (Fed. Cir. 2016). In contrast, the claim limitations herein do not use such a data structure to improve a computer’s functionality or efficiency, or otherwise change the way that device functions. As the court expressly recognized in Enfish, there is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other. Enfish, 822 F.3d at 1335.
While the claim limitations narrow how the cleansed dataset is generated and used for further analysis and business decision making, the “distributed computer system operation” is not improved. The Specification does not provide additional details about the computer system that would distinguish it from generic processing devices that communicate with one another in a network environment. Because the claims do not include an improvement to data transmission, storage, or processing technologies, while the data processing techniques are detailed and specific, they do not transform the abstract idea into a practical application and do not amount to significantly more than the recited abstract idea. As a result, the 35 U.S.C. 101 rejection is proper, maintained, and updated below, as necessitated by amendment.
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 an abstract idea without significantly more. Independent claim 1 recites a device, independent 11 recites a process, and independent 20 recites a product to generate a clean dataset associated with a customer profile of a plurality of customer profiles. The claims recite an abstract idea of obtaining and analyzing customer profile data to generate and display a digital marketing content. Independent claims 1, 11, and 20 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites the following limitations:
obtain constraint data from at least one data source, and store the constraint data in the memory resource for computational access by the one or more processors;
obtain customer profile data of a plurality of customers associated with the system, the customer profile data comprising a customer identifier;
for each customer of the plurality of customers: determine a portion of the customer profile data that corresponds to the global distribution associated with each of the plurality of constraints;
compare the portion of the customer profile data of the customer with the global distribution associated with each of the plurality of constraints;
for each comparison, generate one of a plurality of values for a score and associate the score with the corresponding one of the plurality of constraints;
based on the score of each of the one or more constraints, implement operations that generate an overall score, the overall score indicating a closeness between the customer profile data of the customer to at least the global distribution of each of the one or more constraints; and
based on the customer identifier, associate the overall score with a customer profile of the customer, and store the customer profile and the overall score within a dataset database;
implement operations that automatically generate a clean dataset by performing defragmentation operations that normalize constraint-specific scores using an actual distribution of customer profiles relative to stored global distributions, and by selecting a subset of customer profiles whose aggregated normalized scores satisfy a predefined constraint-consistency threshold, wherein the automatically generated clean dataset comprises an enhanced quality subset of the customer profile data having lower dimensionality and reduced noise relative to the customer profile data;
implement a verification process that computes a divergence metric between cumulative high-dimensional data of the clean dataset and the global distributions and iteratively regenerates the clean dataset when the divergence metric exceeds a predetermined margin of error;
transmit the clean dataset to a plurality of computing systems, including at least one in-store computing system at a physical retail store, wherein each of the plurality of computing systems is associated with a particular channel of an e-commerce entity;
and store the clean dataset at the dataset database;
wherein each of the plurality of computing systems extracts insights from the clean dataset by identifying one of more features in the constraint data such that a first difference between cumulative high dimensional data in the clean dataset and global distributions associated with the one or more constraints is smaller than a second difference between high-dimensional data of the customer profile data and the global distributions associated with the one or more constraints; and
wherein a set of operations associated with the particular channel of the e- commerce entity is implemented based on the extracted insights including those prepared at the at least one in-store computing system to personalize a user experience of a user on the particular channel, including generating personalized digital content for the user based on the extracted insights and controlling at least one physical output device associated with the particular channel and displaying the personalized digital content to automatically present to the user.
Under Step 1 independent claims 1, 11, and 20 recite at least one step or act, including obtaining constraint data.
Under Step 2A Prong One, the limitations recited in claim 1 for obtaining constraint data; obtaining customer profile data; determining a portion of the customer profile data that corresponds to the global distribution; comparing the portion of the customer profile data of the customer with the global distribution; generating one of a plurality values for a score and associating the score with the corresponding one of the plurality of constraints; implementing operations that generate an overall score; associating the overall score with a customer profile of the customer; implementing operations that automatically generate a clean dataset by performing defragmentation operations and selecting a subset of customer profiles whose aggregated normalized scores satisfy a predefined threshold; implementing a verification process that computes a divergence metric; transmitting the clean dataset; extracting insights from the clean dataset; storing the clean dataset; implementing a set of operations associated with a particular channel of the e-commerce entity; and generating personalized digital content, as drafted, fall within the fundamental economic principles or practices grouping of abstract ideas because the claimed subject matter recites to steps to analyze consumer data in relation to various business constraints to determine what digital content most aligns with a customer profile for the business purpose of improving customer relationship management and enterprise sale through personalized marketing, which the MPEP identifies as a method of organizing human activity. See MPEP 2106.04(a)(2)(II)(B). The claimed subject matter involves managing and analyzing customer profile and sales data (per para. [0055-0058] of the Specification: online-transactional data and/or in-store transactional data … “clean data computing device 102 may determine, for each of the set of customer profiles, a week-to-week, during the three-year time period, total purchase amount related to food items”). The focus of the claims as a whole is on managing the personal behavior and commercial interactions of people (e.g., consumers, advertisers, retailers, and product sponsors) for advertising, marketing, and sales activities and behaviors, which fall within the certain methods of organizing human activity grouping of abstract ideas. Therefore, the claims recite an abstract idea.
The claims recite steps to make evaluations and judgments based on collected customer profile data and business constraints, generating a score, generating an overall score, and generating a clean dataset as an output of the data filtering, analysis, and evaluation. The step for extracting insights involves mental judgments of comparing data in the clean dataset with data of the customer profile and constraint data. Because the claims recite limitations for obtaining and analyzing ecommerce customer profile data to make business determinations using steps that could be performed by a data analyst or a marketing and sales specialist, and further because a human could make a sales or marketing business decision based on customer and business constraint data (cleansing, scoring, aggregating, normalizing, and comparing), the claims fall within the mental processes grouping of abstract ideas. See MPEP 2106.04(a)(2)(III). The steps for generating, normalizing, and aggregating scores are mathematical operations that fall within the mathematical concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2)(I).
Under Step 2A Prong Two the judicial exception of the independent claims is not integrated into a practical application. In particular the claims recite a processor, memory, database, and networked computing systems for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraphs [0064-0065] states: “Clean data computing device 102 can include one or more processors 202, working memory 204, one or more input/output devices 206,instruction memory 208… Processors 202 can include one or more distinct processors, each having one or more cores. Each of the distinct processors can have the same or different structure. Processors 202 can include one or more central processing units (CPUs), …, and the like.” The Specification does not provide additional details about the computer system that would distinguish it from any generic processing devices that communicate with one another in a network environment. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h).
The claims do not provide technical details regarding how the set of operations associated with the particular channel of the e-commerce entity is implemented, and only describe displaying personalized digital content as a form of implementation. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more. The amended claim limitations merely amount to the application or instructions to apply the abstract idea on a computer. Accordingly, the 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 but merely use the computer as a tool to perform the data analysis steps.
While the data processing steps narrow how the clean dataset is generated and validated before extracting insights for digital content generation, the limitations for filtering, cleansing, and validating a dataset for further processing is a form of data management that does not improve the functioning of a computer or another technology. The claim limitations are not directed to an improvement to the recited processors/computing systems and memory. These additional elements are broadly and generically claims as tools used to implement to data processing and output steps. The Specification at [para. 0091] states: “… the de-fragmentation operations remove or lessen the effects of fragmentation-related impurities (e.g., lessen the chance fragmented customer profiles of one or more customers are included in the clean dataset 321). In some examples, executed defragmentation engine 305 may implement the defragmentation operations that include normalizing the scores of each constraint associated with each of the set of customer profiles. In some instances, normalizing the scores may include normalizing the size of each discrete bucket of the global distributions of each constraint. For instance, based on the constraint data 316, executed defragmentation engine 305 may determine the actual distribution of the set of customers that have the associated score, and then normalize the score of a particular customer utilizing the determined actual distribution (e.g., dividing the score of the customer with the actual distribution).” As described in the specification the defragmentation and normalizing functions are performed on the “obtained” data as data processing steps, not as steps that improve the functioning of the computer, processor, or memory. Reducing the size of a dataset using constraints, rules, and other data associations is part of the recited abstract idea and does not confer subject matter eligibility.
Under Step 2B the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor, database, and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2 through 10 and 12 through 19 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/ fundamental economic practice by describing have the customer profile data is analyzed or used to generate a score for generating a clean dataset. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. Therefore the claims are directed to an abstract idea. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly independent claims 11 and 20 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1 - 20 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Angell et al. (US 8,639,563) - generating customized marketing messages using current events data. In one embodiment, external marketing data is received from a set of sources to form the current events data. The current events data is processed to form dynamic data. A customized marketing message is generated for the customer using the dynamic data.
Chau et al. (US 9,881,309) - determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.
Gupta et al. (US 11,361,337) - device may obtain customer data, associated with a customer identifier, that includes an indication of a recency of a past purchase, a frequency of past purchases, and/or a monetary value associated with past purchases by a customer associated with the customer identifier. The device may determine, based on comparing the customer data and aggregate customer data, a first score that predicts a current measure of loyalty associated with the customer, and may predict, based on the first score, a predicted frequency of future purchases by the customer and a predicted monetary value associated with the future purchases, to determine a second score that predicts a future measure of loyalty associated with the customer.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623
/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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