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 .
Detailed Status
Request for Continued Examination under 37 CFR 1.1141
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 19, 2010 has been entered.
This action is a Non-Final action in response to communications filed on 02/13/2026.
Claims 1 and 12 have been amended. Claims 1 – 22 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s remarks have been considered.
Applicant argues, “ Applicant submits that Amended Claim 1 now recites specific algorithmic operations that cannot be practically performed in the human mind.” (pg. 17)
Examiner notes the claims recite abstract concepts related to Certain Methods of Organizing Activity related to sales and marketing activities and Mathematical Concepts related to mathematical calculations. See 35 USC 101 rejection below.
Applicant argues,” Applicant respectfully traverses this rejection. As amended, Claim 1 recites specific technical implementations, including "hierarchical clustering methods," data transformation to a "numeric scale," and "Minkowski metric" calculations, that integrate the alleged abstract idea into a practical application, providing a technical improvement to the functioning of the vehicle data system itself.” (pgs. 16-17)
Examiner respectfully disagrees. The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of a targeted incentive database, a non-transitory computer readable medium, a processor, computing devices and a vehicle data system embodied on a server machine. The claimed computer components (see Spec ¶0212) are recited at a high level of generality and invoked as tools to perform generic computer functions (e.g. storing data, receiving input and displaying data).
For instance, the step of a targeted incentive database comprising one or more data tables storing an adaptive mapping structure is considered generic data storing functionality using a generic data structure. The step of a first mapping of vehicle product categories to targeted incentive levels utilizing mapping codes to match the vehicle product categories to the targeted incentive levels and a second mapping of the targeted incentive levels to user segments involves analyzing data. The step of a demand model generated and updated base on a multivariable analysis of a sets of vehicle data analyzing data using complex mathematics. The steps of receiving a user query, collecting a set of features and matching the user query to a product category involves data gathering and analysis functionality. The steps of determining a user segment from collectable observable features, applying machine learning based on segment matching rules utilizing hierarchical clustering methods, generating similarity scores based on Minkowski metric involve analyzing data using complex math. The steps of accessing machine learning model updated second mapping to identify the user segment corresponding to collected observable features and to determine the targeted incentive levels are data analysis. The steps of generating a responsive web page to display the targeted incentive levels and returning the web page in response to user query is a result of the analysis. Examiner notes, storing a demand model in a data store is generic data store functionality. The continuous updating of the demand model based on various data is data gathering activity. The additional information within the wherein clauses seems to be merely informational and not positively recited.
The combination of the additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, even in combination the additional elements do not integrate the abstract idea in to a practical application because they do not impose any meaningful limits on practicing the abstract idea.
In regards to technical implementations cited by Applicant including hierarchical clustering methods and data transformation to a numerical scale are analyzing data through matching and data manipulation. Minkowski metric calculations involve analyzing data using mathematical operations. These features demonstrate abstract concepts that do not provide for a technical improvement in a technology or technical field.
Applicant argues, “Amended Claim 1 recites a specific, non-generic arrangement of components and data structures that improve the accuracy and efficiency of the computer system.” (pgs. 17-18)
Examiner respectfully disagrees. The Federal Circuit has found that "merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea." Intellectual Ventures
I LLC, 792 F.3d at 1369-70; see also Intellectual Ventures I LLC v. Erie Indemnity Co., 711 F. App'x 1012, 1017 (Fed. Cir. 2017) (unpublished) ("Though the claims purport to accelerate the process of finding errant files and to reduce error, we have held that speed and accuracy increases stemming from the ordinary capabilities of a general-purpose computer 'do[] not materially alter the patent eligibility of the claimed subject
matter."').
In regards to a specialized data structure, the data structure/database is behaving as it normally would by updating and storing data based on implemented rules. There is no support in the claims or Specification that demonstrates an improvement in a technology or technical field.
In regards to a specific algorithmic implementation by transforming of features to a numeric scale using a Minkowski metric is considered analyzing data utilizing mathematical concepts. Here, the outcome of the mathematical operations supports matching for segmentation.
Applicant argues similar to DDR, “The amended claims do not merely use a computer as a tool to perform marketing. Instead, the amended claims relate to configuring the computer to perform specific high-speed data clustering and metric-based analysis to solve the distinct Internet-centric problem of realtime incentive optimization.” (pgs. 18-19)
In DDR Holdings the court found that the claims recite a specific way to automate the creation of composite Web page by an outsource provider that incorporates elements from multiple sources in order to solve a problem faced by web sites on the internet, where the claimed solution is necessarily rooted in computer technology. The claims specify how interactions with the Internet are manipulated to yield a desired result, that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. The claimed system generates and directs the visitor to a hybrid (composite) web page that presents product information from the third-party and visual “look and feel” elements from the host website. When the limitations of the asserted claims are taken together as an ordered combination, the claims recite an invention that is not merely the routine or conventional use of the Internet (DDR Holdings, CAFC 2013-1505).
Unlike DDR the instant application is directed to a generic computer components (processor, crm, etc.) performing the steps of a targeted incentive database comprising one or more data tables storing an adaptive mapping structure; storing a demand model in a data store is generic data storage functionality; a demand model generated and updated base on a multivariable analysis of a sets of vehicle data; receiving a user query; collecting a set of features and matching the user query to a product category; accessing machine learning model updated second mapping to identify the user segment corresponding to collected observable features and to determine the targeted incentive levels; generating a responsive web page to display the targeted incentive levels and returning the web page in response to user query. These steps demonstrate abstract concepts and the combination of the additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor).
Further, the problem described by Applicant, realtime incentive optimization, is not a technically driven problem. There is no indication in the claims of solving a problem rooted in technology as generic computer components are performing generic computer functions. Applicant’s claimed invention appears to be an improved business process for providing realtime targeted incentives.
Nothing in the claims demonstrates an improvement in a technology or a technical field.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites,
a first mapping of vehicle product categories to targeted incentive levels utilizing mapping codes to match the vehicle product categories to the targeted incentive levels;
a second mapping of the targeted incentive levels to user segments, wherein the user segments correspond to observable features of users, wherein the second mapping is dynamically updated by a behavioral analytics machine learning model based on historical user data and user interaction data to reflect changing user preferences and market conditions;
a demand model, the demand model generated and updated based on a multivariable analysis of a set of vehicle pricing data, vehicle incentive data, and historical transaction records, and wherein the demand model is trained using historical data collected from one or more sources, including vehicle pricing data, vehicle incentive data, and historical transaction records to analytically predict sales and optimize incentive spending for vehicle models;
a data store communicatively connected to the targeted incentive database, the data store storing a demand model;
wherein the demand model defines demand as a complex, multivariate function of vehicle model prices and other market factors;
receive a user query from the user [computer device via the interface], the user query comprising specific vehicle product configuration information;
responsive to the received user query, control real-time selection of a dynamically optimized targeted incentive level for the user, using the adaptive mapping structure and demand model, by:
matching the user query to a vehicle product category the first mapping of the adaptive mapping structure;
accessing the first mapping to identify a set of potential incentive levels from the targeted incentive levels associated with the matched vehicle product category;
collecting observable features associated with the user, the observable features of the user comprising: a geographic location, an income, and user specified product configuration information; and
wherein the observable features are collected across multiple search sessions to build a comprehensive user profile;
determining, by a processing module, a user segment from the collected observable features by;
applying machine-learning based segment matching rules utilizing hierarchical clustering methods to the collected
generating, by the processing module, similarity scores based on a Minkowski metric between the collected observable features and the
accessing the machine-learning model dynamically updated second mapping to identify the user segment corresponding to the collected observable features;
accessing the machine-learning model updated second mapping to determine the targeted incentive level for the identified user segment and the matched product category;
generating a personalized responsive web page to display the determined targeted incentive along with dynamic pricing information level; and returning the responsive web page to [the user computing device] in real-time, thereby enabling adaptive and dynamic incentive allocation.
The limitations under the broadest reasonable interpretation covers Certain Methods of Organizing Human Activity related to fundamental economic principles or practices and commercial actions dealing with marketing and sales activities, but for the recitation of generic computer components (e.g. a processor). For example, querying a system for a product and determining an incentive level involves marketing and sales activities. Accordingly, the claims recite an abstract idea. Claim 12 is substantially similar to Claim 1 and is abstract.
The claims are also reflective of Mathematical Calculations based on use of demand models to determining price elasticity and predict demand; applying machine learning segment matching and generating similarity scores using Minkowski metrics.
Claim 9 recites:
identifying the first vehicle model and the set of competitive vehicle models;
developing the demand model for the first vehicle model based on the multivariable analysis of a set of vehicle pricing data, vehicle incentive data, and historical transaction records…;
determining a price elasticity of demand for the first vehicle model based on determining a change in demand for a change in a first vehicle model price using the demand model;
applying the price elasticity of demand for the first vehicle model to the first set of incremental changes in the first vehicle model price to determine the demand response for the first set of incremental changes in the first vehicle model price…;
These limitations are related to Mathematical Concepts specifically encompassing mathematical relationships and mathematical formulas or equations. Demand modeling is done through mathematical calculations. Additionally, determining price elasticity is a mathematical relationship concept. Accordingly, this claim recites an abstract idea. Claim 20 substantially recites the subject matter of Claim 9. Claim 9 is also related to Certain Methods of Organizing Human Activity related to fundamental economic practices.
Dependent claims 2-11 encompass the same abstract ideas of Certain Methods of Organizing Human Activity and Mental Processes. For instance, Claim 2 is directed to a set of observable features, Claim 3 is directed to capturing user behavior, Claim 4 is directed to defining user segments, Claim 5 is directed to user query for user specified user attributes, Claim 6 is directed to generating a responsive web page, Claims 7 and 8 are directed to generating an incentive code and Claim 9 is directed to determining price elasticity . Dependent claims 10-11 encompass the same abstract idea of Mathematical Concepts. Claims 13-22 substantially recite the subject matter of Claims 2-11 and encompass the same abstract concepts. The dependent claims further limit the abstract ideas.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a targeted incentive database, a processor, a non-transitory computer readable medium, a processing module, and a user device for providing an interface. Claim 12 recites the additional elements of one or more computing devices, a targeted incentive database, a data store, a vehicle data system on a server machine comprising a processor, a non-transitory crm and a computing device for performing the above limitations. The claimed computer components (see Spec ¶0212) are recited at a high level of generality and invoked as tools to perform generic computer functions (e.g. storing data, receiving input and displaying data).
For instance, the step of a targeted incentive database comprising one or more data tables storing an adaptive mapping structure is considered generic data storing functionality using a generic data structure. The step of a first mapping of vehicle product categories to targeted incentive levels utilizing mapping codes to match the vehicle product categories to the targeted incentive levels and a second mapping of the targeted incentive levels to user segments involves analyzing data. The step of a demand model generated and updated base on a multivariable analysis of a sets of vehicle data analyzing data using complex mathematics. The steps of receiving a user query, collecting a set of features and matching the user query to a product category involves data gathering and analysis functionality. The steps of determining a user segment from collectable observable features, applying machine learning based on segment matching rules utilizing hierarchical clustering methods, generating similarity scores based on Minkowski metric involve analyzing data using complex math. The steps of accessing machine learning model updated second mapping to identify the user segment corresponding to collected observable features and to determine the targeted incentive levels are data analysis. The steps of generating a responsive web page to display the targeted incentive levels and returning the web page in response to user query is a result of the analysis. Examiner notes, storing a demand model in a data store is generic data store functionality. The continuous updating of the demand model based on various data is data gathering activity. The additional information within the wherein clauses seems to be merely informational and not positively recited.
The combination of the additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, even in combination the additional elements do not integrate the abstract idea in to a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B, as noted above the additional elements (e.g. a processor, a crm) in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The claims recite generic computer components as performing generic computer functions such as providing a database, receiving a user query, matching, determining, collecting, generating, etc., which are routinely performed in computer applications.
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Generic computer components recited as performing generic computer functions amount to no more than implementing the abstract idea with a computerized system. The computer components are merely used to automate the incentive allocation process. Therefore, the claim does not amount to significantly more.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, Claims 1-22 are ineligible.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Murphy et al. (US 2010/0153198) discloses an online system presents remote users with static incentives, dynamic incentives and the ability to create and submit customizable incentives related to goods or services of interest to the user.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3683