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
This action is a Final action in response to communications filed on 06/24/2026.
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, “ The USPTO has expressly noted in Example 39 of its Subject Matter Eligibility Guidelines that the training of machine learning models comprises patentable subject matter because, as expressly noted by the USPTO such a claims/limitation 1) does not recite mathematical concepts 2) does not recited a mental process as it is not practically performable in the human mind and 3) does not recite any method of organizing human activity.
Examiner notes the instant claims are not similar to Example 39 directed to facial detection where the claims were found not to reflect any abstract idea based on the scope of the claims .
The instant claims are reflective of abstract concepts encompassing Certain Methods of Organizing Human Activity and Mathematical Concepts.
The limitations under the broadest reasonable interpretation cover 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, storing vehicle pricing, incentive levels, product categories, etc.; training a demand model; 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 generating, training and applying demand models; applying machine learning for segment matching and generating similarity scores using Minkowski metrics.
Regarding the training steps there are no details how the training step is performed. Further, based on MPEP § 2106.05(a)(I), there is no support or demonstration in the claims are Specification of an improved way of training the demand model or an improvement to a computer component or system performance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites, “creating a first training set comprising the modified set of pricing data, the modified set of vehicle incentive data and the historical transaction records; training the demand model at a first time using the first training set; applying, at a second time, the multivariable transformation to the set of pricing data and vehicle incentive data, to generate a second modified set of pricing data and a second modified set of vehicle incentive data; creating a second training set comprising the second modified set of pricing data and the second modified set of vehicle incentive data and the historical transaction records; training the demand model at a second time using the second training set…”
The limitations are not disclosed in the Specification. The cited paragraphs (50-70) of the Specification do not disclose these limitations. The Specification discloses demand model training in para 91 and that models may be retrained in para 96. There is no disclosure in the Specification of the level detail in the claims. Claim 12 is rejected based on the same rational. Claims 2-11 and 13-22 are rejected based on their dependency on Claims 1 and 12 respectively.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-22 are rejected under 35 U.S.C. 112, (b)/second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 1 recites, ”the [first ] modified set” at line 20. There is insufficient antecedent basis for this limitation in the claim. Claim 12 is rejected based on the same rationale. Claims 2-11 and 13-22 are rejected based on their dependency on Claims 1 and 12 respectively.
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 by:
collecting a set of pricing data, vehicle incentive data, and historical transaction records;
applying, at a first time, a multivariable transformation to the set of pricing data and vehicle incentive data, to generate a first modified set of pricing data and a first modified set of vehicle incentive data;
creating a first training set comprising the modified set of pricing data, the modified set of vehicle incentive data and the historical transaction records;
training the demand model at a first time using the first training set;
applying, at a second time, the multivariable transformation to the set of pricing data and vehicle incentive data, to generate a second modified set of pricing data and a second modified set of vehicle incentive data;
creating a second training set comprising the second modified set of pricing data and the second modified set of vehicle incentive data and the historical transaction records;
training the demand model at a second time using the second training set;
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: 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, storing vehicle pricing, incentive levels, product categories, etc.; training a demand model; 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 generating, training and applying demand models; applying machine learning for 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 (multivariable analysis). 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 Mathematical Concepts. 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 by collecting a set of pricing, vehicle and historical data; applying a multivariable transformation to the set of data to generate a first modified set of pricing and incentive data and historical records; creating a first training set; training the demand model using the first training set; applying the multivariable transformation to generate a second modified set of data; creating a second data training set and training the demand model using the second training set is performing complex mathematics (analyzing data). 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 into 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, training 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:
Genc-Kaya et al. (US 10182243) discloses determine one or more promotion pricing parameters for a promotion that is offered by a promotion and marketing service including training demand models.
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.
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
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