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 .
Priority
Examiner acknowledges Applicant’s claim to priority regarding Provisional Application 62/403,576 filed on 10/03/2016 and as a Continuation of 15/723,554 filed on 10/03/2017.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 04/22/2025 has been fully considered.
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-7 are directed to a method, claims 8-14 are directed to a system, and claims 15-20 are directed to a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 8, and 15 recite selecting a supply chain model that fulfills service requirements of each customer business model, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors. Independent claim 1 recites limitations, similarly recited in claims 8 and 15, including “analyzing customers to identify characteristics of the customers; grouping customers into customer clusters; associating customer clusters with customer business models; and selecting a supply chain model that fulfills service requirements of each customer business model.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of the preamble, covers an abstract idea but for the recitation of generic computer components. That is, other than reciting the preamble, nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the preamble language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Step 2A, Prong 2: Independent claims 1, 8, and 15 do not integrate the judicial exception into a practical application. Independent claim 1 recites “a computer-implemented method for articulating customer business models by a computer comprising a processor and a memory, comprising” within the preamble of the claim. Independent claim 8 recites “a system for articulating customer business models, comprising: a computer comprising a processor and a memory and configured to.” Independent claim 15 recites “a non-transitory computer-readable medium embodied with software, the software when executed configured to articulate customer business models by:” within the preamble of the claim. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Step 2B: Independent claims 1, 8, and 15 do not comprise anything significantly more than the judicial exception. Independent claim 1 recites “a computer-implemented method for articulating customer business models by a computer comprising a processor and a memory, comprising” within the preamble of the claim. Independent claim 8 recites “a system for articulating customer business models, comprising: a computer comprising a processor and a memory and configured to.” Independent claim 15 recites “a non-transitory computer-readable medium embodied with software, the software when executed configured to articulate customer business models by:” within the preamble of the claim. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-7, 9-14, and 16-20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kannan et al. (US 20170200104 A1).
Regarding claim 1, Kannan anticipates a computer-implemented method for articulating customer business models by a computer comprising a processor and a memory (Fig. 4 and [0075-0077] teach a computing device that is configured to perform the methods described herein, wherein the computing device including a processor and memory), comprising:
analyzing customers to identify characteristics of the customers ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0010] teaches the price schedule logic is configured to receive electronic communications from the remote computing device of the retailer that communicate price data for items, inventory data for the items, a per-segment demand model for items, and a selected objective function, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0018] teaches the customer segments may include single women, mothers, and retirees; see also: [0015-0017]);
grouping customers into customer clusters ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0018] teaches the customer segments may include single women, mothers, and retirees; see also: [0010, 0016-0017]);
associating customer clusters with customer business models ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0018] teaches the customer segments may include single women, mothers, and retirees, wherein the per-segment demand model specifies the particular behaviors of these different types of customers by including different demand parameter values for each segment, wherein the allocation logic is configured to predict a specific contribution each customer segment provides to the objective function, wherein based on the per-segment demand model, the allocation logic may determine that specific information for each segment, as well as in [0097-0098] teach generating demand parameters for each segment; see also: [0010, 0016-0017]); and
selecting a supply chain model that fulfills service requirements of each customer business model ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0017] teach the allocation logic is configured to allocate the inventory quantity including initial inventory and replenishment quantities amongst each of the plurality of customer segments based on the predicted contribution of each customer segment to the objective function, wherein [0018] teaches the customer segments may include single women, mothers, and retirees, wherein the per-segment demand model specifies the particular behaviors of these different types of customers by including different demand parameter values for each segment, wherein the allocation logic is configured to predict a specific contribution each customer segment provides to the objective function, wherein based on the per-segment demand model, the allocation logic may determine that specific information for each segment, wherein [0029] teaches determining the demand piece constraint that is a linear approximation of the per-segment demand model provided to the optimizer, wherein [0034-0045] teach the demand logic is configured to approximate the demand model over more than two pieces, wherein the demand logic determines an approximation of the objective function in order to allocate the entire inventory quantity includes initial inventory and any replenishment to each segment, as well as in [0066-0067] teach determining an approximate demand model for each time period based on the customer segments demand; see also: [0010, 0016]).
Regarding claims 8 and 15, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 8, Kannan anticipates a system for articulating customer business models, comprising (Fig. 4 and [0075-0077] teach a computing device that is configured to perform the methods described herein, wherein the computing device including a processor and memory): a computer comprising a processor and a memory and configured to (Fig. 4 and [0075-0077] teach a computing device that is configured to perform the methods described herein, wherein the computing device including a processor and memory). Regarding claim 15, Kannan anticipates a non-transitory computer-readable medium embodied with software, the software when executed configured to articulate customer business models by ([0075-0078] teach performing the process by a non-transitory computer readable medium with stored instructions that are implemented by hardware including a processor). Accordingly, claims 8 and 15 are rejected as being anticipated by Kannan.
Regarding claims 2, 9, and 16, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
Kannan further anticipates wherein the customer clusters comprise groups of customers each having particular service requirements ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0018] teaches the customer segments may include single women, mothers, and retirees, wherein the per-segment demand model specifies the particular behaviors of these different types of customers by including different demand parameter values for each segment, wherein the allocation logic is configured to predict a specific contribution each customer segment provides to the objective function, wherein based on the per-segment demand model, the allocation logic may determine that specific information for each segment, as well as in [0097-0098] teach generating demand parameters for each segment; see also: [0010, 0016-0017]).
Regarding claims 4, 11, and 17, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
Kannan further anticipates wherein a number and type of supply chain models is industry-agnostic and determined by customer and product attributes and a required service package for each company ([0068] teaches the constraints that are provided to the optimizer by the allocation logic includes business related constraints that constrain solutions in accordance with the retailer’s pricing policies, wherein [0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0017] teach the allocation logic is configured to allocate the inventory quantity including initial inventory and replenishment quantities amongst each of the plurality of customer segments based on the predicted contribution of each customer segment to the objective function, wherein [0018] teaches the customer segments may include single women, mothers, and retirees, wherein the per-segment demand model specifies the particular behaviors of these different types of customers by including different demand parameter values for each segment, wherein the allocation logic is configured to predict a specific contribution each customer segment provides to the objective function, wherein based on the per-segment demand model, the allocation logic may determine that specific information for each segment, wherein [0029] teaches determining the demand piece constraint that is a linear approximation of the per-segment demand model provided to the optimizer, wherein [0034-0045] teach the demand logic is configured to approximate the demand model over more than two pieces, wherein the demand logic determines an approximation of the objective function in order to allocate the entire inventory quantity includes initial inventory and any replenishment to each segment, as well as in [0066-0067] teach determining an approximate demand model for each time period based on the customer segments demand; see also: [0010, 0016]).
Regarding claims 5, 12, and 18, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
Kannan further anticipates wherein each customer cluster has different business requirements based on one of: delivery and stocking requirements ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0018] teaches the customer segments may include single women, mothers, and retirees, wherein the per-segment demand model specifies the particular behaviors of these different types of customers by including different demand parameter values for each segment, wherein the allocation logic is configured to predict a specific contribution each customer segment provides to the objective function, wherein based on the per-segment demand model, the allocation logic may determine that specific information for each segment, as well as in [0097-0098] teach generating demand parameters for each segment; see also: [0010, 0016-0017]).
Regarding claims 6, 13, and 19, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
Kannan further anticipates wherein the characteristics of the customers comprise one of: a seasonality of customer orders ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0010] teaches the price schedule logic is configured to receive electronic communications from the remote computing device of the retailer that communicate price data for items, inventory data for the items, a per-segment demand model for items, and a selected objective function, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0030] teaches identifying base seasonality of a customer segment; see also: [0015-0018]).
Regarding claims 7 and 14, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
Kannan further anticipates wherein the customer business models comprise groups of customers ([0007] teaches different customer segments, such as single people, young people, parents, retirees, and so on, all respond differently to promotions and markdowns, which should be captured in the price scheduling process, as well as in [0008] teaches a different promotion schedule is created for different customer segments to leverage knowledge about the buying habits of different types of customers and maximize profits, wherein [0012-0013] teach the price schedule logic may query a database that stores such data, such as electronic records encoding price ladders and inventory and replenishment quantities, wherein the queried data includes factors such as elasticity, promotion fatigue, seasonality and so on, wherein the per-segment demand model includes different demand parameter values for different customer segments, wherein the retailer furnishes values for demand parameters based on their own sales histories and experience, wherein [0015] teaches the price logic can consider and generate information based on customer segments that include singles, moms, and more, wherein [0018] teaches the customer segments may include single women, mothers, and retirees; see also: [0010, 0016-0017]).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kannan et al. (US 20170200104 A1) in view of Strauss et al. (US 20170316459 A1).
Regarding claims 3, 10, and 17, Kannan anticipates all the limitations of claims 1, 8, and 15 above.
However, Kannan does not explicitly teach further comprising: translating customer business models into supply chain models, wherein each supply chain model comprises industry-specific characteristics based on customer and product attributes identified in a corresponding customer business model.
From the same or similar field of endeavor, Strauss teaches further comprising: translating customer business models into supply chain models, wherein each supply chain model comprises industry-specific characteristics based on customer and product attributes identified in a corresponding customer business model ([0038] teaches the system may obtain data from one or more sources, determine an incentive for one or more user segments, and persist the determined incentives, wherein the incentives may be determined based on a demand model that incorporates a number of factors such as sales targets, buyer profile including demographics, previous buying behavior, geography, industry specific factors, and more in order to determine incentives for a product on a per-user segment basis on any time scale, wherein [0066] teaches incentive levels and segments associated with vehicles may be defined by the vehicle data system that includes segmenting rules for grouping users based on similarities, wherein the vehicle data system may include one or more models, such as a set of vehicle specific demand models, that are utilized to optimize incentives for vehicles, as well as in [0089-0090] teach the system may determine the demand of a vehicle, wherein the demand of the vehicle is based on the make/model, which can be expressed for a particular segment or overall industry sales unit, wherein the demand model is based on a set of make/models that are defined as competitive in the industry ; see also: [0091]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Kannan to incorporate the teachings of Strauss to include further comprising: translating customer business models into supply chain models, wherein each supply chain model comprises industry-specific characteristics based on customer and product attributes identified in a corresponding customer business model. One would have been motivated to do so in order to optimize the incentive spending by customizing it for customers based on industry specific factors (Strauss, [0038-0040]). By incorporating the teachings of Strauss, one would have been able to determine incentives that are specific to both user groups and product configurations based on demand model input factors including industry-specific factors (Strauss, [0038]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Geldres (US 20130073414 A1) discloses identifying a preference for a product or service based on industry specific brand information
Najmi et al. (US 20110208560 A1) discloses segmentation may classify customers into criticality groups that each have a specified customer service level
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sara G Brown whose telephone number is (469)295-9145. The examiner can normally be reached M-F 8:00 am- 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
Prosecution Insights