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 .
Acknowledgements
Claims 1-20 are pending.
Applicant provided information disclosure statement.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-20 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 8 and 15 recite the limitations of
select one or more metrics with which to aggregate data and classify product performance; aggregate product-store combinations according to the one or more selected metrics; classify the product-store combinations into categories according to classification thresholds; identify individual attribute strength of attributes by counting a number of times each attribute for each classified product-store combination appears in each classified category; filter the attributes according to a frequency of an attribute combination; estimate multi-combination strengths for the attributes; and display one or more multi-combination strengths for potential combinations of the attributes.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of selecting, aggregating, classifying, filtering, estimating, and displaying data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses simply selecting a metric, aggregating product-store combinations, classifying product-store combinations, identifying attributes based on classifying, filtering attributes, estimating attribute strengths, and displaying attribute strengths. These are mere data manipulation steps that do not require a computer. In addition, these steps are in relation to assortment planning which is not novel and has been done before the technological age. Given a real-world example, a store manager would be able to carry out these steps for a retailer by determining assortment planning based on product attributes.
In addition, the fact that the claims recite identifying attributes and determining attribute strengths with respect to product assortment planning make the claims fall in the abstract idea grouping of certain methods of organizing human activity (sales activity, fundamental economic principles or practices; business relations, interactions between people). The claims also recite business terms such as product margin, revenue, and total profit as seen in the dependent claims.
It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components do not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims recite the additional elements of
…load historical data into input data of a database, detect input to one or more input devices, and in response to the detected input, system, server, non-transitory computer-readable medium, assortment planner, and software
These components are recited at a high level of generality and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims, as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further state what the selected metrics are such as total profit. In addition, the dependent claims further state what the historical data includes such as sales history data.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
System, assortment planner, and server
Claim 8 recites method, however method is not considered an additional element.
Claim 8 further recites server and assortment planner
Claim 15 recites non-transitory computer-readable medium and software
Claims 1, 8, and 15 recite load historical data into input data of a database, detect input to one or more input devices, and in response to the detected input
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations as seen in para 0022-0023.
When looking at the additional elements in combination, the computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
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, recitations of generic computer structure to perform generic computer functions that are used 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 amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sahani (US20210019677A1) in further view of Ryks (US20140172502A1).
Regarding claim 1, 8, and 15, Sahani teaches
A system…an assortment planner comprising a server and configured to (See Figure 7 and 8) This shows a system with a processor.
A method (See para 0005-For example, methods can be provided for automatically determining option defining attributes for a category.) This shows a method.
A non-transitory computer-readable medium comprising software (See para 0090-FIG. 7 shows a central processing unit 710 as well as a graphics processing unit or co-processing unit 715. The tangible memory 720, 725 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two, accessible by the processing unit(s).) (See para 0092-information in a non-transitory way and which can be accessed within the computing system 700. The storage 740 stores instructions for the software 780 implementing one or more innovations described herein.) This teaches memory and software.
historical data (See para 0004-Based on the selections, the attributes that are most deterministic of the performance indicators can be identified using historical data and using information gain calculations.) This teaches historical data.
detect input to one or more input devices, and in response to the detected input, select one or more metrics with which to aggregate data and classify product performance (See figure 1) This shows the system detects input from a user device. The input corresponds to selecting a metric (e.g. revenue and profit). The system will aggregate shirt product data which includes classifying product performance as seen here (See table 1, fig. 2, and para 0034-As depicted in Table 1 above, the eight products are ranked according to the sum of profit over the previous time period (e.g., the last three months), from lowest profit (ranked 1) to highest profit (ranked 8). The eight products are also ranked according to the sum of revenue over the previous time period (e.g., the last three months), from lowest profit (ranked 1) to highest profit (ranked 8)).
aggregate product-store combinations according to the one or more selected metrics (See table 1 and figure 2) (See para 0034-As depicted in Table 1 above, the eight products are ranked according to the sum of profit over the previous time period (e.g., the last three months), from lowest profit (ranked 1) to highest profit (ranked 8). The eight products are also ranked according to the sum of revenue over the previous time period (e.g., the last three months), from lowest profit (ranked 1) to highest profit (ranked 8). (See para 0039-FIG. 2 is a diagram depicting an example graph 200 of products ordered by weighted cumulative rank. The depicted products are products A through H from the above example. Each product is depicted along with its sum of revenue and sum of profit (according to the values from Table 1). The depicted products in the example graph 200 are ordered according to their weighted cumulative rank, as depicted at 240.) This shows shirt products aggregated and ranked based on the selected metrics of profit and revenue. The products are product-store combinations since they are sold at a particular retailer (See para 0015-Option planning refers to the selection of products (called product options or product choices) that will be sold by an organization (e.g., a business, such as a retailer).)
classify the product-store combinations into categories according to classification thresholds (See para 0041-As part of the information gain calculation, the ranked products (e.g., ordered by weighted cumulative rank) are classified into two (or more) groups. In some implementations, the classification is performed by identifying a threshold that is used to separate the ranked products into two groups. The threshold can be automatically determined (e.g., the middle of the ranked ranged) or selected by a user (e.g., the user can enter or select a threshold rank value).) (See para 0042-Using the men's shirts category example, with the example eight products A through H, the user could select a threshold value of 4 for the classification. As a result, products with a weighted cumulative rank above 4 would be classified into a first group (in this example, a high performing group due to the high weighted cumulative rank values based on the revenue and profit performance indicators) and products with a weighted cumulative rank of 4 or below would be classified into a second group (in this example, a low performing group). This shows classification categories of low and high performing groups based on a threshold created by the user.
identify individual attribute strength of attributes The system identifies the strength of attributes since it determines the more important attributes with respect to the metrics selected. (See para 0047-FIG. 3 is a diagram depicting an example option planning user interface 300 depicting attributes that are ordered from most deterministic to least deterministic.)
filter the attributes according to a frequency of an attribute combination (See fig. 3 item 330) Attributes are filtered based on being most influential (i.e. most deterministic). This is done based on the attribute combination of a particular brand and size occurring the most in past sales data (i.e. high frequency).
estimate multi-combination strengths for the attributes (See fig. 3 item 330) (See para 0043-Continuing with the men's shirts category example, the Gini gain is calculated for the three attributes associated with the category (brand, color, and size). As discussed further below, the Gini gain is calculated to be Brand, 0.125 Size, 0.045 Color, 0.000) The system estimates the multiple attributes with respect Gini gain calculation. The system determines the combination attributes of brand and size are the most influential (i.e. high strength) for the shirt category.
and display one or more multi-combination strengths for potential combinations of the attributes. (See fig. 3) The system displays the combination of attributes that are the most influential (i.e. high strength).
Even though Sahani teaches historical data such as past sales, it is not clear that it is loaded as input data for a database, however Ryks teaches load historical data into input data of a database (See fig. 1) (See para 0033- Inventory management system 10 stores data in database 18 in some examples. Database 18 may be separate from or part of inventory management system 10. Data stored in database 18 can include information related to items or products analyzed by inventory management system 10. Information about each item in the list of items is included in database 18, including, but not limited to, information such as what product category or categories to which the item belongs, a size of the item (e.g., small, medium, large, super value size, etc.), a volume of the item (e.g., 50 ounce (oz), 100 oz, etc.), amount of the item (4 count (ct), 12 ct, 24 ct, etc.), and seasonality of the item (e.g., winter, first harvest, summer, etc.). Database 18 also includes information related to sales of particular items. For example, a number of units of the item sold over a time period is recorded in database 18.) This shows that historical data about the products are loaded into the database from inventory management system.
Sahani and Ryks are analogous art because they are from the same problem-solving area of product management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sahani’s invention by incorporating the method of Ryks because Sahani could also make walker reports for different items when option planning. This would allow the user to have a better understanding of products by seeing which products the customers are loyal too. This would help decision making in option planning and make the art of Sahani more sophisticated since it is looking at additional product variables.
In addition, Sahani teaches product attributes and classifying products, however it doesn’t teach counting number of times product with attributes appear in a classified category, however Ryks teaches by counting a number of times each attribute for each classified product-store combination appears in each classified category Figure 6 shows the classified category of potential walker items and product class, and it teaches number of times the product with certain attributes appeared in the category by looking at unit sales index count.
Sahani and Ryks are analogous art because they are from the same problem-solving area of product management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sahani’s invention by incorporating the method of Ryks because Sahani could also make walker reports for different items when option planning. This would allow the user to have a better understanding of products by seeing which products the customers are loyal too. This would help decision making in option planning and make the art of Sahani more sophisticated since it is looking at additional product variables. In addition, Sahani can also use a figure like figure 6 in Ryks to determine unit sales count of product/attributes. This would help with option/assortment planning for the retailer.
Regarding claim 2, 9, and 16, Sahani and Ryks teach the limitations of claims 1, 8, and 15, however Sahani further teaches
wherein the one or more selected metrics comprise one or more of: product margin, product revenue and total profit. (See fig. 1) This shows the selected metric of profit and revenue.
Regarding claim 3, 10, and 17, Sahani and Ryks teach the limitations of claims 1, 8, and 15, however Sahani further teaches
wherein the product-store combinations are classified as either high-performing or low-performing. (See para 0042- Using the men's shirts category example, with the example eight products A through H, the user could select a threshold value of 4 for the classification. As a result, products with a weighted cumulative rank above 4 would be classified into a first group (in this example, a high performing group due to the high weighted cumulative rank values based on the revenue and profit performance indicators) and products with a weighted cumulative rank of 4 or below would be classified into a second group (in this example, a low performing group).) This teaches high and low performing groups.
Regarding claim 4, 11, and 18, Sahani and Ryks teach the limitations of claims 1, 8, and 15, however Sahani further teaches
wherein the product-store combinations are classified into three classifications. (See para 0040- As part of the information gain calculation, the ranked products (e.g., ordered by weighted cumulative rank) are classified into two (or more) groups.) This shows the classification can be two or more groups which includes 3.
Regarding claim 5, 12, and 19, Sahani and Ryks teach the limitations of claims 1, 8, and 15, however Ryks further teaches
wherein the server is further configured to: determine whether to further filter remaining attributes according to one or more new specified thresholds; (See fig. 5) The user determines whether they want to further filter the products and their attributes such as by store location, product category, or a new parameter. For example, the user can first filter products with the attribute of adult nutrition and then filter the remaining attributes of products that correspond to performance nutrition.
in response to determining to further filter the remaining attributes, further filter the remaining attributes; (See fig. 5) The user can further filter the chart based on selecting new thresholds on the user interface of fig. 5.
Sahani and Ryks are analogous art because they are from the same problem-solving area of product management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sahani’s invention by incorporating the method of Ryks because Sahani could also make walker reports for different items when option planning. This would allow the user to have a better understanding of products by seeing which products the customers are loyal too. This would help decision making in option planning and make the art of Sahani more sophisticated since it is looking at additional product variables. In addition, Sahani can also use a figure like figure 6 in Ryks to determine unit sales count of product/attributes. This would help with option/assortment planning for the retailer.
In addition, Sahani further teaches and estimate further multi-combination strengths (See fig. 3) This shows the system determines the combination of attributes that are influential (i.e. high strength). This can be done to the remaining attributes as already taught in Ryks as seen above.
Sahani and Ryks are analogous art because they are from the same problem-solving area of product management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Sahani’s invention by incorporating the method of Ryks because Sahani could also determine remaining attributes for shirts such as shirt material and price and further determine the strength of those attributes. This would make the system of Sahani more sophisticated since it adds more variables to be analyzed for the products and it would give the user more insight when they are option planning.
Regarding claim 6, 13, and 20, Sahani and Ryks teach the limitations of claims 1, 8, and 15, however Sahani further teaches
wherein the loaded historical data comprises one or more of: product placement data, product attributes data, product sales history data, and planogram dimension data. (See para 0070- At 420, previous sales data for the plurality of products is obtained. For example, the previous sales data can be historical sales data for a previous number of months or years.) This shows the historical data is pasts sales history.
Regarding claim 7 and 14, Sahani and Ryks teach the limitations of claims 1 and 8, however Sahani further teaches
wherein the one or more selected metrics comprise one or more of: product margin, revenue, profit and inventory turnover. (See fig. 1) This teaches revenue.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Falque-Pierrotin (20210089939) Discloses an automatic supplier recommendation engine for suggesting a product supplier from outside of an existing merchant network is disclosed. A set of optimization solutions may include pricing, performance, shipping, duties, trust score and other inventory or manufacturing factors.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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