DETAILED ACTION
This Office Action is in response to Applicants application filing on September 4, 2024. Claim(s) 1-23 is/are currently pending in the instant application. The application claims priority to U.S. provisional application 63/644,23, filed on May 8, 2024.
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 .
Oath/Declaration
Applicant is reminded that the Office sent a communication on September 17, 2024 regarding the deficiencies in the application. The communication indicates that a properly executed oath or declaration is not been received for each inventor. Applicant is directed to the official communication.
Drawings
The drawings are objected to because the drawings are not clear enough to read. The copies are not of sufficient quality. Examiner has include images below. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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Claim Objections
The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, the remaining claims must not be renumbered. When new claims are presented, they must be numbered consecutively beginning with the number next following the highest numbered claims previously presented (whether entered or not).
The original claims filed contain two claim 7’s. The first one is attached to claim 6 without spacing and the second is directly below. For the purpose of Examination the claims will be considered 7A and 7B. The Applicant needs to correct the error. Additionally, the fee sheet states 23 claims, as it appears that the initial review of the file also missed the extra claim. The Applicants will need to check and determine if they paid the correct fee for claims more than 20.
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Claim 23 contains the word integratively in line 5. The Examiner presumes the Applicant meant “interactively”. Either way, the Applicant needs to correct the claim.
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 1-23 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.
Claims 1-23 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process”, or product or “article of manufacture” and is a method and a system for dynamically determining a sale price of an item or service which is a process (Step 1: YES).
The Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 14 and method Claim 23. Claim 1 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
receiving an item ontology defining categories, properties, and relationships between multiple items;
receiving sales, meta data and exogenous variables;
aggregating sales data for subsets of the items based on the item ontology to create hierarchical sales vectors for the items;
converting the sales data, the meta data and the exogenous variables to vectors;
training the learning model based on data that includes the price vectors and the sales vectors to predict demand at multiple possible sell prices and, based on this prediction, select at least one pricing strategy.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Receiving item ontology, receiving data and assigning variables, aggregating data and creating vectors, and developing a model to predict demand and sell prices recites a commercial interaction. Accordingly, the claim recites an abstract idea. The computer system in Claim 14 is just applying generic computer components to the recited abstract limitations. The machine learning model in Claim 23 appears to be just software. Claims 14 and 23 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Receiving item ontology, receiving data and assigning variables, aggregating data and creating vectors, and developing a model to predict demand and sell prices recites a concept performed in the human mind. But for the “machine learning model” language, the claim encompasses collecting item ontology, assessing and aggregating data to create vectors, and developing a model for demand and price prediction using his/her mind and/or pen and paper. The mere nominal recitation of a generic and well known machine learning model does not take the claim out of the mental processes grouping. Accordingly, the claim recites an abstract idea. The computer system in Claim 14 is just applying generic computer components to the recited abstract limitations. The machine learning model in Claim 23 appears to be just software. Claims 14 and 23 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Claim 23 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
extracting features from aggregated data associated with a category, wherein the feature extraction is performed at least one of, a) independently, prior to being input into the learning model, or b) integratively, within the learning model itself;
utilizing the extracted features in the machine learning model to represent the category.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Extracting features from aggregated data and assigning categories to the features a concept performed in the human mind. But for the “machine learning model” language, the claim encompasses collecting item ontology, assessing and aggregating data to create vectors, and developing a model for demand and price prediction using his/her mind and/or pen and paper. The mere nominal recitation of a generic and well known machine learning model does not take the claim out of the mental processes grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite machine learning model (Claim 1) a computer system (claim 14) and/or machine learning model (Claim 23). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 14, and 23 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0071] about implementation using general purpose or special purpose computing devices (Each element in FIG. 3 can include computing hardware and software, which when executed by a processor, causes the hardware to accomplish the described functionality. For example, databases 304, 306, and 308 can include one or more database systems, such as relational database systems.) and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 14, and 23 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-13 and 15-22 further define the abstract idea that is present in their respective independent claims 1 and 14 and thus correspond to Certain Methods of Organizing Human Activity and/or Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception.
Claims 2 and 15 relate to MPEP 2106.05(d)II. v. Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
Claims 3-6 and 16-19 constitute MPEP 2106.05(f)(2) i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
Claims 7A and 20 relate to MPEP 2106.05(f)(2) v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Claim 7B, 8, 21 and 22 relate to MPEP 2106.05(d)II. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim 9 includes MPEP 2106.05(d)II. ii. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
Claims 10-12 are equivalent to MPEP2106.05(g)(3) iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim 13 is consistent with MPEP 2106.05(d)II. v. Determining an estimated outcome and setting a price, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93;
Therefore, the claims 2-13 and 15-22 are directed to an abstract idea. Thus, the claims 1-23 are not patent-eligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pyati U.S. Publication 2019/0311301 A1 (hereafter Pyati).
Regarding claim 23, extracting features from aggregated data associated with a category, wherein the feature extraction is performed at least one of, a)independently, prior to being input into the learning model, or b) [interactively], within the learning model itself;
utilizing the extracted features in the machine learning model to represent the category. (see at least [0012] a computing system of a network-based service or application can receive attribute values for attributes of a new item listing, such as a category, title, description, and photographs and videos of an item. Concurrently, the computing system can analyze previous item listings related to the new item listing, such as item listings in the same category as the new item listing, item listings of items purchased by a specific user or set of users sharing common traits, or item listings published within a specified time period. The computing system may process the attributes and associated attribute values of the new item listing and previous item listings to extract their features and associated feature values.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited prior art generally refers to dynamic and optimal price determination including associated methods and systems.
U.S. Publication 2015/0363805 A1 - A dynamic pricing algorithm is used to price a large set of items so that their aggregate margin is above a pre-specified level even under uncertain demand. The algorithm automatically hedges the profit loss associated to low-margin items with profit gains associated to higher-margin items, and does so even when the realized demand is different than the expected one. The algorithm leverages the separability of a re-formulation of the robust counterpart of the nominal revenue maximization problem. This separability results into a nested bisection algorithm where each iteration in the procedure requires only computation of a number of independent, one-dimensional optimization problems, one for each product to price. The algorithm is easily implemented in a parallel architecture such a multi-core computer or a cluster of computers, where each core handles an independent one-dimensional problem corresponding to an item and its data is stored locally.
U.S. Publication 2025/0069104 A1 - Techniques for generating a retail forecasting model from product-cluster-based estimated elasticity values to forecast the effects of price changes on the demand for a set of products are disclosed. A system generates cluster-based price-elasticity values for a set of products by applying a set of regressive elasticity-estimation algorithms to a set of product data and clustering products based on product descriptions and estimated price-elasticity values. The system uses the cluster-based price-elasticity values for the products to generate the retail forecasting model.
U.S. Publication 2021/0103945 A1 - Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by machine learning models. A set of constraints are then received and used, along with the elasticities to compute the optimal prices for deployment in retailers for further testing.
U.S. Publication 2020/0143439 A1 - The disclosed technology improves the process of generating recommended prices for retail products by optimizing revenue and profit while complying with a set of business rules by assigning a monetary value to each business rule. Then for each decision price that violates a business rule constraint, a penalty value is added to the monetary value. If the monetary value including the penalty is better than an original monetary value, the decision price is included in the recommended prices.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 January 24, 2026.