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 ACTION
1. This Office Action is in response to the Amendment filed on April 13, 2026, which paper has been placed of record in the file.
2. Claims 1-10 and 20-21 are considering in this application.
Claim Rejections - 35 USC § 112
3. 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.
4. Claims 1-10 and 20-21 are 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 claims contain 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. The claims added the new features “wherein the machine-learning model is configured to reduce database queries by indexing the first set of the historical transaction data in the relational database comprising the first, second, third, fourth, and fifth distinct sets of attributes” and “wherein the iteratively trained machine-learning model is configured to reduce database queries by indexing the second set of historical transaction data in the relational database comprising the first, second, third. fourth, and fifth distinct sets of attributes” recited in the independent claims 1 and 20-21, are not described in the Specification. The only section described the “indexing” new features added in the Specification is para [0006], which described “Indeed, the present techniques of providing a procurement modeling system including one or more computer servers for predicting price reasonableness for procuring a product or service by a purchaser entity may provide technical improvements to previous or existing procurement- related cloud-computing based platforms. For example, in accordance with the presently disclosed techniques, the procurement modeling system may provide technical improvements to previous or existing procurement-related cloud-computing based platforms by increasing processing speeds of data processors and reducing database queries by indexing and organizing entities of data and manages data in a predetermined structured manner, such that a number of queries to the database in order to surface desired data is reduced as compared to performing a brute-force search of all of the entities of data stored in the database. For example, in accordance with the presently disclosed embodiments, the procurement modeling system may utilize one or machine-learning models to surface and generate desired data including, for example, preferential supplier entities for a purchaser entity and supplier entity pricing power”, nowhere the Examiner can find the features “wherein the machine-learning model is configured to reduce database queries by indexing the first set of the historical transaction data in the relational database comprising the first, second, third, fourth, and fifth distinct sets of attributes” and “wherein the iteratively trained machine-learning model is configured to reduce database queries by indexing the second set of historical transaction data in the relational database comprising the first, second, third. fourth, and fifth distinct sets of attributes.”
Novelty and Non-Obviousness
5. No prior arts were applied to the claims because the Examiner is unaware of any prior arts, alone or in combination, which disclose at least the limitations of “input data associated with the user input into the machine-learning model to generate: a purchaser-specific preferential ranking for each of the at least one supplier from the plurality of suppliers, wherein the purchaser-specific preferential ranking is based, at least in part, on the familiarity of the purchaser with each of the at least one supplier from the plurality of suppliers to supply the particular line-item to the purchaser; and a selection of one of the at least one supplier from the plurality of suppliers to supply the particular line-item to the purchaser, wherein the selection is based on the purchaser-specific preferential ranking; obtain a second set of training data at least by retrieving a second set of historical transaction data comprising the first, second, third, fourth, and fifth distinct sets of attributes from the relational database, wherein the second set of historical transaction data comprises one or more price quotes and the selection of one of the at least one supplier from the plurality of suppliers to supply particular line-item to the purchaser; and iteratively train, based on the second data set of training data, the machine learning model to generate updated outputs corresponding to the at least one supplier from the plurality of suppliers” recited in the independent claims 1, 20, and 21.
Response to Arguments/Amendment
6. Applicant’s arguments with respect to claims 1-10 and 20-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
Claims 1-10 and 20-21 are eligible because the claims recite significantly more than the abstract idea and integrate the abstract idea into a practical application, specially the limitations “wherein the machine-learning model is configured to reduce database queries by indexing the first set of the historical transaction data in the relational database comprising the first, second, third, fourth, and fifth distinct sets of attributes” and “wherein the iteratively trained machine-learning model is configured to reduce database queries by indexing the second set of historical transaction data in the relational database comprising the first, second, third. fourth, and fifth distinct sets of attributes”, provide technological improvements to the machine-learning model to reduce database queries by indexing the first set of the historical transaction data in the relational database comprising the first, second, third, fourth, and fifth distinct sets of attributes. Therefore, the claims are eligible.
Accordingly, the 101 rejection has been withdrawn.
Conclusion
7. 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 extension fee 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 date of this final action.
8. Claims 1-10 and 20-21 are rejected.
9. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Bikumala et al. (US 2021/0158236) disclose a machine learning (ML) module that can continuously learn from the market data and historical orders to dynamically recommend an optimum supplier portfolio to the manufacturer for a specific product.
Koch et al. (US 2020/0279191) disclose mechanisms and processes for generating dynamic merchant scoring predictions.
Kumar et al. (US 2020/0005192) disclose a machine learning engine for identification of related vertical groupings may be trained using artificial intelligence and machine techniques and used according to techniques.
Beh et al. (US 2016/0048852) disclose an apparatus includes a demand module that determines a demand for a product offered from a plurality of suppliers. A pricing module receives cost factors associated with the product to determine a base per unit cost of the product.
Cox et al. (US 2009/0327039) disclose a method for enhancing the procurement processes and sourcing strategy options of an organization, including registering purchase item data, supply item data, demand market data, and supply market data, and generating one or more suggested procurement and sourcing strategies using a power and leverage positioning methodology.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (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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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 June 25, 2026
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