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. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 24, 2025 has been entered.
2. Claims 1-10 and 20-21 are considering in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-10 and 20-21 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 21, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a system for generating a machine-learning output of a recommended supplier. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a system for generating a machine-learning output of a recommended supplier for supplying a particular line-item to a purchaser. The claim recites the steps: receive historical transaction data…; store the historical transaction data…, the plurality of distinct sets of data attributes comprising: a first distinct set of attributes of a plurality of line-items associated with the set of transactions…, a second distinct set of attributes of the plurality of suppliers including the at least one supplier, a third distinct set of attributes comprising categories master data…, a firth distinct set of attributes comprising line-item master data…, a fifth distinct set of attributes of the plurality of purchasers including the purchaser, generate: a purchaser-specific preferential ranking…; and a selection of the at least on supplier to supply the particular line-times to the purchaser…, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations. See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “one or more processors coupled to the one or more storage media”, “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction”, “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs.”
The additional elements “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction”, are mere data gathering, storing, retrieving, transmitting, and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering, storing, retrieving, transmitting and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, transmitting and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the data ingestion server, the data cleaner, the prediction query server, they are just merely used as general means for collecting, storing, retrieving, transmitting, and outputting data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
The additional elements “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs” are used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, these limitations only recite the outcome of “generating the outputs and the update outputs corresponding to at least one supplier” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction”, “generate: a purchaser-specific preferential ranking…; and a selection of the at least on supplier to supply the particular line-times to the purchaser…”, are recited as being performed by the processors. The processors are recited at a high level of generality. In the limitations “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction”, the processors are used as tools to perform the functions of gathering, storing, retrieving, transmitting, an outputting data. In the limitations “generate: a purchaser-specific preferential ranking…; and a selection of the at least on supplier to supply the particular line-times to the purchaser…”, the processors are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processors, the storage media, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “train, based on the first set of training data, the machine-learning model to generate outputs”, “input data associate with the user input into the machine learning model”, and “iteratively train, based on the second set of training data, the machine learning model to generate updated outputs” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering, storing, retrieving, transmitting, and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction” are recited at a high level of generality. These elements amount to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); 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;
As discussed in Step 2A, Prong Two above, the recitation of the processors to perform limitations “receive, by a data ingestion server, historical transaction data”, “store, by the data ingestion server, the historical transaction data”, “obtain a first set of training data at least by: retrieving a first set of the historical transaction data from the relational database, and cleaning, using a data cleaner, the first, second, third, fourth, and fifth distinct set of attributes”, “obtain a second set of training data at least by retrieving a second set of historical transaction data”, “receiving, through a prediction query server, a user input specifying a potential procurement transaction”, “generate: a purchaser-specific preferential ranking…; and a selection of the at least on supplier to supply the particular line-times to the purchaser…”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 1 and 20, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 1 directed to a method, independent claim 20 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 21.
Regarding dependent claims 2-10, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claim 2, the claim simply refines the abstract idea by further reciting wherein each of the set of transactions comprise a line-item and an identification of a corresponding purchaser, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 3, the claim simply refines the abstract idea by further reciting wherein the line-item comprises a price of the particular line-item, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 4, the claim simply refines the abstract idea by further reciting providing, based on the purchaser-specific preferential ranking, and ordered list of preferential suppliers of supplying the particular line item to the purchaser, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 5, the claims recite the additional elements causing one or more electronic devices associated with the purchaser to display the ordered list of preferential suppliers, which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the electronic devices, they are just merely used as general means for collecting and displaying data (See claim 21 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 6, the claim simply refines the abstract idea by further reciting wherein the purchaser-specific preferential ranking is determined based on a market competitiveness score…, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 7, the claim simply refines the abstract idea by further reciting wherein the purchaser-specific preferential ranking is determined based on a monetary value of a sum of procurement transactions…, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 8, the claim simply refines the abstract idea by further reciting wherein the purchaser-specific preferential ranking is determined based on a determined negotiation behavior…, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 9, the claim simply refines the abstract idea by further reciting wherein the purchaser-specific preferential ranking is determined based on one or more attributes of the purchaser with respect to historical transactions…, that fall under the category of Organizing Human activity grouping of abstract ideas as described above in the independent claim 21. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 10, the claim recites the additional elements wherein the machine learning model comprises a gradient boosting model, and adaptive boosting model…, which is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, these limitations only recite the outcome of “generating the outputs and the update outputs corresponding to at least one supplier” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see claim 21 above). However, theses recited models are well-known in the art of machine learning model. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-10 and 20-21 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Conclusion
7. Claims 1-10 and 20-21 are rejected.
8. 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.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 January 9, 2026