DETAILED ACTION
This is a Final Office Action in response to claims on 12/11/2024. Claims 1-2, 5-20 are pending. The effective filing date is 12/11/2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/11/2024 and 08/04/2026 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-2, and 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1- Claims 1-2, 5-13 are directed to a method, a patent eligible subject matter. Claims 14-17 are directed to a system, a patent eligible subject matter. Claims 18-20 are directed to a non-transitory computer-readable storage medium, which is an article of manufacture, a patent eligible subject matter. Claims 1-20 pass step 1.
Step 2A, Prong 1-The independent claims 1, and similarly claim 14 and 18, recite:
automatically identifying, for a first organization and from an electronic data repository, historical sourcing event data that includes sourcing event field values, line item values, and historical sourcing event template selections for historical sourcing event database objects (identification of event data is a method of collecting information, and is considered a mental process under MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
training, using the automatically-identified historical sourcing event data, a machine-learning sourcing event template recommendation model for the first organization to automatically recommend, from among a plurality of candidate sourcing event template objects in an electronic repository of sourcing event template objects, a sourcing event template object for an upcoming sourcing event, wherein (making a recommendation on where to source items from based on data is making a decision about sales activities, as it is a method to analyze sales information to obtain more product, see MPEP 2106.04(a)(2)(II)(B));
the training includes determining feature weights for sourcing event database objects for sourcing event template object selection for features that can determine, for the first organization, a sourcing event template object for a sourcing event database object (training is described as making a determination, and making a determination is analyzing data, and is considered a mental process under MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)); and
the training determines different feature weighs for he firs organization than other feature weights previously determined for a second organization different from the first organization (training is described as making a determination, and making a determination is analyzing data, and is considered a mental process under MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
automatically identifying, using a machine-learning sourcing event creation intent model, from a plurality of automatically-processed electronic messages, a first electronic message as corresponding to a request to create a sourcing event database object in a database of a sourcing system server for a first upcoming sourcing event (requesting to create a sourcing event is a method to obtain more items for inventory, and is a method of creating more inventory for sales, and is a sales activity see MPEP 2106.04(a)(2)(II)(B));
in response to automatically identifying the request to create the sourcing event database object in the database of the sourcing system server for the first upcoming sourcing event, automatically extracting, using a machine-learning automatic sourcing event field extraction model, extracted sourcing event field data from the request to create the sourcing event database object (extracting information about the request is a method of obtaining information, and is considered a mental process under MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
providing extracted sourcing event field data for the first upcoming sourcing event to the machine-learning sourcing event template recommendation model (providing information is a way to collect and analyze information, and is considered a mental process under MPEP 2106.04(a)(2)(III)(A) a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016));
receiving, from the machine-learning sourcing event template recommendation model, a first recommended sourcing event template object for the first upcoming sourcing event from the electronic repository of sourcing event template objects (receiving the recommendation from an analysis, would be using data to make decisions about the sales activities, based on analysis of data, and is a sales activity, under MPEP 2106.04(a)(2)(II)(B)); and
automatically invoking an application programming interface of the sourcing system server for creating the sourcing event database object in the database of the sourcing system server for the first organization for the first upcoming sourcing event, wherein parameters for the application programming interface are selected based on the extracted sourcing event field data for the first upcoming sourcing event and the first recommended sourcing event template object (creating a sourcing event is a method to obtain new objects from a source, and the act of ordering inventory is a sales activity, and is considered a sales activity under MPEP 2106.04(a)(2)(II)(B)).
Step 2A, Prong 2- The independent claims 1, and similarly claim 14 and 18, provide a machine learning model, computing device, a computer-readable storage device and processors are additional elements. This judicial exception is not integrated into a practical application because all of the elements are being used as tools to implement the abstract idea. Under MPEP 2106.05(f)(2) when a computer, processor and storage devices are used in their ordinary capacity, then the elements are recited as a tool to perform an existing process, such as obtaining data from a database. The machine learning model is described as the idea of an outcome, and under MPEP 2106.05(f)92) when the claims fails to recite details on how a solution is accomplished, and in this case, the machine learning model is how the solution is accomplished, but other than reciting machine learning, there are no details to describe how this is accomplished, and therefore the claims fail to integrate the abstract ideas into a practical application.
Step 2B- The independent claims 1, and similarly claim 14 and 18, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are described in general terms, and in combination a computer device having processors and storage devices, to implement a machine leaning model does not provide significantly more than the abstract idea, as it uses these elements as tools to perform the abstract idea, under MPEP 2106.05(f).
Dependent Claims
Claims 2, 5-13, 15-17, and 19-20 add additional training, determination and details about the data being analyzed, which creates additional mental process steps to create sales activities, to be an abstract idea under MPEP 2106.04(a)(2). There are also no additional elements that would showcase integration into a practical application or be significantly more under MPEP 2106.05(f).
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.
Claims 1-2, 5-6, 9-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0097139 A1 Gowda et al. (hereinafter Gowda) in view of US 2019/0179937 A1 Banerjee et al. (hereinafter Banerjee)
Regarding claim 1, 14 and 18, Gowda teaches a computer-implemented method comprising:
automatically identifying, for a first organization and from an electronic data repository, historical sourcing event data that includes sourcing event field values, line item values, and historical sourcing event template selections for historical sourcing event database objects (Gowda [0012-0013] input can be an organization line items, including description price; [0022] sourcing system automatically creates sourcing event);
training, using the automatically-identified historical sourcing event data, a machine-learning sourcing event template recommendation model for the first organization to automatically recommend, from among a plurality of candidate sourcing event template objects in an electronic repository of sourcing event template objects, a sourcing event template object for an upcoming sourcing event, wherein (Gowda [0025] user feedback can be used to change the suggested item; [0022] sourcing system automatically creates sourcing event);
the training includes determining feature weights for sourcing event database objects for sourcing event template object selection for features that can determine, for the first organization, a sourcing event template object for a sourcing event database object (Gowda [0056] determination can be changed by currency type, and given a specific weight once the type of currency is determined; [0024] taking feedback to train the models);
and
the training determines different feature weighs for the first organization than other feature weights previously determined for a second organization different from the first organization (Gowda [0056] determination can be changed by currency type, and given a specific weight once the type of currency is determined; [0024] taking feedback to train the models);
automatically identifying, using a machine-learning sourcing event creation intent model, from a plurality of automatically-processed electronic messages, a first electronic message as corresponding to a request to create a sourcing event database object in a database of a sourcing system server for a first upcoming sourcing event (Gowda [0033] the client device executes and sends requests to the sourcing system; [0022] sourcing system automatically creates sourcing event);
in response to automatically identifying the request to create the sourcing event database object in the database of the sourcing system server for the first upcoming sourcing event, automatically extracting, using a machine-learning automatic sourcing event field extraction model, extracted sourcing event field data from the request to create the sourcing event database object (Gowda [0023] the machine learning engine is able to identify the item lines and then be used as an input);
providing extracted sourcing event field data for the first upcoming sourcing event to the machine-learning sourcing event template recommendation model (Gowda [0023] one machine learning engine can identify line items for an input, and then use the model to map to create a sourcing system automatic analysis);
receiving, from the machine-learning sourcing event template recommendation model, a first recommended sourcing event template object for the first upcoming sourcing event from the electronic repository of sourcing event template objects (Gowda [0023] the machine learning model then sends the recommendation to the sourcing application to obtain specific items); and
automatically invoking an application programming interface of the sourcing system server for creating the sourcing event database object in the database of the sourcing system server for the first organization for the first upcoming sourcing event, wherein parameters for the application programming interface are selected based on the extracted sourcing event field data for the first upcoming sourcing event and the first recommended sourcing event template object (Gowda [0025-0026] the models sent to the sourcing application to order items).
Gowda fails to explicitly disclose multiple machine learning models.
Banerjee teaches multiple machine learning models, for identification analyzing and outputting (Banerjee [0022-0023] multiple machine learning models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Gowda with the opportunity to use multiple models as taught by Banerjee. The motivation for doing so would be to use an advanced analysis system to make connections and better sourcing determinations (Banerjee [0002] relationships between data objects).
Regarding claim 2, Gowda teaches the computer-implemented method of claim 1, further comprising preprocessing the historical sourcing event data (Gowda [0053-0054] generative model to analyze historical data to learn and better analyze).
Regarding claim 5, 17 and 19, Gowda teaches the computer-implemented method of claim 1, wherein the features include commodity, category, department, currency, baseline spend, and line item information of historical sourcing event database object (Gowda [0056] determination can be changed by currency type, and given a specific weight once the type of currency is determined).
Regarding claim 6, 16 and 20, Gowda teaches the computer-implemented method of claim 1, wherein the features include recency data for historical selections of sourcing event template objects (Gowda [0018] the models can also identify newly added terms, and therefore are able to determine the most recent items sourced).
Regarding claim 9, Gowda teaches the computer-implemented method of claim 1, wherein the first electronic message comprises a free-form text message (Gowda [0022] the input may be electronic messaging from buyer).
Regarding claim 10, Gowda teaches the computer-implemented method of claim 1, further comprising: providing a recommendation to use the first recommended sourcing event template to a user of the first organization (Gowda [0024] the sourcing application may present the buyer with the translations); and receiving confirmation from the user for using the first recommended sourcing event template object (Gowda [0025] first customer can confirm the translation); and wherein the application programming interface of the sourcing system server is invoked in response to receiving the confirmation (Gowda [0026] the user confirms, and then the sourcing application moves forward).
Regarding claim 11, Gowda teaches the computer-implemented method of claim 1, further comprising: receiving, from the machine-learning sourcing event template object recommendation model, a second recommended sourcing event template for a second upcoming sourcing event (Gowda [0024] the sourcing application may present the buyer with the translations); providing a recommendation to use the second recommended sourcing event template object for the second upcoming sourcing event to a user of the first organization (Gowda [0025] first customer can confirm or reject the translation); and receiving a rejection from the user regarding using the second recommended sourcing event template object for the second upcoming sourcing event (Gowda [0025] first customer can reject the translation).
Regarding claim 12, Gowda teaches the computer-implemented method of claim 11, further comprising determining, by the machine-learning sourcing event template recommendation model, in response to the rejection, a third recommended sourcing event template for the second upcoming sourcing event (Gowda [0024] when the buyer rejects the buyer may select the correct term).
Regarding claim 13, Gowda teaches the computer-implemented method of claim 11, further comprising updating the machine-learning sourcing event template recommendation model based on the rejection (Gowda [0024] the confirmation and rejections are used as feedback training for he models).
Claims 7-8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gowda, Banerjee and in further view of US 2021/0035069 A1 Parikh (hereinafter Parikh).
Regarding claim 7, Gowda teaches the computer-implemented method of claim 1. Gowda fails to explicitly disclose wherein the machine-learning sourcing event template recommendation model comprises a XG Boost model. Parikh teaches wherein the machine-learning sourcing event template recommendation model comprises a XG Boost model (Parikh [0235] the machine learning model may be convolutional or XG boost methods). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Gowda with the XG Boost taught in Parikh. The motivation for doing so would be to use a specific machine learning model based on the specific outcome desired (Parikh [0235] different algorithms for different tasks).
Regarding claim 8, and 15 Gowda teaches the computer-implemented method of claim 1. Gowda fails to explicitly disclose wherein the machine-learning sourcing event template recommendation model comprises a convolutional XG Boost model. Parikh teaches wherein the machine-learning sourcing event template recommendation model comprises a convolutional XG Boost model (Parikh [0235] the machine learning model may be convolutional or XG boost methods). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Gowda with the XG Boost taught in Parikh. The motivation for doing so would be to use a specific machine learning model based on the specific outcome desired (Parikh [0235] different algorithms for different tasks).
Response to Arguments
Applicant's arguments filed 07/22/2026 have been fully considered but they are not persuasive.
Regarding 101, Applicant points to the MPEP and the distinction between the claim involving an abstract idea, or it being directed to an abstract idea. Here the claims recites identifying historical events, using a machine learning model to make predictions, and then applying those predictions to create a sourcing event. Overall, it is a method of using data, analyzing the data, and then using the information to influence a task. This showcases how a person would think about the task, using historical information to influence the decision making process, and therefore is directed to the abstract idea, and specifically a mental process for making economical decisions. Further analysis must be applied to the abstract idea.
Examiner agrees that the claims are being performed on a computer, but the use of a computer does not itself integrate the judicial exception into a practical application. As shown in MPEP 2106.04(a)(2)(III)(C) a claim that requires a computer may still recite a mental process. When the mental process is being performed on generic computers, and the computer is presented as a tool to perform the mental process, if the concept may be performed by a person, then the additional elements are being used as a tool.
The additional elements are ML engine, which is described as inputting information and outputting the solution, which does not show any technical means to perform the action, and therefore is not a computing solution, but rather application of a manual solution on a computer.
Desjardins is recited in the Applicant arguments, which was found eligible because the claims provided technical improvements over conventional systems by reducing storage requirements and preserving task performance across sequential training. This is distinct from the training in the instant claims, because as recited in the Present Application [0028] which recites the efficiency comes from choosing the correct template to apply to the analysis. The selection of one template versus another, does not show a technical improvement, it shows an improvement in the decision making process. The reasons for eligibility needs to be focused on how the template identification improves the functionality of a computer, and not just using a magic black box decision making, and therefore, does not claim a technical improvement.
Regarding 103, Gowda in [0056] is able to teach the use of weighted factors to make a determination, and in [0024] there is details about when a user confirms, rejects or make alternative selections, this is able to train the prediction models. Therefore, when a specific organization changes the model response, the model is able to take the feedback and be “trained” to better accommodate the specific organization. Therefore, the claims remain rejected under 103.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0123527 A1 Michael et al. teaches a machine learning model to predict failures (Abstract).
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/JESSICA E SULLIVAN/ Examiner, Art Unit 3627
/FAHD A OBEID/ Supervisory Patent Examiner, Art Unit 3627
