DETAILED ACTION
Status of Claims
This communication is in response to applicant’s response filed on 04/08/2026.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1, 3, 5, 10, 13, 15 and 17 are currently amended. Claims 4, 6, 9, 11, 12, 16 and 18 are canceled. New claims 21-23 have been added Claims 1-3, 5, 7-8, 10, 13-15 and 17, 19-23 are currently pending and have been examined.
Priority
This application claims priority of US Provisional Application 63/400,630 filed on August 24, 2022. Priority claim for the benefit of this prior-filed application is acknowledged.
Examiner Request
Filed documents (i.e. claims) are not illegible enough for use of machine transaction (i.e. OCR (Optical Character Recognition)) for searching, copying text for use in the office action. Examiner respectfully request copy future claim amendment in OCR compliant format if possible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 3 and 15 recites the limitation “the complexity request” There is insufficient antecedent basis for this limitation in claims 1 and 14, from which they respectively depend. It is unclear whether “the complexity request” refers back to “complexity of the procurement request” or not. For examination purposes, the examiner considers “the complexity request” to refer back to “complexity of the procurement request.”
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 1-3, 5, 7-8, 10, 13-15 and 17, 19-23 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 1-3, 5, 7-8, 10, 21-23 are drawn to a method which is within the four statutory categories (i.e., a process). Claims 13-15, 17 and 19 are drawn to a system which is within the four statutory categories (i.e. a machine). Claim 20 is drawn to a non-transitory computer-readable medium which is within the four statutory categories (i.e., a manufacture).
Independent claims 1, 13 and 20 as a whole directed toward an abstract idea of procuring goods or services (i.e. receiving a procurement request from a user, the procurement request including details of goods or services being requested; determining a complexity of the procurement request; in response to determining that the complexity of the procurement request is above a certain high complexity threshold: presenting a plurality of procurement questions to the user about the procurement request;
receiving responses to the plurality of procurement questions from the user; applying a trained classifier model to the procurement request including the received responses to the plurality of procurement questions to identify a plurality of possible procurement professionals capable of handling the procurement request from a plurality of available procurement professionals; wherein the trained classifier model is a random forest multi-class classifier model trained to output the plurality of possible procurement professionals with the plurality of possible procurement professionals corresponding to a plurality of possible output classes;
wherein the trained classifier model is trained using training data comprising historical procurement requests comprising a plurality of historical responses and historical selected procurement professionals, the training data obtained by encoding a plurality of historical responses into a plurality of categorical variables of binary values; wherein the trained classifier model is trained to determine feature importance for the plurality of categorical variables to identify the plurality of possible procurement professionals; receiving, from the user, a selecting selection of a procurement professional from the plurality of possible procurement professionals based on a procurement workload of each possible procurement professional; and transmitting the procurement request to the selected procurement professional; in response to determining that the complexity of the procurement request is below a low complexity threshold, presenting the user with self-serve procurement information; receiving: an indication from the selected procurement professional that they are not able to handle the procurement request, the indication that the selected professional is not capable of handling the procurement request comprising an indication of a different procurement professional for handling the procurement request; and an indication from the user comprising feedback of the selected procurement professional; and further training the trained classifier model to identify the plurality of possible procurement professionals using the indication of the different procurement professional for handling the procurement request and the feedback of the selected procurement professional.)
The claim recites steps of: Receiving a procurement request with goods/services details; Determining complexity of the request; Presenting questions and receiving responses; Applying a trained classifier (random forest multi-class) to identify possible procurement professionals; Training the classifier using historical data and feature importance; Receiving selection from user and transmitting request; Presenting self-serve information for low complexity requests; Receiving indications and feedback, and further training the classifier which falls under abstract idea bucket of Certain Methods of Organizing Human Activities.
Because the claim recites abstract ideas, the analysis proceeds to determine whether the claim recites additional elements that recite a practical application of the abstract ideas. According to MPEP 2106.04(d), additional elements that recite an instruction to apply the abstract ideas using a processor and memory, that recite that generally link the use of the abstract ideas to a particular technological environment or field of use are not indicative of a practical application. Here, the additional elements of the processor memory fail to recite a practical application because they are instructions to apply the abstract ideas using computers. The claim recites a trained classifier model, but does not specify a technical improvement to computer functionality or machine learning technology itself. The steps are performed in their ordinary capacity, automating a business process (procurement matching) using conventional machine learning techniques (random forest classifier, feature importance). The claim does not solve a technological problem or improve the operation of computers or networks. Therefore, the claim as a whole fails to recite a practical application of the abstract ideas.
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons as given above.
Regarding claims 2 and 14: recite abstract ideas similar to those discussed above in connection with independent claims, presenting a plurality of initial questions to the user; and receiving responses to the plurality of initial questions, the responses providing the procurement request used to determine the complexity. Regarding claims 3 and 15: recite abstract ideas similar to those discussed above in connection with independent claim, presenting a plurality of follow-up questions to the user; and receiving follow-up responses to the plurality of follow-up questions, the follow-up responses added to the procurement request used to identify the plurality of possible procurement professionals. Regarding claims 5 and 17: recite abstract ideas similar to those discussed above in connection with independent claims, assigning the procurement request to an individual from a plurality of individuals capable of handling low complexity procurement requests, the individual selected based on workloads of the plurality of individuals. Regarding claims 7 and 19: recite abstract ideas similar to those discussed above in connection with independent claims, adding one or more procurement professionals and their associated expertise to the plurality of available procurement professionals. Regarding claim 8: recite abstract ideas similar to those discussed above in connection with independent claims, wherein the procurement process comprises: providing one or more detail questions from the selected procurement professional; receiving answers to the one or more detail questions from the user; and completing by the procurement professional the procurement request. Regarding claim 10: recite abstract ideas similar to those discussed above in connection with independent claims, wherein the indication comprises one or more of: an indication that the workload of the selected professional is too high to complete the procurement request; and an indication that the selected professional is not capable of handling the procurement request. Regarding claim 21: recite abstract ideas similar to those discussed above in connection with independent claims wherein the training data further comprises a business of a historical selected procurement professional and a type of a historical procurement request. Regarding claim 22: recite abstract ideas similar to those discussed above in connection with independent claims retraining the trained classifier based on updated procurement data and updated plurality of available procurement professionals. Regarding claim 23: recite abstract ideas similar to those discussed above in connection with independent claims wherein the procurement request and the indication from the user are received from a first device of the user using a procurement request interface; and wherein the indication from the selected procurement professional is received from a second device of the selected procurement professional using a procurement professional interface, the procurement professional interface comprising a first button to request further information on the procurement request, a second button for accepting the procurement request, and a third button for providing the indication of the different procurement professional for handling the procurement request.
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Reasons for Allowability Over the Prior Art
Closes prior art of records Burger et al. US 11,011,266 teaches A health provider matching service is provided to match patients to health providers based on a semantic relationship graph of data associated with conditions of patients. Schoenberg (US 2013/0054288) teaches The computerized system 110 includes an availability or presence tracking module 112 for tracking the availability of the service providers 130. Availability or presence is tracked actively or passively. In an active system, one or more of the service providers 130 provides an indication to the computerized system 110 that the one or more service providers are available to be contacted by consumers 120 and an indication of the mode by which the provider may be contacted. In some examples of an active system, the provider's computer, phone, or other terminal device periodically provides an indication of the provider's availability (e.g., available, online, idle, busy) to the system 110 and a mode (e.g., text, voice, video, etc.) by which he can be engaged. In a passive system, the computerized system 110 presumes that the service provider 130 is available by the service provider's actions, including connecting to the computerized system 110 or registering the provider's local phone number with the system. In some examples of a passive system, the system 110 indicates the provider 130 to be available at all times until the provider logs off, except when the provider is actively engaged with a consumer 120. Gounares et al (US 20090259488 A1) teaches The claimed subject matter provides systems and/or methods that identify healthcare professionals appropriate to treat diseases. The system can include mechanisms that employ patient symptoms, diagnoses associated with the symptoms, proposed treatment plans, or treatment outcomes based on proposed treatment plans, to construct and utilize dependency graphs to infer a score. The inferred score can then be employed to identify qualified healthcare professionals appropriate to treat the disease as presented by the patient and indicated by the symptoms. However upon further search and consideration, claims limitations as whole when considered in orderly combination are non-obvious combination of limitations which are not fairly taught by prior art of records.
Response to Arguments
As to the remark, Applicant asserted that claims 1 and 13 do not recite an abstract idea, as they do not direct the user to perform procurement or specify how procurement is performed because, The claims are limited to processing procurement requests using a trained classifier model and collecting feedback for further training, not to fundamental economic practices or organizing human activity. The claims also are not directed to a mental process, because the steps (e.g., training a random forest classifier, encoding responses, determining feature importance) require computational resources and cannot be performed in the human mind or with pencil and paper. Applicant analogizes to USPTO Example 39, arguing that the claims recite technical machine learning training steps, not mathematical relationships or mental processes. Applicant asserted that claims specify a random forest multi-class classifier model, trained with encoded historical responses and capable of determining feature importance—steps that require a technical environment and computational resources. Even if considered a judicial exception, applicant argues the claims are integrated into a practical application by providing a specific technical solution for matching procurement requests to professionals using machine learning and the claims solve a technical problem in machine learning—how to adapt and improve a classifier for procurement professional identification—through specific training and feedback mechanisms.
Examiner respectfully traverses Applicant’s remark for the following reasons:
With respect to (a) Examiner would like to point out to applicant that While the claims recite the use of a trained classifier model, the overall process automates the matching of procurement requests to professionals—a business method that falls within the “certain methods of organizing human activity” judicial exception. The steps of receiving requests, presenting questions, collecting responses, and matching professionals are routine in business and procurement management. The inclusion of machine learning techniques (random forest classifier, feature importance, encoding responses) does not transform the nature of the claim, as these are applied to facilitate a conventional business process. The claim does not recite a specific improvement to computer technology or a technical field beyond automating known business practices.
The claim recites steps that require computational resources, but the underlying process—analyzing data and matching professionals—remains a business decision-making activity. The use of a classifier model and encoded variables is a generic application of machine learning to automate data analysis and selection. The claim does not recite a specific technological improvement or solve a technical problem rooted in computer technology; rather, it applies known machine learning techniques to a business context. In Example 39 and Ex Parte Desjardins, the claims were found eligible because they improved the functioning of computer technology or solved a technical problem rooted in computer technology. Here, the claims do not recite such improvements. The use of machine learning to automate professional selection is a generic application of known techniques to a business process, not a technical improvement.
The claims are directed to an abstract idea (business method for procurement management and professional selection).The recited technical features are applied in a conventional manner to automate a business process, not to provide a specific technical improvement. The § 101 rejection is maintained.
Conclusion
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/NEHA PATEL/ Supervisory Patent Examiner, Art Unit 3699
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