DETAILED ACTION
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 .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 06/30/2025, Applicant, on 12/29/2025, amended claims 1 and 17; added claims 21-22. Claims 1-2, 4-17, and 19-22 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 12/29/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 rejection of claims 1-7, 9-17, and 19-22 are applied in light of Applicant's amendments.
The Applicant argues “The Applicant respectfully submits that if the claims recite or describe a judicial exception, any such judicial exception is integrated into a practical application of computing, the claims recite additional elements and limitations representing significantly more than any judicial exception, and the claims embody inventive concepts.” (Remarks 12/29/2025)
In response, the Examiner respectfully disagrees. The Examiner has thoroughly reviewed and analyzed the claims, arguments, and specification. The Examiner attempted to find eligible subject matter in the claims and/or specification, but was unsuccessful; and thus, is unable to provide any suggestions/Examiner’s amendment to overcome the 101 rejection.
The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding statistical data. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract ideas (mathematical concepts). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for purchase requisitions , and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding purchase information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
For instance, Applicant claims "wherein the probability value is generated by executing a neural network." While this claim appears to have a practical application, a closer examination reveals that the core of the invention is the underlying mathematical model and its training process. Furthermore, even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. The Applicant has not created a new learning algorithm, but rather optimizing existing algorithm(s) or the application of known techniques to a new dataset. Such incremental advancements, while potentially valuable for business, do not automatically confer patent eligibility or a technological improvement. As highlighted in the Alice framework, the mere recitation of known components or processes does not necessarily amount to an inventive concept.
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 1-7, 9-17, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-7, 9-17, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is 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.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7, 9-16, and 22) and computer program product (claims 17 and 19-21) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process (receiving/analyzing request data) for performing certain mathematical concepts (generating statistical/probability information).
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The limitations reciting the abstract idea(s) (mental process and mathematical concepts), as set forth in exemplary claim 1, are: receiving…local statistical information and global statistical information for electronic digital past request data representing past requests of client devices that were …and that were reviewed in an approval chain… the local statistical information being associated with past requests of client devices of a particular enterprise and the global statistical information being associated with past requests of other enterprises; …receiving… representing a request before the request is submitted to the computer system for processing in the approval chain, the request comprising a purchase requisition comprising request data values and the past request data for each of the past requests, the request data values and the past request data comprising a plurality of different requisition data values; generating, in real time, as each of the request data values are received from the client device, and based on the local statistical information, the received external data and global statistical information for electronic digital past request data representing past requests, a probability value indicating a high likelihood that the request will be rejected if the request is submitted to the computer system for processing in the approval chain; …that includes the request and a message that includes the probability value; wherein the probability value …using request data values of the request; …to receive, from the requisition data, at least total price, quantity, first supplier, second supplier, delivery date, and percentage of contract; determining suggestions for rewriting the request, wherein the suggestions decrease the probability value; …the suggestions for rewriting the request; receiving … modified, revised, or updated request data values; requesting updated external data associated with the updated request data values …; producing in real time an updated probability value based on the updated request values, updated external data, and the global statistical information for electronic digital past request data representing past requests; producing updated suggestions for rewriting the request using the updated request data values. Independent claim 17 recites the CRM for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: “a computer system and digitally storing … submitted via a computer system… that comprises a series of approval computers… from a client device electronic digital requisition data… requesting external data associated with the request from an external device; receiving the external data from the external device; causing, in real time as each of the request data values are received from the client device…causing the client device to display a graphical user interface … is generated by executing a neural network… wherein the neural network comprises an input layer configured with neurons… causing the client device to display in the graphical user interface the suggestions for rewriting the request; receiving the updated external data from the external device; causing the client device to display in the graphical user interface the updated probability value and the updated suggestions: the computer system receiving, from the client device, the request for processing in the approval chain. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform…; the neural network comprising a Fast Artificial Neural Network (FANN); training the neural network by providing the past request data to the neural network as training data; repeating the training, until output generated by the neural network indicates, within an error value not exceeding a threshold value, that processing the past request data by the neural network results in the neural network predicting an approval of the past request data the neural network comprising an input layer configured with neurons to receive, from the requisition data, at least total price, quantity, first supplier, second supplier, delivery date, and percentage of contract; further comprising causing to display the probability value in a computer display screen logically near or on the request data or a portion of the request data” (as recited in claims 1, 3-4, 13, and 17). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: “a computer system and digitally storing … submitted via a computer system… that comprises a series of approval computers… from a client device electronic digital requisition data… requesting external data associated with the request from an external device; receiving the external data from the external device; causing, in real time as each of the request data values are received from the client device…causing the client device to display a graphical user interface … is generated by executing a neural network… wherein the neural network comprises an input layer configured with neurons… causing the client device to display in the graphical user interface the suggestions for rewriting the request; receiving the updated external data from the external device; causing the client device to display in the graphical user interface the updated probability value and the updated suggestions: the computer system receiving, from the client device, the request for processing in the approval chain. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform…; the neural network comprising a Fast Artificial Neural Network (FANN); training the neural network by providing the past request data to the neural network as training data; repeating the training, until output generated by the neural network indicates, within an error value not exceeding a threshold value, that processing the past request data by the neural network results in the neural network predicting an approval of the past request data the neural network comprising an input layer configured with neurons to receive, from the requisition data, at least total price, quantity, first supplier, second supplier, delivery date, and percentage of contract; further comprising causing to display the probability value in a computer display screen logically near or on the request data or a portion of the request data” for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
In addition, Applicant’s Specification (paragraph [0039]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2, 5-7, 9-12, 14-16, and 18-22) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2, 5-12, 14-16, and 22 “receiving…revised electronic digital requisition data representing a revised request; generating, in real time in response to the revised request, and based on the revised request and the local statistical information and global statistical information for electronic digital past request data representing past requests, a revised probability value indicating a likelihood that the revised request will be rejected if the request is submitted to the computer system for processing in the approval chain; generating the revised probability value by executing a neural network using request data values of the revised request; and causing the client device to display a second graphical user interface that includes the revised request and a second message that includes the revised probability value; the training data being obtained from requisition forms that were submitted and approved or rejected previously, history logs maintained for previously reviewed requisitions, profiles of users, profiles of approval chains, profiles of suppliers, inventory logs of suppliers; the request comprising any of an invoice, expense report, personal request, employee suggestion submission; retrieving from storage and causing displaying an explanation of why the request data indicates a high probability that a particular request will be rejected if submitted; further comprising determining suggestions for rewriting the request, when the request data indicates a high probability that a particular request will be rejected if submitted; wherein the suggestions for rewriting the request comprise at least one of: changing supplier from whom items are requested, changing a requested delivery date of requested items, changing a quantity of requested items; the local statistical information and global statistical information comprising any of a count of requested items in a request, availability of items in a request, whether requested items were approved in the past, whether requested items meet a quality threshold, whether a supplier of requested items is local, whether fulfilling similar requests for similar items was successful in the past; further comprising parsing justification content of a justification in the request to identify keywords and testing whether the keywords have been also used in requests that were approved in the past; further comprising determining suggestions for rewriting the request by changing supplier from whom items are requested, changing a requested delivery date of requested items, changing a quantity of requested items; the request comprising a purchase requisition, the method further comprising generating the probability value by determining a K nearest requisition-neighbors to the request and determining the probability value as a percentage of the K nearest requisition-neighbors that were rejected; the local statistical information and global statistical information comprising any of a count of requested items in a request, availability of items in a request, whether requested items were approved in the past, whether requested items meet a quality threshold, whether a supplier of requested items is local, whether fulfilling similar requests for similar items was successful in the past; the request comprising a purchase requisition, the method further comprising generating the probability value by determining a K nearest requisition-neighbors to the request and determining the probability value as a percentage of the K nearest requisition-neighbors that were rejected; wherein the neural network has been trained using training data comprising a plurality of features comprising at least: on behalf of; # of policies; submitter's self-approval limit; # of req lines; has justification; days until need- by date; address on req does not equal user's default, freeform requests, toral price of order, non- preferred suppliers, # of commodities, % of items on contract, % of items with p-card, # of billing strings that do not match default, has RFQ happened for an item on the req, split billing extra allocation count, submitter rejection %, approver rejection %, # of approvers, buyer action required, over budget(s), # of lines with items already in stock, req was previously rejected, # of items on separate order already, user has previously ordered all items on list ”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (18-21) recite the CRM for performing the method of claims 2-16 and 22. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beddo; Michael Ervin. SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR FORECASTING PRODUCT SALES, .U.S. PGPub 20140108094 The present invention relate to systems, methods, and computer program products for determining forecasting data relating to a product using a neural network and accessing that forecasting data. In some embodiments, a system is provided that includes (a) forecasting apparatus, which stores product information and a neural network; and (b) a computing system that access the forecasting apparatus via a web portal and transmits some or all of the product information to the forecasting apparatus. In some embodiments, the forecasting apparatus is configured to determine an initial sales forecast using at least a portion of the product information and the neural network, modify the initial sales forecast to generate a final sales forecast, and present the final sales forecast to the computing system via the web portal.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625