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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/05/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/05/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 and 103 rejections of claims 1-20 are applied in light of Applicant's amendments.
The Applicant argues “Even if the claims were found to recite a judicial exception, they integrate that exception into a practical application.” (Remarks 12/05/2025)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set forth in the 2019 PEG. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mathematical concepts. Thus, the claim recites mathematical concepts for performing certain methods of organizing human activity.
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 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.
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.
The claimed subject matter is merely claims a method for calculating and analyzing (forecasting) information regarding agent demand. 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 (modeling and projecting) data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract idea. 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 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 service project(s), 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 a service system, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
Applicant’s arguments with respect to the rejection to the claims of 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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 1-20 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 1-20 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), computer program product (claims 8-14), and system (claims 15-20) 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 mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set forth in the 2019 PEG. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mathematical concepts. Thus, the claim recites mathematical concepts for performing certain methods of organizing human activity.
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) (mathematical concepts), as set forth in exemplary claim 1, are: receiving a request to determine a number of agents working at a future time;Independent claims 8 and 14 recite the CRM and system 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 using a combination engine… wherein the multiple modeling engines run independently of one another and of the combination engine, wherein training and inference workloads of the multiple modeling engines are distributed across different computing servers; A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising…; An apparatus comprising: a memory; and a processor configured to execute instructions stored in the memory to…; (as recited in claims 1, 8, and 15). 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.
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: using a combination engine… wherein the multiple modeling engines run independently of one another and of the combination engine, wherein training and inference workloads of the multiple modeling engines are distributed across different computing servers; A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising…; An apparatus comprising: a memory; and a processor configured to execute instructions stored in the memory to…; (as recited in claims 1, 8, and 15) 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.
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). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0042]) 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 (2-7, 9-14, and 16-20) 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 2-7 “obtaining, at the future time, contact center data of the contact center, wherein the contact center data comprises at least one of a number of agents currently working, engagement volume data, engagement length data, contact center user wait time data, or contact center agent availability data; and training, using online learning, the combination engine and the multiple modeling engines based on the obtained contact center data; wherein the output comprises a prompt to accept an overtime assignment that is transmitted to a device of at least one agent; wherein the output comprises a graphical indication of the future time, a graphical indication of the number of agents, and a graphical indication of a number of agents currently scheduled to work at the future time; receiving an input representing a service level target, wherein the number of agents is determined based on the service level target, wherein the service level target represents a proportion of contact center users who are connected to a contact center agent within a given time period after requesting connection to the contact center agent; wherein the multiple modeling engines comprise at least one of a weighted weekly moving average engine, a long short-term memory engine, or a time-series forecasting engine; determining a number of agents who were working at the future time and a service level of the contact center at the future time; and training the multiple modeling engines based on the number of agents who were working and the service level ”, 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 (9-13 and 15-20) recite the CRM and system for performing the method of claims 2-7. 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.
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 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub Pub 20220027837 (hereinafter “D’Attilio”) et al., in view of U.S. PGPub 20230015083 to (hereinafter “Shete”) et al., in further view of U.S. PGPub 20190385087 to (hereinafter “Martin”) et al.
As per claim 1, D’Attilio teaches A method, comprising:
receiving a request to determine a number of agents working at a future time; (D’Attilio: 0015-0023 and 0045-0060)
(D’Attilio: 0068-0071, 0079)
training, based on historical contact center data, …to generate agent demand data representing a number of agents working at a given time; and (D’Attilio: 0046-0063)
wherein training the combination engine includes leveraging an error metric representing a difference between a measured average user wait time and an average user wait time predicted by at least one of the modeling; engines; and providing an output representing the number of agents; (D’Attilio: 0126-0135)
D’Attilio may not explicitly teach the following. However, Shete teaches:
the multiple modeling engines … training the combination engine, based on the historical contact center data and performance data of the modeling engines by combining outputs from multiple modeling engines… to generate the prediction by combining the outputs of the modeling engines,; (Shete: 0060-0070)
D’Attilio and Shete are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified D’Attilio with the aforementioned teachings from Shete with a reasonable expectation of success, by adding steps that allow the software to utilize models with the motivation to more efficiently and accurately organize and analyze information [Shete 0060].
D’Attilio and Shete may not explicitly teach the following. However, Martin teaches:
wherein the multiple modeling engines run independently of one another and of the combination engine, wherein training and inference workloads of the multiple modeling engines are distributed across different computing servers, and; (Martin, Abstract : “A method for large-scale distributed machine learning using input data comprising formal knowledge and/or training data. The method consisting of independently calculating discrete algebraic models of the input data in one or many computing devices, and in sharing indecomposable components of the algebraic models among the computing devices without constraints on when or on how many times the sharing needs to happen…0009-0012: The method uses asynchronous communication among computing devices, each device computing its own discrete algebraic model and all collectively computing a distributed model that provides a solution for the scaling-up of machine learning systems. This method enables the distributive learning without the need for the synchronization of computing devices… This specification describes a method to resolve the problem of scaling-up distributed machine learning systems with multiple computing devices. The method for distributed machine learning uses formal knowledge, training data or both combined as input data. The method can be applied to computing devices working in the same or related learning tasks for cooperative, distributed machine learning… The computing devices can operate without waiting or interrupting their ongoing calculations due to the reception of the sharing from other computing devices and without the obligation to use a sharing load every time it is received…0018: Computing devices here refer to computing hardware, i.e. to machines and also to computing threads within the same or different machines.”
D’Attilio, Shete, and Martin are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified D’Attilio and Shete with the aforementioned teachings from Martin with a reasonable expectation of success, by adding steps that allow the software to utilize multiple engines with the motivation to more efficiently and accurately organize and analyze information [Martin 0019].
As per claim 2, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
In addition, D’Attilio teaches:
obtaining, at the future time, contact center data of the contact center, wherein the contact center data comprises at least one of a number of agents currently working, engagement volume data, engagement length data, contact center user wait time data, or contact center agent availability data; (D’Attilio: 0058, 0080-0081, 0084, 0126-0127)
and training, using online learning, the combination engine and the multiple modeling engines based on the obtained contact center data;(D’Attilio: 0071-0081).
As per claim 3, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
D’Attilio may not explicitly teach the following. However, Shete:
wherein the output comprises a prompt to accept an overtime assignment that is transmitted to a device of at least one agent; (Shete: 0023-0024, 0044-0057, 0098)
D’Attilio and Shete are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified D’Attilio with the aforementioned teachings from Shete with a reasonable expectation of success, by adding steps that allow the software to send data with the motivation to more efficiently and accurately organize and analyze information [Shete 0044].
As per claim 4, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
In addition, D’Attilio teaches:
wherein the output comprises a graphical indication of the future time, a graphical indication of the number of agents, and a graphical indication of a number of agents currently scheduled to work at the future time; (Shete: 0053, see Fig. 5)
D’Attilio and Shete are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified D’Attilio with the aforementioned teachings from Shete with a reasonable expectation of success, by adding steps that allow the software to display information with the motivation to more efficiently and accurately organize and analyze information [Shete 0053].
As per claim 5, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
In addition, D’Attilio teaches:
receiving an input representing a service level target, wherein the number of agents is determined based on the service level target, wherein the service level target represents a proportion of contact center users who are connected to a contact center agent within a given time period after requesting connection to the contact center agent; (D’Attilio: 0077-0081)
As per claim 6, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
In addition, D’Attilio teaches:
wherein the multiple modeling engines comprise at least one of a weighted weekly moving average engine, a long short-term memory engine, or a time-series forecasting engine; (D’Attilio: 0064-0069, 0071)
As per claim 7, D’Attilio, Shete, and Martin teach all the limitations of claim 1.
In addition, D’Attilio teaches:
determining a number of agents who were working at the future time and a service level of the contact center at the future time; and training the multiple modeling engines based on the number of agents who were working and the service level; (D’Attilio: 0068, 0081-0083)
Claims 8-9, 12-16, and 17-18, and 19-20 are directed to the CRM and apparatus for performing the method of claim 1-2 and 5-7 above. Since D’Attilio, Shete, and Martin teach the CRM and apparatus, the same art and rationale apply.
Claims 10-11 and 17-18 are directed to the CRM and apparatus for performing the method of claim 3-4 above. Since D’Attilio, Shete, and Martin teach the CRM and apparatus, the same art and rationale apply.
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.
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