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previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/7/2026 has been entered.
The claims 1, 6, 7, 9, 14, 17 and 20 have been amended. Claims 19 remain canceled. Claims 1-18 and 20 are pending.
Response to Amendment
With respect to Applicant’s arguments, the 101 rejection remains and is updated below.
Applicant’s arguments have been considered. However, the art rejections remain and are updated below.
Response to Argument
With respect to the 101 arguments, Applicant argues that claim 1, as amended, “is a computer-implemented technological process that uses calculated monotony indices as control signals to automatically control the operation of distributed remote computing devices,” such that the claimed invention improves computer-implemented task distribution and scheduling in distributed computing environments (See Remarks at pg. 10). Applicant alleges that “the claims as amended, therefore, are not directed to a mental process and, at a minimum, integrate any alleged abstract idea into a practical application and recite significantly more” (See Remarks at pg. 10). Applicant further argues that “transmitting instructions to remote computing devices based on the calculated monotony indices, wherein the instructions cause the remote computing devices to automatically execute computer operations, including automatic assignment of tasks or interactions… is not mere data analysis or insignificant post-solution activity” (See Remarks at pg. 13). However, Examiner respectfully disagrees. Examiner notes that the amended claim language, “transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer” recites an additional element that sends mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea [Emphasis added]. Additionally, “receiving or transmitting data over a network, e.g., using the Internet to gather data” is recognized by the courts as a well-understood, routine, and conventional function (See 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), MPEP 2106.05(d).). The amended claim language fails to practically apply the judicial exception or amount to significantly more. See the updated 101 rejection below.
With respect to the 101 arguments, Applicant argues that the “claims are not mental steps if “the human mind is not equipped to perform the claim limitations” (See Remarks at pg. 11). Specifically, Applicant asserts that the claim limitations, “selecting a remote computing device based stored data items; and calculating monotony indices for selected remote computing devices based on one or more of the tasks and one or more time windows, in the mind or by pen and paper” are “computer centric tasks, which a human cannot perform as a practical matter” (See Remarks at pg. 11). However, Examiner respectfully disagrees. Examiner first notes that Applicant’s Specification, at ¶0034, recites that a “plurality of agents or remote computers may be chosen or selected by embodiments of the invention for monotony assessment or evaluation based on, for example, existing or stored agent queue reports and/or data items and a plurality of conditions and/or criteria and/or predetermined thresholds.” Also, Applicant’s Specification, at ¶0058 recites the “monotony index associated with a specific computer may be associated also with the person assigned to that computer, e.g. the agent associated with a computer... In step 1040, a processor may then control a computer system based on the calculation or quantification of monotony, for example by sending or transmitting instructions to a remote computer. For example, a processor may assign computer- dependent tasks to a certain computer based on the calculated monotony index for that computer, or for the person associated with that computer.” Examiner notes that from the interpretation of Applicant’s Specification the selection of remote computing devices is merely an evaluation performed according to data and reports about the computing devices. It is evident that the remote computing devices are associated with a person assigned to the computer and their task interactions with the computer. Also, the calculated “monotony indices” is an evaluative assessment that quantifies the amount of data about the computing device operated by an agent against a condition and/or criteria and/or threshold. Therefore, the assessment that contributes to the calculated monotony index is an observation of data items and/or tasks that are evaluated according to conditions and/or criteria and/or predetermined thresholds. The selection of remote computing devices is also a mere selection in which devices associated with agents are being observed for the evaluation of the monotony index. Accordingly, these claims, in view of Applicant’s Specification, recite mental processes, as they recite observations and evaluations.
Also, with respect to the 101 arguments, Applicant argues that independent claim 1, as amended, expressly requires calculating monotony indices using one or more processors based on task execution data collected from remote computing devices and further transmitting instructions over a communication network to remote computing devices to automatically execute computer operations or assign tasks or interactions,” such that the claims cannot be characterized as human thought, observation, or judgement and require physical transmission of data to control computer operations (See Remarks at pgs. 11-12). Examiner respectfully disagrees. Examiner notes that data gathering steps, as well as the transmission of information are steps that are applied for the implementation of the abstract idea. As explained in the arguments above, mere instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea are not indicative of an inventive concept that practically applies the judicial exception. Therefore, the amended claims fail to practically apply the judicial exception or amount to significantly more. See the updated 101 rejection below.
Also, with respect to the 101 arguments, Applicant argues that the claims amount to significantly more, such that the “independent claim 1, as amended , recites sequence of operations in which task execution data associated with remote computing devices is processed, monotony indices are calculated over one or more time windows, the calculated monotony indices are documented or published in a database, and instructions are transmitted to remote computing devices to automatically execute computer operations or assign tasks or interactions based on those indices. The inventive concept lies not in the mere presence of a generic computer, but in the specific use of calculated monotony indices as automated control signals for distributed computing systems” (See Remarks pgs. 15-16). However, Examiner respectfully disagrees. Applicant alleges that “Applicant's as-filed specification makes clear that the contribution of the invention resides in quantifying task monotony and using that quantification to automatically control the operation of remote computing devices” (See Remarks, at pg. 16). Examiner first notes that the claims directed to quantifying task monotony are methods of observing task and interaction types to evaluate an index of monotony, which recites a judicial exception. The claim limitations reciting “sending” or “transmitting” instructions to a remote computer does not amount to automatically controlling an operation. The “one or more instructions” claimed merely instruct a computing device to implement some assignment or operation. The claimed computer systems in the independent claims do not automate the operation. These computer systems are merely transmitting instructions. As clarified in the arguments above, “receiving or transmitting data over a network, e.g., using the Internet to gather data” is recognized by the courts as a well-understood, routine, and conventional function (See 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), MPEP 2106.05(d).). Therefore, the claims as amended fail to recite additional elements that amount to significantly more than the judicial exception. See the updated 101 rejection below.
With respect to the 102 arguments and 103 arguments, Applicant argues that the cited reference Paul et al. (United States Patent Application Publication, 2010/0049574, hereinafter referred to as Paul) fails to disclose the amended limitations of the amended independent claims (See Remarks at pgs. 17-21). Examiner notes that this argument is now moot, as the claims are now rejected by Smutko et al. (United States Patent Application Publication, 2020/0394577, hereinafter referred to as Smutko) in view of Sekar et al. (United States Patent Application Publication, 2021/0201359, hereinafter referred to as Sekar). See the updated art rejections below.
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 therefore, 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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with Step 1, it is first noted that the claimed methods in claims 1-8 and 17-20; and the claimed computerized system in claims 9-16 are directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 1-20.
In accordance with Step 2A, Prong One, claims 1-20, the claimed invention recites an abstract idea.
Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements
recited in bold):
Claim 1:
A method for controlling a computer system based on quantifying repetitive computer- based tasks, the method comprising: in a computerized-system comprising one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items describing one or more of the remote computing devices, wherein one or more of the data items describe one or more tasks executed by one or more of the remote computing devices, the one or more tasks associated with one or more tasks types:
selecting, by one or more of the processors, one or more of the remote computing devices based on one or more of the stored data items;
calculating, by one or more of the processors, one or more monotony indices for one or more of the selected remote computing devices based on one or more of the tasks and one or more time windows; and
automatically documenting one or more of the calculated monotony indices in a database, the documenting including changing one or more of: fields included in the database. or metadata linked to the database.
transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer.
Claim 9:
A computerized system for controlling a computer system based on quantifying repetitive computer-based tasks, the system comprising: one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items describing one or more of the remote computing devices, wherein one or more of the data items describe one or more tasks executed by one or more of the remote computing devices, the one or more tasks associated with one or more tasks types; wherein the one or more processors are to:
select one or more of the remote computing devices based on one or more of the stored data items;
calculate one or more monotony indices for one or more of the selected remote computing devices based on one or more of the tasks and one or more time windows; and
automatically document one or more of the calculated monotony indices in a database, the documenting including changing one or more of: fields included in the database, or metadata linked to the database.
transmit one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer.
Claim 17:
A method for identifying monotony, the method comprising: in a computerized-system comprising one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items describing one or more of the remote computing devices, wherein one or more of the data items describe one or more interactions involving one or more of the remote computing devices, the one or more interactions associated with one or more interaction types:
computing, by one or more of the processors, a monotony index for a remote computing device based on one or more of the interactions and one or more timeframes; and
automatically publishing one or more of the calculated monotony indices, the publishing including changing one or more of: fields included in a database, or metadata linked to the database.
sending one or more instructions to a given remote computer based on the publishing of one or more of the monotony indices, the one or more instructions to automatically assign at least one interaction to the remote computer.
The above-recited limitations viewed as an abstract idea are mental processes (i.e., concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Specifically, the claimed invention recites steps for observing task and interaction types to evaluate an index of monotony and data to be changed. Therefore, the claims recite a mental process.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application because the use of bolded additional elements for receiving/transmitting data (e.g., automatically documenting one or more of the calculated monotony indices in a database, the documenting including changing one or more of: fields included in the database. or metadata linked to the database; automatically publishing one or more of the calculated monotony indices, the publishing including changing one or more of: fields included in a database, or metadata linked to the database; transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer; sending one or more instructions to a given remote computer based on the publishing of one or more of the monotony indices, the one or more instructions to automatically assign at least one interaction to the remote computer; etc.); processing information (e.g., select one or more of the remote computing devices based on one or more of the stored data items; calculate one or more monotony indices for one or more of the selected remote computing devices based on one or more of the tasks and one or more time windows; computing, by one or more of the processors, a monotony index for a remote computing device based on one or more of the interactions and one or more timeframes; etc.); storing data; displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. Mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform (e.g. transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer; sending one or more instructions to a given remote computer based on the publishing of one or more of the monotony indices, the one or more instructions to automatically assign at least one interaction to the remote computer; etc.) an abstract idea are note indicative of an inventive concept. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims.
In accordance with Step 2B, the claims only recite the above bolded additional elements. The additional elements are recited at a high-level of generality (i.e., as a generic computer for identifying and quantifying monotony, and associated degradations in performance statistics) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as evidence of generic computer implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification at paragraph 0018-0020, that a “computing device100 may include a controller or processor105 (or, in some embodiments, a plurality of processors) that may be, for example, a central processing unit processor (CPU), a chip or any suitable computing or computational device, an operating system115, a memory120, a storage130, input devices135 and output devices140 such as a computer display or monitor displaying for example a computer desktop system. Each of the procedures and/or calculations discussed herein, and the modules and units discussed, such as for example those included in Figs. 2-10, may be or include, or may be executed by, a computing device such as included in Fig. 1, although various units among these modules may be combined into one computing device. Operating system115 may be or may include any code segment designed and/or configured to perform tasks involving coordination, scheduling, arbitration, supervising, controlling or otherwise managing operation of computing device100, for example, scheduling execution of programs. Memory120 may be or may include, for example, a Random Access Memory (RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short term memory unit, a long term memory unit, or other suitable memory units or storage units. Memory120 may be or may include a plurality of, possibly different memory units. Memory120 may store for example, instructions (e.g. code125) to carry out a method as disclosed herein, and/or a data store of a plurality of data items describing one or more remote computing devices as further disclosed herein. Executable code 125 may be any executable code, e.g., an application, a program, a process, task or script.” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that additional elements that “receive or transmit data over a network, e.g., using the Internet to gather data” (e.g. transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer; sending one or more instructions to a given remote computer based on the publishing of one or more of the monotony indices, the one or more instructions to automatically assign at least one interaction to the remote computer; etc.) and “performing repetitive calculations” to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
With respect to the dependent claims, claims 2-8, 10-16, 18 and 20 recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, claims 2-3, 8, 10-11, 16 and 18 further narrow the calculation and processing of a monotony or frequency of a task of a particular task type. Claims 4-7, 12-15, and 20 further narrow the receiving and transmitting of calculated monotony index in formation and scheduling data. The dependent claims do not remedy these deficiencies.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smutko et al. (United States Patent Application Publication, 2020/0394577, hereinafter referred to as Smutko) in view of Sekar et al. (United States Patent Application Publication, 2021/0201359, hereinafter referred to as Sekar).
As per Claim 1, Smutko discloses a method for controlling a computer system based on quantifying repetitive computer-based tasks, the method comprising: in a computerized-system comprising one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items describing one or more of the remote computing devices, wherein one or more of the data items describe one or more tasks executed by one or more of the remote computing devices, the one or more tasks associated with one or more tasks types (Smutko: ¶0023-0024: A number of human users such as call-center agents may use agent terminals which may be for example personal computers or terminals. Terminals may include one or more software programs to operate and display a computer desktop system (e.g. displayed as user interfaces such as a GUI). Client data collection software may execute on or by terminals and may monitor input to programs. For example, client data collection software may receive, gather or collect a user's desktop activity or actions, e.g. low-level user action information or descriptions, and send or transmit them to a remote server. Client data collection software may access or receive actions via an API (application programming interface) interface with the operating system and/or specific applications (e.g. the Chrome browser) for the computer or terminal on which it executes. Remote server may collect or receive data such as user action information or descriptions.):
selecting, by one or more of the processors, one or more of the remote computing devices based on one or more of the stored data items (Smutko: ¶0021: Distinct remote user terminals are identified and linked to the performed unique actions performed at each of the terminals. See Fig. 5 where the selected terminal stored items are displayed.);
calculating, by one or more of the processors, one or more monotony indices for one or more of the selected remote computing devices based on one or more of the tasks and one or more time windows (Smutko: ¶0021: Data may be received describing low-level user action information or items that describe series of actions that repeat across data. See Table 2 and ¶0048 and 0051-0054 where the low-level action sequences indices are determined by an algorithm that identifies the number of occurrences of the action sequences responsive to their timestamps in a time window. See also ¶0094 where the number of occurrences is scored for automation. Examiner notes that Applicant’s Specification, ¶0034-0035 describe the calculation of monotony indices as calculating a plurality of subcomponents such as a task type and monotony frequency. Therefore, the use of the calculated number of occurrences for action sequences in a time window would be a calculation of monotony indices.); and
automatically documenting one or more of the calculated monotony indices in a database, the documenting including… or more of: fields included in the database or metadata linked to the database (Smutko: ¶0090: The subprocess in the database is linked to the human agent ID performing the action.);
transmitting one or more instructions to a remote computer based on the documenting of one or more of the calculated monotony indices, the one or more instructions to automatically execute at least one computer operation on the remote computer (Smutko: ¶0098: An automation process is created to according to a calculated score derived from the cumulative measure of action sequences in a duration of time. The automated process is transmitted to a remote system to perform the human agent process.).
Smutko does not explicitly disclose, however Sekar discloses: C) automatically documenting… in a database, the documenting including changing one or more of: fields included in the database or metadata linked to the database (Sekar: ¶0276 and 0286: Interaction predictors may update a campaign dataset to create an enriched dataset for scheduling agents to scheduled hours.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Smutko with Sekar’s schedule workforce in accordance with automated bots because the references are analogous/compatible since each is directed toward features for optimizing workforce by analyzing scheduled tasks and automating certain tasks to automated systems, and because incorporating Sekar’s schedule workforce in accordance with automated bots in Smutko would have served Smutko’s pursuit of identifying automation opportunities to manual human tasks as an effective business-oriented decision (See Smutko, ¶0101); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9 and 17 recite the limitations already addressed by the rejection of Claim 1; therefore, the same rejection applies.
As per Claim 2, Smutko in view of Sekar discloses the method of claim 1. wherein the calculating of one or more monotony indices comprises calculating one or more task type indices, each task type index to quantify a relative weight of one or more of the tasks of a given type among one or more of the tasks (Smutko: ¶0067: For scoring subprocesses or series of actions, a factor may be applied to action types according to its importance.).
Claim 10 recites the same limitations already addressed by the rejection of Claim 2; therefore, the same rejection applies.
As per Claim 3, Smutko in view of Sekar discloses the method of claim 1, wherein the calculating of one or more monotony indices comprises calculating one or more time window indices, each time window index to quantify a relative weight of one or more consecutive tasks of a given type within one or more time windows (Smutko: ¶0067: For scoring subprocesses or series of actions, a factor may be applied to action types according to its importance. This score may measure how much time is given to the action type in a total time duration.).
Claim 11 recites the same limitations already addressed by the rejection of Claim 3; therefore, the same rejection applies.
As per Claim 4, Smutko in view of Sekar discloses the method of claim 1, comprising generating one or more reports, the one or more reports describing the execution of tasks by one or more of the remote computing devices, based on at least one of: one or more of the stored data items, one or more conditions, and one or more predetermined thresholds (Smutko: ¶0096-0098: Identified processes are associated with their scores derived from the cumulative measure of action sequences, where the action sequences are stored data items, in a duration of time are reported to the user terminal in a report, visualization or graph. Automated processes are created subject to the condition of the calculated score.).
Claim 12 recites the same limitations already addressed by the rejection of Claim 4; therefore, the same rejection applies.
As per Claim 5, Smutko in view of Sekar discloses the method of claim 4, comprising determining a time window length based on one or more of the data items (Smutko: ¶0041 and 0043: Actions and series of actions according to data items are identified for which they occurred in a determined time window.); and wherein at least one of: the generating of one or more reports, the selecting of one or more of the remote computing devices, the calculating of one or more monotony indices, and the documenting of one or more of the calculated monotony indices is performed periodically based on the time window (Smutko: ¶0096-0098: Identified processes are associated with their scores derived from the cumulative measure of action sequences in a duration of time are reported to the user terminal in a report, visualization or graph.).
Claim 13 recites the same limitations already addressed by the rejection of Claim 5; therefore, the same rejection applies.
As per Claim 6, Smutko in view of Sekar discloses the method of claim 1, wherein the one or more instructions cause the remote computer to automatically assign at least one task to be executed by the remote computer (Smutko: ¶0098: An automation process is created to according to a calculated score derived from the cumulative measure of action sequences in a duration of time. The automated process is transmitted to a remote system to perform the human agent process.).
Claim 14 recites the same limitations already addressed by the rejection of Claim 6; therefore, the same rejection applies.
As per Claim 7, Smutko in view of Sekar discloses the method of claim 6.
Smutko does not explicitly disclose; however, Sekar discloses comprising minimizing one or more inefficiency functions for one or more block scheduling options; and choosing an optimal scheduling option based on the minimization, wherein the transmitting one or more instructions is performed based on the chosen optimal scheduling option (Sekar: ¶0056-0058: The analytics module includes an optimizer used to minimize functions according to processes of an agent and automated processes. See ¶0250 where neural networks are used to schedule the workforce by predictor coefficients related to a worker’s ability to handle a task efficiently. The schedule produced by the model may schedule time blocks according to the predictor coefficients likelihood of success.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Smutko with Sekar’s neural networks to schedule workforce in accordance with automated bots because the references are analogous/compatible since each is directed toward features for optimizing workforce by analyzing scheduled tasks and automating certain tasks to automated systems, and because incorporating Sekar’s neural networks to schedule workforce in accordance with automated bots in Smutko would have served Smutko’s pursuit of identifying automation opportunities to manual human tasks as an effective business-oriented decision (See Smutko, ¶0101); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 15 recites the same limitations already addressed by the rejection of Claim 7; therefore, the same rejection applies.
As per Claim 8, Smutko in view of Sekar discloses the method of claim 7.
Smutko does not explicitly disclose; however, Sekar discloses wherein one or more of inefficiency functions comprise one or more coefficients; and wherein the method comprises generating, using a neural network, one or more of the coefficients (Sekar: ¶0250: Neural networks are used to schedule the workforce through the use of predictor coefficients related to a worker’s ability to handle a task efficiently.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Smutko with Sekar’s neural networks to schedule workforce in accordance with automated bots because the references are analogous/compatible since each is directed toward features for optimizing workforce by analyzing scheduled tasks and automating certain tasks to automated systems, and because incorporating Sekar’s neural networks to schedule workforce in accordance with automated bots in Smutko would have served Smutko’s pursuit of identifying automation opportunities to manual human tasks as an effective business-oriented decision (See Smutko, ¶0101); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16 recites the same limitations already addressed by the rejection of Claim 8; therefore, the same rejection applies.
As per Claim 18, Smutko in view of Sekar discloses the method of claim 17, wherein the computing of one or more monotony indices comprises calculating one or more monotony frequency indices, each monotony frequency index quantifying a consecutive handling of tasks belonging to the same type within one or more timeframes (Smutko: ¶0067: For scoring subprocesses or series of actions, a factor may be applied to action types according to its importance. This score may measure how much time is given to the action type in a total time duration.).
As per Claim 20, Smutko in view of Sekar discloses the method of claim 17.
Smutko does not explicitly disclose; however, Sekar discloses comprising comparing one or more values calculated using inefficiency functions for one or more scheduling options and selecting a scheduling option based on the comparison, wherein the sending of one or more instructions is performed based on the selected scheduling option (Sekar: See ¶0250 where neural networks are used to schedule the workforce by predictor coefficients related to a worker’s ability to handle a task efficiently. The schedule produced by the model may schedule time blocks according to the predictor coefficients likelihood of success.).
It would have been obvious to one of ordinary skill in the before the effective filing date of the claimed
invention to combine Smutko with Sekar’s neural networks to schedule workforce in accordance with automated bots because the references are analogous/compatible since each is directed toward features for optimizing workforce by analyzing scheduled tasks and automating certain tasks to automated systems, and because incorporating Sekar’s neural networks to schedule workforce in accordance with automated bots in Smutko would have served Smutko’s pursuit of identifying automation opportunities to manual human tasks as an effective business-oriented decision (See Smutko, ¶0101); and further obvious since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Swinke et al. (US 2018/0144126): A computer-implemented method for assisting a user perform a task in a computer system includes deploying a bot to monitor user interactions with the computer system and using machine learning to recognize a pattern in a user's actions in repetitively performing the task in the computer system. The method includes processing the recognized pattern to automatically establish a suggested rule that can be used by the computer system for performing a future instance of the task in lieu of the user performing the future instance of the task.
Garvey et al. (US 2007/0234301): Method and system for determining a management complexity factor for an environment, such as an information technology (IT) environment, is disclosed. A management complexity factor represents the needless complexity, inefficiencies and waste in an environment. Au IT environment includes different platforms, systems, and components that have an effect on changes and upgrades within the environment. The impact of the changes or upgrades is reduced if the environment has increased coordination, few single points of failure, high information availability, a high level of automation and the like.
Mudi et al. (US 2021/0304064): A method and system for automating repetitive task on a user interface is disclosed. The method includes identifying a plurality of Document Object Model (DOM) elements from a repetitive pattern. The method further includes identifying a set of dynamic DOM elements from the plurality of DOM elements. The method further includes determining a path and a path position within the application code of the repetitive pattern for each of the set of dynamic DOM elements. The method further includes training an Artificial Intelligence (AI) model to identify the repetitive pattern and to identify the corresponding path and the path position within the application code component, for each of the set of dynamic DOM elements.
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/ALLISON M NEAL/Primary Examiner, Art Unit 3625
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