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 . Claims 1, 3, 8, 10, 12, 13, 15, 117, 119, and 121-131 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 04/23/2026, amended claims and cancelled/previously cancelled 4-7, 9, 11, 14, 16-116, 118, and 120. Claims 1, 3, 8, 10, 12, 13, 15, 117, 119, and 121-131 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The102/103 Rejections were overcome and withdrawn in the Final Office action dated 06/18/2025.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that claims are not directed towards mental processes and further points to specific aspects of the amended claims and points to storage mediums, servers, etc.
Examiner respectfully disagrees. The cited elements such as different data structures, automatically converting, standardizing, computer-specific data processing operations, etc. are each are additional elements which amount to “apply it” on a general purpose computer (see MPEP 2106.05(f)) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The conversions as described in the specification include standardizing/normalizing data and converting data signals into data entries and not a conversion of format into a standardized format as described in Example 42. The claims are directed towards the mental processes of determining sales projections, determining projected staffing needs, and determining one or more recommended staffing actions and further certain methods of organizing human activity.
Applicant contends that similar to McRo, Inc v. Bandai… replace subjective discretionary human judgement with a defined automated process and as such is patent eligible.
Examiner respectfully disagrees. The present claims do not follow the same fact pattern as recited in McRO as MCRO is directed towards automatic lip synchronization while the present claims are directed towards determining staffing recommendations applied to a general purpose computer.
Applicant further contends that claims are not directed towards mental processes as the claims recite the additional elements (listed on pg. 22-24 of the arguments) and are not capable of being performed in the human mind.
Examiner respectfully disagrees. The cited elements such as servers, automatically, API, machine learning (recited at ahigh level of generality), real-time (implies use of a general purpose computer, automatically (implies use of a general purpose computer), modules, sub-modules, etc. are each are additional elements which amount to “apply it” on a general purpose computer (see MPEP 2106.05(f)) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Applicant contends the claims recite multiple algorithms which are machine implemented technical computations and not mental processes. Applicant further asserts the claims improve system performance and coordination of automated staffing.
Examiner finds the argument unpersuasive. An algorithm alone does not constitute an additional element. The inclusion of a machine learning algorithm (recited at a high level of generality) amounts to “apply it” on a general purpose computer (see MPEP 2106.05(f)) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The servers, modules, APIs, etc. are addressed similarly. Examiner further notes the cited improvements merely improve upon the abstract idea itself and not the technology as a whole.
Applicant contends at Step 2A- P2 that the claims recite an improvement in the functioning of a computer, or an improvement to other technology or technical field. Applicant points to several limitations. Applicant further points to Enfish… and Visual Memory…
Examiner respectfully disagrees. The cited elements are each are additional elements which amount to “apply it” on a general purpose computer (see MPEP 2106.05(f)). The use of algorithms are merely narrowing the abstract idea as algorithms can be performed mentally (i.e. pen and paper) and would further certain methods of organizing human activities. The present claims are not analogous to the cited case. Enfish claims methods and apparatuses for storing and retrieving data using assertedly improved database techniques through use of a self-referential table while the present claims are directed towards the methods for managing labor for a workforce. Further with respect to Visual Memory, the claims do not recite similar elements as Visual Memory is directed towards “a programmable operational characteristic of said system determines a type of data stored by said cache.” The claims and additional elements are addressed in detail below.
Applicant contends that the claims recite an improvement to the technological field and points to the specification in [03]. Applicant further asserts that the ingestion of heterogeneous data, real-time conversion, etc. reflect an improvements and not merely using the computer as a tool.
Examiner respectfully disagrees. The asserted improvements merely improve upon the abstract idea itself and do not constitute an improvement to the technology nor technological field as a whole.
Applicant contends with respect to McRo that the claims rather than merely claiming an idea of a solution or outcome, solve a technological problem. Applicant further contends that the Examiner makes not expand on why the claims of McRo were eligible but not the present claims as the present claims use a combined order of rules that render information in a specific format similar to McRo. Applicant further asserts that similar to McRo that the claims replace subjective human decision making with a defined automated process.
Examiner respectfully disagrees. While there are some similarities with respect to automating processes, the present claims do not follow the same fact pattern as recited in McRO as MCRO is directed towards automatic lip synchronization while the present claims are directed towards determining staffing recommendations applied to a general purpose computer. While McRo recites limitations such as “obtaining a first set of rules that define output morph weight set stream as a function of phoneme sequence and time of said phoneme sequence; obtaining a timed data file of phonemes having a plurality of sub-sequences; generating an intermediate stream of output morph weight sets and a plurality of transition parameters between two adjacent morph weight sets by evaluating said plurality of sub-sequences against said first set of rules; generating a final stream of output morph weight sets at a desired frame rate from said intermediate stream of output morph weight sets and said plurality of transition parameters; and applying said final stream of output morph weight sets to a sequence of animated characters to produce lip synchronization” which provide an improvement to the technology allowing computers to produce accurate and realistic lip synchronization and facial expressions in animated characters that previously could only be produced by human animators. As such the present claims do not match the fact patterns of the cited case.
Applicant contends that similar to Example 42 that the claims recite a specific improvement to a technological field and solve challenges described in the specification. Applicant further asserts that similar to Example 42 that the claims recite real-time data conversion of heterogeneous inputs.
Examiner respectfully disagrees. The cited example is not analogous to the present claims. The Examiner further points to the applicant’s specification wherein the conversion of the present application is described as “one or more normalization algorithms may function to convert the one or more manually received order guides” and “Normalization may include converting images to recognizable characters, adjusting scales of the input” (which could include optical character recognition as currently recited and further recites scaling of data; Specification, [97, 99]). The cited example provides explicit details of the data conversion such as “encoding device is responsive to a precoded message-to-be-transmitted M and an encoding key E to provide a ciphertext word C for transmission to a particular decoding device. The message-to-be-transmitted is precoded by converting it to a numerical representation which is broken into one or more blocks MA of equal length. This precoding may be done by any conventional means. The resulting message MA is a number representative of a message-to-be-transmitted… The encoding key E is a pair of positive integers e and n, which are related to the particular decoding device. The encoding device distinctly encodes each of the n possible messages. The transformation provided by the encoding device is described by the relation CA=MAe (mod n) where e is a number relatively prime to (p-1)*(q-1). The encoding device transmits the ciphertext word signal CA to the decoding device over the communications channel” and as such the claims do not match the fact pattern of the cited example.
Applicant contends that similar to Example 40 because it provides a specific improvement over prior system resulting in an improved system.
Examiner respectfully disagrees. The cited example requires collecting network traffic data including delay, jitter, or packet loss, comparing the traffic to a predefined threshold, and collecting data that is greater than a predefined threshold which integrates the judicial exception into a practical application and as such the claims do not match the fact pattern of the cited example.
Applicant contends with respect to 2106.05(b) that the additional elements are not just mere instructions to apply to a generic computer. Applicant further points to sub-modules, algorithms executed by a processor, a system backbone, etc. Applicant submits that the elements provide functionality of the method and a general computer would not be inherently enabled to perform the steps.
Examiner respectfully disagrees. Each of the additional element is identified below and analysed both individually as well as in combination and determined to be “apply it” on a general purpose computer (see MPEP 2106.05(f)) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The servers, modules, APIs, etc. are addressed similarly. Examiner further notes the cited improvements merely improve upon the abstract idea itself and not the technology as a whole. The Examiner further points to the Applicant’s Specification wherein the system is described a general purpose computing device “(t)he system may include one or more computing devices. The one or more computing devices may function to allow a user to interact with an application; execute one or more modules, algorithms, methods, and/or processes; receive and/or transmit one or more data signals, convert one or more data signals to data entries, retrieve one or more data entries from one or more storage devices, or any combination thereof The one or more computing devices may include and/or be in communication with one or more processors, storage devices, servers, networks, user interfaces, other computing devices, the like, or any combination thereof The one or more or more computing devices may communicate via one or more interaction interfaces (e.g., an application programming interface ("API")), networks, and/or the like. The computing device may be one or more personal computers (e.g., laptop or desktop), mobile devices (e.g., mobile phone, tablet, smart watch, etc.), or any combination thereof” (Specification, [66]).
Applicant contends with respect to 2106.05(c) that the claims provide a transformation of raw workplace inputs, recommend staffing actions, and provide alerts.
Examiner respectfully disagrees. The conversion of data is addressed above. The recommended staffing actions and providing alerts (Examiner notes that as recited alerts do not require displaying or transmission of data) are included as abstract portions which are both mental processes and further certain methods of organizing human activity.
Applicant contends that claims are using judicial exception in a meaningful way and further points to Diamond v. Diehr and Alice Corp. asserting that claims do not merely use a computer to implement the idea as the claims utilize an application, sub-modules, algorithms, databases, etc. and are not just “apply it.”
Examiner respectfully disagrees. Each of the cited elements are additional elements which are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f)and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Examiner further points to the Applicant’s Specification wherein it is described through the use of a general purpose computer (Specification, [66]; The system may include one or more computing devices…. The one or more computing devices may include and/or be in communication with one or more processors, storage devices, servers, networks, user interfaces, other computing devices, the like, or any combination thereof. The one or more or more computing devices may communicate via one or more interaction interfaces (e.g., an application programming interface ("API")), networks, and/or the like. The computing device may be one or more personal computers (e.g., laptop or desktop), mobile devices (e.g., mobile phone, tablet, smart watch, etc.), or any combination thereof).
Applicant contends with respect to Bascom… and Rapid Litig… that the combination of elements in combination is significantly more than the judicial exception itself
Examiner respectfully disagrees. The present claims are not analogous to Bascom as the claims in Bascom are directed to generating network access requests, remote ISP servers associating network accounts, filtering schemes, and filtering elements, while the present application is directed towards managing labor for a workplace (and further the abstract portions identified below). Further Rapid Litigation is directed towards cryopreservation techniques to preserve hepatocytes which prior methods would damage hepatocytes that lead to poor recovery of viable cells which is not analogous to the presently cited limitations. The claims are analyzed both individually as well as in combination and determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f)and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Applicant further contends at Step 2B that the additional elements or combination of elements amount to significantly more than the judicial exception. Applicant points to transmitting … to a labor sub-module and asserts that the limitations is not well-understood, routine, or conventional (WURC). Applicant further points to the arrangement of API driven data ingestion and multi-source real-time integration, etc. Applicant further asserts that the Examiner does not address the limitations other than transmitting of data with respect to being WURC.
Examiner respectfully disagrees. The transmitting of data over a network is concept that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362). The limitations merely use general purpose computer elements to perform the limitation. The remaining additional elements have been analyzed both individually as well as in combination are determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f)and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Examiner notes that the remaining elements addressed above are not directed towards WURC activities.
The 101 Rejection is updated and maintained 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 therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 8, 10, 12, 13, 15, 117, 119, and 121-131 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1, 3, 8, 10, 12, 13, 15, 117, 119, and 121-131 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1 and 117 recite a series of steps for managing labor for a workforce;
Regarding Claims 1, (additional elements bolded)
A computer-implemented method for managing labor for a workplace with a labor module comprising:
a) providing a system including one or more servers, wherein the one or more servers include one or more storage mediums and one or more processors, the one or more servers being part of a system backbone, configured to automatically coordinate execution of multiple sub-modules of the labor module, wherein the one or more servers are in communication with the internet, and wherein the one or more servers are in communication with a plurality of computing devices including one or more on-site computer systems and one or more individual computing devices via the internet;
b) providing an application to a user for downloading onto an individual computing device with a user interface so the user can interact with the labor module, wherein the application can access the one or more servers of the system via the internet;
c) the one or more processors automatically executing a sales sub-module initiated by the system backbone and stored as computer-executable instructions in the one or more storage mediums and automatically (automatically implies the use of a general purpose computer) determining projected sales for the workplace in real time, wherein the sales sub-module is part of the labor module;
wherein the workplace is in a hospitality industry;
wherein the sales sub-module receives one or more workplace inputs, and the one or more workplace inputs include a workplace type, a workplace location, and information about goods for sale and/or services offered by the workplace, wherein the workplace type includes one or more sectors of the hospitality industry, and wherein the workplace location includes one or more geographic locations of the workplace, wherein the workplace type includes one or more sectors of the hospitality industry, and wherein the workplace location includes one or more geographic locations of the workplace;
wherein the sales sub-module is in communication with a plurality of external sources via an application programming interface, the plurality of external sources include one or more weather databases, one or more events databases, and one or more promotions databases;
wherein the sales sub-module automatically receives one or more external inputs from each of the plurality of external sources, the one or more external inputs including weather data, events data and promotions data;
wherein the sales sub-module is configured to automatically receive from the one or more weather databases the weather data is weather related information based on a geographic location of the workplace and automatically receive from the one or more events databases information about scheduled events occurring within a proximity of 50 km or less to the workplace;
wherein the one or more workplace inputs and the one or more external inputs are received from heterogeneous external sources, each having different data structures;
wherein the sales sub-module automatically converts the one or more workplace inputs and the one or more external inputs into one or more structured input data entries suitable for analysis and storage, including standardizing the heterogeneous inputs into a common structured format usable by the one or more projected sales algorithms, and stores the structured input data entries as one or more input records in one or more databases;
wherein the sales sub-module automatically executes one or more projected sales algorithms to analyze one or more inputs, including the one or more external inputs, the structured input data entries, and the one or more input records to determine the projected sales; and
wherein the one or more projected sales algorithm includes a machine learning model trained on historical sales and historical external inputs records retrieved from one or more databases, including previous weather, previous promotions, and previous events;
wherein the one or more projected sales algorithm automatically determines one or more sales trends in real time based on one or more weather patterns, one or more events, one or more promotions, calendar data, and the one or more workplace inputs;
wherein the projected sales are automatically updated by re-executing the one or more projected sales algorithms in response to newly received external inputs without user intervention;
wherein the projected sales include expected volume of the sales, expected timeframe for the sales, and expected volume of the sales by good and/or and service offered by the workplace; and
wherein the projected sales for the workplace are stored in one or more databases in the one or more storage mediums;
d) the one or more processors automatically transmitting the projected sales to a labor sub-module, wherein the labor sub-module is part of the labor module, wherein the transmitting is controlled by the system backbone;
e) the one or more processors automatically executing the labor sub-module backbone and stored as computer-executable instructions in the one or more storage mediums upon receiving the projected sales from the sales sub-module, wherein executing the labor sub-module includes executing one or more labor algorithms to determine projected staffing needs which is staffing needed to support the projected sales, the labor sub-module further receiving staff profile inputs including one or more employee records stored in one or more staff databases;
wherein the labor sub-module automatically converts the received projected sales and the staff profile inputs into one or more labor data entries and stores the labor data entries as labor records in one or more databases prior to executing the one or more labor algorithms; and
wherein the labor sub-module retrieves the labor records from the one or more databases and executes the one or more labor algorithms using the retrieved labor records to determine the projected staffing needs;
f) the one or more processors automatically transmitting the projected staffing needs to a scheduling sub-module, wherein the scheduling sub-module is part of the labor module, and wherein the transmitting is controlled by the system backbone;
g) the one or more processors automatically executing the scheduling sub-module initiated by the system backbone and stored as computer-executable instructions in the one or more storage mediums upon receiving the projected staffing needs;
wherein the scheduling sub-module automatically receives one or more scheduling inputs including one or more schedule inputs and one or more attendance inputs;
wherein the scheduling sub-module converts the one or more scheduling inputs into standardized scheduling data entries and stores the standardized scheduling data entries as scheduling records in one or more scheduling databases prior to executing the one or more scheduling algorithms;
wherein the scheduling sub-module retrieves the scheduling records from the one or more scheduling databases and executes the one or more scheduling algorithms using the retrieved scheduling records and the projected staffing needs;
wherein executing the scheduling sub-module includes executing one or more scheduling algorithms to determine one or more recommended staffing actions in real time;
and automatically generating one or more staffing action alerts based on the one or more recommended staffing actions;
h) the one or more processors automatically transmitting the one or more staffing action alerts to an action module, wherein the transmitting is controlled by the system backbone;
wherein automatically generating the one or more staffing action alerts includes generating standardized alert data entries derived from the recommended staffing actions; and
wherein the one or more staffing action alerts are machine-generated control outputs representing execution-ready staffing actions based on the one or more recommended staffing actions;
i) automatically displaying the one or more staffing action alerts on the user interface of the individual computing device via the application; and
wherein the labor module is executable computer instructions residing on the one or more storage mediums on the one or more servers and is accessible for execution by the one or more processors.
Regarding Claim(s) 117, A computer-implemented method for managing labor for a workplace with a labor module comprising:
a)providing a system including one or more servers, wherein the one or more servers include one or more storage mediums and one or more processors, the one or more servers being part of a system backbone configured to automatically coordinate execution of multiple sub-modules of the labor module, wherein the one or more servers are in communication with the internet, wherein the one or more servers are in communication with a plurality of computing devices including one or more on-site computer systems and one or more individual computing devices via the internet;
b)providing an application to a user for downloading onto an individual computing device with a user interface so that the user can interact with the labor module, wherein the application can access the one or more servers via the internet;
c) the one or more processors automatically executing a sales sub-module initiated by the system backbone and stored as computer-executable instructions in the one or more storage mediums and automatically determining projected sales for the workplace in real time, wherein the sales sub-module is part of the labor module;
wherein the workplace is in a hospitality industry;
wherein the sales sub-module receives one or more workplace inputs, and the one or more workplace inputs include a workplace type, a workplace location, and information about goods for sale and/or services offered by the workplace;
wherein the sales sub-module is in communication with a plurality of external sources via an application programming interface, the plurality of external sources include from one or more weather databases, one or more events databases, and one or more promotions databases;
wherein the sales sub-module automatically receives one or more external inputs from each of the plurality of external sources, the one or more external inputs including weather data, events data, and promotions data;
wherein the one or more workplace inputs and the one or more external inputs are received from heterogeneous external sources each having different data structures;
wherein the sales sub-module automatically converts the one or more workplace inputs and the one or more external inputs into one or more structured input data entries suitable for analysis and storage, including standardizing the heterogeneous inputs into a common structured format usable by the one or more projected sales algorithms, and stores the structured input data entries as one or more input records in one or more databases;
wherein the sales sub-module automatically executes one or more projected sales algorithms to analyze one or more inputs, including the one or more external inputs, to determine the projected sales;
wherein the one or more projected sales algorithms includes a machine learning model trained on historical sales and historical external inputs records retrieved from one or more databases, including previous weather, previous promotions, and previous events;
wherein training of the machine learning model includes reinforcement learning;
wherein the training of the machine learning model is in a live mode, the live mode including real-time training based on the outputs associated with one or more inputs;
wherein the one or more projected sales algorithm automatically determines one or more sales trends in real time based on one or more weather patterns, one or more events, one or more promotions, calendar data, and the one or more workplace inputs; and
wherein the projected sales are automatically updated by re-executing the one or more projected sales algorithms in response to newly received external inputs without user intervention;
wherein the projected sales include expected volume of the sales, expected timeframe for the sales, and expected volume of the sales by good and/or service offered by the workplace; and
wherein the projected sales for the workplace are stored in one or more databases in the one or more memory storage mediums;
d) the one or more processors automatically transmitting the projected sales to a labor sub-module, wherein the labor sub-module is part of the labor module, wherein the transmitting is controlled by the system backbone;
e) the one or more processors automatically executing the labor sub-module, initiated by the system backbone and stored as computer-readable instructions in the one or more storage mediums and determining projected staffing needs;
wherein the labor sub-module is automatically executed upon receiving the projected sales from the sales sub-module;
wherein the labor sub-module automatically executes one or more labor algorithms to determine the projected staffing needs to support the projected sales;
wherein the labor sub-module receives one or more staff profile inputs for executing the one or more labor algorithms;
wherein the one or more staff profile inputs are automatically received from one or more staff databases which store one or more staff profiles which include one or more employee records; and
wherein the one or more labor algorithms automatically identify, what roles need to be fulfilled to support the projected sales; and
which staff is adequate to support the roles to support the projected sales;
wherein the labor sub-module automatically converts the received projected sales and the staff profile inputs into one or more labor data entries and stores the labor data entries as labor records in one or more databases prior to executing the one or more labor algorithms; and
wherein the labor sub-module retrieves the labor records from the one or more databases and executes the one or more labor algorithms using the retrieved labor records to determine the projected staffing needs;
f) the one or more processors automatically transmitting the projected staffing needs to a scheduling sub-module, wherein the scheduling sub-module is part of the labor module, and wherein the transmitting is controlled by the system backbone; and
g) the one or more processors automatically executing the scheduling sub-module initiated by the system backbone and stored as computer-executable instruction sin the one or more storage mediums upon receiving the projected staffing needs from the labor sub-module;
wherein the scheduling sub-module automatically receives one or more scheduling inputs including one or more schedule inputs and one or more attendance inputs;
wherein the scheduling sub-module converts the one or more scheduling inputs into standardized scheduling data entries and stores the standardized scheduling data entries as scheduling records in one or more scheduling databases prior to executing the one or more scheduling algorithms;
wherein the scheduling sub-module retrieves the scheduling records from the one or more scheduling databases and executes the one or more scheduling algorithms using the retrieved scheduling records and the projected staffing needs;
wherein executing the scheduling sub-module includes executing one or more scheduling algorithms to determine one or more recommended staffing actions in real time; and
automatically generating one or more staffing action alerts based on the one or more recommended staffing actions;
wherein automatically generating the one or more staffing action alerts includes generating standardized alert data entries derived from the recommended staffing actions; and
wherein the one or more staffing action alerts are machine-generated control outputs representing execution-ready staffing actions based on the one or more recommended staffing actions;
h) the one or more processors automatically transmitting the one or more staffing action alerts to an action module, wherein the transmitting is controlled by the system backbone;
i) automatically displaying the one or more staffing action alerts on the user interface of the individual computing device via the application; and
wherein the labor module is executable computer instructions residing on the one or more storage mediums on the one or more servers and is accessible for execution by the one or more processors.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards determining sales projections, determining projected staffing needs, and determining one or more recommended staffing actions.
Further the claims are directed towards “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards identifying inventory discrepancies, inventory patterns, labor and scheduling recommendations (Specification, [02]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f)and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Further the additional elements of transmitting the projected sales to a labor sub-module; automatically transmitting the projected staffing needs to a scheduling sub-module; and transmitting the one or more staffing action alerts to an action module are activities that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Regarding Claim(s) 8, 10, 12, 15, 119, 121-128, and 130-131 the claims further narrow the abstract idea or recite additional elements previously addressed in the independent claims. (i.e. databases; modules; automatically; etc. )
Regarding Claim(s) 3, the claim further recite the additional element(s) of one or more databases, APIs, web portals, or a combination thereof. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 13 and 129, the claim further recite the additional element(s) of scheduling systems.. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/ Examiner, Art Unit 3624
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