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 .
DETAILED ACTION
This non-final Office action is in response to applicant’s communication received on May 07, 2026 (examining claims filed April 20, 2026), wherein claims 1-20 are currently pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 07, 2026 has been entered.
Response to Arguments
Applicant's arguments/remarks have been fully considered but they are geared towards newly amended claims with newly added limitations. These newly amended claims with the newly added limitations/terms are considered for the first time in the rejection below.
35 USC §101 discussion:
Applicant amendments and newly added limitation discuss analyzing abstract information (time) where the information is then used for making staffing determination. The newly added additional elements (in the newly added limitations) of “training a supervised linear regression model with training records to derive the model” (claim 1), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (claim 12), and “training a machine-learning model (model) as a supervised linear regression model” (claim 19 – note the added limitation “to derive relationships between the total number of transactions, POS traffic duration, and SCO traffic duration in order to predict a POS traffic duration coefficient and a SCO traffic duration coefficient, wherein the model leverages historical transaction data and operational constraints to predict an optimal mix of POS cashiers and SCO attendants” is abstract and shows the generic machine-learning technique is used in an “apply-it” fashion) are very broad and generically stated and is an old and well-known generic/general purpose machine-learning feature – used in an “apply-it” fashion in Applicant’s claims. There is no improvement to the actual technical environment or technical elements themselves shown in the claims as Applicant’s core concept is directed to managing workers/employees in a retail environment based on abstract information and abstract known-type algorithms. There is no improvement in any technology/computer/devices itself and/or no improvement in the technical environment itself. The technology used is old well-known generic/general-purpose computers, and/or computer/computing elements/components, etc., (see rejection below) where the main concept remains obtaining/receiving information/data (where the information itself is abstract in nature – e.g. historical transaction data, durations, total, etc.,), data/information analysis/manipulation to determine more data (e.g. comparing and organizing information, using limitation/constraints on information, etc.,; and also including using mathematical calculations and equations using abstract numerical data to give mathematical results), and providing/displaying this determined data/information for further analysis and decision-making (e.g. staffing, reducing costs, improving efficiency, optimization, etc.,). The claimed invention further uses mathematical steps to analyze and determine further data (see discussion above). The limitations of independent claims (1, 12, 19) and dependent claims (2-11, 13-18, 20), under the broadest reasonable interpretation, covers methods of organizing human activity (commercial interactions (including sales activities or behaviors and business relations in a retail setting involving retail POS/checkout transactions setting with customer traffic) and managing personal interactions between people (including scheduling/staffing employees and following rules or instructions based on constraints)) and mathematical concepts (using mathematical calculations and equations using abstract numerical data to give mathematical results).
The generic/general-purpose computers, processors, and/or computer/computing components/elements/devices, etc., terms/limitations (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1 and its dependent claims 2-11); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12 and its dependent claims 13-18); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19 and its dependent claim 20)) are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1 and its dependent claims 2-11); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12 and its dependent claims 13-18); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19 and its dependent claim 20)). The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use computers and a familiar network as a tool to perform abstract processes involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
The independent claims (1, 12, 13) and dependent claims (2-11) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The core limitations of the independent claims and dependent claims require no more than the above listed known and/or generic computing devices and software functions (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1 and its dependent claims 2-11); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12 and its dependent claims 13-18); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19 and its dependent claim 20)) and, without more, this is insufficient to transform them into patent-eligible applications of the abstract idea at their core. See Alice, 134 S. Ct. at 2358, 2360; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 610–11 (2010); Diamond v. Diehr, 450 U.S. 175, 191 (1981); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, Fig. 1 and paras. 0018-0022, 0050, 0061, and 0073 [general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice, at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claim into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it.
See full rejection 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Note: The above §101 discussion under the “Response to Argument” section are is fully incorporated into this rejection and should be considered part of the this rejection.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, Claims 1-18 are directed to a method (i.e., process), claims 12-18 are also directed to a method (i.e., process), and claims 19-20 are directed to a system (i.e. machine). Accordingly, all claims are directed to one of the four statutory categories of invention.
(Under Step 2) The claimed invention is directed to an abstract idea without significantly more.
(Under Step 2A, Prong 1 (MPEP 2106.04)) The independent claims (1, 12, 19) recite obtaining/receiving information/data (where the information itself is abstract in nature – e.g. historical transaction data, durations, total, etc.,), data/information analysis/manipulation to determine more data (e.g. comparing and organizing information, using limitation/constraints on information, etc.,; and also including using mathematical calculations and equations using abstract numerical data to give mathematical results), and providing/displaying this determined data/information for further analysis and decision-making (e.g. staffing, reducing costs, improving efficiency, optimization, etc.,). The claimed invention further uses mathematical steps to analyze and determine further data (see discussion above). The limitations of independent claims (1, 12, 19), under the broadest reasonable interpretation, covers methods of organizing human activity (commercial interactions (including sales activities or behaviors and business relations in a retail setting involving retail POS/checkout transactions setting with customer traffic) and managing personal interactions between people (including scheduling/staffing employees and following rules or instructions based on constraints)) and mathematical concepts (using mathematical calculations and equations using abstract numerical data to give mathematical results). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including scheduling, social activities, teaching, and following rules or instructions), then it falls within the “organizing human activities” grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57). If a claims limitation, under its broadest reasonable interpretation, covers the performance of the limitation as mathematical relationships, mathematical formulas or equations, mathematical calculations then it falls within the Mathematical concepts grouping of abstract ideas. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance - Federal Register, Vol. 84, Vol. 4, January 07, 2019, pages 50-57).
Accordingly, since Applicant's claims fall under organizing human activities grouping and mathematical concepts grouping, the claims recite an abstract idea.
(Under Step 2A, prong 2 (MPEP 2106.04(d))) This judicial exception is not integrated into a practical application because but for the recitation of generic/general-purpose computers/terminals and/or computing components/devices/elements/etc., (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19)) in the context of the independent claims (1, 12, 19), the claims encompass the above stated abstract idea (organizing human activity (commercial interactions (including sales activities or behaviors and business relations in a retail setting involving retail POS/checkout transactions setting with customer traffic) and managing personal interactions between people (including scheduling/staffing employees and following rules or instructions based on constraints)) and mathematical concepts (using mathematical calculations and equations using abstract numerical data to give mathematical results)). As shown above, the claims and specification recite generic components which are recited at a high level of generality performing generic computer functions. (MPEP 2106.04; and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The generic/general-purpose computers/terminals and/or computing components/devices/elements/etc., (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19)) limitations are no more than mere instructions to apply the judicial exception (the above abstract idea) in an apply-it fashion using generic/general-purpose computers/terminals, processors, and/or computer components/elements/ devices, etc. The CAFC has stated that it is not enough, however, to merely improve abstract processes by invoking a computer merely as a tool.. Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1364 (Fed. Cir. 2020). The focus of the claims is simply to use generic/general-purpose computers/terminals and/or computing components/devices/elements/etc., and a familiar network as a tool to perform abstract processes (as discussed above) involving simple information exchange. Carrying out abstract processes involving information exchange is an abstract idea. See, e.g., BSG, 899 F.3d at 1286; SAP America, 898 F.3d at 1167-68; Affinity Labs of Tex., LLC v. DIRECTV, LLC, 838 F.3d 1253, 1261-62 (Fed. Cir. 2016). And use of standard computers and networks to carry out those functions—more speedily, more efficiently, more reliably—does not make the claims any less directed to that abstract idea. See Alice Corp., 573 U.S. at 222-25; Customedia, 951 F.3d at 1364; Trading Techs. Int'l, Inc. v. IBG LLC, 921 F.3d 1084, 1092-93 (Fed. Cir. 2019); SAP America, 898 F.3d at 1167; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1314 (Fed. Cir. 2016); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353, 1355 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 1370 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014). Accordingly, the additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 12, 19) and dependent claims (2-11, 13-18, 20) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the independent claims (1, 12, 19) and dependent claims (2-11, 13-18, 20) do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The claims recite using known and/or generic/general-purpose computers/terminals and/or computing components/devices/elements/etc., (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19))and software. For the role of a computer in a computer implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of "well-understood, routine, [and] conventional activities previously known to the industry." Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014), at 2359 (quoting Mayo, 132 S. Ct. at 1294 (internal quotation marks and brackets omitted)). These activities as claimed by the Applicant are all well-known and routine tasks in the field of art – as can been seen in the specification of Applicant’s application (for example, see Applicant’s specification at, for example, Fig. 1 and paras. 0018-0022, 0050, 0061, and 0073 [general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,]) and/or the specification of the below cited art (used in the rejection below and on the PTO-892) and/or also as noted in the court cases in §2106.05 in the MPEP. Further, "the mere recitation of a generic computer cannot transform a patent ineligible abstract idea into a patent-eligible invention." Alice, at 2358. None of the hardware offers a meaningful limitation beyond generally linking the system to a particular technological environment, that is, implementation via computers. Adding generic computer components to perform generic functions that are well‐understood, routine and conventional, such as gathering data, performing calculations, and outputting a result would not transform the claim into eligible subject matter. Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might impede innovation more than it would promote it. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims require no more than a generic computer to perform generic computer functions. The additional elements (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a supervised linear regression model with training records to derive the model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) by calculating (using abstract mathematics with abstract information)…and generating training records” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), “training a machine-learning model (model) as a supervised linear regression model” (actual training not shown and training a model is generically states), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19)) or combination of elements in the claims other than the abstract idea per se amount to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Applicant is directed to the following citations and references: Digitech Image., LLC v. Electronics for Imaging, Inc.(U.S. Patent No. 6,128,415); and (2) Federal register/Vol. 79, No 241 issued on December 16, 2014, page 74629, column 2, Gottschalk v. Benson. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself. See Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (U.S. 2014).
The dependent claims (2-11, 13-18, 20) further define the independent claims and merely narrow the described abstract idea, but not adding significantly more than the abstract idea. The above rejection includes and details the discussion of dependent claims and the above rejection applies to all the dependent claim limitations. In summary, the dependent claims further state using obtained data/information (where the information itself is abstract in nature), data/information analysis/manipulation to determine more data (e.g. comparing and organizing information, using limitation/constraints on information, etc.,; and also including using mathematical calculations and equations using abstract numerical data to give mathematical results), and providing/displaying this determined data/information for further analysis and decision-making (e.g. staffing, reducing costs, improving efficiency, optimization, etc.,). The claimed invention further uses mathematical steps to analyze and determine further data (see discussion above). These claims are directed towards organizing human activity (commercial interactions (including sales activities or behaviors and business relations in a retail setting involving retail POS/checkout transactions setting with customer traffic) and managing personal interactions between people (including scheduling/staffing employees and following rules or instructions based on constraints)) and mathematical concepts (using mathematical calculations and equations using abstract numerical data to give mathematical results). This judicial exception is not integrated into a practical application because the claims and specification recite generic/general-purpose computers/terminals and/or computing components/devices/elements/etc., (for example, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in Independent claim 1 and its dependent claims 2-11); terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (in independent claim 12 and its dependent claims 13-18); and system, processors, non-transitory computer-readable storage medium, terminals (POS and Self-checkout (SCO)), machine learning model (only stated without any technical details – where the models are mathematical in nature), transmitting using generic/general-purpose communication devices/components (in transactions and creating logs), graphical display/interfaces, etc., (independent claim 19 and its dependent claim 20)) which are recited at a high level of generality performing generic computer functions. (MPEP 2106.04 and also see 2019 Revised Patent Subject Matter Eligibility Guidance – Federal Register, Vol. 84, Vol. 4, January 07, 2019, page 53-55). The dependent claims, as discussed above, also merely recites post-solution/extra-solution activities (with generic/general-purpose computers and/or computing components/devices/etc.,). The additional elements do not integrate the abstract idea in to a practical application because it does not impose any meaningful limits on practicing the abstract idea – i.e. they are just post-solution/extra-solution activities. The dependent claims merely use the same general technological environment and instructions to implement the abstract idea without adding any new additional elements. Also, the dependent claims also do not include additional elements that are sufficient to amount to significantly more than the juridical exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself.
Prior art discussion (not a prior art rejection – but remains rejected under §101 above)
As per the independent claim 1, 12, and 19 the closet prior art are DeBardlebon et al., (US 2021/0241249), Pachigar et al., (US 2023/0419201), and Hurwitz et al., (US 2006/0065717).
However, neither DeBardlebon, Pachigar, and Hurwitz specifically disclose claim 1 combined limitations of determine relationships between the traffic durations and traffic totals…calculating an optimal staffing combination for SCO terminals and POS terminals of a store in a user-defined interval using a traffic total for the user-defined interval… providing the optimal staffing combination through an interface to a user. Also, Neither DeBardlebon, Pachigar, and Hurwitz specifically disclose claims 12 and 19 combined limitations of obtaining a total number of transactions for the interval; providing the total number of transactions to a machine learning model (model) that leverages historical transaction data and operational constraints to predict an optimal mix of POS cashiers and SCO attendants; receiving a POS traffic duration coefficient and a SCO traffic duration coefficient as output from the model; calculating the optimal staffing combination based on the total number of transactions, the POS traffic duration coefficient, the SCO traffic duration coefficient, and at least one operational constraint. Additionally, given the specific ordered combination of the claim elements in the independent claims cannot be found in the prior art (including art cited in PTO-892) and can only be found in Applicant’s Specification. The prior art of record (including art cite on PTO-892) does not teach or suggest (the reference individually or in combination) Applicant’s current independent claims as a whole (it is the entire claimed concept described by the limitations collectively coming together that is not rejected under prior art (the core concept is shown in the claim as a whole — limitations organized in the specific form and coming together collectively to form the concept)). Furthermore, any combination of the cited references and/or additional references to teach all of the claim elements would not be obvious and would result in impermissible hindsight reconstruction.
As per the dependent claims, these claims depend on the independent claims above and incorporate the limitations thereof, and are therefore not rejected under prior art for at least the same rationale as applied to the independent claims above, and incorporated herein.
Note that all the claims are still rejected under §101 rejection and are therefore not allowable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GURKANWALJIT SINGH whose telephone number is (571)270-5392. The examiner can normally be reached on M-F 8:30-5:30.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625
Prosecution Insights