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 final Office action is in response to applicant’s communication received on September 29, 2025, wherein claims 1, 3-8, and 10-14 are currently pending.
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive.
35 USC §101 discussion:
Applicant states that Applicant’s claims are subject matter eligible because the claims “are similar to the claim of example 39” (where example 39 was presented by the USPTO under 2019 Patent Subject Matter Eligibility Guidance (PEG). Examiner respectfully disagrees with the Applicant that Applicant’s claims are subject matter eligible.
Example 39 is directed to the field of facial detection using technology (i.e. is a computer technology for identifying human faces in digital images). Example 39’s invention addresses this issue by using a combination of features to more robustly detect human faces. The first feature is the use of an expanded training set of facial images to train the neural network. The steps presented in the example 39 are clearly directed to a specific technology and effects and improves the technology and technical environment. This is not the case for the claims presented in this current application by the Applicant. The independent claims and dependent claims of the current application recite obtaining/receiving/accessing/etc., information/data (where the information itself is abstract/non-technical in nature – e.g. (in a scheduling and shift based setting) attributes, shift information, metric (math), event information, identifiers, etc.,), data analysis and manipulation (including using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,) to determine more abstract/non-technical information/data, and providing/displaying this determined information/data for further analysis and/or decision making in scheduling/calendaring. The claimed invention further uses mathematical steps to analyze and determine further data (e.g. defining values, generating metrics, determining/obtaining various scores/values, metrics adjustments, using mathematical models, etc.,). Applicant’s claim is nothing like the claim presented in example 39. Applicant’s current independent and dependent claims are directed to organizing human activity (shift assignments and scheduling (people – employees/etc.,)) and mathematical concepts (see above – (using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,)). The Applicant only recites generic/general-purpose computers and computing components/elements/devices/etc., (for example, no technical elements or computers recited in independent claim 1 and its dependent claims 2-7 but the specification points to only generic/general-purpose computers and/or computing elements/components/etc., - for example see Applicant’s specification at fig.1 and paras. 0022, 0075-0076); and computing device, communication interface, processor, , etc., – in independent claim 8 and its dependent claims 9-14) in an “apply-it” fashion. Applicant does recite “training” but, as discussed also discussed below, specification also just uses the term “training” generically as “training data” and nothing more is provided. Training data is simply understanding relationships between information which can be used for predicting (note that training data is used in (teaching) machine learning for machine learning to happen; but “training” itself is just data relationships and understanding those relationships (e.g. patterns, etc.,)). In the current application (claim and specification), Applicant does not even detail what specifically the training entails and how the training happens technically. The generic/general-purpose elements/terms/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, 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 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 activities.
Applicant’s core concept are also not directed to any technical environment and there is no improvement to technology and none of the generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., 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. Therefore, Applicant’s independent claims and dependent claims 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.
See detailed rejection below.
Prior art discussion:
Applicant’s alleges that Cameron does not disclose “classification model,” a “classifier,” a “machine learning algorithm” and “therefore, Cameron does not disclose (the limitations added to the independent claim of) ‘generating and training a classification model based on (i) the labels and (ii) values for the first attribute from the previous shifts,’ and ‘executing the trained classification model to obtain a score associated with the target identifier’ where ‘the score corresponding to a first attribute of the set of attributes.’” Examiner respectfully disagrees.
Regarding “machine learning,” neither the claims nor the specification disclose “machine learning” nor do the claims disclose any details on technical function of “machine learning.” Applicant has not claimed “machine learning.” So it is not necessary for the examiner to reject or address something Applicant does not have in the original disclosure/specification and claims and specification. It is also noted that if the Applicant were to add this in the claims it would constitute new matter and will result in 35 USC §112 issues. Applicant only uses the term “training” but provides not technical details on what this training. The specification also just uses the term “training” generically as “training data” and nothing more is provided. Training data is simply understanding relationships between information which can be used for predicting (note that training data is used in (teaching) machine learning for machine learning to happen; but “training” itself is just data relationships and understanding those relationships (e.g. patterns, etc.,)). In the current application (claim and specification), Applicant does not even detail what specifically the training entails and how the training happens technically. Cameron discloses, for example, “varying in the number of workers drawn from each classification. Each particular staff mix enumeration 150 comprises a fixed number of workers (P, F) in each non-flexible classification (PT 120P, FT 120F) and computed range of workers in the flexible classification (120X)….range results from the fact that each planning period has multiple divisions (e.g., multiple weeks), and each division has a specific number of flexible workers….logic 100 also computes an average number of flex time workers for the enumeration…scheduling a workforce 100 generates multiple shift assignments 210 for each scheduling interval in a planning period, where each shift assignment 210 corresponds to the same planning period division but includes different numbers of workers of each classification,” in paragraphs 0033-0035 and in paragraph 0100-103 discusses recognizing/understanding patterns (also see, for example 0027 which discusses logic…pattern (where the pattern recognition and other data relationships are shown in paragraph 0026 and above in 0033-0035 and see 0039 [relationship between shift distance and constraints…shift template (pattern and relationship set)… how many of each variation of a shift template 330 are needed to cover demand 140, and creates that number of shift instances 320])).
Regarding “classifier,” Applicant, in the claim set, has not claimed “classifier.” So it is not necessary for the examiner to reject/address what Applicant has not claimed.
Regarding “classification model,” Applicant uses the term very broadly. Cameron discloses “determine the number of workers of a flexible classification that are needed to handle predicted demand in a scheduling period, given a fixed number of workers in a non-flexible classification (or classifications)…multiple potential schedules--multiple shift assignments for the same scheduling interval--varying in the number of workers drawn from each classification…contains a specific count of workers in each classification, and this count of workers in each classification combines to form a staff mix enumeration” in paragraphs 0026-0031. Cameron is clearly disclosing a classification model in detail. Cameron further shows classification model in detail at paragraphs 0033-0035 where Cameron states “varying in the number of workers drawn from each classification…staff mix enumeration 150 comprises a fixed number of workers (P, F) in each non-flexible classification (PT 120P, FT 120F) and computed range of workers in the flexible classification (120X)…range results from the fact that each planning period has multiple divisions (e.g., multiple weeks), and each division has a specific number of flexible workers…computes an average number of flex time workers for the enumeration…scheduling a workforce 100 generates multiple shift assignments 210 for each scheduling interval in a planning period, where each shift assignment 210 corresponds to the same planning period division but includes different numbers of workers of each classification.” It is clear that Cameron discloses classification model.
Furthermore, as shown in the discussion above and the rejection below, Cameron indeed discloses Applicant’s limitations of “generating and training a classification model based on (i) the labels and (ii) values for the first attribute from the previous shifts…executing the trained classification model to obtain a score associated with the target identifier…the score corresponding to a first attribute of the set of attributes.”
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, and 10-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Step 1 (MPEP 2106.03) of the subject matter eligibility test per MPEP 2106.03, Claims 1 and 3-7 are directed to a method (i.e., process) and claims 8 and 10--14 are directed to a computing device (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, 8) and dependent claims (3-7, 10-14) recite obtaining/receiving/accessing/etc., information/data (where the information itself is abstract/non-technical in nature – e.g. (in a scheduling and shift based setting) attributes, shift information, metric (math), event information, identifiers, etc.,), data analysis and manipulation (including using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,) to determine more abstract/non-technical information/data, and providing/displaying this determined information/data for further analysis and/or decision making in scheduling/calendaring. The claimed invention further uses mathematical steps to analyze and determine further data (e.g. defining values, generating metrics, determining/obtaining various scores/values, metrics adjustments, using mathematical models, etc.,). The limitations of independent claims (1, 8) and dependent claims (3-7, 10-14), under the broadest reasonable interpretation, covers methods of organizing human activity (shift assignments and scheduling (people – employees/etc.,)) and mathematical concepts (see above – (using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,)). 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 and computing components/elements/devices/etc., (for example, no technical elements or computers recited in independent claim 1 and its dependent claims 3-7 but the specification points to only generic/general-purpose computers and/or computing elements/components/etc., - for example see Applicant’s specification at fig.1 and paras. 0022, 0075-0076); and computing device, communication interface, processor, , etc., – in independent claim 8 and its dependent claims 10-14) in the context of the claims, the claim encompasses the above stated abstract idea (organizing human activity (shift assignments and scheduling (people – employees/etc.,)) and mathematical concepts (see above – (using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,))). As shown above, the independent claims (1, 8) and dependent claims (3-7, 10-14) and specification recite generic/general-purpose computers and computing components/elements/devices/etc., 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 elements/terms/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, 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 independent claims (1, 8) and dependent claims (3-7, 10-14) 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 activities.
(Under Step 2B (MPEP 2106.05)) The independent claims (1, 8) and dependent claims (3-7, 10-14) 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 claims recite using known and/or generic/general-purpose computers, processors, and/or computer components/elements/ devices, etc., and software (for example, no technical elements or computers recited in independent claim 1 and its dependent claims 3-7 but the specification points to only generic/general-purpose computers and/or computing elements/components/etc., - for example see Applicant’s specification at fig.1 and paras. 0022, 0075-0076); and computing device, communication interface, processor, , etc., – in independent claim 8 and its dependent claims 10-14). 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, ¶¶ 0017 and 0022 [general-purpose/generic computers/processors/etc., and generic/general-purpose computing components/devices/etc.,], 0075-0076 [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 independent claims (1, 8) and dependent claims (3-7, 10-14) 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, no technical elements or computers recited in independent claim 1 and its dependent claims 3-7 but the specification points to only generic/general-purpose computers and/or computing elements/components/etc., - for example see Applicant’s specification at fig.1 and paras. 0022, 0075-0076); and computing device, communication interface, processor, , etc., – in independent claim 8 and its dependent claims 10-14) or combination of elements in the independent claims (1, 8) and dependent claims (3-7, 10-14) other than the abstract idea per se amount(s) 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 (3-7, 10-14) 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/non-technical in nature – e.g. (in a scheduling and shift based setting) attributes, shift information, metric (math), event information, identifiers, etc.,), data analysis and manipulation (including using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,) to determine more abstract/non-technical information/data, and providing/displaying this determined information/data for further analysis and/or decision making in scheduling/calendaring. The claimed invention further uses mathematical steps to analyze and determine further data (e.g. defining values, generating metrics, determining/obtaining various scores/values, metrics adjustments, using mathematical models, etc.,). These claims are directed towards organizing human activities and further geared towards mathematical relationships (as discussed above – (organizing human activity (shift assignments and scheduling (people – employees/etc.,)) and mathematical concepts (see above – (using mathematical concepts – e.g. metrics, scores determination, using models (mathematical), metrics adjustments, etc.,)))). This judicial exception is not integrated into a practical application because the claims and specification recite generic/general-purpose computers/devices and/or generic/general-purpose computing components/elements/etc., which are recited at a high level of generality performing generic/general-purpose 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 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 (as discussed above).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, and 10-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cameron et al., (US 2008/0300954).
As per claim 1, Cameron discloses a method, comprising:
generating, for a target identifier, a plurality of shift candidates, each shift candidate defining values for a set of attributes ((note that “target identifier” corresponds to the employee – Applicant’s spec. para. 0030) ¶¶ 0005-0006 [workers…shift constraints…forecasted demand over a planning period], 0020-0023 [workforce…description of workers…constraints], 0024-0026 [predicted demand… generates shift assignments to cover predicted demand]);
generating a metric corresponding to each shift candidate (see citations above and also see ¶¶ 0005, 0007, 0020 [each worker…classified], 0025, 0054-0055 [considers each worker separately]; see also 0060-0065 [detailed mathematics and algorithm]);
detecting a plurality of events corresponding to previous shifts associated with the target identifier in a previous schedule; determining a label for each of the events; generating and training a classification model based on (i) the labels and (ii) values for the first attribute from the previous shifts; executing the trained classification model to obtain a score (¶¶ 0026-0031 [automatically scheduling a workforce 100 uses inventive techniques (described in more detail later) to determine the number of workers of a flexible classification that are needed to handle predicted demand in a scheduling period, given a fixed number of workers in a non-flexible classification (or classifications). Furthermore, logic for automatically scheduling a workforce 100 generates multiple potential schedules--multiple shift assignments for the same scheduling interval--varying in the number of workers drawn from each classification. Each schedule thus contains a specific count of workers in each classification, and this count of workers in each classification combines to form a staff mix enumeration ], 0033-0035 [varying in the number of workers drawn from each classification. Each particular staff mix enumeration 150 comprises a fixed number of workers (P, F) in each non-flexible classification (PT 120P, FT 120F) and computed range of workers in the flexible classification (120X). The range results from the fact that each planning period has multiple divisions (e.g., multiple weeks), and each division has a specific number of flexible workers. In the embodiment of FIG. 2, logic 100 also computes an average number of flex time workers for the enumeration…scheduling a workforce 100 generates multiple shift assignments 210 for each scheduling interval in a planning period, where each shift assignment 210 corresponds to the same planning period division but includes different numbers of workers of each classification], 0098-0100 [customer center environment – see with above paras. and also para. 0103 [schedules…historical patterns]);
[obtain the score] associated with the target identifier, the score corresponding to a first attribute of the set of attributes (¶¶ 0026-0035 [showing scores and metrics and the mathematics used in calculations], 0037-0040; also see 0056-0064+ [the mathematical concept used and the scores corresponding to attributes]);
determining a metric adjustment for each shift candidate based on (i) the obtained score for the target identifier, and (ii) a value of the first attribute defined by the shift candidate; selecting a shift candidate for the target identifier, based on the metrics and the metric adjustments (¶¶ 0016 [adjustment], 0054-0055 [shift adjustment performed], 0042 [demand is adjusted], 0052-0055 [assignments are adjusted…shift adjustment performed…optimizing algorithm]; see also 0075-0077); and
deploying a schedule containing the selected shift candidate (see citations above and also see, for example ¶¶ 0020-0026 [automatically scheduling…scheduler…automatically scheduling workforce…generates schedule], 0103 [creating…schedules]).
As per claim 8, claim 8 discloses substantially similar limitations as claim 1 above; and therefore claim 8 is rejected under the same rationale and reasoning as presented above for claim 1.
As per claim 3, Cameron discloses the method of claim 2, wherein the plurality of events includes a first set of events initiated by the target identifier, and a second set of events initiated by an administrator identifier; and wherein generating the classification model includes: generating a first classification model from the first set of events, and a second classification model from the second set of events (¶¶ 0037-0042, 0099-0102 [contact center example with agent and customer interaction; see with Cameron’s claim 1 and citations above for claims 1-2], 0102-0105 [contact center example with agent and supervisors; see with Cameron’s claim 1 and citations above for claims 1-2]).
As per claim 10, claim 10 discloses substantially similar limitations as claim 3 above; and therefore claim 10 is rejected under the same rationale and reasoning as presented above for claim 3.
As per claim 4, Cameron discloses the method of claim 3, wherein obtaining the score comprises obtaining a first score via execution of the first classification model, and obtaining a second score via execution of the second classification model; and wherein determining the metric adjustment is based on (i) the first score, (ii) the second score, (iii) respective weights corresponding to the first and second scores, and (iv) the value of the first attribute (¶¶ 0016, 0020-0022, 0036-0040 [generates multiple shift assignments 210 for each scheduling interval in a planning period, where each shift assignment 210 corresponds to the same planning period division but includes different numbers of workers of each classification. The number of workers in each classification is a staff mix enumeration…a classification 120 that is based on a minimum and a maximum number of hours that the worker 110 is expected to work in each scheduling period…multiple classifications 120 exist. For at least one non-flexible classification, logic 100 attempts to assign shifts so that the number of hours assigned to non-flexible workers is between the minimum and a maximum defined by the classification. (In FIG. 1, full time classification FT 120F and part time classification PT 120P are both non-flexible.) For at least one other classification, logic 100 allows a flexible amount of hours to be assigned to the worker 110. (In FIG. 1, classification 120X is flexible.) As described above, a per-classification count of the number of workers in the shift assignment 210 is tracked as a staff mix enumeration 150 associated with the shift assignment], 0042-0044, 0052-0055 [with paragraph 0049+ – adjustments discussed]; see also 0065-073 [showing the detailed mathematics]).
As per claim 11, claim 11 discloses substantially similar limitations as claim 4 above; and therefore claim 11 is rejected under the same rationale and reasoning as presented above for claim 4.
As per claim 5, Cameron discloses the method of claim 2, wherein each event corresponding to a previous shift is selected from the group consisting of: a modification of at least one attribute of the previous shift; a request for assignment of the previous shift to or from the target identifier; an assignment of the previous shift to or from the target identifier; and timestamps associated with the previous shift and the target identifier (¶¶ 0020-0025 [scheduling period…attempts to assign shifts to meet these minimums and maximums. The example of FIG. 1 shows two non-flexible classifications, full time (FT 120F) and part time (PT 120P), where the maximum number of hours for full time classification FT 120F is greater than the maximum number of hours for part time classification PT 120P…logic for automatically scheduling a workforce 100 determines, for each division of the planning period, the number of flexible employees needed to cover demand, given a specific number of non-flexible employees This process is iterated for various numbers of non-flexible employees, and determines, for each, the number of flexible employees needed to cover demand. A particular number of non-flexible workers, and the associated (computed) number of flexible workers, will be referred to herein as an "enumeration". Logic 100 produces ranges for the number of flexible employees for each enumeration. Some embodiments also produce per-enumeration statistics for the count of flexible workers (e.g., average, minimum, maximum)…input…shift constraints…"shift"--for instance, a portion of a defined period (e.g., 24-hour period) that a worker 110 is scheduled, or assigned, to work…8 AM to 5 PM, 9 AM to 5 PM, and 12 AM to 6 AM. A shift constraint 130 describes how a shift is limited, for example, in duration (an 8 hour shift, a 6 hour shift), in start time (9 AM or earlier), or in stop time (before 11 PM)… include break times and durations (e.g., an 8 hour shift includes a thirty-minute lunch break and a fifteen-minute morning break)], 0033-0035, 0045-0047 [receives as input shift constraints 130, and produces a set of shift templates 330. Shift templates 330 and demand 140 are used as input by shift instance generation logic 520 to produce a set of shift instances 320. Each individual shift instance 320 represents a particular date (530) that is "covered" with shifts…iteration loop over all the staff mix enumerations, which vary over the number of non-flexible workers (from 0 to a maximum N). Next, block 610 initializes counts of the minimum and maximum number of flexible workers for the current enumeration (Enum.sub.n). Next, block 620 iterates through scheduling divisions in the planning period. In the example flowchart of FIGS. 6A-B, the scheduling division is a week, so the loop is iterated over each week. However, other embodiments may use a different scheduling division, for example, a bi-week, and the second-level iteration would then be per-bi-week – see with para. 0050 [include more than one non-flexible classification can be handled by modifying the portion of process 640 which assigns unassigned shifts to non-flexible workers (block 630), so that it first assigns workers of the highest priority non-flexible classification (e.g., full-time), then assigns workers of lower priority non-flexible classifications (e.g., part-time), before assigning flexible workers (blocks 675-695). With this modification, any number of non-flexible classifications can be supported]]; see also 0055-0063).
As per claim 12, claim 12 discloses substantially similar limitations as claim 5 above; and therefore claim 12 is rejected under the same rationale and reasoning as presented above for claim 5.
As per claim 6, Cameron discloses the method of claim 2, wherein determining a label for each of the events includes: comparing the event to a labelling criterion corresponding to the event; assigning a first label when the event satisfies the labelling criterion; and assigning a second label when the event does not satisfy the labelling criterion (note that the “label” as described/used in the specification is just assigned information (which can be anything – broad); ¶¶ 0037-0040, 0042 [data describing predicted demand (140) over a planning period is received. Next, at block 420, a description of shift constraints (130) and of the workers (110) is received. In some embodiments, since the description of shift constraints and the description of the workers are already known to, or internal to, logic for automatically scheduling a workforce 100, block 420 is optional. At block 430 (which is optional), demand 140 is adjusted to account for non-customer-facing activities. For example, where the staffing environment is a bank, non-customer-facing activities for a teller may include servicing the automatic teller machine or the night deposit box], 0098-0106 [exampled in a customer service environment – e.g. performs many functions…a customer center supervisor or manager with information about agents and contacts, both historical and real-time. Another function is supplying the supervisor with information on how well each agent complies with customer center policies. Yet another function is calculating staffing levels and creating agent schedules based on historical patterns of incoming contacts. The functionality of the entire WFMS 1090 is typically divided among several applications, some of which have a user interface component, and WFMS 1090 comprises the suite of applications….how well agents adhere to scripts, identify which agents are "good" sales people and which ones need additional training. As such, speech analytics can be used to find agents who do not adhere to scripts. Yet in another example, speech analytics can measure script effectiveness, identify which scripts are effective and which are not, and find, for example, the section of a script that displeases or upsets customers (e.g., based on emotion detection)]).
As per claim 13, claim 13 discloses substantially similar limitations as claim 6 above; and therefore claim 13 is rejected under the same rationale and reasoning as presented above for claim 6.
As per claim 7, Cameron discloses the method of claim 1, further comprising, prior to deploying the schedule: generating a secondary schedule by selecting a shift candidate for the target identifier based on the metrics; comparing an efficiency of the schedule with an efficiency of the secondary schedule; and determining that the efficiency of the schedule exceeds the efficiency of the secondary schedule (¶¶ 0054 [shift adjustment…optimizing algorithm…scheduling…optimizing utilization – see with 0075-0078 [ adjusts the assignment of workers to shift instances for a particular scheduling interval…comparisons: w.sub.j+s.sub.i'j'<m.sub.j; and (w.sub.j'-s.sub.i'j'+u.sub.i)<m.sub.j'; where u.sub.i is length of unassigned shift instance on day i, s.sub.i'j' is length of shift instance assigned to worker j on day i, w.sub.j is total hours assigned to worker j during the scheduling interval, and m.sub.j is maximum hour capacity during the scheduling interval for employee j. If either comparison in block 850 is False, then processing returns to block 840 to select the next worker j'….comparisons are True, then block 860 adjust shift assignments]]; see also 0106).
As per claim 14, claim 14 discloses substantially similar limitations as claim 7 above; and therefore claim 14 is rejected under the same rationale and reasoning as presented above for claim 7.
Conclusion
The prior art made of record on the PTO-892 and not relied upon is considered pertinent to applicant's disclosure. For example, some of the pertinent art is as follows:
Yang (US 12,106,385): Generate a set of shift candidates based on labor demand data; determine a cost function including a labor cost, wherein the cost function is expressed at least in part in terms of a set of decision variables; determine a set of constraints based at least in part on the set of decision variables, worker data, and scheduling configuration data; and determine simultaneously, using a MIP solver, a subset of the shift candidates selected in a final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of the shift candidates.
Thompson et al., (US 2004/0039628): Discusses managing a health clinic, and in particular to managing/scheduling employees to work in the clinic. The system and method relates to a computer program for computing the needs of patients, determining adequate staffing requirements and displays these needs and requirements in connection with actual scheduling values. Thus, the system provides a tool for quickly determining whether the clinic is overstaffed or understaffed, for the entire day based on patient needs, both direct and indirect patient care needs. The system and method may further use facility limitation information to provide overall efficiency information.
Kober et al., (US 2022/0391801): Provides for worker ranking, worker scheduling, and enterprise labor sharing are described. A method includes: receiving objective evaluation data associated with members of a workforce; receiving subjective evaluation data associated with the members; generating composite evaluation data associated with the first members based on the objective evaluation data, the subjective evaluation data, or both; and generating scheduling information associated with the members based on the composite evaluation data. Another method includes: assigning members of a workforce to candidate temporal periods of a work schedule, where the assigning is based on a priority order corresponding to composite evaluation data associated with the members. Another method includes: identifying a work task associated with a first member of a network; selecting a worker associated with a second member of the network based on the work task or worker data; and outputting a notification at a device associated with the worker.
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/Gurkanwaljit Singh/
Primary Examiner, Art Unit 3625