DETAILED ACTION
This FINAL office action is in response to Applicant’s amendment filed August 24, 2026. Applicant’s August 24th amendment amended claims 1 and 11 and canceled claims 5 and 15. Currently claims 1-4, 6-14 and 16-20 are pending. Claims 1 and 11 are the independent claims.
The instant application is a continuation in part of Application No. 18142670 now U.S. Patent No. 12045649.
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 .
Response to Amendment
The 35 U.S.C. 101 rejection of claims 1-4, 6-14 and 16-20 in the previous office action is maintained.
Response to Arguments
Applicant's arguments filed August 24, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues claims are not directed to an abstract idea (e.g. not a mental process, not organizing human activity, no one of the enumerated groupings of abstract ideas, not mathematical concepts; does not recite the concept of task allocation as an economic or commercial practice, specific computational architecture built around a defined machine learning pipeline; Remarks: Last Paragraph, Page 11; Pages 12, 13); the claims cannot be performed practically in the human mind (e.g. efficiency index - outlier cluster determined by an impact metric machine learning model....; iteratively retraining Remarks: Last Paragraph, Page 5; Pages 6, 7; Paragraph 1, Page 10); the claims integrates the abstract idea into a practical application (e.g. outlier cluster determined by an impact metric machine learning model...is a specific and describes how the machine learning model operates - not merely a result; Remarks: Last Two Paragraphs, Page 8; Page 9); the claims recite a technical improvement in the operation of the reallocation system itself (e.g. two stage machine learning, not how task allocation has historically been performed, Remarks: Paragraph 1, Page 10) and the claims significantly more than the abstract idea (e.g. recites specific improvements in relevant technology; non-conventional arrangement of steps provides a technical improvement; Remarks: Last Paragraph, Page 10; Paragraph 1, Page 11).
In response to applicant's argument that the claims are patent eligible under 35 U.S.C. 101, it is noted that the features upon which applicant relies (i.e., “This requires simultaneously computing, using a trained model whose parameters encode population-level statistical relationships across the full training corpus, a numerical impact metric for each attribute cluster of each second resource, computing a population average of those metrics across all second resources, and identifying which clusters deviate substantially from that average." Paragraph 1, Page 6; machine learning pipeline, Remarks: Last Paragraph, Page 11) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea (organizing human activity, economic practice, sales, etc.) and/or cannot be practically performed in the human mind, the examiner respectfully disagrees.
The claims are directed to a well-known business practice – human task allocation/assignment (Title: Apparatus and Method For Task Allocation”; Claim 11 "A method for optimal task reallocation...."; Figures 7, 8), task allocation being both an method of organizing human activity in the subcategories of managing person behavior or relationships or interactions between people as well as a mental process performed using a generic computer or other generic machinery (e.g. generic classifier, ML, etc.).
Specifically, the claimed invention reallocating assignable tasks to resources (users) based on resource classification/labels as a function of resource efficiency and task completion time. The “resources” as disclosed in at least Specification Paragraph 24 are human users represented using user profiles (“A “first resource,” as used herein, is data that contains descriptions of actions. In an embodiment, first resource 106 may be a user profile. A “user profile,” as used herein, is at least an element of data describing the user in some aspect. In a nonlimiting example, first resource 112 may be free-form text generated by a user that includes some descriptions of a user's habits and tasks performed on a day-to-day basis.” (i.e. data describing users’ habits, tasks, etc.)). The labels/classifications of the ‘resources’ as disclosed in at least Specification Paragraph 48 include “Example label values may be described by data and may include “Certainty” and “Uncertainty” indicators, as well as sub-labels of “Importance,” “Breakthrough,” and “Advantage.” Those skilled in the art will recognize that fewer, greater, or alternative descriptions of labels may be generated and used in connection with the described processes, such as on a case-by-case basis.” (i.e. data, meta-data). See also Specification Paragraphs 19, 24, 37 and 41.
While the claims may represent an improvement to the fundamental economic process of task allocation/reallocation, the claims in no way either claimed or disclosed provide a technical solution to a technical problem; improve any of the underlying technology (, processor, memory, machine learning, label classifier, etc.) and/or improve another technological field (e.g. human task management/assignment/allocation is not a technical field).
Additionally, the claims are directed to a mental processing practically capable of being performed in the human mind via observation, evaluation, judgement and opinion. Representative claim 1: The step of identifying a plurality of tasks associated with a first resource may be performed in the human mind using observation of data and evaluation. The step of generating a completion time constraint may be performed in the human mind using evaluation and judgement. The step of generating a determining a projected completion time may be performed in the human mind using evaluation and judgement. The step of determining at least an assignable task of the plurality of tasks may be performed in the human mind using evaluation and judgement. The step of identifying a plurality of second resources comprising an efficiency index… and an outlier cluster determined by an impact metric machine learning model may be performed in the human mind via judgement. Additionally this step recites a mathematical concept/operation – determining an outlier cluster using a trained machine learning model. The step of generate label training data comprising correlations may be performed by a human mind via evaluation. The step of train a label classifier may be performed in the human mind via observation and judgement. The step of classify the second datum of each of the plurality of second may be performed in the human mind using evaluation and opinion. The step of reclassify the second resource datum of each of the plurality of second resources may be performed in the human mind using evaluation and judgement. The step of iteratively updating the label training data may be performed in the human mind using evaluation and judgement. The step of retraining the label classifier may be performed in the human mind using evaluation and judgement. The step of reclassifying the second resource datum may be performed in the human mind via evaluation. The step of determine a probability datum related to at least a resource label mind using evaluation and judgement. The step of generate an optimal reallocation as a function of the probability datum T may be performed in the human mind using judgement and opinion. The step of reallocation the at least one assignable task judgement may be performed in the human mind using evaluation and opinion.
Other than the recitation of a processor, memory, label classifier (software per se) nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea. The limitations directed to a hardware device including a processor, computer readable memory, display device, sensors, network, control devices and/or database are each recited at a high level of generality and amount to no more than mere instructions to apply the exception using a generic computer/computer hardware. See MPEP 2106.05(f). Further the mere nominal recitation of a generic computer (i.e., processor, memory, label classifier (software per se)) does not take the claim limitation out of the mental processes grouping. The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of “visualizing flow direction is a distribution network” (Claim 20, preamble). In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, memories, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
The fact that a generic computing elements (processor, etc.) and other machinery (e.g. classifier, machine learning model) "necessarily exist[s] in the physical, rather than purely conceptual, realm," is beside the point. There is no dispute that a computer is a tangible system (in § 101 terms, a "machine"), or that many computer-implemented claims are formally addressed to patent-eligible subject matter. But if that were the end of the in § 101 inquiry, an applicant could claim any principle of the physical or social sciences by reciting a computer system configured to implement the relevant concept. Such a result would make the determination of patent eligibility "depend simply on the draftsman’s art," Flook, supra, at 593, 98 S. Ct. 2522, 57 L. Ed. 2d 451, thereby eviscerating the rule that "‘[l]aws of nature, natural phenomena, and abstract ideas are not patentable,’" Myriad, 133 S. Ct. 1289, 186 L. Ed. 2d 124, 133).
Regarding the recited label classifier trained/retrained to classify/reclassify the second resource datum of each of a plurality of resources, the label classifier is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic label classifier on a generic computer (processor, memory, etc.), also recited at a high level of generality. The label classifier is used to generally apply the abstract idea without limiting how the trained label classifier functions. The trained/retrained/updated label classifier is described at a high level such that it amounts to using a generic computer with a generic label classifier to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The trained/retrained/updated label classifier in this claim does not negate the mental nature of these limitations because the trained/retrained/updated label classifier is merely used at a tool to perform an otherwise mental process.
Regarding the recited (impact metric) machine learning model trained on historical data to determine an outlier cluster by identifying a generated impact metric being substantially different from a population average, the machine learning model is recited and disclosed at a high level of generality and is used to generally apply the abstract idea (e.g. outlier cluster determination – a well-known mathematical concept/operation) without limiting how the trained machine learning model functions. The impact metric machine learning model is only mentioned briefly in Specification Paragraph 47, wherein merely discloses that the model may be trained on data sets including historical attribute clusters and historical target processes associated with ratings of the degree to which the historical attribute clusters support historical attribute processes and the that model may accept attributes clusters and target processes as inputs and may output an impact metric (i.e. a black box, automagically generates an impact metric). The trained machine learning model recites only outcomes/results without any details as to how the outcomes/results are accomplished (that an outlier has a substantially different impact metric than a population average is insufficient details related to how the trained machine learning model functions). The trained impact metric machine learning model does not negate the mental nature of these limitations because the trained impact metric machine learning model is merely used at a tool to perform an otherwise mental process.
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in the claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Even when considered in combination the additional elements represent mere instructions to apply an exception and insignificant extra solution activity which cannot provide an inventive concept.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims integrate the abstract idea into a practical application, the examiner respectfully disagrees.
The claims are directed to a well-known business practice –task allocation/reallocation – in this case reallocating assignable tasks to resources based on resource classification/labels as a function of resource efficiency and task completion time. While the claims may represent an improvement to the business process of task allocation/reallocation they in no way either claimed or disclosed represent a practical application.
Under the see MPEP § 2106.05, the claims are evaluated to determine if additional elements that integrate the judicial exception into a practical application (see Manual of Patent Examining Procedure ("MPEP") §§ 2106.05(a)-(c), (e)- (h)). A claim that integrates a judicial exception into a practical application applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
For example, limitations that are indicative of "integration into a practical application" include:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP § 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP § 2106.05(e).
In contrast, limitations that are not indicative of "integration into a practical application" include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP § 2106.05(±);
Adding insignificant extra-solution activity to the judicial exception- see MPEP § 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use - see MPEP 2106.05(h).
In view of the MPEP § 2106.05, one must consider whether there are additional elements set forth in the claims that integrate the judicial exception into a practical application. The identified additional non-abstract elements recited in the independent claims are the generic processor, memory, label classifier (software per se). These generic computer hardware merely performs generic computer functions of processing data and represent a purely conventional implementation of applicant’s task allocation/reallocation in the general field of task management and do not represent significantly more than the abstract idea. See at least MPEP § 2106.05(a) ("Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field").
These recited additional elements are merely generic computer components. The claims do present any other issues as set forth in the MPEP § 2106.05 regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding the recited label classifier, the label classifier is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic label classifier on a generic computer, also recited at a high level of generality. The label classifier is used to generally apply the abstract idea without limiting how the trained/retrained/updated label classifier functions. The trained/retrained/updated label classifier is described at a high level such that it amounts to using a generic computer with a generic label classifier to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Regarding the recited (impact metric) machine learning model trained on historical data to determine an outlier cluster by identifying a generated impact metric being substantially different from a population average, the machine learning model is recited and disclosed at a high level of generality and is used to generally apply the abstract idea (e.g. outlier cluster determination – a well-known mathematical concept/operation) without limiting how the trained machine learning model functions. The impact metric machine learning model is only mentioned briefly in Specification Paragraph 47, wherein merely discloses that the model may be trained on data sets including historical attribute clusters and historical target processes associated with ratings of the degree to which the historical attribute clusters support historical attribute processes and the that model may accept attributes clusters and target processes as inputs and may output an impact metric (i.e. a black box, automagically generates an impact metric). The trained machine learning model recites only outcomes/results without any details as to how the outcomes/results are accomplished (that an outlier has a substantially different impact metric than a population average is insufficient details related to how the trained machine learning model functions).
Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)- (h)), the claims do not integrate the judicial exception into a practical application.
There is a fundamental difference between computer functionality improvements, on the one hand, and uses of existing computers as tools to perform a particular task, on the other — a distinction that the Federal Circuit applied in Enfish, in rejecting a § 101 challenge at the first stage of the Mayo/Alice framework because the claims at issue focused on a specific type of data structure, i.e., a self-referential table, designed to improve the way a computer stores and retrieves data in memory, and not merely on asserted advances in uses to which existing computer capabilities could be put. See Enfish, 822 F.3d at 1335-36. Here the claims simply use a computer as a tool and nothing more.
For the reasons outlined above, that the claims recite a method of organizing human activity, i.e., an abstract idea, and that the additional element recited in the claim beyond the abstract idea (i.e., processor, memory, label classifier (software per se) is no more than a generic computer component used as a tool to perform the recited abstract idea. As such, it does not integrate the abstract idea into a practical application. See Alice Corp., 573 U.S. at 223-24 (“[Wholly generic computer implementation is not generally the sort of ‘additional featur[e]’ that provides any ‘practical assurance that the process is more than a drafting effort designed to monopolize the [abstract idea] itself.’” (quoting Mayo, 566 U.S. at 77)).
Accordingly, the claims are directed to an abstract idea.
Step Two of the Mayo/Alice Framework (Step 2B)
Having determined under step one of the Mayo/Alice framework that the claims are directed to an abstract idea, we next consider under Step 2B of the Guidance, the second step of the Mayo/Alice framework, whether the claims include additional elements or a combination of elements that provides an “inventive concept,” i.e., whether an additional element or combination of elements adds specific limitations beyond the judicial exception that are not “well-understood, routine, conventional activity” in the field (which is indicative that an inventive concept is present) or simply appends well-understood, routine, conventional activities previously known to the industry to the judicial exception. See MPEP § 2106.05.
Under step two of the Mayo/Alice framework, the elements of each claim are considered both individually and “as an ordered combination” to determine whether the additional elements, i.e., the elements other than the abstract idea itself, “transform the nature of the claim” into a patent-eligible application. Alice Corp., 573 U.S. at 217 (citation omitted); see Mayo, 566 U.S. at 72-73 (requiring that “a process that focuses upon the use of a natural law also contain other elements or a combination of elements, sometimes referred to as an ‘inventive concept,’ sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the natural law itself’ (emphasis added) (citation omitted)).
Here the only additional element recited in the claims beyond the abstract idea is a processor, memory, label classifier (software per se)” i.e., generic computer component. See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Applicant has not identified any additional elements recited in the claim that, individually or in combination, provides significantly more than the abstract idea.
Similar to the discussion in Uniloc USA, Inc. v. LG Electronics USA, Appeal No. 19-1835 (Fed. Cir. Apr. 30, 2020), where the Federal Circuit reaffirmed that software inventions are patentable in the U.S. with a bright-line statement: “Our precedent is clear that software can make patent-eligible improvements to computer technology, and related claims are eligible as long as they are directed to non-abstract improvements to the functionality of a computer or network platform itself.” the instant application merely applies the abstract idea using a generic computer as a conduit/tool for the abstract idea and does not improve the functioning of a computer or computer networks, does not improve another technical field and does not provide a technical solution to a technical problem.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s arguments that the claims are patent eligible under 35 U.S.C. 101 as the recite a technical improvement, the examiner respectfully disagrees.
The claims are directed are directed to both a well-known economic practice (human task allocation – subcategories of managing personal behavior or relationships or interactions between people) as well as a mental process, wherein the method steps can be readily performed in the human mind or via pen and paper. The claims do not recite or disclose improvements to a computer or any other technology (only a generic processor is disclosed), MPEP 2106.05(a). The claims do not apply or involve a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. The claims do not apply or perform the abstract idea with a particular machine, MPEP 2106.06b. The claims to do transform or reduce a particular article to a different state or thing (data remains data when processed by a computer), MPEP 2106.05c. The claims do not apply or use the abstract idea in a meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (i.e. a processor), such that the claims are a drafting effort to monopolize the abstract idea (i.e. the claims do not integrate the abstract idea into a practical application of the abstract idea).
The claims are directed to a series of method steps that can be performed in a human mind or via pen and paper and/or utilizes a generic computer (processor memory) and generic label classifier and a generic machine learning model. The claims merely recite a general linking to the use of the abstract idea to a particular technological environment (e.g. processor). The recited processor merely performs generic computer functions of processing data. The performance of the processor is not improved in any way. Further Applicant’s disclosure lacks any discussion of improving the performance of the underlying technological environment. The processor merely ‘executes’ the abstract idea and is used merely a tool. The claims are not directed to improving computer performance and do not recite any such benefit. Further Applicant’s specification does not disclose any teachings related to improving computer performance.
As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must “play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly”).
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
The pending claims, more specifically their use of a generic machine learning model and label classifer, are more akin to the recent Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025) decision.
Recentive sued Fox in November 2022 for infringement of four patents – Network Map patents and Machine learning training patents. Recentive asserted that its patents claim eligible subject matter because they involve “the unique application of machine learning to generate customized algorithms, based on training the machine learning model, that can then be used to automatically create . . . event schedules that are updated in real-time.” Recentive characterized its patents as introducing “the application of machine learning models to the unsophisticated, and equally niche, prior art field of generating network maps for broadcasting live events and live event schedules.” The court did not find Recentive’s arguments persuasive and found the patents ineligible under 35 U.S.C. 101.
The instant application fails to be patent eligible under 35 U.S.C. 101 for very similar reasons the court found Recentive’s patents ineligible, namely the claims do no more than apply established methods of machine learning (label classifier, impact machine learning model) to a new data environment (task allocation/reallocation).
Similar to the discussion on Page 12 of the Recentive decision, the Applicant has failed to provide support or substantiative arguments that the disclosed invention or the claimed invention improves the recited trained/retrained/updated label classifier now claimed (“But Recentive also admits that the patents do not claim a specific method for “improving the mathematical algorithm or making machine learning better.” Oral Arg. at 4:40–4:44.). As such the recited trained/retrained/updated label classifier is merely a tool/conduit for the abstract idea – recited at a high level and applied using a generic computer/processor which is likewise not improved by the recited or disclosed invention (i.e. claims lack a specific technological improvement). More specifically not only does Applicant’s specification fail to disclose an improvement the trained/retrained/updated label classifier, Applicant’s disclosure and subsequent arguments fila to delineate steps through which the trained/retrained/updated label classifier, now claimed, achieve an improvement.
See, e.g., IBM v. Zillow Grp., Inc., 50 F.4th 1371, 1381 (Fed. Cir. 2022) (holding abstract a claim that “d[id] not sufficiently describe how to achieve [its stated] results in a non-abstract way,” because “[s]uch functional claim language, without more, is insufficient for patentability under our law.” (quoting Two-Way Media Ltd v. Comcast Cable Commc’ns, LLC, 874 F.3d 1329, 1337 (Fed. Cir. 2017))); see also Intell. Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342 (Fed. Cir. 2017) (similar); Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016) (similar). “[T]he patent system represents a carefully crafted bargain that encourages both the creation and the public disclosure of new and useful advances in technology, in return for an exclusive monopoly for a limited period of time.” Pfaff v. Wells Elecs., 525 U.S. 55, 63 (1998); Sanho Corp. v. Kaijet Tech. Int’l Ltd., 108 F.4th 1376, 1382 (Fed. Cir. 2024). Allowing a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system. In this respect, the patents’ claims are materially different from those in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), and Koninklijke, the cases on which Recentive relies.
Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment. This new environment is identifying and performing process (worklow) variants.
Similar to the court’s conclusion that simply applying machine learning to a new field of use does not result in patent eligibility, Applicant’s disclosure makes clear that the recited impact metric machine learning model nor the trained/retrained/updated label classifier are improved in any way and do not result in an improvement in an underlying technology or another technical field) (“We see no merit to Recentive’s argument that its patents are eligible because they apply machine learning to this new field of use. We have long recognized that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015); see also Alice, 573 U.S. at 222; Parker v. Flook, 437 U.S. 584, 593 (1978); Stanford, 989 F.3d at 1373 (rejecting argument that a claim was not abstract where patentee contended “the specific application of the steps [was] novel and enable[d] scientists to ascertain more haplotype information than was previously possible”).
As Applicant’s specification makes clear, the invention as disclosed merely automates previous manual methods (e.g. Specification Paragraphs 2 – personalized task allocation; Figures 7, 9) wherein the court found that merely using existing machine learning technology to perform tasks previously undertaken by humans does not render a claim patent eligible under 35 U.S.C. 101 (“Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”); compare McRo, 837 F.3d at 1314– 16 (finding eligibility of claims to use specific computer techniques different from those humans use on their own to produce natural-seeming lip motion for speech).”)
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
In response to Applicant’s argument that the claims are patent eligible under 35 U.S.C. 101 as the claims recite significantly more than the abstract idea, the examiner respectfully disagrees.
The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of task allocation/assignment. In re TLI Commc’ns LLC Pat. Litig., 823 F.3d 607, 612 (Fed. Cir. 2016). The recited technology (processor, etc.), are used as a “conduit for the abstract idea,” not to provide a technological solution to a specific technological problem. Id.; see also id. at 611–13 (holding claims reciting the use of a cellular telephone and a network server to classify an image and store the image based on its classification to be abstract because the patent did “not describe a new telephone, a new server, or a new physical combination of the two” and did not address “how to combine a camera with a cellular telephone, how to transmit images via a cellular network, or even how to append classification information to that data”).
Regarding the recited label classifier, the label classifier is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic label classifier on a generic computer, also recited at a high level of generality. The label classifier is used to generally apply the abstract idea without limiting how the trained label classifier functions. The trained/retrained/updated label classifier is described at a high level such that it amounts to using a generic computer with a generic label classifier to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
Regarding the recited (impact metric) machine learning model trained on historical data to determine an outlier cluster by identifying a generated impact metric being substantially different from a population average, the machine learning model is recited at a high level of generality and is used to generally apply the abstract idea (e.g. outlier cluster determination – a well-known mathematical concept/operation) without limiting how the trained machine learning model functions. The trained machine learning model recites only outcomes/results without any details as to how the outcomes/results are accomplished (that an outlier has a substantially different impact metric than a population average is insufficient details related to how the trained machine learning model functions).
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps recited in the claims through anything other than well understood technology used in a routine and conventional manner. Therefore, the claims lack an inventive concept. See also, e.g., Elec. Power Grp., 830 F.3d at 1355 (holding claims lacked inventive concept where “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”); Content Extraction, 776 F.3d at 1348 (holding claims lacked an inventive concept where the claims recited the use of “existing scanning and processing technology”).
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
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 1-4, 6-14 and 16-20 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.
Regarding independent claims 1 and 11, the claims are directed to the abstract idea of task allocation. This is a process (i.e. a series of steps) which (Statutory Category – Yes –process).
The claims recite a judicial exception, a method for organizing human activity, task allocation (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to reallocating assignable tasks to resources based on resource classification/labels as a function of resource efficiency and task completion time, wherein task allocation is a fundamental economic practice. Further all of the steps of “identify”, “generating”, “determining”, “determine”, “identify”, “generate”, “train”, “classify”, “reclassify”, “iteratively updating” “retraining”, “reclassifying”, “determine”, “generate” and “reallocate” recite functions of the task allocation are also directed to an abstract idea. The steps determining a projected completion time and determine a probability datum, of are also directed to an abstract idea because they recite a mathematical concept/operation. The intended purpose of independent claims 1 and 11 appears to be to reallocate tasks assigned to (human) resources (personnel – claims 10, 20).
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of managing person behavior or relationships or interactions between people. The exceptions are the generic computer elements: processor, memory, label classifier (software per se).
Accordingly, the claims recite an abstract idea under Step 2A, Prong One, we proceed to Step 2A, Prong Two. Considering whether the additional elements set forth in the claim integrate the abstract idea into a practical application, the previously identified non-abstract elements directed to generic computing components include: processor, memory, label classifier (software per se). These generic computing components are merely used to process data as described extensively in Applicant’s specification (Specification: Paragraph 10; Figure 10). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea.
Moreover, when viewed as a whole with such additional elements considered as an ordered combination, the claim modified by adding a generic computer would be nothing more than a purely conventional computerized implementation of applicant's task allocation in the general field of business management/marketing and would not provide significantly more than the judicial exception itself. Note McRo, Inc. v. Bandai Namco Games America Inc. (837 F.3d 1299 (Fed. Cir. 2016)), guides: "[t]he abstract idea exception prevents patenting a result where 'it matters not by what process or machinery the result is accomplished."' 837 F.3d at 1312 (quoting O'Reilly v. Morse, 56 U.S. 62, 113 (1854)) (emphasis added). The claims are not directed to a particular machine nor do they recite a particular transformation (MPEP § 2106.05(b)).
Additionally, the claims do not recite any specific claim limitations that would provide a meaningful limitation beyond generally linking the use of the judicial exception to a particular technological environment. Nor do the claims present any other issues regarding a determination of whether the additional generic elements integrate the judicial exception into a practical application. Rather, the claims merely use instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea. Thus, under Step 2A, Prong Two (MPEP §§ 2106.05(a)-(c) and (e)¬ (h)), claims 1-4, 6-14 and 16-20 do not integrate the judicial exception into a practical application.
Regarding the use of the generic (known, conventional) recited processor, memory, label classifier (software per se)," the Supreme Court has held "the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. 208, 223. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The claims as a whole do not recite more than what was well-known, routine and conventional in the field (see MPEP § 2106.05(d)). In light of the foregoing, that each of the claims, considered as a whole, is directed to a patent-ineligible abstract idea that is not integrated into a practical application and does not include an inventive concept.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
Additionally, the claims recite a judicial exception, a mental processes, which can be performed in the human mind or via pen and paper (Judicial Exception – Yes – mental process).
The claimed identify a plurality of tasks, generating a completion time constraint, determine a projected completion time, determine at least an assignable task, identify a plurality of second resources, generate label training data, train a label classifier, classify the second resource datum, reclassify the second resource datum, iteratively updating the label training data, retraining the label classifier, reclassifying the second resource datum, determine a probability datum, generate an optimal reallocation as a function of the probability datum and reallocate the assignable task all describe the abstract idea. These limitations as drafted are directed to a process that under its reasonable interpretation covers performance of the steps in the mind but for the recitation of the generic computer components. Other than the recitation of a processor, memory nothing in the claimed steps precludes the step from practically being performed in the mind. The claims do not recite additional elements that are sufficient to amount to significantly more than the abstract idea. The mere nominal recitation of a generic processor/computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process. (Judicial Exception recited – Yes – mental process).
The claims do not integrate the abstract idea into a practical application. The generic processor, memory are each recited at a high level of generality merely performs generic computer functions of retrieving, processing or displaying data. The generic processor/computer merely applies the abstract idea using generic computer components. The elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not recite improvements to the functioning of a computer or any other technology field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, the claims to do apply the abstract idea with a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (e.g. data remains data even after processing; MPEP 2106.05(c)), the claims no not apply or use the abstract idea in some other meaningful way beyond generally linking the user of the abstract idea to a particular technological environment (i.e. a generic computer) such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea (MPEP 2106.05(e)). The recited generic computing elements are no more than mere instructions to apply the exception using a generic computer component.
Regarding the recited label classifier, the label classifier is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic label classifier on a generic computer (processor), also recited at a high level of generality. The label classifier is used to generally apply the abstract idea without limiting how the trained label classifier functions. The trained/retrained/updated label classifier is described at a high level such that it amounts to using a generic computer with a generic label classifier to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The trained/retrained/updated label classifier in this claim does not negate the mental nature of these limitations because the trained/retrained/updated label classifier is merely used at a tool to perform an otherwise mental process.
Regarding the recited (impact metric) machine learning model trained on historical data to determine an outlier cluster by identifying a generated impact metric being substantially different from a population average, the machine learning model is recited at a high level of generality and is used to generally apply the abstract idea (e.g. outlier cluster determination – a well-known mathematical concept/operation) without limiting how the trained machine learning model functions. The trained machine learning model recites only outcomes/results without any details as to how the outcomes/results are accomplished (that an outlier has a substantially different impact metric than a population average is insufficient details related to how the trained machine learning model functions). The impact metric machine learning model is only mentioned briefly in Specification Paragraph 47, wherein merely discloses that the model may be trained on data sets including historical attribute clusters and historical target processes associated with ratings of the degree to which the historical attribute clusters support historical attribute processes and the that model may accept attributes clusters and target processes as inputs and may output an impact metric (i.e. a black box, automagically generates an impact metric). The trained impact metric machine learning model does not negate the mental nature of these limitations because the trained impact metric machine learning model is merely used as a tool to perform an otherwise mental process.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (Integrated into a Practical Application – No).
As discussed above the additional elements in the claims amount to no more than a mere instruction to apply the abstract idea using generic computing components, wherein mere instructions to apply an judicial exception using generic computer components cannot integrate a judicial exception into a practical application or provide an inventive concept. For the steps that were considered extra-solution activity, this has been re-evaluated and determined to be well-understood, routine, conventional activity in the field. Applicant’s specification does not provide any indication that the computer/processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is ineligible (Provide Inventive Concept – No).
Accordingly, the claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 2-4, 12-14 and 16-20, the claims are directed to the abstract idea of task allocation and merely further limit the abstract idea claimed in independent claims 1 and 11.
Claims 2 and 12 further limits the abstract idea by identifying a time deficit for each task and determining at least an assignable task as a function of the time deficit and a threshold (a more detailed abstract idea remains an abstract idea). Claims 3 and 13 further limit the abstract idea by generating a gap datum and categorizing each of the plurality of second resources and the resource label based on the gag datum (a more detailed abstract idea remains an abstract idea). Claims 4 and 14 further limit the abstract idea by organizing the resource labels sequentially (a more detailed abstract idea remains an abstract idea). Claims 6 and 16 further limit the abstract idea by generating temporal attribute training data, train a temporal attribute machine learning model and determine the temporal attribute using the trained machine learning model (a more detailed abstract idea remains an abstract idea). Claims 7 and 17 further limit the abstract idea by generating probability training data, training a probability machine learning model, a determining the probability datum using the trained machine learning model (a more detailed abstract idea remains an abstract idea). Claims 8 and 18 further limit the abstract idea by generating an interface query data structure, receive user input and display the probability datum (insignificant post-solution activity; a more detailed abstract idea remains an abstract idea). Claims 9 and 19 further limit the abstract idea by receive internal personnel assignment data and determine internal personnel additional tasks (a more detailed abstract idea remains an abstract idea). Claims 10 and 20 further limit the abstract idea by generating a personnel list and generating a personnel assignment (a more detailed abstract idea remains an abstract idea).
Regarding the steps directed to train/retrain/update a label classifier the label classifier is recited at a high level of generality and amounts to no more than mere instructions to apply the abstract idea using a generic trained label classifier on a generic computer, also recited at a high level of generality. The trained classifier is used to generally apply the abstract idea without limiting how the trained classifier functions. The trained classifier is described at a high level such that it amounts to using a generic computer with a generic trained classifier to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. The recitation of a trained classifier in this claim does not negate the mental nature of these limitations because the trained/retrained/updated label classifier is merely used at a tool to perform an otherwise mental process.
None of the limitations considered as an ordered combination provide eligibility because taken as a whole the claims simply instruct the practitioner to apply the abstract idea to a generic computer.
Further regarding claims 1-4, 6-14 and 16-20, Applicant’s specification discloses that the claimed elements directed to a processor, memory at best merely comprise generic computer hardware which is commercially available (Specification: Paragraph 10; Figure 10). More specifically Applicant’s claimed features directed to a system do not represent custom or specific computer hardware circuits, instead the terms merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims merely recite manipulating data utilizing generic computer hardware (e.g. memory, processor, etc.). Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. Further the lack of detail of the claimed embodiment in Applicant’s disclosure is an indication that the claims are directed to an abstract idea and not a specific improvement to a machine.
Accordingly given the broadest reasonable interpretation and in light of the specification the claims are interpreted to include the process steps being performed by a human mind or via pen and paper. The claim limitations which recite a computer implemented method is at best recite generic, well-known hardware. However, the recited generic hardware simply performs generic computer function of displaying or processing data. Generic computers performing generic, well known computer functions, alone, do not amount to significantly more than the abstract idea. Further the recited memories are part of every conventional general-purpose computer.
Applicant has not demonstrated that a special purpose machine/computer is required to carry out the claimed invention. A special purpose machine is now evaluated as part of the significantly more analysis established by the Alice decision and current 35 U.S.C. 101 guidelines. It involves/requires more than a machine only broadly applying the abstract idea and/or performing conventional functions.
Applicant’s specification discloses that the claimed elements directed to a processor, memory, label classifier (software per se) merely comprise generic computer hardware which is commercially available (Specification: Paragraph 10; Figure 10). More specifically Applicant’s claimed features directed to a system and components do not represent custom or specific computer hardware circuits, instead the term system merely refers to commercially available software and/or hardware. Thus, as to the system recited, "the system claims are no different from the method claims in substance. The method claims recite the abstract idea implemented on a generic computer; the system claims recite a handful of generic computer components configured to implement the same idea." See Alice Corp. Pry. Ltd., 134 S.Ct. at 2360.
Accordingly, the claims are not patent eligible under 35 U.S.C. 101.
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 SCOTT L JARRETT whose telephone number is (571)272-7033. The examiner can normally be reached M-TH 6am-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
Prosecution Insights