DETAILED ACTION
This non-final office action is in response to Applicant’s amendment and request for continued examination filed April 7, 2026. Applicant’s April 7th amendment amended claim 1. Claims 1, 12 and 20 are the independent claims.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 7, 2026 has been entered.
Response to Amendment
The objection to the Title in the previous office action is withdrawn in response to Applicant’s amendment to the title.
The 35 U.S.C. 101 rejection of claims 1-20 in the previous office action is maintained.
Response to Arguments
Applicant's arguments filed April 7, 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues that the claims are patent eligible under 35 U.S.C. 101 as the claims are not directed to an abstract idea (e.g. claims cannot be performed mentally/by human mind, are not directed to a method of organizing human activity, directed to a specific technical machine learning pipeline; Remarks: Page 10); the claims integrate the abstract idea into a practical application (e.g. specific ordered technical pipeline; concrete improvement on how predictive analytics systems function; machine learning not a black box, produces technologically improved result; Remarks: Page 11; Paragraph 1, Page 12); the claims contain an inventive concept (e.g. specific ordered combination, not well-known, routine and conventional; Remarks: Paragraph 3, Page 12); claims now positively a machine learning predictive model that is trained (e.g. consistent with Subject Matter Eligibility Examples 47-49; Remarks: Paragraph 2, Page 13); the claims are similar to Dejardins et al., (e.g. recite specific machine learning operations, not merely generic application of a model, technical pipeline; Remarks: Paragraph 3, Page 13).
Initially it is noted that Applicant’s April 7, 2026 amendment only amended independent claim 1. Independent claims 12 and 20 (claims 12-20) have not been amended. Accordingly, Applicant’s arguments, with regards to the rejection of the claims under 35 U.S.C. 101 focus primarily only on features found in amended independent claim 1 (e.g. 12 and 20 fail to recite machine learning, as newly recited in amended claim 1).
Further 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. iterative mathematical optimization, gradient computation, loss minimization, parameter adjustment, millions of data entries, multi-dimensional feature vectors, assign derived labels, iteratively adjusting model parameters to minimize a loss function – Remarks: Paragraph 3, Page 10) 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 that the claims are not directed to an abstract idea, the examiner respectfully disagrees.
The claims remain directed to predictive analytics and/or marketing audience selection, both well-known economic practices. Specifically, the claims are directed to enabling a user integration endpoint (e.g. client machines; Figure 1, Element 108) associated with an audience service (e.g. client applications; Figure 1, Element 110) via an interactive user interface (i.e. mere data input); transmitting predictive trait values to the integration endpoint (i.e. mere data output); and automatically selecting users in a test set based on predictive trait values (generating an audience) (the audience configured to receive one or more transmitted messages, no messages are actually generated or sent to the audience).
While the claims may represent an improvement to the fundamental economic process of audience selection (automatically selecting users in a test set based on predictive trait values) and/or predictive analytics (as argued by Applicant) by enabling a huma user to select an integration endpoint associated with an audience service, the claims in no way either claimed or disclosed provide a technical solution to a technical problem; improve any of the underlying technology; and/or improve another technology or 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 detecting, at a predictive trait user interface (UI), a selection of a predictive trait of a plurality of predictive traits and a selection of a configuration setting for the predictive trait may be performed in the human mind using observation of data wherein the trait user interface can be visualized/implemented via pen and paper (e.g. a list of predictive user traits; also insignificant pre-solution activity, data input).The step of retrieve user data for a plurality of users (onboarding flow) may be performed in the human mind using observation of data. The step of generate a training dataset based on use data, feature set and a label may be performed by human mind by judgement and opinion. The step of run the trained machine learning predictive trait model on a test dataset comprising data representing one or more users, to compute predictive trait values for the one or more users in the test set may be performed in the human mind using evaluation and judgement of data. This step is also directed to a mathematical operation/concept. The step of detecting, at an interactive UI element displayed in the predictive trait UI, a user selection of an integration endpoint associated with an audience service may be performed by observation of data (also insignificant pre-solution activity, data input). The step of transmitting the predictive trait values to the integration endpoint may be performed by observation of data and is also directed to insignificant post solution activity (i.e. data output). The step of automatically selecting users in the test set based on the predictive trait values may be performed in the human mind via judgement and opinion. Examiner notes that no messages are actually generated or transmitted or received by the selected – that the generated audience is configured to receive transmitted messages is non-functional descriptive material.
Other than the recitation of a computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions 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 computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions 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. See MPEP 2106.05(f). Further the mere nominal recitation of a generic computer (used for its well-understood, conventional and routine purpose) does not take the claim limitation out of the mental processes grouping.
Regarding the recited machine learning predictive trait model (Claim 1 ONLY) trained to compute predictive values for one or more users in the test set, the trained machine learning predictive trait model 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 machine learning predictive trait model on a generic computer, also recited at a high level of generality. The trained machine learning predictive trait model is used to generally apply the abstract idea without limiting how the trained machine learning predictive trait model functions. The trained machine learning predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model 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 machine learning predictive trait model in claim 1 only does not negate the mental nature of these limitations because the trained machine learning predictive trait model is merely used as a tool to perform an otherwise mental process.
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, memory, 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”).
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”).
Reevaluating the steps of detecting a selection, detecting a user selection and transmitting the predictive trait values which are considered insignificant extra solution activity, these limitations are mere data gathering and output recited at a high level of generality and amount to nothing more than receiving data which are both well-understood, routine and conventional activities. The limitations remain insignificant extra solution activity even upon reconsideration. 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 – predictive analytics – in this case automatically selecting an audience to receive one or more transmitted messages. While the claims may represent an improvement to the business process of predictive analytics or audience selection 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 computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions . These generic computer hardware merely performs generic computer functions of receiving, processing and transmitting data and represent a purely conventional implementation of applicant’s audience selection/predictive analytics in the general field of business analytics 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 machine learning predictive trait model (Claim 1 ONLY) trained to compute predictive values for one or more users in the test set, the trained machine learning predictive trait model 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 machine learning predictive trait model on a generic computer, also recited at a high level of generality. The trained machine learning predictive trait model is used to generally apply the abstract idea without limiting how the trained machine learning predictive trait model functions. The trained machine learning predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model 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 predictive trait model (Claims 12 and 20) trained to compute predictive trait values for one or more users in a test set, 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 predictive trait model on a generic computer, also recited at a high level of generality. The trained predictive trait model is used to generally apply the abstract idea without limiting how the trained machine-learning model functions. The trained predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained predictive trait model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
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., computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions) 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 computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions” 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.
Regarding the recited machine learning predictive trait model (Claim 1 ONLY) trained to compute predictive values for one or more users in the test set, the trained machine learning predictive trait model 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 machine learning predictive trait model on a generic computer, also recited at a high level of generality. The trained machine learning predictive trait model is used to generally apply the abstract idea without limiting how the trained machine learning predictive trait model functions. The trained machine learning predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
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.
With regards to Applicant’s argument that the claimed invention recites a ‘concrete’ improvement on how predictive analytics systems function and/or produces a technologically improved result, the examiner respectfully disagrees. Utilizing computers, even using ‘trained’ machine learning predictive models’ (Claim 1) or trained predictive model (Claims 12, 20), to predict values and select users/audience to transmit messages to is conventional, routine and well-known. The computer is merely used for well-known, conventional and routine data input, data processing and data output. Nothing in the claims or Applicant’s invention suggests that the conventional computer is used in anything other than is routine and conventional purpose nor is there any suggestion that the claimed method improves the functioning of a computer or resolves a technical computer inherent in computers or computer networks. The wished for ‘improvement’ is at best organizing user data and/or enabling automatic message transmission are at best business improvements – i.e. improvements in the abstract idea itself. Examiner encourages Applicant to more specifically identify the ‘concrete’ improvement, clarify what ‘technologically’ improved is achieved and map the specific claimed steps to specific disclosed improvements.
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/contain an inventive concept, the examiner respectfully disagrees.
The claims use “conventional or generic technology in a nascent but well-known environment” to implement the abstract idea of business metric forecasting. 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”).
Nothing in Applicant’s disclosures suggests that the Applicant intended to accomplish any of the steps 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”).
As discussed in MPEP 2106.05(I)(A): "It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2B. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception is not in itself an inventive concept and does not guarantee eligibility:
The fact that a computer "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§ 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).
Further 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.
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 now positively recite machine learning and/or are consistent with Subject Matter Eligibility Examples 47-49, the examiner respectfully disagrees.
As noted previously, claims 12-20 fail to positively recite machine learning and therefore are not consistent with SME 47-49.
Examiner suggests Applicant provide a clearer mapping of the claimed invention to one or more of the specific example claims in SME 47-49.
Applicant’s amended claim 1, is more similar 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 to a new data environment (workflow/process automation).
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 first/machine learning processes or the unsupervised machine learning process 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 recite machine learning processes are merely tools/conduits for the abstract idea – recited at a high level and applied using a generic computer/computing device 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 a improvement which the newly claimed machine learning processes (first/second machine learning, unsupervised machine learning), Applicant’s disclosure and subsequent arguments fila to delineate steps through which the machine learning processes, 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 predictive analytics – audience selection.
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 machine learning (machine learning predictive model) is not 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”).
The invention as disclosed merely automates previous manual methods 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 more similar to those the court found ineligible under 35 U.S.C. 101 and are therefore also ineligible 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 are similar to the recent Appeals Review Panel review of Ex parte Desjardins et al., related to U.S. Patent Application No. 16/319,040, assigned to DeepMind Technologies Limited, the examiner respectfully disagrees.
While the Desjardins decision cautions against overbroad application of 35 U.S.C. 101 to artificial intelligence inventions, such inventions not categorically excluded from patentability, the thrust of the decision made clear that improvements to an AI model itself can be sufficient for the purpose of patent eligibility, even when the claims recite, on their face, an ostensibly “abstract idea.” Specifically, the Appeals Review Panel found that the claims under review provided a technical improvement in the functioning of machine learning models by enabling continual learning, reducing storage requirements, and preserving performance across tasks. In particular, the decision emphasized that the claimed invention addresses a technical problem ("catastrophic forgetting") and improves the operation of AI systems, not just through generic computer implementation but by a specific training strategy. To support this determination, the Appeals Review Panel looked to the specification which, on its own, disclosed how the invention would improve functioning of an AI model--in particular, the specification explained how the proposed invention would use less “storage capacity” and lead to “reduced system complexity." These improvements, which the Appeals Review Panel found were incorporated into the claims as a whole, constituted an “improvement to how the machine learning model itself operates”.
None of Applicant’s arguments, disclosure or claims discusses at any level that the generically applied/utilization of machine learning predictive trait model (independent claim 1) represents or provides an improvement in machine learning itself.
Independent claim 1, as newly amended, recites that the step of training and running a trained machine learning predictive model to compute predictive train values for one or more users in a test set, the trained machine learning predictive model 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 machine learning predictive model on a generic computer, also recited at a high level of generality. The trained machine learning predictive model is used to generally apply the abstract idea without limiting how the machine learning functions. The machine learning is described at a high level such that it amounts to using a generic computer with generic machine learning to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished. Further nowhere in Applicant’s disclosure is there any discussion at any level that the utilization of a trained machine learning predictive model to compute predictive train values improves the general field of machine learning or addresses a technical problem in the field of machine learning or provides an improvement to a specific machine learning model, algorithm, technique or the like.
Accordingly, the claims are nothing like those in the Desjardins decision and are therefore 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 1-20 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, 12 and 20, the claims are directed to the abstract idea of predictive analytics – specifically audience selection based on predicted user traits. 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, predictive analytics – audience selection (Judicial Exception – Yes – organizing human activity). Specifically, the claims are directed to providing a predictive user interface to enable a person to select, configure, train and run a predictive trait model for the purposes of selecting a set of users (audience), wherein predictive analytics is a fundamental economic practice that falls into the abstract idea subcategories of sales activities and/or commercial interactions. Further all of the steps of “detecting”, “executing”, “retrieve”, “generate”, “run”, “detecting”, “transmitting” and “automatically selecting” (representative claim 1) recite functions of the predictive analytics – audience selection are also directed to an abstract idea that falls into the abstract idea subcategories of sales activities and/or commercial interactions. The step of train a machine learning predictive trait model (Claim 1), train a predictive model (Claims 12, 20) are also directed to an abstract idea because it is a mathematical concept. The intended purpose of independent claims 1, 12, and 20 appears to be to automatically select users in a test set based on predictive trait values (generating an audience; e.g. Lifetime Value) wherein the users in the test set are configured to receive one or more transmitted messages (no messages are actually generated, transmitted or received).
Accordingly, the claims recite an abstract idea – fundamental economic practice, specifically in the abstract idea subcategories of sales activities and/or commercial interactions. The exceptions are the user (who is a person) and additional limitations of generic computer elements: computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions.
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: computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions. These generic computing components are merely used to receive/access, process or display data as described extensively in Applicant’s specification (Specification: Figures 13, 14). 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 predictive analytics 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 as set forth in the guidance 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 1-20 do not integrate the judicial exception into a practical application.
Regarding the use of the generic (known, conventional) recited computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions," 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.
Regarding the recited machine learning predictive trait model (Claim 1 ONLY) trained to compute predictive values for one or more users in the test set, the trained machine learning predictive trait model 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 machine learning predictive trait model on a generic computer, also recited at a high level of generality. The trained machine learning predictive trait model is used to generally apply the abstract idea without limiting how the trained machine learning predictive trait model functions. The trained machine learning predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model to apply the abstract idea. These limitations only recite outcomes/results of the steps without any details about how the outcomes are accomplished.
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 steps of generate a training dataset, train a machine learning predictive trait model (Claim 1), train a predictive model (Claims 12, 20), run the trained machine learning predictive trait model (Claim 1), run the predictive trait model (Claims 12, 20) and automatically selecting users in the test set based on the predictive trait 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 component storing instructions, user interface, orchestrator (software per se), computer-readable storage medium 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 because the steps detecting a selection of a predictive trait, retrieve user data is directed to insignificant pre-solution activity (i.e. data gathering/data input). The step of transmitting the predictive trait values is directed to insignificant post-solution activity (i.e. data output). 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 component storing instructions, user interface, orchestrator (software per se), computer-readable storage medium 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 machine learning predictive trait model (Claim 1 ONLY) trained to compute predictive values for one or more users in the test set, the trained machine learning predictive trait model 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 machine learning predictive trait model on a generic computer, also recited at a high level of generality. The trained machine learning predictive trait model is used to generally apply the abstract idea without limiting how the trained machine learning predictive trait model functions. The trained machine learning predictive trait model is described at a high level such that it amounts to using a generic computer with a generic trained machine-learning model 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 machine learning predictive trait model in claim 1 only does not negate the mental nature of these limitations because the trained machine learning predictive trait 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 retrieving and trasnmitting 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).
The claims are ineligible under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding dependent claims 2-11 and 13-19, the claims are directed to the abstract idea of predictive analytics and merely further limit the abstract idea claimed in independent claims 1, 12 and 20.
Claims 2 and 13 further limits the abstract idea by associating the configuration settings with at least ONE of event type or condition or time window or training audience or test audience (a more detailed abstract idea remains an abstract idea). Claims 3 and 14 further limit the abstract idea by selecting a set of users, generate a training data set and generate a trained predictive model (a more detailed abstract idea remains an abstract idea). Claims 4 and 15 further limit the abstract idea by selecting a set of users and generating a test set (a more detailed abstract idea remains an abstract idea). Claims 5 and 16 further limit the abstract idea by generating feature importance explanations and computing percentile statistics (a more detailed abstract idea remains an abstract idea). Claims 6 and 17 further limit the abstract idea by synchronizing the computer predictive trait values, receiving a user selection of one or more destinations and transmitting the computed predicted trait values (a more detailed abstract idea remains an abstract idea). Claims 7 and 18 further limit the abstract idea further comprising specifying a condition require or precluding a first user action, configuring a time window and specifying a second user action (a more detailed abstract idea remains an abstract idea). Claims 8 and 20 further limit the abstract idea by limiting the selected trait to lifetime value, limiting the configuration setting with the event type, and detecting a user selection of a completed order event (a more detailed abstract idea remains an abstract idea). Claim 9 further limits the abstract idea by training a first model to predict likelihood that a user is a zero or non-zero LTV, training a second model to predict a LTV score (a more detailed abstract idea remains an abstract idea). Claim 10 further limits
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 1-20, Applicant’s specification discloses that the claimed elements directed to a processor, memory component storing instructions, user interface, orchestrator (software per se), computer-readable storage medium at best merely comprise generic computer hardware which is commercially available (Specification: Figures 13, 14). 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 computer, processor, memory, user interface, orchestrator (software per se) and computer readable medium storing instructions s merely comprise generic computer hardware which is commercially available (Specification: Figures 13, 14). 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
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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SCOTT L. JARRETT
Primary Examiner
Art Unit 3625
/SCOTT L JARRETT/Primary Examiner, Art Unit 3625
Prosecution Insights