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 .
Specification
The disclosure is objected to because of the following informalities: paragraph [0063] refers to "outlier process 140," but reference character 140 is used consistently throughout the remainder of the specification and in FIG. 1 to designate the "outlier cluster"; "outlier process" is separately designated by reference character 160.
Appropriate correction is required.
Claim Objections
Claims 4 and 14 are objected to because of the following informalities: "imputing the target process into the impact machine-learning model" appears to intend "inputting the target process into the impact metric machine-learning model," consistent with the parallel terminology used in claims 3 and 13. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Written Description
Claims 8, 9, 18, and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claims 8 and 18 recite "inputting an outlier cluster into an outlier process machine learning model; and receiving an outlier process from the outlier process machine learning model." The specification describes the outlier process machine learning model only in functional, result-oriented terms. At ¶[0056], the specification states that the model "may accept as an input outlier cluster 140 and may output outlier process 160" and is "trained using historical attribute clusters associated with historical processes." At ¶[0057], the specification states that the model "may include a k-means clustering model" or "a particle swarm clustering model." A k-means clustering model and a particle-swarm clustering model are unsupervised algorithms that partition a set of input data points into groups (see the specification's own description of k-means at ¶[0038]–[0042]). The specification does not describe how such a clustering model receives a single outlier cluster and produces a categorically different "outlier process" output — i.e., "a process in which the entity possesses an advantage" (¶[0008]). Mapping a cluster to a process is a supervised prediction/labeling task, yet the only disclosed model species (clustering algorithms) do not perform that task, and the specification discloses no bridging algorithm — no model architecture, no representation by which a "process" is emitted as an output, no training objective or loss, and no inference procedure. The specification therefore describes the desired result of the outlier process machine learning model without describing how that result is achieved, and does not reasonably convey to a person of ordinary skill in the art that the inventor had possession of the claimed model. See MPEP § 2161.01(I). Moreover, the recitation of a "machine learning model" for this function is a functional genus for which the specification discloses neither a representative species that performs the recited function nor common structure defining the genus. See MPEP § 2163.
Claims 9 and 19 further recite "receiving historical attribute clusters associated with historical processes; and training the outlier process machine-learning model using the historical attribute clusters associated with historical processes." These claims inherit the deficiency of claims 8 and 18. Because the specification does not disclose how the outlier process machine learning model maps an outlier cluster to an outlier process, its statement that the model is "trained using historical attribute clusters associated with historical processes" (¶[0056]) merely identifies the training data without disclosing the training methodology — the objective/loss function, the feature-to-process label encoding, or the resulting model — needed to produce that mapping. Identifying training data is not the same as conveying possession of the process for training a model to perform the claimed function. See MPEP § 2161.01(I). (It is further noted that claim 9 depends from claim 7 and claim 19 depends from claim 17, neither of which introduces "the outlier process machine-learning model," so the term lacks antecedent basis in the claim chain — an issue addressed separately under 35 U.S.C. 112(b)).
Enablement
Claims 8, 9, 18, and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
The factors to be considered in determining whether a disclosure meets the enablement requirement of 35 U.S.C. 112(a) have been described in In re Wands, 858 F.2d 731, 737 (Fed. Cir. 1988). See MPEP § 2164.01.
(1) Breadth of the claims: The limitation "outlier process machine learning model" (claims 8, 18) and its training (claims 9, 19) read on any machine learning model capable of outputting an outlier process from an outlier cluster — a broad functional genus.
(2) Nature of the invention: Determining the outlier process is the central inventive result of the application, and the claimed model is the recited means of achieving it; a high degree of enabling disclosure is therefore expected for this limitation.
(3) State of the prior art: Although machine learning techniques are generally known, the art does not supply a standard method for causing a clustering model — or any model — to emit a discrete "process" from a single cluster input; the specification's own named species (k-means and particle-swarm clustering, ¶[0057]) are unsuited to this supervised mapping.
(4) Level of one of ordinary skill: The level of skill is high (a software/machine-learning engineer). Even so, a skilled artisan cannot practice the limitation without a disclosed output representation of a "process" and a corresponding model and training scheme.
(5) Level of predictability in the art: While software is generally a predictable art, the internal inconsistency between the claimed supervised cluster→process mapping and the only disclosed (clustering) species removes predictability as to how the claimed model is to be built.
(6) Amount of direction or guidance provided by the inventor: Minimal to none for this model. Paragraphs [0056]–[0057] state only that the model is trained on historical attribute clusters associated with historical processes and "may include" a clustering model; the specification provides no algorithm, feature/label definition, output format, loss function, or step-by-step procedure for the model.
(7) Existence of working examples: None. No example in the specification shows an outlier cluster being input to the model and a specific outlier process being output. The concrete outlier-process examples in the specification instead use the non-machine-learning lookup of predetermined associations (¶[0058]).
(8) Quantity of experimentation necessary: Undue. To practice claims 8, 9, 18, and 19, a person of ordinary skill would have to independently devise the output representation of a "process," select and architect a suitable (non-clustering) model, design a feature-to-process labeling and training scheme, and validate the result — an open-ended research effort rather than the routine implementation the disclosure would need to support.
Considering the above factors, the specification does not enable a person of ordinary skill in the art to make and use the full scope of the claimed invention of claims 8, 9, 18, and 19 without undue experimentation.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites, in relevant part, "locate in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters; determine an outlier process as a function of an outlier cluster..." Having already established a single outlier cluster with definite antecedent basis ("the outlier cluster"), the claim's subsequent reference to "an outlier cluster" using the indefinite article renders it unclear whether the same, previously-recited outlier cluster is meant, or whether a new and entirely undefined outlier cluster is being introduced. Claim 11 recites the corresponding method limitation and is indefinite for the same reason. For purposes of examination, "an outlier cluster" in this limitation is interpreted under BRI to refer back to the same outlier cluster previously located in the claim.
Claim 2 recites "wherein locating in the plurality of attribute clusters an outlier cluster comprises: ... determining an outlier cluster as a function of the first impact metric and the second impact metric." Because claim 2 purports to further define the single "locating ... an outlier cluster" step of claim 1, the reintroduction of "an outlier cluster" with an indefinite article, rather than "the outlier cluster," leaves it unclear whether the resulting outlier cluster is the same one recited in claim 1. Claim 12 is indefinite for the same reason with respect to claim 11. For purposes of examination, "an outlier cluster" is interpreted under BRI to refer to the same outlier cluster recited in the independent claim.
Claims 4 and 14 recite "imputing the target process into the impact machine-learning model." The parent claims (3 and 13, respectively) introduce "an impact metric machine learning model," not "an impact machine-learning model." There is insufficient antecedent basis for "the impact machine-learning model" as recited. Additionally, claims 4 and 14 recite "imputing" the target process into the model, whereas the parent claims and the specification (Para. [0084]) consistently recite "inputting" for this operation; the use of a different term with a different ordinary meaning renders the scope of the step uncertain. For purposes of examination, "the impact machine-learning model" is interpreted under BRI as referring to the "impact metric machine learning model" of the parent claim, and "imputing" is interpreted as "inputting".
Claims 5 and 15 recite "the population average." No antecedent basis for "population average" appears anywhere in claims 1-5 or 11-15. For purposes of examination, "the population average" is interpreted under BRI to mean an average value of the relevant impact metric across a population of entities, consistent with specification ¶[0046]-[0047].
Claims 5 and 15 recite "the impact metric indicates higher aptitude in the attribute cluster than the population average." The parent claims (2 and 12, respectively) introduce two impact metrics ("a first impact metric" and "a second impact metric") and two attribute clusters ("a first attribute cluster" and "a second attribute cluster"), but never an unqualified singular "impact metric" or "attribute cluster." It is therefore unclear which of the two previously-recited impact metrics or attribute clusters is meant. For purposes of examination, "the impact metric" and "the attribute cluster" are interpreted under BRI to refer to "the first impact metric" and "the first attribute cluster" of the parent claim, respectively, consistent with specification ¶[0047].
Claims 6 and 16 recite "determining an outlier cluster as a function of an impact metric and an external impact metric." As with claims 2/12, "an outlier cluster" is reintroduced with an indefinite article notwithstanding the single outlier cluster already established in the independent claim. In addition, the parent claims (2 and 12) introduce only "a first impact metric" and "a second impact metric," not an unqualified "impact metric," so it is unclear whether "an impact metric" refers to the first impact metric of the parent claim or to some other, undefined metric. For purposes of examination, "an outlier cluster" is interpreted under BRI to refer to the outlier cluster of the independent claim, and "an impact metric" is interpreted to refer to "the first impact metric" of the parent claim.
Claims 7 and 17 recite "the impact metric indicates higher aptitude in the attribute cluster than the external impact metric." Claims 7 and 17 depend from claims 2 and 12, respectively — not from claims 6 and 16, which are the only claims that introduce "an external impact metric." Because claims 6/16 are sibling dependent claims and not ancestors of claims 7/17 in the claim dependency chain, there is no antecedent basis for "the external impact metric" in claims 7/17. For purposes of examination, "the external impact metric" is interpreted under BRI to refer to the external impact metric described in claims 6/16 and specification ¶[0046], describing an impact metric associated with an external attribute cluster relative to which an outlier cluster may be determined.
As in claims 5/15, claims 7 and 17 recite unqualified "the impact metric" and "the attribute cluster" notwithstanding that the parent claims (2 and 12) introduce two of each ("first" and "second"), leaving the referent ambiguous. For purposes of examination, "the impact metric" and "the attribute cluster" are interpreted under BRI to refer to "the first impact metric" and "the first attribute cluster" of the parent claim, respectively.
Claims 8 and 18 recite "inputting an outlier cluster into an outlier process machine learning model; and receiving an outlier process from the outlier process machine learning model." Both "an outlier cluster" and "an outlier process" were already established with definite antecedent basis in the independent claim (claim 1 or claim 11), and their reintroduction here with indefinite articles creates ambiguity as to whether the same previously-recited elements, or new, undefined elements, are meant. For purposes of examination, "an outlier cluster" and "an outlier process" are interpreted under BRI to refer to the same outlier cluster and outlier process, respectively, established in the independent claim.
Claims 9 and 19 recite "training the outlier process machine-learning model using the historical attribute clusters associated with historical processes." Claim 9 depends from claim 7 (dependency chain 9→7→2→1); claim 19 depends from claim 17 (dependency chain 19→17→12→11). "Outlier process machine learning model" is introduced only in claims 8 and 18, respectively, which are sibling dependent claims of claims 7 and 17 (all depending, ultimately, from the same independent claim) but are not ancestors of claims 9 and 19 in their respective dependency chains. There is accordingly no antecedent basis for "the outlier process machine-learning model" within claims 9's or 19's own dependency chain. For purposes of examination, "the outlier process machine-learning model" in claims 9 and 19 is interpreted under BRI to refer to the outlier process machine learning model described in claims 8/18 and specification ¶[0056]-[0057] (outlier process machine learning model 156), which is trained using historical attribute clusters associated with historical processes.
Appropriate correction is required.
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 an abstract idea without significantly more.
CLAIM 1
Step 1: Claim 1 recites “An apparatus for data structure generation” comprising “at least a processor” and a “memory,” which falls within the statutory category of a machine. See MPEP 2106.03. Accordingly, claim 1 satisfies Step 1 of the eligibility analysis.
Step 2A, Prong 1: Claim 1 is directed to an abstract idea. Specifically, claim 1 recites a mental process — the evaluation and judgment of an entity’s attributes to identify a standout (“outlier”) attribute cluster and to determine a process in which the entity has an advantage. The claim recites “identify a plurality of attribute clusters”, “locate in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters”, and “determine an outlier process as a function of an outlier cluster.” Under their broadest reasonable interpretation, these limitations encompass acts of observation, evaluation, and judgment that can practically be performed in the human mind, or by a human using pen and paper — for example, a business analyst can mentally review an entity’s attributes (skills, knowledge, assets), recognize which grouping of attributes stands out relative to a population, and determine a process in which the entity is thereby advantaged. Concepts performed in the human mind, such as evaluations and judgments, are mental processes. See MPEP 2106.04(a)(2)(III). Independently of the mental-process grouping, these same limitations also recite a certain method of organizing human activity. The recited steps of locating “an outlier cluster” of an entity’s attributes and “determin[ing] an outlier process as a function of an outlier cluster” — that is, selecting for an entity a process in which its comparatively distinctive attributes give it an advantage — describe the fundamental economic practice of allocating effort and resources according to comparative advantage. See MPEP 2106.04(a)(2)(II). This grouping is set forth only as an independent, alternative basis; the mental-process grouping above is by itself sufficient to establish that the claim recites an abstract idea. The specification’s framing of the invention as “systematically determin[ing] optimal processes for entities with specific attributes” (specification at ¶[0003]) to achieve “a more efficient allocation of resources” (specification at ¶[0008]) confirms this commercial character.
Step 2A, Prong 2: The claim recites the following additional elements…“at least a processor”; “a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to” perform the recited functions; “determine a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process”; and “configure a user device to display the visual element to a user.”
The recited “processor” and “memory … containing instructions configuring the at least processor to” perform the functions invoke generic computer equipment used in its ordinary capacity to carry out the abstract idea, which cannot integrate the exception into a practical application. See MPEP 2106.05(f). The “visual element data structure” that is “configured to generate a visual element” and the step to “configure a user device to display the visual element to a user,” constitute generic generation and outputting/display of data which is insignificant extra-solution (post-solution) activity that likewise does not integrate the exception. See MPEP 2106.05(g). To the extent the display merely situates the abstract idea in a generic computer or graphical-user-interface environment, it does no more than generally link the exception to a technological environment. See MPEP 2106.05(h).
The judicial exception is not integrated into a practical application. The additional elements, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application.
Applying the two-step improvement analysis of Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025), the specification does not disclose an improvement to the functioning of a computer or to any other technology or technical field. The specification describes only a business benefit — “a more efficient allocation of resources” (specification at ¶[0008]) and revealing to an entity “one or more outlier processes” in which “the entity possesses an advantage” (specification at ¶[0008]) — and frames the problem as “failures to systematically determine optimal processes for entities with specific attributes” (specification at ¶[0003]). These are improvements to a business or commercial process, not to technology. The computer components are described in wholly generic terms: the processor may be “any computing device … including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC)” (specification at ¶[0009]), the memory is a conventional memory (specification at ¶[0096]), and the data structures are generic (e.g., “Boolean, integer, float, string … linked list, tree, array” (specification ¶[0059])). Because the specification identifies no technological improvement — and, unlike the specification in Desjardins, discloses no improvement to how any machine-learning model itself operates — the improvement consideration of MPEP 2106.05(a) does not apply. Even if an improvement were disclosed, the claim does not reflect it: the claim recites the result at a high level of generality without reciting any particular technological means of achieving it, thereby claiming the idea of an outcome rather than a particular way to achieve it. See McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016).
Accordingly, claim 1 does not integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. The “processor” is a generic computer component performing generic computer functions, which the courts have recognized as well-understood, routine, and conventional. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 226 (2014); MPEP 2106.05(d). The “memory … containing instructions” performs the well-understood, routine, and conventional functions of storing and retrieving information in memory. See Versata Dev. Group v. SAP Am., Inc.; OIP Techs., Inc. v. Amazon.com, Inc.; MPEP 2106.05(d)(II). Generating a “visual element data structure” and configuring a “user device to display the visual element” amount to well-understood, routine, and conventional receipt, formatting, and presentation of data for display. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); TLI Communications LLC v. AV Automotive, LLC; MPEP 2106.05(d)(II). Considered as an ordered combination, these generic components add nothing beyond what they contribute individually; there is no unconventional arrangement (cf. BASCOM Global Internet Servs. v. AT&T Mobility LLC).
Accordingly, claim 1 does not amount to significantly more than the abstract idea, does not satisfy Step 2B, and is rejected under 35 U.S.C. 101.
CLAIM 2
Step 1: Claim 2 depends from claim 1 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 2 additionally recites: “generating a first impact metric associated with a first attribute cluster … and a second impact metric associated with a second attribute cluster …” and “determining an outlier cluster as a function of the first impact metric and the second impact metric.” Generating an impact metric is a scoring/evaluation of the degree to which an attribute cluster supports a target process, and determining the outlier cluster from a comparison of two such metrics is a comparison and judgment; both can practically be performed in the human mind. These are further mental-process steps of the same nature as those in claim 1. See MPEP 2106.04(a)(2)(III).
Step 2A, Prong 2 & Step 2B: No new additional elements are introduced. The analysis from parent claim is maintained.
CLAIM 3
Step 1: Claim 3 depends from claim 2 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: No additional abstract idea limitations are introduced.
Step 2A, Prong 2: The following additional elements are introduced: “inputting the first attribute cluster into an impact metric machine learning model; and receiving a first impact metric from the impact metric machine learning model.” Generating the impact metric remains the same abstract evaluation identified in claim 2. Consistent with USPTO Example 39 and the August 4, 2025 guidance, generically “inputting” data into a “machine learning model” and “receiving” an output does not itself set forth or describe any mathematical relationship, calculation, formula, or equation, and therefore does not recite a further judicial exception; rather, the “impact metric machine learning model” is a new additional element applied as a tool.
The judicial exception is not integrated into a practical application. With respect to the previously recited elements, for the same reasons discussed in the Step 2A, Prong 2 analysis of claim 2, the additional elements do not integrate the judicial exception into a practical application. With respect to the new “impact metric machine learning model,” it is recited at a high level of generality as a generic class of computer algorithm used as a tool to perform the abstract scoring; the specification describes it as a conventional model “trained on data sets including historical attribute clusters … associated with ratings … of experts” (specification at ¶[0046]) and discloses no improvement to how the model itself operates. Mere instructions to apply the abstract idea using a generic machine learning model do not integrate the exception. See MPEP 2106.05(f); Ex Parte Desjardins. Accordingly, claim 3 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. For the same reasons discussed in the Step 2B analysis of claim 2, the additional elements are well-understood, routine, and conventional and do not amount to significantly more than the judicial exception. With respect to the “impact metric machine learning model,” using a generic machine learning model to compute a score or classification is a well-understood, routine, and conventional use of a generic class of computer algorithms, as confirmed by the specification’s generic description of conventional models (e.g., k-means, K-nearest neighbors, naïve Bayes, and neural networks) (specification at ¶[0033]–[0038]). This well-understood, routine, and conventional finding is supported by an express statement in the specification — its own description of these as conventional models (Spec. ¶[0033]–[0038]) — which is the first category of Berkheimer evidence. See MPEP 2106.05(d), subsection (II); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018). Considered as an ordered combination, the model adds nothing unconventional to the generic components already addressed. Accordingly, claim 3 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 4
Step 1: Claim 4 depends from claim 3 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 4 additionally recites “identifying a target process” and “imputing the target process into the impact machine-learning model.” Identifying a target process is a further mental step of selection and evaluation. See MPEP 2106.04(a)(2)(III). Inputting the target process into the machine-learning model merely applies the same generic “impact metric machine learning model” already introduced in claim 3.
Step 2A, Prong 2 & Step 2B: No new additional elements are introduced. The analysis from parent claim is maintained.
CLAIM 5
Step 1: Claim 5 depends from claim 2 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 5 additionally recites that “the impact metric indicates higher aptitude in the attribute cluster than the population average.” This is a further mental evaluation — a comparison of a score to a population average — that can practically be performed in the human mind. See MPEP 2106.04(a)(2)(III).
Step 2A, Prong 2 & Step 2B: No new additional elements are introduced. The analysis from parent claim is maintained.
CLAIM 6
Step 1: Claim 6 depends from claim 2 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 6 additionally recites “identifying an external attribute cluster”, inputting it into and receiving an external impact metric from an “impact metric machine learning model,” and “determining an outlier cluster as a function of an impact metric and an external impact metric.” Identifying an external attribute cluster and comparing impact metrics are mental acts of evaluation and comparison. See MPEP 2106.04(a)(2)(III). Consistent with USPTO Example 39 and the August 4, 2025 guidance, the generic recitation of inputting data into and receiving output from a machine learning model does not recite a mathematical concept.
Step 2A, Prong 2: The claim recites the following additional elements: “inputting the external attribute cluster into the impact metric machine learning model; receiving an external impact metric from the impact metric machine learning model”. The inputting and receiving steps are insignificant extra-solution data gathering. See MPEP 2106.05(g). For the same reasons discussed in the Step 2A, Prong 2 analysis of claim 3, applying the generic impact metric machine learning model as a tool does not integrate the exception, and, for the same reasons discussed in the Step 2A, Prong 2 analysis of claim 2, the additional elements do not integrate the judicial exception into a practical application. Accordingly, claim 6 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. Gathering data and applying the generic impact metric machine learning model are well-understood, routine, and conventional. This is supported under MPEP 2106.05(d), subsection (II) (Berkheimer); for the machine learning model, see the express Berkheimer evidence discussed in the Step 2B analysis of claim 3. Accordingly, claim 6 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 7
Step 1: Claim 7 depends from claim 2 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: Claim 7 additionally recites that “the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.” This is a further mental comparison between two scores that can practically be performed in the human mind. See MPEP 2106.04(a)(2)(III).
Step 2A, Prong 2 & Step 2B: No new additional elements are introduced. The analysis from parent claim is maintained.
CLAIM 8
Step 1: Claim 8 depends from claim 1 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: No additional abstract idea limitations are introduced.
Step 2A, Prong 2: The claim recites the following additional elements: “inputting an outlier cluster into an outlier process machine learning model; and receiving an outlier process from the outlier process machine learning model.” The inputting and receiving steps are insignificant extra-solution data gathering. See MPEP 2106.05(g). The “outlier process machine learning model” is recited at a high level of generality as a generic class of computer algorithm used as a tool; the specification describes it generically as, e.g., “a k-means clustering model” or “a particle swarm clustering model” (specification at ¶[0056]–[0057]) and discloses no improvement to how the model itself operates. Mere instructions to apply the abstract idea using a generic machine learning model do not integrate the exception. See MPEP 2106.05(f); Ex Parte Desjardins. Accordingly, claim 8 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. Using a generic machine learning model to produce an output is a well-understood, routine, and conventional use of a generic class of computer algorithms, as confirmed by the specification’s description of conventional clustering models (specification at ¶[0056]–[0057]). This well-understood, routine, and conventional finding is supported by an express statement in the specification — its description of these conventional clustering models (specification ¶[0056]–[0057]) — which is the first category of Berkheimer evidence. See MPEP 2106.05(d), subsection (II); Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018). Furthermore, gathering data and applying the generic machine learning model are well-understood, routine, and conventional. This is supported under MPEP 2106.05(d), subsection (II) (Berkheimer). Accordingly, claim 8 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 9
Step 1: Claim 9 depends from claim 7 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: No additional abstract idea limitations are introduced.
Step 2A, Prong 2: The claim recites the following additional elements: “receiving historical attribute clusters associated with historical processes” is insignificant extra-solution data gathering. See MPEP 2106.05(g). “training the outlier process machine-learning model using the historical attribute clusters associated with historical processes” is generic model training recited at a high level of generality. Critically, under the two-step analysis of Ex Parte Desjardins, the specification discloses no improvement to how the machine learning model itself operates as a result of this training: unlike Desjardins (where training overcame “catastrophic forgetting” while protecting knowledge of prior tasks), the specification here describes only ordinary supervised training of a conventional model on historical data (Spec. ¶¶[0056]–[0057]) to produce a business output. The training therefore amounts to no more than mere instructions to apply the abstract idea using a generic machine learning model. See MPEP 2106.05(f). Accordingly, claim 9 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. Receiving/gathering data and training a generic machine learning model on that data are well-understood, routine, and conventional activities. See MPEP 2106.05(f), (g); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016). The ordered combination adds nothing unconventional. Accordingly, claim 9 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
CLAIM 10
Step 1: Claim 10 depends from claim 1 and recites an apparatus, which falls within the statutory category of a machine. See MPEP 2106.03.
Step 2A, Prong 1: No additional abstract idea limitations are introduced.
Step 2A, Prong 2: The clam recites the following additional elements: “the visual element is configured to highlight the outlier process.” This limitation merely specifies a display detail for the previously recited visual element; it adds no new abstract concept and introduces no new type of additional element beyond the display element already identified in claim 1.
The judicial exception is not integrated into a practical application. Configuring the visual element to “highlight” the outlier process is a generic display/output feature and insignificant post-solution activity. See MPEP 2106.05(g). For the same reasons discussed in the Step 2A, Prong 2 analysis of claim 1, the display of results does not integrate the exception. Accordingly, claim 10 does not satisfy Step 2A, Prong 2.
Step 2B: The claim does not include additional elements that amount to significantly more than the judicial exception. Highlighting an element on a display is a well-understood, routine, and conventional presentation of data. See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); MPEP 2106.05(d)(II). For the same reasons discussed in the Step 2B analysis of claim 1, the additional elements are well-understood, routine, and conventional and do not amount to significantly more than the judicial exception. Accordingly, claim 10 does not satisfy Step 2B and is rejected under 35 U.S.C. 101.
Claims 11-20 are substantially similar in scope and spirit to claims 1-10. Therefore, the rejections of claims 1-10 are applied accordingly. The only main difference in the analysis for each rejection would be for Step 1, where claims 1-10 recites an apparatus. Claims 11-20 a method of data structure generation, which falls within the statutory category of a process. See MPEP 2106.03. Accordingly, claims 11-20 satisfies Step 1 of the eligibility analysis.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over to US Pat. Pub. No. 2014/0143249A1 to Cazzanti et al. (hereinafter Cazzanti) in view of (US Pat. Pub. No. 2021/0358065A1 to Achiaga et al. (hereinafter Achiaga).
Per claim 1, Cazzanti discloses An apparatus for data structure generation (Cazzanti: ¶[0043], FIG. 2:222/230…Cazzanti discloses a computing device having a processor and a memory that operates on entity attribute data and produces cluster analyses and displays, which constitutes the apparatus for generating data structures under BRI, "client device 200 includes a processing unit (CPU) 222 in communication with a mass memory 230 via a bus 224"), the apparatus comprising:
at least a processor (Cazzanti: ¶[0043], FIG. 2:222…Cazzanti's device includes a processing unit (CPU) that performs the recited cluster-analysis actions, reading on the claimed at least a processor, "client device 200 includes a processing unit (CPU) 222 in communication with a mass memory 230 via a bus 224"); and
a memory communicatively connected to the at least processor, the memory containing instructions (Cazzanti: ¶[0043], FIG. 2:224…Cazzanti's mass memory is communicatively connected to the CPU over a bus, reading on the claimed communicatively connected memory, "a processing unit (CPU) 222 in communication with a mass memory 230 via a bus 224"; ¶[0050]…that memory stores the processor's instructions, "Mass memory 230 illustrates another example of computer readable storage media for storage of information such as computer readable instructions, data structures, program modules, or other data"), configuring the at least processor to:
identify a plurality of attribute clusters (Cazzanti: ¶[0065], FIG. 4:357/360… Cazzanti's Cluster Analyzer generates, by machine analysis, a plurality of clusters each grouping entities according to their attribute values, which constitutes identifying a plurality of attribute clusters under BRI, an attribute cluster being a group of related entity attributes, "generate clusters from a dataset of characteristics (attributes) that describe entities with the dataset");
locate in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters (Cazzanti: ¶[0079]–[0081], FIG. 5:508–512 and FIG. 6…Cazzanti computes, for each cluster, a dissimilarity from a reference cluster and orders the clusters by that dissimilarity so that the most differentiated cluster is surfaced, which constitutes locating an outlier cluster among the plurality under BRI, the specification defining an outlier cluster as an attribute cluster whose impact metric differs substantially from a population average, "for each cluster, a dissimilarity is computed for each aggregate attribute to its corresponding aggregate attribute in the reference cluster…the clusters are ordered based on their dissimilarity to the reference");
determine a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process (Cazzanti: ¶[0081], FIG. 6…Cazzanti determines and renders a display/visual data structure from the cluster analysis that generates a visual element (a color-coded circle) representing the differentiated (outlier) cluster, which constitutes determining a visual element data structure configured to generate a visual element as a function of at least the outlier cluster under BRI, "Each cluster may be represented by a circle with area proportional to the size of the cluster according to a user-chosen colormap"); and
configure a user device to display the visual element to a user (Cazzanti: ¶[0081], FIG. 6…Cazzanti displays the resulting visualization to a user, which constitutes configuring a user device to display the visual element to a user, "the ordering may be displayed visually, such as illustrated in FIG. 6").
Cazzanti does not expressly disclose, but Achiaga does teach:
determine an outlier process as a function of an outlier cluster (Achiaga: ¶[0110], FIG. 7:780…Achiaga determines, from an entity's clustered anchor-skill data (a distinctive attribute cluster of the entity), a recommended process such as a job opportunity or role, which constitutes determining an outlier process as a function of an outlier cluster under BRI, "identifying one or more job opportunities for the employee based on the clustered anchor skill data").
Cazzanti and Achiaga are analogous art because they are from the same field of endeavor, specifically the machine-learning analysis of an entity's attributes by clustering to surface the attribute clusters that most distinguish the entity and to drive a resulting recommended action. They also address the same problem of automatically identifying which of an entity's attribute clusters are most distinctive and determining what action or process to take in response.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art (PHOSITA) to combine the outlier-cluster identification and visualization system of Cazzanti with the machine-learning process-recommendation of Achiaga so as to determine an outlier process as a function of the identified outlier cluster and present it to the user, because doing so combines prior-art elements according to known methods to yield the predictable result of not only surfacing an entity's distinctive attribute cluster but also recommending the process in which the entity is advantaged.
The suggestion/motivation for doing so is provided by Cazzanti itself, which teaches that the ordered/visualized clusters are surfaced so as "to provide insight on what action or treatment might be made to address that specific segment of the underlying population" (Cazzanti: Abstract); Achiaga supplies precisely such an action-determination mechanism by "identifying one or more job opportunities for the employee based on the clustered anchor skill data" (Achiaga: ¶[0110]), so a PHOSITA would have been motivated to use Achiaga's recommendation model to determine the action/process foreshadowed by Cazzanti.
Per claim 2, Cazzanti combined with Achiaga discloses claim 1. Cazzanti further teaches wherein locating in the plurality of attribute clusters an outlier cluster comprises generating a first impact metric associated with a first attribute cluster of the plurality of attribute clusters and a second impact metric associated with a second attribute cluster of the plurality of attribute clusters; and determining an outlier cluster as a function of the first impact metric and the second impact metric (Cazzanti: ¶[0079]–[0080], FIG. 5:508/510…Cazzanti computes for each cluster (including first and second clusters) a single cluster dissimilarity (impact metric) from the reference and orders/selects the outlier cluster as a function of those per-cluster dissimilarities, "the attribute dissimilarities are combined into one cluster dissimilarity. The result of this step is a single number that captures the cluster's dissimilarity from the reference cluster").
Per claim 3, Cazzanti combined with Achiaga discloses claim 2. Achiaga further teaches wherein generating the first impact metric further comprises inputting the first attribute cluster into an impact metric machine learning model; and receiving a first impact metric from the impact metric machine learning model (Achiaga: ¶[0020], FIG. 1C:115…Achiaga processes the entity's skill/attribute data with a machine-learning similarity model that outputs similarity scores (impact metrics) for the attributes, which constitutes inputting an attribute cluster into an impact-metric machine-learning model and receiving an impact metric therefrom, "the skills platform may process the employee data and the skill data, with a similarity model, to determine similarity scores between the skills of the skill data based on the employee data"). The rationale to combine Achiaga with Cazzanti is the same as the parent claim.
Per claim 4, Cazzanti combined with Achiaga discloses claim 3. Achiaga further teaches wherein generating the first impact metric further comprises identifying a target process; and imputing the target process into the impact machine-learning model (Achiaga: ¶[0024], FIG. 1D:120…Achiaga identifies predefined target skill profile categories (target processes) and uses them together with the skill data in its similarity/skill-scoring model, which constitutes identifying a target process and inputting it into the impact machine-learning model, "As shown in FIG. 1D and by reference number 120, the skill platform may add and/or remove skills to/from the skill data for predefined target skill profile categories, based on the similarity scores and to generate modified skill data"). The rationale to combine Achiaga with Cazzanti is the same as the parent claim.
Per claim 5, Cazzanti combined with Achiaga discloses claim 2. Cazzanti further teaches wherein the impact metric indicates higher aptitude in the attribute cluster than the population average (Cazzanti: ¶[0077], FIG. 5:504…Cazzanti's dissimilarity (impact metric) is computed against a reference cluster that is the union of all clusters, i.e., the population, so a differentiated cluster's metric indicates how it stands apart from the population average, "the reference cluster could be the cluster obtained from the union of all clusters – which comprises all entities…").
Per claim 6, Cazzanti combined with Achiaga discloses claim 2. Cazzanti further teaches wherein locating in the plurality of attribute clusters an outlier cluster further comprises identifying an external attribute cluster; inputting the external attribute cluster into the impact metric machine learning model; receiving an external impact metric from the impact metric machine learning model; and determining an outlier cluster as a function of an impact metric and an external impact metric (Cazzanti: ¶[0077], FIG. 5:504…Cazzanti permits the reference cluster to be defined from entities of a different (external) dataset and computes each cluster's dissimilarity against it, which constitutes identifying an external attribute cluster, obtaining an external impact metric, and determining the outlier cluster from that comparison, "the reference cluster might comprise entities from other than any of the entities within the dataset. That is, the reference cluster might be provided from a different dataset").
Per claim 7, Cazzanti combined with Achiaga discloses claim 2. Cazzanti further teaches wherein the impact metric indicates higher aptitude in the attribute cluster than the external impact metric (Cazzanti: ¶[0081], FIG. 6…Cazzanti orders the clusters by their dissimilarity relative to the defined reference (which may be an external/different-dataset reference per ¶[0077]), so an outlier cluster's impact metric indicates greater differentiation than that of the external reference, "Flowing next to block 512, the clusters are ordered based on their dissimilarity to the reference. The ordering function may be chosen by the user and can be ascending or descending. Continuing to block 514, the ordering may be displayed visually, such as illustrated in FIG. 6").
Per claim 8, Cazzanti combined with Achiaga discloses claim 1. Achiaga further teaches wherein determining an outlier process as a function of an outlier cluster comprises inputting an outlier cluster into an outlier process machine learning model; and receiving an outlier process from the outlier process machine learning model (Achiaga: ¶[0064], FIG. 3:305/310…Achiaga inputs an entity's anchor-skill-data cluster as a new observation into a trained machine-learning model that outputs a recommendation such as a job opportunity, which constitutes inputting an outlier cluster into an outlier-process machine-learning model and receiving an outlier process therefrom, "The machine learning system may apply the trained machine learning model 305 to the new observation to generate an output"). The rationale to combine Achiaga with Cazzanti is the same as the parent claim.
Per claim 9, Cazzanti combined with Achiaga discloses claim 7. Achiaga further teaches receiving historical attribute clusters associated with historical processes; and training the outlier process machine-learning model using the historical attribute clusters associated with historical processes (Achiaga: ¶[0124], FIG. 8:870…Achiaga trains its clustering/recommendation model using historical groups of anchor-skill data and historical data for a plurality of employees, which constitutes training the outlier-process machine-learning model using historical attribute clusters associated with historical processes, "the clustering model is trained with historical groups of anchor skill data and historical similarity scores associated with historical skill data and historical employee data for a plurality of employees (block 870)"). The rationale to combine Achiaga with Cazzanti is the same as the parent claim.
Per claim 10, Cazzanti combined with Achiaga discloses claim 1. Cazzanti further teaches wherein the visual element is configured to highlight the outlier process (Cazzanti: ¶[0081], FIG. 6…Cazzanti's visual element emphasizes the most differentiated cluster by color-coding it according to degree of dissimilarity, and in the combination that highlighting is applied to the recommended outlier process, which constitutes a visual element configured to highlight the outlier process, "Each cluster may be represented by a circle with area proportional to the size of the cluster, and the circle can optionally be color-coded, where the color represents a degree of dissimilarity according to a user-chosen colormap").
Claims 11-20 are substantially similar in scope and spirit as claims 1-10. Therefore the rejections of claims 1-10 are applied accordingly.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-10 and 13-18 of U.S. Patent No. 11,868,859. Although the claims at issue are not identical, they are not patentably distinct from each other because each limitation of the instant claims is either recited verbatim by, or is an obvious variant of, a corresponding limitation of the claims of U.S. Patent No. 11,868,859, as shown in the claim-correspondence table below. Independent instant claims 1 (apparatus) and 11 (method) recite identifying a plurality of attribute clusters, locating an outlier cluster therein, determining an outlier process as a function of the outlier cluster, determining a visual element data structure as a function of the outlier process, and configuring a user device to display the visual element to a user. Patent claims 1 and 9 (apparatus) and 10 and 18 (method) recite each of these limitations, and further recite the narrower step of locating the outlier cluster by inputting a target process and an attribute cluster into an impact metric machine learning model. A generic claim is not patentably distinct from a reference claim reciting a species that falls within the genus (In re Goodman); accordingly, instant claims 1 and 11 are not patentably distinct from the corresponding patent claims. The instant dependent claims map to the patent claims as set forth in the table. Instant claims 9, 10, 19, and 20 add only the routine, conventional steps of training the already-claimed outlier-process machine learning model on historical data, and of highlighting the outlier process within the displayed visual element; these are obvious variants within the level of ordinary skill and are supported by nothing more than the patent's own claims. The instant application claims are therefore not patentably distinct from the patent claims.
Instant Claim 1 ↔ U.S. Pat. No. 11,868,859 Claim 1
Instant Application Claims
U.S. Pat. No. 11,868,859 Claims
at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:
at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to: (Claim 1)
identify a plurality of attribute clusters;
identify a plurality of attribute clusters; (Claim 1)
locate in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters;
locate in the plurality of attribute clusters an outlier cluster, wherein locating the outlier comprises identifying a target process, inputting the target process and an attribute cluster into an impact metric machine learning model, receiving an impact metric, and determining the outlier cluster in the plurality of attribute clusters as a function of the impact metric; (Claim 1) — the patent recites a narrower species of the instant genus 'locate ... an outlier cluster.'
determine an outlier process as a function of an outlier cluster;
determine an outlier process as a function of outlier cluster; (Claim 1)
determine a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process; and
determine a visual element data structure as a function of the outlier process (Claim 1); determine a visual element as a function of the visual element data structure (Claim 9)
configure a user device to display the visual element to a user.
configure a user device to display the visual element to a user. (Claim 9)
Instant Claim 2 ↔ U.S. Pat. No. 11,868,859 Claim 7
generating a first impact metric associated with a first attribute cluster of the plurality of attribute clusters and a second impact metric associated with a second attribute cluster of the plurality of attribute clusters; and
inputting a first attribute cluster into an impact metric machine learning model; receiving a first impact metric from the impact metric machine learning model; inputting a second attribute cluster into impact metric machine learning model; receiving a second impact metric from the impact metric machine learning model; and
determining an outlier cluster as a function of the first impact metric and the second impact metric.
determining outlier cluster as a function of the first impact metric and the second impact metric, wherein the first impact metric is associated with the first attribute cluster and the second impact metric is associated with the second attribute cluster.
Instant Claim 3 ↔ U.S. Pat. No. 11,868,859 Claim 7
inputting the first attribute cluster into an impact metric machine learning model; and
inputting a first attribute cluster into an impact metric machine learning model; and
receiving a first impact metric from the impact metric machine learning model.
receiving a first impact metric from the impact metric machine learning model.
Instant Claim 4 ↔ U.S. Pat. No. 11,868,859 Claim 1
identifying a target process; and
identifying a target process;
imputing the target process into the impact machine-learning model.
inputting the target process into an impact metric machine learning model;
Instant Claim 5 ↔ U.S. Pat. No. 11,868,859 Claim 4
the impact metric indicates higher aptitude in the attribute cluster than the population average.
the impact metric indicates higher aptitude in the attribute cluster than the population average.
Instant Claim 6 ↔ U.S. Pat. No. 11,868,859 Claim 5
identifying an external attribute cluster;
identifying external attribute cluster;
inputting the external attribute cluster into the impact metric machine learning model;
inputting the external attribute cluster into the impact metric machine learning model;
receiving an external impact metric from the impact metric machine learning model; and
receiving an external impact metric from the impact metric machine learning model; and
determining an outlier cluster as a function of an impact metric and an external impact metric.
determining outlier cluster as a function of impact metric and external impact metric.
Instant Claim 7 ↔ U.S. Pat. No. 11,868,859 Claim 6
the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
Instant Claim 8 ↔ U.S. Pat. No. 11,868,859 Claim 8
inputting an outlier cluster into an outlier process machine learning model; and
inputting outlier cluster into an outlier process machine learning model; and
receiving an outlier process from the outlier process machine learning model.
receiving outlier process from the outlier process machine learning model.
Instant Claim 9 ↔ U.S. Pat. No. 11,868,859 Claim 8
receiving historical attribute clusters associated with historical processes; and
The patent claims the outlier process machine learning model (Claim 8). Receiving historical attribute clusters associated with historical processes to train that model is a routine, conventional prerequisite to employing a machine learning model.
training the outlier process machine-learning model using the historical attribute clusters associated with historical processes.
inputting outlier cluster into an outlier process machine learning model; and receiving outlier process from the outlier process machine learning model (Claim 8). Training the already-claimed outlier-process machine learning model on historical data is an obvious variant requiring only ordinary skill.
Instant Claim 10 ↔ U.S. Pat. No. 11,868,859 Claim 9
the visual element is configured to highlight the outlier process.
determine a visual element as a function of the visual element data structure; and configure a user device to display the visual element to a user (Claim 9). Configuring the displayed visual element to highlight the outlier process is an obvious variant of displaying a visual element generated from the outlier process.
Instant Claim 11 ↔ U.S. Pat. No. 11,868,859 Claim 10
identifying, by at least a processor, a plurality of attribute clusters;
using at least a processor, identifying a plurality of attribute clusters; (Claim 10)
locating, by at least a processor, in the plurality of attribute clusters an outlier cluster, wherein the outlier cluster is an attribute cluster of the plurality of attribute clusters;
using the at least a processor, identifying a target process, inputting the target process and an attribute cluster into an impact metric machine learning model, receiving an impact metric, and determining the outlier cluster in the plurality of attribute clusters as a function of the impact metric; (Claim 10) — a narrower species of the instant 'locating ... an outlier cluster.'
determining, by at least a processor, an outlier process as a function of an outlier cluster;
using the at least a processor, determining an outlier process as a function of outlier cluster; (Claim 10)
determining, by at least a processor, a visual element data structure as a function of the outlier process, wherein the visual element data structure is configured to generate a visual element as a function of at least one of the outlier cluster and the outlier process; and
using the at least a processor, determining a visual element data structure as a function of the outlier process (Claim 10); determining a visual element as a function of the visual element data structure (Claim 18)
configuring, by at least a processor, a user device to display the visual element to a user.
configuring a user device to display the visual element to a user. (Claim 18)
Instant Claim 12 ↔ U.S. Pat. No. 11,868,859 Claim 16
generating a first impact metric associated with a first attribute cluster of the plurality of attribute clusters and a second impact metric associated with a second attribute cluster of the plurality of attribute clusters; and
inputting a first attribute cluster into an impact metric machine learning model; receiving a first impact metric; inputting a second attribute cluster into impact metric machine learning model; receiving a second impact metric; and
determining an outlier cluster as a function of the first impact metric and the second impact metric.
determining outlier cluster as a function of the first impact metric and the second impact metric, wherein the first impact metric is associated with the first attribute cluster and the second impact metric is associated with the second attribute cluster.
Instant Claim 13 ↔ U.S. Pat. No. 11,868,859 Claim 16
inputting the first attribute cluster into an impact metric machine learning model; and receiving a first impact metric from the impact metric machine learning model.
inputting a first attribute cluster into an impact metric machine learning model; receiving a first impact metric from the impact metric machine learning model.
Instant Claim 14 ↔ U.S. Pat. No. 11,868,859 Claim 10
identifying a target process; and imputing the target process into the impact machine-learning model.
using the at least a processor, identifying a target process; using the at least a processor, inputting the target process into an impact metric machine learning model;
Instant Claim 15 ↔ U.S. Pat. No. 11,868,859 Claim 13
the impact metric indicates higher aptitude in the attribute cluster than the population average.
the impact metric indicates higher aptitude in the attribute cluster than the population average.
Instant Claim 16 ↔ U.S. Pat. No. 11,868,859 Claim 14
identifying an external attribute cluster; inputting the external attribute cluster into the impact metric machine learning model;
identifying an external attribute cluster; inputting the external attribute cluster into the impact metric machine learning model;
receiving an external impact metric from the impact metric machine learning model; and determining an outlier cluster as a function of an impact metric and an external impact metric.
receiving an external impact metric from the impact metric machine learning model; and determining outlier cluster as a function of impact metric and external impact metric.
Instant Claim 17 ↔ U.S. Pat. No. 11,868,859 Claim 15
the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
the impact metric indicates higher aptitude in the attribute cluster than the external impact metric.
Instant Claim 18 ↔ U.S. Pat. No. 11,868,859 Claim 17
inputting an outlier cluster into an outlier process machine learning model; and receiving an outlier process from the outlier process machine learning model.
inputting outlier cluster into an outlier process machine learning model; and receiving outlier process from the outlier process machine learning model.
Instant Claim 19 ↔ U.S. Pat. No. 11,868,859 Claim 17
receiving historical attribute clusters associated with historical processes; and
The patent claims the outlier process machine learning model (Claim 17). Receiving historical attribute clusters associated with historical processes to train that model is a routine, conventional prerequisite to employing a machine learning model.
training the outlier process machine-learning model using the historical attribute clusters associated with historical processes.
inputting outlier cluster into an outlier process machine learning model; and receiving outlier process from the outlier process machine learning model (Claim 17). Training the already-claimed outlier-process machine learning model on historical data is an obvious variant requiring only ordinary skill.
Instant Claim 20 ↔ U.S. Pat. No. 11,868,859 Claim 18
the visual element is configured to highlight the outlier process.
determining a visual element as a function of the visual element data structure; and configuring a user device to display the visual element to a user (Claim 18). Configuring the displayed visual element to highlight the outlier process is an obvious variant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN CHEN whose telephone number is (571)272-4143. The examiner can normally be reached M-F 10-7.
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/ALAN CHEN/ Primary Examiner, Art Unit 2125
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