DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following NON-FINAL office action is in response to Applicant communication filed on 12/17/2025 regarding application 18/234,724. Claims 1, 5, 10, 14 and 19 have been amended. Thus, Claims 1-5, 7-14 and 16-20 have been rejected.
Response to Amendments
2. Applicant’s amendment filed on 12/17/2025 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
3. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/17/2025 has been entered.
Response to 35 U.S.C. § 101 Arguments
4. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-5, 7-14 and 16-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 12-18, dated 12/17/2025). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1-5, 7-14 and 16-20 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 13-15, dated 12/17/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the claims are not integrated into a practical application under Step 2A prong 2 of its 35 U.S.C. § 101 analysis because, in part, the additional elements are generic computing elements or instructions that do not integrate the judicial exception into a practical application.
In response to Applicant’s arguments pertaining to Independent Claims 1, 10 and 19 for step 2a prong 2, Examiner notes that receiving a request, mandatory constraints, and preferences are steps that are categorized as insignificant extra-solution activities. They represent mere data-gathering and data-entry functions necessary to implement the abstract idea of resource allocation on a computer. Determining a minimal unsatisfied constraints set via mixed-integer optimization: While this step uses a specific mathematical technique, it does so in a functional, ends-oriented manner. In 2025, the Federal Circuit in Recentive Analytics v. Fox clarified that applying established machine learning or mathematical methods to a new data environment—without specific technical details on how the model itself is improved—remains abstract. Determining whether a respective constraint must be altered: This is a generic "apply it" instruction. It uses the mathematical algorithm to perform a standard evaluative task (checking for needed changes) without a particularized technological implementation. Identifying impossible conditions and determining infeasibility: These are mental processes that are merely automated. They do not transform the abstract idea because they do not improve the computer's functioning; they only automate the logical deduction of a problem's state. Generating an explanation and determining if an issue exists: These steps are generic computer outputs of information. Providing a human-readable explanation is a communication task that does not provide a technical solution to a technical problem.
Each of these additional steps fails to provide a technological improvement or a meaningful limitation that moves the claim beyond the abstract idea: "receiving... a third input that relates to an adjustment": This is insignificant extra-solution activity. Receiving data to update a request is a conventional data-gathering step required to perform any iterative administrative process. "performing... a debugging mechanism, to revise the request": While the term "debugging" sounds technical, the claim describes it in a functional, result-oriented manner. It does not recite a specific technical protocol or novel AI architecture for the debugging; it simply automates the human task of revising a plan based on feedback. "iteratively executing... to generate a revised explanation": Iteration is a fundamental characteristic of human planning and basic computer processing. Simply repeating a set of abstract steps (checking for infeasibility) until a result is achieved is not a technological improvement to the computer's functionality. "displaying... allocation information": This is a classic generic computer output. Displaying the result of a calculation to a user is "extra-solution activity" that does not provide a technical solution to a technical problem.
Independent Claims 1, 10 and 19 fails the "Practical Application" test for three primary reasons established in 2025 guidance: Lack of Technological Improvement: The claim does not improve the "functioning of a computer" or another "technical field". It describes a business/administrative result (resource allocation) rather than a technical innovation like reducing CPU load or improving optimization convergence speed. Mere Application to a Field of Use: As affirmed in Recentive Analytics v. Fox (April 2025), simply introducing machine learning or mathematical optimization to a new field (like resource management) is not a practical application. The court ruled that iterative training and dynamic updates are "incidental to the very nature of machine learning" and do not count as technological improvements. No Meaningful Limitation: The claim recites "at least one processor" and "artificial intelligence" in a generic way. It does not require specialized hardware, novel AI architectures, or a specific transformation of data that imposes a "meaningful limit" on the abstract idea.
Under the August 2025 USPTO Memo, for an AI claim to be integrated into a practical application, it must solve a technical problem with a technical solution. These steps describe a user-interface feedback loop for administrative planning. The problem (conflicting constraints) is administrative, and the solution (adjusting inputs based on explanations) is a logical business process, not a technological one. As emphasized in recent 2025 Federal Circuit case law (e.g., Recentive Analytics v. Fox), using "debugging" or "iterative execution" as a black box to achieve a desired result is insufficient. Because the claim does not explain how the processor is specifically modified or how the AI's internal weights or structures are uniquely adjusted to enable this debugging, it is merely "applying" the abstract idea. The steps merely describe the "field of use" for the AI (resource allocation). They do not limit the exception to a specific machine or a transformation of data that creates a new technological effect. In 2025, a claim that simply "helps a user fix an error" through an explanation is viewed as a high-level administrative tool. Because these steps are generic computer functions (receiving, iterating, displaying) applied to a method of organizing human activity (resource planning), they do not provide an "additional element" that integrates the judicial exception into a practical application under Step 2A, Prong 2.
Therefore, Claims 1-5, 7-14 and 16-20 are patent ineligible under 35 U.S.C. § 101 step 2a prong 2 as they do not recite additional elements that integrate the judicial exception into a practical application.
Argument #2:
(B). Applicant argues that Claims 1-5, 7-14 and 16-20 are directly parallel to the Recent Precedential Decision in Ex Parte Desjardins 2025 case where an Appeals Review Panel convened and signed by Director Squires reversed a 35 U.S.C. § 101 rejection of machine learning claims (see Applicant Remarks, Page 15, dated 12/17/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks here, Examiner points out that the provided Independent Claims 1, 10 and 19 claim limitations remain patent-ineligible under 35 U.S.C. § 101 despite the 2025 precedential decision in Ex parte Desjardins. While Desjardins signaled a more favorable environment for AI, it specifically protected inventions that provide technical improvements to the computer or AI model itself.
While Desjardins signaled a more favorable environment for AI, it specifically protects inventions that improve the internal functioning of the machine learning (ML) model itself. These claim limitations recited for Independent Claims 1, 10 and 19, applies mixed-integer optimization to a resource allocation task, which lacks the critical "model improvement" elements found in Desjardins. Three Reasons why these claim limitation steps for Independent Claims 1, 10 and 19 of the instant application are not analogous to Ex parte Desjardins: Reason #1. “Improvement to Model Training vs. Application of an Algorithm” -> Desjardins: The claims were directed to a novel way of training a model to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks". This solved the specific technical problem of "catastrophic forgetting" in AI. Independent Claims 1, 10 and 19 of the instant application: These steps describe the application of a known mathematical algorithm (mixed-integer optimization) to a specific business or administrative environment (resource allocation). In 2025, the USPTO distinguishes between "how a model learns" (eligible) and "what a model does for a user" (often abstract). Reason #2. “Technical Performance vs. Administrative Result” -> Desjardins: The specification and claims clearly identified improvements such as "using less storage capacity," "reduced system complexity," and "preserved task performance". These were viewed as technological improvements to the computer's functioning analogous to Enfish. Independent Claims 1, 10 and 19 of the instant application: The result of these steps is an "explanation for the infeasibility" and "allocation information". These are administrative or informational outputs. Unlike Desjardins, these claims do not recite a specific change to the internal architecture of the AI that makes the processor more efficient or the model more generalized. Reason #3: Integration into a Practical Application (Step 2A, Prong 2) -> Desjardins: The Appeals Review Panel (ARP) found integration because the claims modified the model's parameters in a specific way that reflected a disclosed technical improvement. Independent Claims 1, 10 and 19 of the instant application: These steps of the instant application are considered computer implementations of an abstract idea. Simply using AI to identify impossible conditions or generate human-readable explanations is viewed in 2025 as a mental process or a method of organizing human activity that lacks the "meaningful limit" required to reach the level of a practical application.
Moreover, in Desjardins, the claims addressed a specific technical challenge within the field of AI: "catastrophic forgetting," a problem where a model loses old knowledge when learning new tasks. The solution was a novel method of training that improved the model's internal technological function. These steps of Independent Claims 1, 10 and 19 describe the automation of a human administrative task: reviewing an explanation of a problem (infeasible resource request) and adjusting inputs ("debugging mechanism"). This simply mimics human evaluative judgment on a generic computer. In the Desjardins case: Eligibility was supported by clear technical improvements, such as "using less storage capacity," "reduced system complexity," and "preserved task performance". These were concrete, physical improvements to the computer's operation. In contrast to the Desjardins case, the result of these steps of Independent Claims 1, 10 and 19 are "allocation information" and a "revised explanation". These are informational outputs related to the business problem (resource management), not a physical or technical improvement to the computer system's performance (e.g., reduced memory usage, faster processor speed). Furthermore, in the Desjardins case: The claims were tightly integrated with specific technical steps for "modifying a first set of parameters associated with a first neural network" and other detailed model architecture changes. In contrast to the Desjardins case, these steps of Independent Claims 1, 10 and 19 uses broad, functional language like "at least one processor," "performing a debugging mechanism," and "iteratively executing". This language is "ends-oriented" and does not impose meaningful limits on the abstract idea, contrasting sharply with the specific, technical limitations that made Desjardins eligible.
In conclusion, Claims 1-5, 7-14 and 16-20 are patent ineligible under 35 U.S.C. § 101 step 2a prong 2 as they do not recite additional elements that integrate the judicial exception into a practical application and is not analogous to 2025 case of Ex parte Desjardins (Appeal 2024-000567).
Argument #3:
(C). Applicant argues that Claims 1-5, 7-14 and 16-20 recite additional elements that amount to significantly more than the recited judicial exception under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 16-17, dated 12/17/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that the Office has not established that amended claims 1, 10 and 19 fails to recite significantly more than the abstract idea under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 16, dated 12/17/2025). Examiner respectfully disagrees.
Examiner refers Applicant to Examiner’s 35 U.S.C. 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1, 10 and 19. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
In response to Applicant’s remarks for step 2B of the 35 U.S.C. § 101 analysis for Independent Claims 1, 10 and 19, Examiner points out why each step fails to provide an inventive concept. Reason #1: Data-Gathering Steps (Receiving Inputs): The steps of "receiving... a request," "receiving... a first input [constraints]," and "receiving... a second input [preferences]." These are characterized as conventional pre-solution activity. In 2025, receiving data from a user to initiate a process is considered a well-understood and routine function of a generic computer. These steps do not transform the abstract idea because they do not impose a technological limit on how the data is gathered or used. Reason #2: Mathematical Optimization (Mixed-Integer Optimization). The steps of "determining... via an artificial intelligence algorithm... [using] mixed-integer optimization." While optimization is a powerful tool, applying a known mathematical technique to a particular data set (resource allocation) is considered routine mathematical activity in the field of computer science. Because the claim does not recite a specific improvement to the optimization algorithm itself—such as a novel data structure that reduces the computational complexity of the mixed-integer math—it does not amount to significantly more than the mathematical concept. Reason #3: Automated Logic (Determining Infeasibility and Identifying Conditions): "identifying... at least one condition that is impossible to satisfy" and "determining... that an infeasibility exists." These steps represent the automation of mental processes. The logic of checking if rules (constraints) conflict is a fundamental human cognitive task. Using a processor to do this more quickly or on a larger scale is not an inventive concept. Per the August 2025 Memo, "mere automation" of a task previously performed by humans, without a specific technological "how-to," is conventional. Reason #4: Informational Output (Generating Explanations). The steps of "generating... an explanation for the infeasibility" and "determining whether an issue exists... based on the explanation." This is categorized as insignificant post-solution activity. Generating a report or an explanation for a user is a standard communication function. It does not solve a technological problem in the computer's hardware or the AI's internal architecture; it merely provides an administrative result to a human user. Reason #5: Functional Results (Reducing Resource Requirements). The clause of "to generate the minimal unsatisfied constraints set and reduce resource requirements" is a functional statement of a goal (efficiency) rather than a technical description of the means to achieve it. In 2025, "results-oriented" claim language that lacks specific technical limitations is insufficient to establish an inventive concept.
The step of "receiving... a third input that relates to an adjustment" lacks an inventive concept due to Conventional Data Gathering: Receiving user input to update a data set is a basic function of any interactive software. In 2025, the USPTO maintains that "data-gathering" is a routine pre-solution activity that does not contribute to an inventive concept. The step of "performing... a debugging mechanism, to revise the request" recites Functional/Black-Box Language: The claim does not recite a specific technological debugging protocol or a novel AI architecture. "Debugging" here is used functionally to describe the result of fixing a request. Automating a logical correction process on a generic processor is considered routine automation of a mental process. The step of "iteratively executing... to generate a revised explanation" Routine Computer Behavior: Computers are inherently designed to perform iterative loops. Simply repeating a set of abstract steps (checking for feasibility) until a condition is met is a well-understood and conventional use of a processor to handle business logic. The step of "displaying... allocation information that relates to an action" recites Insignificant Extra-Solution Activity: Displaying the final result of a calculation to a user is the quintessential "generic computer output."
Independent Claims 1, 10 and 19 when factoring the additional elements in view of the claim limitation steps both individually and as an ordered combination, these steps describe a high-level administrative process for managing resources. Because they utilize generic computer components to perform their standard functions (receiving, calculating, identifying, and displaying), the claim lacks an inventive concept. It does not provide a technological breakthrough in the field of AI or computing, but rather applies existing AI tools to a business problem, which is insufficient to meet the "significantly more" threshold in 2025.
The 2025 guidance emphasizes that for AI claims to be eligible, they must recite concrete, technical improvements to the machine learning model or computer system (e.g., reduced memory usage, faster training times, or novel architectures). Because the claim limitation steps of Independent Claims 1, 10 and 19 merely applies existing AI optimization techniques to a business/administrative problem using generic processors, it does not amount to significantly more than the abstract idea of resource planning.
In conclusion, Claims 1-5, 7-14 and 16-20 are patent ineligible under 35 U.S.C. § 101 step 2B as they do not recite additional elements that amount to significantly more than the recited judicial exception under step 2B of the 35 U.S.C. § 101 analysis.
Argument #4:
(D). Applicant argues that Claims 1-5, 7-14 and 16-20 recite an ordered combination of claim elements that define a non-conventional arrangement, such that the ordered combination set forth in the claims is not routine or conventional. This conclusion is supported by the fact that no prior art is applied to reject these claimed features. Moreover, Examiner has not established that the combination of claim features is well-understood, routine, or conventional, as required by MPEP § 2106.05 (d) (I) under step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 16-17, dated 12/17/2025). Examiner respectfully disagrees.
In response to Applicant’s remarks here, Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018).
Applicants argue that the claims are patent-eligible under step 2B and contain an inventive concept due to the lack of application of prior art against Applicant’s claims. Examiner submits that the question of novelty and non-obviousness evidence (application of prior art) is not relevant to the question of determining whether the claims as constructed contain an inventive concept. Lastly, Examiner cites the case of (Two-Way Media v. Comcast, (Fed. Cir. 2017)) and the District Court from this case concluded that “the proffered materials are irrelevant to the § 101 motion for judgment on the pleadings. None of the proffered materials addresses a § 101 challenge to claims of the asserted patents. The novelty and non-obviousness of the claims under §§ 102 and 103 does not bear on whether the claims are directed to patent-eligible subject matter under § 101. . . . Because the proffered materials are irrelevant to the instant § 101 issue, I have not considered them.” The appeal to Federal Circuit Court affirmed the District Court’s ruling that “eligibility and novelty are separate inquiries.”
Additionally, certain/particular claim limitations in Independent Claims 1, 10 and 19 recite steps of “receiving data” when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), which have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or Transmitting Data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional element of “artificial intelligence (AI) algorithm” in Independent Claims 1, 10 and 19 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2021/0117873 A1) hereinafter Vakhutinsky, et. al. Vakhutinsky at ¶ [0002]: “One embodiment is directed generally to a computer system, and in particular to a computer system that provides artificial intelligence based room assignment optimization.” Vakhutinsky at ¶ [0027]: “Most hotel operators generally perform the room assignment manually by assigning the rooms to the individual reservations through an intuitive domain understanding, which is labor-intensive and, in many cases, results in assignments that are far from optimal. Some known solutions solve the problem using Mixed Integer Linear Programming (“MILP”).”
See for example; US PG Pub (US 2022/0366360 A1) hereinafter Terrazas-Moreno, et. al. Terrazas-Moreno at ¶ [0043]: “The math model constructor 222 implements a data-driven algorithm that creates an equation-oriented model 223a-n out of a black-box, input-output model 221a-n, using statistics, machine learning, and/or Artificial Intelligence techniques.” Terrazas-Moreno at ¶ [0050]: “If the problem (solving the equation-oriented models 223a-n) is feasible the algorithm converges; if the problem is infeasible, a new constraint is written exclusively in terms of Category 1 and Category 2 variables and added to the master problem 225, e.g., as another equation in the master problem 225. This constraint is referred to as a “cut” and it can be generated in several ways. For instance, the constraint can be generated through: (i) an integer cut, (ii) a Benders cut, (iii) a logic-based cut, and (iv) a cut generated through data-driven and/or artificial intelligence means.”
See for example; US PG Pub (US 2010/0318207 A1) hereinafter Yee, et. al. Yee at ¶ [0044] notes: “FIG. 22 illustrates a high-level overview of database federation and "Extract, Transform, and Load" ("ETL") database management to query, search, and selectively extract data, present the data, analyze and present the data using complex optimization, for example, integer programming, mixed integer programming, heuristics, and artificial intelligence, among other techniques, human intervention and requesting additional data, as carried out by a sourcing agent.” See for example; US PG Pub (US 2004/0193473 A1) hereinafter Robertson, et. al. Robertson notes at ¶ [0060]: “An effective schedule is formed in step 820 using the worker data from steps 811, 812, and 813. In the field of employee staffing and scheduling, several techniques are known to create an optimized schedule using the worker data, such as the information described above in steps 811, 812, and 813. For instance, an optimized schedule for a security checkpoint may be formed using linear programming, quadratic or mixed-integer programming, nonlinear optimization, global optimization, non-smooth optimization using genetic and evolutionary algorithms, and constraint programming methods from artificial intelligence.”
Returning now to the elderly “Bascom”, Examiner reveals that what made it eligible was its capability, at the time of the invention, to go beyond mere reliance on specific processes [as argued here at Applicant Remarks, Page 16, dated 12/17/2025], by clearly and deliberately installing a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user, such design providing the benefits of both filtering on a local computer and the filtering on the ISP server (“Bascom Glob. Internet Servs. v. AT&T Mobility, LLC, U.S. Court of Appeals Federal Circuit, No. 2015-1763, June 27, 2016, 2016 BL 204401, 827 F.3d 1341” hereinafter “Bascom”, p.1242 ¶5) which was ultimately found by the Federal Circuit as a “software-based invention that improved performance of the computer system itself (“Bascom” p.1243 last ¶).
Therefore, in conclusion, Appellant’s suggestion that specific limitations (or the claimed invention as a whole) must be shown to be well-understood, routine, and conventional to support the conclusion of subject matter ineligibility for 35 U.S.C. § 101 of Independent Claims 1, 10 and 19 is not persuasive.
Argument #5:
(E). Applicant argues that Claims 1-5, 7-14 and 16-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Page 17, dated 12/17/2025). Examiner respectfully disagrees.
Specifically, Applicant argues that amended claims 1, 10 and 19 recite AI algorithms for determining unsatisfied constraints and reducing resource requirements and asserts that no people are managed and no interactions between people are prescribed and also states that this computer-dependent process is incapable of being practically being performed in the human mind as required by MPEP § 2106.04 (a) (2) III (A) (see Applicant Remarks, Page 17, dated 12/17/2025). Examiner respectfully disagrees.
With respect to “Mental Processes” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (see Applicant’s Specification ¶ [0037]: “The processor 104 may be a general-purpose processor or may be part of an application specific integrated circuit (ASIC).”), or 2) in a computer environment (see Applicant’s Specification ¶ [0035-0036]: “For example, the computer system 102 may include, or be included within, any one or more computers, servers, systems, communication networks or cloud environment. Even further, the instructions may be operative in such cloud-based computing environment.”), or 3) is merely using a computer as a tool to perform these concepts.” Thus, based on these 3 factors, Examiner maintains that the claims still recite a mental process. Also, Examiner refers Applicant to MPEP § 2106.04 (a) III (B): “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
With respect to “Mathematical Concepts” category, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (I) (C): “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping.” “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea).” Furthermore, see MPEP § 2106.05 (c): “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360, 31 USPQ2d 1754, 1755, 1759 (Fed. Cir. 1994)).”
For Independent Claims 1, 10 and 19, the claim limitation step of "receiving... a request for a resource allocation" depicts receiving information and making requests are fundamental administrative tasks and communication steps that can be practically performed in the mind. These are abstract idea under Organizing Human Activity / Mental Process. The step of "receiving... a first input [mandatory constraints] and a second input [non-mandatory preferences]" depicts collecting and organizing rules or preferences for an activity is a basic administrative and data entry task. These are abstract idea under Organizing Human Activity / Mental Process. The step of "determining... a minimal unsatisfied constraints set... [using] mixed-integer optimization" depicts a mixed-integer optimization is a specific mathematical formula/algorithm used for solving optimization problems. This explicitly recites a mathematical concept. The step of "determine a least number of possible conditions... and reduce resource requirements" describes the intended mathematical result or calculation of the optimization algorithm. This is a Mathematical Concept. The step of "identifying... at least one condition that is impossible to satisfy... based on the minimal unsatisfied constraints set" depicts evaluating data to identify a conflict or impossibility is a form of judgment or observation performable in the human mind. This is classified as a Mental Process. The step of "determining... that an infeasibility exists" depicts drawing a conclusion (judgment) from an evaluation is a classic mental process. This is classified as a Mental Process. The step of "generating... an explanation for the infeasibility" depicts creating a human-understandable report or explanation involves cognitive communication and analytical steps. This is classified as a Mental Process. The step of "determining whether an issue exists... based on the generated explanation" depicts a final evaluative judgment based on previously processed information, a quintessential mental process.
Moreover, the step of "receiving... a third input that relates to an adjustment of... information" depicts receiving input and adjusting a request is a fundamental communicative and administrative task performable by a human (e.g., editing a calendar request after a conflict is noted). This is abstract idea classified under Mental Process / Organizing Human Activity categories. The step of "performing... a debugging mechanism, to revise the request to re-specify the resource allocation, based on the generated explanation" describes ore logic of interpreting feedback (the "explanation") and making a logical adjustment ("debugging," "revising the request") is a form of human judgment and problem-solving. This automates that mental/administrative process. This is abstract idea classified under Mental Process / Organizing Human Activity categories. The step of "iteratively executing... the determining that the infeasibility exists and the generating of the explanation for the infeasibility" describes a repetition of the core abstract idea (determining infeasibility and explaining it) which is a standard administrative feedback loop and a basic function of a computer (iteration) applying that logic. This is abstract idea classified under Mental Process / Organizing Human Activity categories. Lastly, the step of "displaying... allocation information that relates to an action that satisfies the at least one constraint" recites displaying the final result (the allocation information) to the user is a generic communication step and the end-result of a business process (resource management). This is abstract idea classified under Mental Process / Organizing Human Activity categories.
In conclusion, Examiner maintains that Claims 1-5, 7-14 and 16-20 are directed to abstract ideas under “Mental Processes” or “Mathematical Concepts” or “Certain Methods of Organizing Human Activities” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #6:
(E). Applicant argues that Dependent Claims 2-5, 7-9, 11-14, 16-18 and 20 are patent eligible under 35 U.S.C. § 101 due to the reasons given for Independent Claims 1, 10 and 19, and because they recite features that are patentable in their own right (see Applicant Remarks, Page 18, dated 12/17/2025). Examiner respectfully disagrees.
Examiner maintains that Dependent Claims 2-5, 7-9, 11-14, 16-18 and 20 are patent ineligible under the 35 U.S.C. § 101 analysis due to the reasons explained above in Argument Sections #1-5 for Independent Claims 1, 10 and 19, whereby similar reasons and rationale are also applied here to the Dependent Claims.
Thus, Claims 1-5, 7-14 and 16-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-5, 7-14 and 16-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5, 7-14 and 16-20 are each focused to a statutory category namely a “method” or a “process” (Claims 1-5 and 7-9), a “apparatus” or a “system” (Claims 10-14 and 16-18) and a “non-transitory computer readable storage medium” or an “article of manufacture” (Claims 19-20).
Step 2A Prong One: Independent Claims 1, 10 and 19 recite limitations that set forth the abstract idea(s), namely (see in bold except via strikethrough):
“” (see Independent Claim 10);
“” (see Independent Claim 10);
“” (see Independent Claim 10);
“” (see Independent Claim 10);
“” (see Independent Claim 19);
“receiving, from a user, a request for a resource allocation that includes first information that relates to at least one resource to be allocated” (see Independent Claim 1);
“receiving, , a first input that includes second information that relates to at least one constraint that is mandatory with respect to the request” (see Independent Claim 1);
“receiving, a second input that includes third information that relates to at least one preference that is not mandatory” (see Independent Claim 1);
“determining, , a minimal unsatisfied constraints set with respect to the request, wherein uses mixed-integer optimization to determine a least number of possible conditions required for satisfying the resource allocation to generate the minimal unsatisfied constraints set and reduce resource requirements, and wherein the mixed-integer optimization further determines whether a respective constraint of the at least one constraint must be altered for the satisfying of the resource allocation” (see Independent Claim 1);
“identifying, at least one condition that is impossible to satisfy with respect to the request, based on the minimal unsatisfied constraints set, wherein the at least one condition includes at least one from among the at least one preference and the at least one constraint” (see Independent Claim 1);
“determining, , that an infeasibility exists with respect to fulfilling the request in a manner that satisfies each of the at least one constraint and the at least one preference, based on the identifying of the at least one condition” (see Independent Claim 1);
“generating, , an explanation for the infeasibility based on a result of the determining that the infeasibility exists” (see Independent Claim 1);
“determining whether an issue exists with the request, based on the generated explanation” (see Independent Claim 1);
“responsive to a determination that at least one issue exists with the request” (see Independent Claim 1);
“receiving, , a third input that relates to an adjustment of at least one from among the first information and the second information” (see Independent Claim 1);
“performing, , a debugging , to revise the request to re-specify the resource allocation, based on the generated explanation” (see Independent Claim 1);
“iteratively executing, the determining that the infeasibility exists and the generating of the explanation for the infeasibility to generate a revised explanation, based on the third input that relates to an adjustment of at least one from among the first information and the second information, and further based on the revised request” (see Independent Claim 1);
“responsive to a determination that an issue does not exist with the request, displaying, by the at least one processor, allocation information that relates to an action that satisfies the at least one constraint” (see Independent Claim 1);
“receive, from a user , a request for a resource allocation that includes first information that relates to at least one resource to be allocated” (see Independent Claim 10);
“receive, , a first input that includes second information that relates to at least one constraint that is mandatory with respect to the request” (see Independent Claim 10);
“receive a second input that includes third information that relates to at least one preference that is not mandatory” (see Independent Claim 10);
“determine, , a minimal unsatisfied constraints set with respect to the request, wherein uses mixed-integer optimization to determine a least number of possible conditions required for satisfying the resource allocation to generate the minimal unsatisfied constraints set and reduce resource requirements, and wherein the mixed-integer optimization further determines whether a respective constraint of the at least one constraint must be altered for the satisfying of the resource allocation” (see Independent Claim 10);
“identify, at least one condition that is impossible to satisfy with respect to the request, based on the minimal unsatisfied constraints set, wherein the at least one condition includes at least one from among the at least one preference and the at least one constraint” (see Independent Claim 10);
“determine that an infeasibility exists with respect to fulfilling the request in a manner that satisfies each of the at least one constraint and the at least one preference, based on the identifying of the at least one condition” (see Independent Claim 10);
“generate an explanation for the infeasibility based on a result of the determination that the infeasibility exists” (see Independent Claim 10);
“determine whether an issue exists with the request, based on the generated explanation” (see Independent Claim 10);
“responsive to a determination that at least one issue exists with the request” (see Independent Claim 10);
“receive a third input that relates to an adjustment of at least one from among the first information and the second information” (see Independent Claim 10);
“perform a debugging , to revise the request to re-specify the resource allocation, based on the generated explanation” (see Independent Claim 10);
“iteratively execute the determining that the infeasibility exists and the generating of the explanation for the infeasibility to generate a revised explanation, based on the third input that relates to an adjustment of at least one from among the first information and the second information, and further based on the revised request” (see Independent Claim 10);
“responsive to a determination that an issue does not exist with the request, display allocation information that relates to an action that satisfies the at least one constraint” (see Independent Claim 10);
“receive, from a user, a request for a resource allocation that includes first information that relates to at least one resource to be allocated” (see Independent Claim 19);
“receive a first input that includes second information that relates to at least one constraint that is mandatory with respect to the request” (see Independent Claim 19);
“receive a second input that includes third information that relates to at least one preference that is not mandatory” (see Independent Claim 19);
“determine, , a minimal unsatisfied constraints set with respect to the request, wherein uses mixed-integer optimization to determine a least number of possible conditions required for satisfying the resource allocation to generate the minimal unsatisfied constraints set and reduce resource requirements, and wherein the mixed-integer optimization further determines whether a respective constraint of the at least one constraint must be altered for the satisfying of the resource allocation” (see Independent Claim 19);
“identify, at least one condition that is impossible to satisfy with respect to the request, based on the minimal unsatisfied constraints set, wherein the at least one condition includes at least one from among the at least one preference and the at least one constraint” (see Independent Claim 19);
“determine that an infeasibility exists with respect to fulfilling the request in a manner that satisfies each of the at least one constraint and the at least one preference, based on the identifying of the at least one condition” (see Independent Claim 19);
“generate an explanation for the infeasibility based on a result of the determining that the infeasibility exists” (see Independent Claim 19);
“determine whether an issue exists with the request, based on the generated explanation” (see Independent Claim 19);
“responsive to a determination that at least one issue exists with the request” (see Independent Claim 19);
“receive a third input that relates to an adjustment of at least one from among the first information and the second information” (see Independent Claim 19);
“perform a debugging to revise the request to re-specify the resource allocation, based on the generated explanation” (see Independent Claim 19);
“iteratively execute the determining that the infeasibility exists and the generating of the explanation for the infeasibility to generate a revised explanation, based on the third input that relates to an adjustment of at least one from among the first information and the second information, and further based on the revised request” (see Independent Claim 19);
“responsive to a determination that an issue does not exist with the request, display allocation information that relates to an action that satisfies the at least one constraint” (see Independent Claim 19).
Here, for Independent Claims 1, 10 and 19, these steps recite a series of steps such as series of steps: "receiving... information," "determining... a minimal unsatisfied constraints set," "identifying... an impossible condition," and "generating... an explanation" which recite abstract ideas. For example; these steps are often categorized as mental processes because they describe evaluative actions (identifying, determining, explaining) that can be practically performed in the human mind or with pen and paper. Moreover, these claims relate to resource allocation, which is a fundamental economic practice or administrative task. Allocating resources based on mandatory constraints and non-mandatory preferences is a basic business or organizational activity. Without a specific technological tie-in (like optimizing network bandwidth or hardware cycles), this is considered an abstract method of organizing human activity. The steps of iteratively executing... the determining that the infeasibility exists... and the generating of the explanation... to generate a revised explanation” is an iterative feedback loop for administrative planning. It organizes the human activity of refining a request (e.g., a meeting room booking or budget request) until it fits mandatory rules. Also, the step of "displaying... allocation information that relates to an action that satisfies the at least one constraint." Displaying the final result of a successful request is considered the end-result of a method of organizing human activity (managing a resource).
Thus, the sequence of receiving data, evaluating it for infeasibility, and generating a human-readable explanation for an issue are “Mental Processes”. The general concept of managing and allocating resources based on user-defined constraints and preferences are “Certain Methods of Organizing Human Activities” and the use of mixed-integer optimization to solve a constraint satisfaction problem are “Mathematical Concepts”.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior (including teachings or following rules or instructions) or alternatively as “Mathematical Concepts” which pertains to (2) mathematical calculations.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under the broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid, in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude these claims from reciting an abstract idea. See MPEP § 2106.04(a) III C.
That is, other than reciting the additional elements of (e.g., “a memory” & “communication interface” & “display” & “debugging mechanism” & “non-transitory computer readable storage medium” & “a processor”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (2) mathematical calculations and additionally or alternatively as “Mental Processes” which pertains to (3) concepts performed in the human mind (including observations or evaluations or judgments) or (4) using pen and paper as a physical aid.
Moreover, the mere recitation of generic computer components such as (e.g., “a memory” & “a processor”) does not take the claims out of “Certain Methods of Organizing Human Activities” or “Mental Processes” or “Mathematical Concepts” Groupings.
Therefore, at step 2a prong 1, Yes, Claims 1-5, 7-14 and 16-20 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 10 and 19 recites additional elements directed to: (e.g., “a memory” & “communication interface” & “display” & “non-transitory computer readable storage medium” & “a processor”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 10 and 19: With respect to reliance on (e.g., “debugging mechanism” & “artificial intelligence (AI) algorithm”) as additional elements when considered individually and as a ordered combination (as a whole) for the claim limitations for Independent Claims 1, 10 and 19, these additional elements do not provide limitations that are indicative of integration into a practical application due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting to a particular field of use or technological environment pertaining to monitoring and analyzing infeasibility in order to identify at least one from among the at least one constraint that is impossible to satisfy for responding to a request for resource allocation in a business enterprise environment (see MPEP § 2106.05 (h)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-5, 7-14 and 16-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 10 and 19 recites additional elements directed to: (e.g., “a memory” & “communication interface” & “display” & “non-transitory computer readable storage medium” & “a processor”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (h) and See MPEP § 2106.05 (f). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0037]: “The processor 104 may be a general-purpose processor or may be part of an application specific integrated circuit (ASIC). The processor 104 may also be a microprocessor, a microcomputer, a processor chip, a controller, a microcontroller, a digital signal processor (DSP), a state machine, or a programmable logic device.”).
Independent Claims 1, 10 and 19: With respect to reliance on (e.g., “debugging mechanism” & “artificial intelligence (AI) algorithm”) as additional elements when considered individually and as an ordered combination (as a whole) in view of the claim limitations for Independent Claims 1, 10 and 19, these additional elements do not amount to significantly more than the judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) limiting to a particular field of use or technological environment pertaining to monitoring and analyzing infeasibility in order to identify at least one from among the at least one constraint that is impossible to satisfy for responding to a request for resource allocation in a business enterprise environment (see MPEP § 2106.05 (h)).
Additionally, certain/particular claim limitations in Independent Claims 1, 10 and 19 recite steps of “receiving data” when evaluated as additional elements, these activities at most amount to insignificant extra-solution activities (see MPEP § 2106.05 (g)), which have been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or Transmitting Data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The additional element of “artificial intelligence (AI) algorithm” in Independent Claims 1, 10 and 19 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example; US PG Pub (US 2021/0117873 A1) hereinafter Vakhutinsky, et. al. Vakhutinsky at ¶ [0002]: “One embodiment is directed generally to a computer system, and in particular to a computer system that provides artificial intelligence based room assignment optimization.” Vakhutinsky at ¶ [0027]: “Most hotel operators generally perform the room assignment manually by assigning the rooms to the individual reservations through an intuitive domain understanding, which is labor-intensive and, in many cases, results in assignments that are far from optimal. Some known solutions solve the problem using Mixed Integer Linear Programming (“MILP”).”
See for example; US PG Pub (US 2022/0366360 A1) hereinafter Terrazas-Moreno, et. al. Terrazas-Moreno at ¶ [0043]: “The math model constructor 222 implements a data-driven algorithm that creates an equation-oriented model 223a-n out of a black-box, input-output model 221a-n, using statistics, machine learning, and/or Artificial Intelligence techniques.” Terrazas-Moreno at ¶ [0050]: “If the problem (solving the equation-oriented models 223a-n) is feasible the algorithm converges; if the problem is infeasible, a new constraint is written exclusively in terms of Category 1 and Category 2 variables and added to the master problem 225, e.g., as another equation in the master problem 225. This constraint is referred to as a “cut” and it can be generated in several ways. For instance, the constraint can be generated through: (i) an integer cut, (ii) a Benders cut, (iii) a logic-based cut, and (iv) a cut generated through data-driven and/or artificial intelligence means.”
See for example; US PG Pub (US 2010/0318207 A1) hereinafter Yee, et. al. Yee at ¶ [0044] notes: “FIG. 22 illustrates a high-level overview of database federation and "Extract, Transform, and Load" ("ETL") database management to query, search, and selectively extract data, present the data, analyze and present the data using complex optimization, for example, integer programming, mixed integer programming, heuristics, and artificial intelligence, among other techniques, human intervention and requesting additional data, as carried out by a sourcing agent.” See for example; US PG Pub (US 2004/0193473 A1) hereinafter Robertson, et. al. Robertson notes at ¶ [0060]: “An effective schedule is formed in step 820 using the worker data from steps 811, 812, and 813. In the field of employee staffing and scheduling, several techniques are known to create an optimized schedule using the worker data, such as the information described above in steps 811, 812, and 813. For instance, an optimized schedule for a security checkpoint may be formed using linear programming, quadratic or mixed-integer programming, nonlinear optimization, global optimization, non-smooth optimization using genetic and evolutionary algorithms, and constraint programming methods from artificial intelligence.”
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-5, 7-9, 11-14, 16-18 and 20 recite additional elements directed to: (e.g., “first algorithm” & “SME knowledge database” & “graphical user interface (GUI)”), and when considered individually and as an ordered combination (as a whole) with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 10 and 19 along with further steps/details that could be performed as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior (including teachings or following rules or instructions) and additionally or alternatively as “Mathematical Concepts” which pertains to (4) mathematical calculations.
Dependent Claims 7-9 and 16-18 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and 2B for Independent Claims 1, 10 and 19. Dependent Claims 2-5, 11-14 and 20: With respect to reliance on (e.g., “first algorithm” (see Dependent Claims 2, 11 and 20) & “mixed-integer optimization technique” (see Dependent Claims 2, 11 and 20) & “subject matter expertise (SME) database” (see Dependent Claims 3 and 12) & “graphical user interface (GUI)” (see Dependent Claims 4-5 & 13-14)) as additional elements shown in Dependent Claims 2-5, 11-14 and 20 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)) or (2) the claims as a whole are limited to a particular field of use or technological environment pertaining to monitoring and analyzing infeasibility in order to identify at least one from among the at least one constraint that is impossible to satisfy for responding to a request for resource allocation in a business enterprise environment (see MPEP § 2106.05 (h)).
Furthermore, certain/particular limitations in Dependent Claims 4-5 & 13-14 even if the steps of “mere data outputting/data displaying” (e.g., “further comprising displaying, via & graphical user interface (GUI), a result of the generating of the explanation” & “displaying via a GUI, a prompt that facilitates a reception of the third input”) are evaluated as additional elements, these activities at most amount to insignificant extra-solution activities, which has been recognized as Well-Understood, Routine and Conventional (WURC), and thus insufficient to add significantly more to the abstract idea. See MPEP § 2106.05(d) ii - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-5, 7-14 and 16-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-5, 7-14 and 16-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
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/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625