DETAILED ACTION
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 .
Status
This action is in response to the amendment filed on 6/16/2026. Claims 1-4, 7-15, 18-20 are pending. Claims 1-2, 10-12, and 20 are amended. No claims have been added. Claims 5-6 and 16-17 have been cancelled.
Response to Arguments
Applicant's arguments filed 6/16/2026 have been fully considered but they are not persuasive. The applicant has argued the previous 101 rejection. Specifically “Rather, amended claim 1 is directed to a particular computer-implemented data-processing architecture for determining resilience using specifically recited processor-executed operations on structured data. As amended, claim 1 requires receiving entity data including function data, selecting at least one probability indicator as a function of that function data, determining a life probability using a life machine-learning model trained from life training data that correlates probability indicators to life probabilities, and then generating a growth approach through a further lookup-table workflow. In particular, claim 1 now requires querying a lookup table comprising an array of data that maps input values to output values, wherein the querying replaces a runtime computation with an array indexing operation, inputting an indicator class label and at least one probability deviation to the lookup table, looking up the growth approach associated with the indicator class label, and selecting, from the lookup table, the growth approach and at least one growth deviation that falls within a particular range corresponding to the at least one probability deviation. Those limitations do not recite conducting commerce, forming legal relationships, managing interpersonal conduct, or instructing a person how to behave. Instead, they recite a specific sequence of machine-executed training, lookup-table, array-indexing, and selection operations for generating a resilience-related output from structured inputs.” The examiner respectfully disagrees. The applicant appears to be arguing the mechanics of the claims without focusing on what those mechanics are recited for. The claim includes a growth approach which the claim itself defines as part of identifying a growth strategy. A growth strategy for the business entity is a plan for conducting the entity’s commercial affairs, how it allocates resources, pursues customers, and scales operations. The specification confirms that the growth approach is used to inform decisions about how an entity continues operating and grows which falls within business relation and managing business risk. The recited lookup-table steps are the means by which the business strategy determination is reached, a claim does not leave a functional grouping merely by reciting the generic computer mechanics used to arrive at the abstracted business output.
The applicant has argued “Amended claim 1 is not limited to a person merely observing information, evaluating it, and making a judgment. Rather, claim 1 recites a particular processor-implemented sequence in which the processor receives entity data including function data, selects at least one probability indicator as a function of the function data, determines a life probability using a life machine-learning model that is trained from life training data correlating a plurality of probability indicators to a plurality of life probabilities, and further determines that life probability such that the life probability comprises at least one probability deviation associated with the at least one probability indicator. Claim 1 then requires generating a growth approach through a further processor-executed lookup-table workflow, including querying a lookup table comprising an array of data that maps input values to output values, wherein the querying replaces a runtime computation with an array indexing operation, inputting an indicator class label and the probability deviation to the lookup table, looking up the growth approach associated with the indicator class label, and selecting, from the lookup table, the growth approach and at least one growth deviation that falls within a particular range corresponding to the probability deviation. That claimed sequence is not a mere mental evaluation, but a multi-step manipulation of interrelated training data, machine-learning model output, lookup-table input values, array-based mappings, and range-based selection logic.” The examiner respectfully disagrees. The MPEP is explicit that a mental process does not become non-abstract merely because a computer performs it. The claimed sequence evaluated step by step does not exceed what a human analyst using a paper could perform. Specifically reading reported characteristic of a business, picking out the relevant metric to examine, arriving at a judgement of survival likelihood by reference to historical patterns for similar business, and consulting a reference chart organized by category and range to identify a corresponding recommendations. Each of these is at most an evaluation or judgment expressly contemplated by the mental-processes grouping. Applicant’s reliance on Synopsys is misplaced. The claim in that case was held not to recite a performable mental process involved converting a hardware-description-language specification into a gate level netlist. This was a transformation over an effectively unbounded design space requiring iterative computation that has no manual analog. By contrast, applicant’s claims involve a lookup table as an array of data that maps input values and output values. A bounded table lookup organized by category and range is squarely the kind of operation a person can and historically performed manually, using no more than a printed table. That the claim implements this table as a computer-resident array accessed by an indexing operation describes the manner in which a general purpose computer performs a task humans already do, it does not convert the underlying evaluative and lookup activity into something a human could not conceivably perform.
The claim recites “training a life machine learning model as a function of the life training data” and “determining the life probability as a function of the life machine learning model” at a purely functional level, without reciting any algorithm, architecture, or technical training methodology. Under is broadest reasonable interpretation this functional recitation covers a human deriving a probability estimate from correlated historical examples. The claim itself specifies no particular technical means for performing the calculation.
The applicant has argued “Applicant asserts that representative claim 1, at least as amended, recites additional elements that integrate any alleged judicial exception into a practical application by improving the manner in which a computing system processes correlated entity data, machine-learning outputs, and lookup-table inputs to generate a structured resilience output. More particularly, claim 1 is not directed to merely evaluating whether an entity may continue operating or generally recommending a growth strategy at a high level. Instead, claim 1 recites a specific, staged computational architecture in which a processor receives entity data including function data, selects at least one probability indicator as a function of the function data, and determines a life probability of the entity using a life machine-learning model that is trained using life training data comprising a plurality of probability indicators correlated to a plurality of life probabilities. Claim 1 further requires determining the life probability as a function of the trained life machine-learning model, wherein the life probability comprises at least one probability deviation associated with the at least one probability indicator. Claim 1 then continues that technical pipeline by generating a growth approach through a particular lookup-table workflow that includes querying a lookup table comprising an array of data that maps input values to output values, wherein querying the lookup table replaces a runtime computation with an array indexing operation, inputting an indicator class label and the at least one probability deviation to the lookup table, looking up the growth approach associated with the indicator class label, and selecting, from the lookup table, the growth approach and at least one growth deviation that falls within a particular range corresponding to the at least one probability deviation, wherein the growth approach identifies a growth strategy. In this way, claim 1 recites a practical integration in which trained machine-learning operations and a particular array-based lookup-table architecture are used in a coordinated computational workflow to transform raw entity data into progressively refined outputs including a life probability, a probability deviation, a growth approach, and a growth deviation. These limitations improve the functionality of the computing system itself by requiring a specific sequence of machine-executed training, data correlation, array-indexed lookup, and range-based output selection, rather than merely invoking a computer as a tool to perform an abstract idea.” The examiner respectfully disagrees. The claims recite no technical means and merely recites only that “querying the lookup table replaces a runtime computation with an array indexing operation” a characterization of what the lookup table does relative to an unclaimed alternative, not any specific related to the structure that achieves a measurable technical result. Substituting a precomputed lookup for a live computation is known in computer science and using an unspecified “array of data that maps input values to output values” does not, describe any improvement over what a generic array or hash table already provides. The applicant has not identified and the claim does not recite any particular data structure, indexing algorithm, or memory arrangement that departs from a conventional array lookup, nor any technical benefit. Without such a recitation the claim, at most, uses a computer as a tool to perform the steps of the invention which does not integrate the exception into a practical application.
The applicant has argued “Claim 1 as amended recites significantly more than any alleged abstract idea because it does not merely state a desired result or invoke generic machine learning or generic data analysis at a high level. Instead, claim 1 requires a specific, non-generic arrangement of additional elements that carry out a defined sequence of machine-implemented operations. In particular, claim 1 requires receiving entity data including function data, selecting at least one probability indicator as a function of the function data, determining a life probability of the entity using a life machine-learning model that is trained using life training data comprising a plurality of probability indicators correlated to a plurality of life probabilities, and determining the life probability such that the life probability comprises at least one probability deviation associated with the at least one probability indicator. Claim 1 further requires generating a growth approach through a particular lookup-table workflow, including querying a lookup table comprising an array of data that maps input values to output values, wherein querying the lookup table replaces a runtime computation with an array indexing operation, inputting an indicator class label and the at least one probability deviation to the lookup table, looking up the growth approach associated with the indicator class label, and selecting, from the lookup table, the growth approach and at least one growth deviation that falls within a particular range corresponding to the at least one probability deviation. These are concrete processing steps that impose meaningful limits on the claim and define a particular technological implementation, not merely an instruction to apply an alleged abstract idea on a computer.” The examiner respectfully disagrees. The additional elements identified by the applicant specifically the lookup table replacing a runtime computation reflects nothing more than storing a result in advance so it can be retrieved by an index rather than recalculated. Reciting this substitution in generic terms, without specifying any particular indexing scheme, memory structure, or algorithm detail does not describe anything that would be considered significantly more than the abstract idea. Applying the substitution to a trained models output is a generic application of the concept to a computational pipeline that produces a bounded output whether or not that pipeline includes machine learning. The claim does not recite anything about how the lookup table interacts with the model beyond consuming its output as an input value. The class label and range based lookup structure involves organizing a lookup table by category and then by a sub-range within that category which is a standard way any table, chart, or database index is organized whenever an exact match key isn’t available. Naming that structure as “multiple arrays” per class label is a description or an ordinary two level indexing scheme, not a novel structure.
The claims sequence of train a model, apply it to obtain a classification and a numeric value, use those as lookup keys, retrieve a corresponding output is exactly what would be expected if asked to combine a model that classifies and scores something with a table that converts scores and recommendations. The applicant has not identified any interaction between the machine learning state and the lookup table stage that produces an effect beyond what each performs independently.
Applicant’s arguments, see pages 9-11, dated 6/16/2026, with respect to the previous 103 rejections have been fully considered and are persuasive in view of applicant’s amendments. The previous 103 rejections have been withdrawn. The combination of Gembicki and Selvadurai, while facially capable of mapping most limitations of claim 1 in combination with additional references, is not being pursued because it rests on hindsight reasoning. Gembicki and Selvadurai are reasonably combinable, as both are directed to the same field of computer-implemented entity scoring, but the combination does not specifically teach the lookup-table mechanism. The prior art does not specifically teach “wherein querying the lookup table replaces a runtime computation with an array indexing operation; inputting an indicator class label and the at least one probability deviation to the lookup table; looking up, using the lookup table, the growth approach associated with the indicator class label, wherein each indicator class label contains multiple arrays and each array is associated with a range of probability deviations; and selecting, from the lookup table, the growth approach and at least one growth deviation that falls within a particular range corresponding to the at least one probability deviation, wherein the growth approach identifies a growth strategy.” Much of the rejection of the claimed limitations would involve prior art that includes non-analogous reference and converts what should be a showing of independently motivated combinations into a single-reference reconstruction of the claim, assembled with the benefit of hindsight rather than from any articulated reason a person of ordinary skill would have had, at the time of the invention, to look to solve a business-resilience scoring problem. It is not based on one limitation but the combination of claim limitations that the previous 103 rejection is withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 7-15, 18-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-4, 7-11, are directed to an apparatus, claims 12-15, 18-20 are directed to a method. Therefore, claims 1-4, 7-15, 18-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 12, recite generating or updating a map of a dining environment layout, constituting an abstract idea based on “A Mental Process” and “Certain Methods of Organizing Human Activity” related to determining the ability of an entity to continue operating over a given period of time. Specifically the independent claims recite:
(a) mental process: as drafted, the claim recites the limitations of receiving data, selecting data, determining data, and generating data which includes querying, inputting, looking up, and selecting data which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting by a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “by at least a processor” language, the claim encompasses the user manually taking data and generating a growth approach. The mere nominal recitation of a generic computing devices does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
(c) certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for determining the ability of an entity to continue operating over a given period of time and generating a growth strategy. “Commercial interactions” or “legal interactions” include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.” Specifically estimating business survival and optimizing organizational growth. Thus, the claim recites an abstract idea.
The claimed limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “at least a processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “at least a processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “at least a processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 3-4, 7-9, 14-15, 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 10-11, 13, 20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 12, do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions… training a life machine learning model… querying a lookup table comprising an array of data that maps input values to output values… wherein querying the lookup table replaces a runtime computation with an array indexing operation.” Claim 12 is a method that recites limitations performed “by at least a processor…training a life machine learning model… querying a lookup table comprising an array of data that maps input values to output values… wherein querying the lookup table replaces a runtime computation with an array indexing operation.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, generate, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3-4, 7-9, 14-15, 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 2 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 10 introduces the additional element of “create a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmit the user interface data structure; and the apparatus further comprises a display communicatively connected to the at least a processor, the display configured to: receive the user interface data structure; and display the life probability and the growth approach as a function of the user interface data structure.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein the life probability further comprises at least one probability deviation, wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection of the at least one probability deviation.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 20 introduces the additional element of “creating, by the at least a processor, a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmitting, by the at least a processor, the user interface data structure to a display; displaying, using the display, the life probability, and the growth approach as a function of the user interface data structure.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 12, do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a system comprising “at least a processor; a memory communicatively connected to the at least a processor, the memory containing instructions… training a life machine learning model… querying a lookup table comprising an array of data that maps input values to output values… wherein querying the lookup table replaces a runtime computation with an array indexing operation.” Claim 12 is a method that recites limitations performed “by at least a processor…training a life machine learning model… querying a lookup table comprising an array of data that maps input values to output values… wherein querying the lookup table replaces a runtime computation with an array indexing operation.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 3-4, 7-9, 14-15, 18-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 2 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 10 introduces the additional element of “create a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmit the user interface data structure; and the apparatus further comprises a display communicatively connected to the at least a processor, the display configured to: receive the user interface data structure; and display the life probability and the growth approach as a function of the user interface data structure.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 11 introduces the additional element of “wherein the life probability further comprises at least one probability deviation, wherein the display is configured to display at least one growth deviation of the growth approach as a function of a selection of the at least one probability deviation.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 13 introduces the additional element of “training an indicator machine learning model as a function of the indicator training data; and selecting at least one probability indicator as a function of the indicator machine learning model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 20 introduces the additional element of “creating, by the at least a processor, a user interface data structure, wherein the user interface data structure comprises the life probability and the growth approach; and transmitting, by the at least a processor, the user interface data structure to a display; displaying, using the display, the life probability, and the growth approach as a function of the user interface data structure.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-4, 7-15, 18-20 are rejected under 35 USC 101.
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 1-4, 7-15, 18-20 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.
Claims 1 and 12 recite the limitation “each indicator class label.” There is insufficient antecedent basis for this limitation in the claim. The applicant previously claims “an indicator class label” this is a singular label, therefore there is no support in the claim for “each.”
Claim 2 recites the limitation “at least one probability indicator as a function of the entity data.” There is insufficient antecedent basis for this limitation in the claim.
The dependent claims inherit the rejections of the claims from which they depend.
Pertinent pieces of prior art include Gembicki (US 20160034838 A1) which discloses the resiliency of an organization by obtaining resiliency intelligence, determining confidence in the intelligence, generating resiliency scores, bundling scores into multiple level of granularity, and providing score bundles to users based on a membership level. Selvadurai et al. (US 11341517 B2) discloses methods to evaluate and compare entities, such as businesses. Cagan (US 20060271472 A1) which discloses automated valuation model valuations and the forecast standard deviations. Torkoly (US 20190265684 A1) which discloses integration of multilevel production processes in which the end product produced by a production entity. Prieto (US 20140156323 A1) which discloses a resilience management engine that can generate a resiliency metric representative of how resilient a program is with respect to a particular event.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270 5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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