Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant's arguments filed 2/24/26 have been fully considered but they are not persuasive.
Response to Arguments
Modeling is a mental process of modeling with assistance of pen and paper, and a machine learning model is an additional element (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)). The present claims read on a series of “what-if” scenarios where a human can use a mental process and/or modeling to answer questions.
It is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do").
The claims amount to data analysis/manipulation and using some form of AI as a tool. The transformation of data, or the mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic 'abstract idea,"' is not a transformation sufficient to integrate a judicial exception into a practical application. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994)).
Claiming AI and/or models on a high level can amount to using a black box without specifying any real details of how the AI operates or what’s in the black box. The claims need to specify the technical details of the AI.
"The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea." MPEP § 2106.04(a)(2).III. "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." Id. For the purposes of this abstract idea, "[t]he courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
Claims that receive, store, retrieve, and convey data, are classic examples of insignificant extra-solution activity. See, e.g., Bilski, 545 F .3d at 963. Insignificant Extra-Solution Activity MPEP § 2106.05(g):
Applicant argues the present claims are similar to Ex 39.
In response USC 101 example 39 specifies only additional elements and applying one or more transformations to each digital facial image including mirroring, rotating, smoothing or contrast reduction to create a modified set of digital facial images.
A computer-implemented method of training a neural network for facial detection comprising:
• collecting a set of digital facial images from a database;
• applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images;
• creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images;
• training the neural network in a first stage using the first training set;
• creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and
• training the neural network in a second stage using the second training set.
Therefore the present claims are not similar to example 39.
In response to applicant’s remarks pertaining to 112(f) and that “there is no requirement that any of the claim steps must be implemented by hardware”, if applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are e.g., model configured for, component configured to, component configured to store, component configured to model in claim 1. See below
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The claims and almost all the disclosure are directed to software/code and do not specify the hardware/interfaces that would be used other than a few examples.
Claim 1
Explainable model configured to (spec says INNs may function as a locally explainable model but fails to define the structure/hardware used);
Explanation component configured to (“The model explanation component 906 may include the answer 9061, model explanation 9062, justification 9063, and model fusion and links 9064 components”, spec. 0068 but fails to define the structure/hardware used);
model fusion and links component is configured to (“Figure 3 is an exemplary embodiment of an EIGS output from an XNN, CNN-XNN model fusion;”, 0018 but fails to define the structure/hardware used);
causal component, configured to (“Figure 8 illustrates an exemplary structure for an explanation scaffolding 9101. The explanation scaffolding 9101 may be structured into four exemplary components: the model explanation component 906, the hypothetical and causal component 1510,”, spec. 0067 but fails to define the structure/hardware used);
claim 2
abductive logic system for (“Abductive hypotheses may be implemented via the appropriate abductive logic system appropriate for the specific EIGS embodiment, for example, Pierce's abductive logic system, and so on.”, 0078 but fails to define the structure/hardware used);
claim 3
causal component, configured to (“Figure 8 illustrates an exemplary structure for an explanation scaffolding 9101. The explanation scaffolding 9101 may be structured into four exemplary components: the model explanation component 906, the hypothetical and causal component 1510,”, spec. 0067 but fails to define the structure/hardware used);
claim 4
interpretation component configured to (spec does not define it);
claims 5, 11
hypotheses and concepts component configured to (“the implementation in hypotheses and concepts component 1511 may use a causal DAG, and so on”, 0078 but fails to define the structure/hardware used);
quality component (“The controls and quality component 1512 may contain information”, 0083);
claim 6
interactions and moderators component is configured to
(“interactions and moderators component may further contain a Latent Variable Model (LVM) to enable the EIGS to relate observed data points”, 0090);
Claims 7, 8
the mediations component is configured to (“The mediations component 1514 may contain information about the statistical and causal mediations applicable in the model 904, the EIGS and its components. Statistical and causal mediation models are stored in mediations component 1514 and may use a suitable machine learning process for the identification and creation of such mediation models, which may be serial or parallel in structure.”, 0092);
claim 10
interventions component is configured to (“Causal mediation models in mediation component 1514 may utilize the appropriate causal logic system appropriate for the specific EIGS embodiment, with transmission and exchange of information with associations and assumptions component 1515, interventions component 1516 and counterfactuals component 1517.”, 0097).
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
2A Prong 1:
Return a model output, wherein the model output comprises an answer and a model explanation (amounts to a mental process in the same way that a human can produce an explanation or predict the weather with or without a computer);
produce an explanation using an explanation scaffolding (reads on model or decision tree created on paper or with a generic computer), the explanation scaffolding comprising a plurality of components and a plurality of defined linkages between the plurality of components (mental process of modeling with assistance of pen and paper);
model output indicating the answer and further comprising the model explanation (mental process of modeling with assistance of pen and paper);
model at least one cause-and-effect relationship(mental process of modeling with assistance of pen and paper);
a plurality of interpreters (reads on humans) comprising a plurality of interpretation components, the plurality of interpretation components forming an interpretation scaffolding (applicants disclosure defines it as human (“An exemplary interpreter may be a knowledgeable human”, 0032) Abstract idea of analyzing data/reasoning. Mental process. A human- mind with pen and paper can generate/determine data);
produce an interpretation (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A system for providing explanations and interpretations, the system comprising a processor (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a memory on which is stored computer program code instructions (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
an explainable model configured [to receive an input query] and [return a model output], wherein the model output comprises an answer and a model explanation; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
receive an input query and return a model output (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
an explanation component configured to [receive the model output] to [produce an explanation] using an explanation scaffolding, the explanation scaffolding comprising a plurality of components and a model fusion and linkage information defining a plurality of connections between the plurality of components (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
an explanation model component, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a hypothetical and causal component, the hypothetical and causal component configured to [model at least one cause-and-effect relationship] (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
configured to receive the answer, [the model explanation], and the explanation scaffolding to [produce an interpretation] (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system for providing explanations and interpretations, the system comprising a processor (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a memory on which is stored computer program code instructions (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
an explainable model configured [to receive an input query] and [return a model output], wherein the model output comprises an answer and a model explanation; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f));
receive an input query and return a model output (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
an explanation component configured to [receive the model output] to [produce an explanation] using an explanation scaffolding, the explanation scaffolding comprising a plurality of components and a model fusion and linkage information defining a plurality of connections between the plurality of components (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
an explanation model component, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
a hypothetical and causal component, the hypothetical and causal component configured to [model at least one cause-and-effect relationship] (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
configured to receive the answer, [the model explanation], and the explanation scaffolding to [produce an interpretation] (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Further, the receiving/transmitting steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/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). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
2. The system of claim 1, wherein the hypothetical and causal component further comprises an abductive logic system for diagnosing an observed effect to identify a cause of the observed effect and one or more recommendations, the recommendations comprising a course of action to remedy the observed effect (further expand mental process user can perform a mental process of modeling with assistance of pen and paper; A human- mind with pen and paper can generate/determine data).
3. The system of claim 1, wherein the hypothetical and causal component further comprises one or more hypothesis evaluation components and a plurality of concepts associated with groupings of one or more hypotheses, wherein each grouping of one or more hypotheses connects one or more concepts in the plurality of concepts by identifying one or more expected relationships between propositions for the one or more concepts, and wherein a conceptual framework is formed from the one or more expected relationships, (further expand mental process user can perform a mental process of modeling with assistance of pen and paper; A human- mind with pen and paper can generate/determine data) and wherein the hypothetical and causal component is further configured to cluster different types of explanations into concepts using a cognitive chunk model, implement a confirmatory factor analysis, and/or implement an explanatory factor analysis(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
4. The system of claim 1, wherein the interpretation scaffolding comprises: an explanation and interpretation scenario component; a framing, protocol, and contextual component; and an interpretation model component, and wherein receiving, on the explanation component, the model output using the explanation scaffolding comprises: receiving, on the explanation component, an explanation scaffolding data structure, the explanation scaffolding data structure comprising each of the plurality of components and defined linkages between the plurality of components; and deconstructing the explanation scaffolding data structure into an explanation (further expand mental process user can perform a mental process of modeling with assistance of pen and paper; A human- mind with pen and paper can generate/determine data).
5. The system of claim 1, wherein the hypothetical and causal component further comprises: a hypotheses and concepts component configured to store information corresponding to one or more hypotheses applicable to the explanation, wherein the one or more hypotheses include one or more of: a trial hypothesis comprising a suggested outcome based on evidence, wherein the hypotheses and concepts component is configured to test the evidence to confirm or reject the trial hypothesis; an abductive hypothesis comprising a suggested explanation regarding a goal to be achieved; a statistical hypothesis; or a causal hypothesis identifying whether one or more of a plurality of features recognized by the explainable model is an effect of a cause triggered by an interaction of one or more of the plurality of features; a controls and quality component configured to: generate an output within one or more predetermined parameters; identify and apply one or more predetermined compliance constraints with tolerance parameters; store and retrieve information indicating a state of qualitative or quantitative information of a plurality of variables and data within the system, and determine whether the variables and data within the system are internally consistent(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
apply one or more of standardization, cleansing, data transforms, data profiling, data matching, data linking, data conformity checks, data accuracy checks, data precision checks, data bias checks, and data interpolation methods to the variables and data within the system(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); apply one or more data privacy and access rules to the variables and data within the system (user can make decisions and apply rules or guidelines); trigger one or more actions or modify and configure one or more constraints and activating events and triggers in a behavioral model; validate, compare, and analyze the variables and data within the system in relation to a set of validated reference data to identify one or more new or discrepant values(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); and apply one or more of data transforms, timestamp checks, data freshness checks, and data retention policy compliance of the variables and data within the system against a defined service level agreement, an interactions and moderators component; a mediations component; an associations and assumptions component; an interventions component; and a counterfactuals component(further expand mental process user can perform a mental process of modeling with assistance of pen and paper; A human- mind with pen and paper can generate/determine data).
6. The system of claim 5, wherein the interactions and moderators component is configured to discover a plurality of moderators, the moderators comprising categorical or quantitative variable that affects a relationship between one or more interactions(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation), wherein the interactions and moderators component is configured to discover the plurality of moderators by at least one of a correlation analysis method and a variance analysis method; and wherein the interactions and moderators component further comprises a latent variable model(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
7. The system of claim 5, wherein the system includes the mediations component, and wherein the mediations component comprises statistical and causal mediations applicable to the explainable model (user can model data) and wherein the mediations component is configured to identify one or more mediator variables indicating a relationship between one or more independent variables and one or more dependent variables; and. wherein the mediations component is further configured to: identify moderators from the interaction and moderator component affecting the relationship between one or more independent variables and one or more dependent variables(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); create a new mediated moderation path associated with a new mediator value by applying a moderator effect via the new mediator value and a new indirect path from the independent variables to the dependent variables; and assign a label for the moderator effect and the independent variables and the dependent variables according to the identified moderators(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
8. The system of claim 5, wherein the associations and assumptions component is configured to determine one or more of: statistical associations between sets of data variables; conditional probabilities between data variables; inferences and associations obtained from data using conditional expectation methods; answers to conditional probability sentences of the form P(ylx) = p, where the probability of an event Y = y, given that X = x was observed, is equal to p; and a scenario analysis(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
9. The system of claim 5, wherein the interventions component is configured to identify a plurality of potential interventions, and is configured to use the potential interventions to determine (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data) one or more of: conditional probabilities that distinguish between causal relationships from correlative relationships stored in the associations and assumptions component; one or more of: causal adjustments; multiple interventions; back-door identification and estimation methods; front-door identification and estimation methods; conditional interventions; covariate-specific effect identification and estimation methods; inverse probability weighting and estimation methods; confounder identification; and suppressor variable identification; causal inference obtained from using causal interventions; answers to conditional probability sentences; and a scenario analysis(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
10. The system of claim 5, wherein the controls and quality component is further configured to integrate with a semiotics component and a domain knowledge component by performing one or more checks, comprising: checks against a predetermined range of values or static interrelationships; checks against aggregated processes and functions held in domain knowledge of the domain knowledge component; outlier checks and exception case flagging; drift checks against one or more nominal conditions that are prespecified or automatically discovered by a machine learning system; checks against one or more predefined business as usual expectations; checks using an explainable autoencoder/decoder system for drift, shift and abnormality detection, wherein the checks are performed using one or more of: simple generic aggregation rules, complex logic functions on a group of attributes of data input to the processes and functions held in the domain knowledge, and automatically discovered checks discovered via a machine learning process ran against the well-known processes and functions held in the domain knowledge(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
11. The system of claim 5, wherein the interactions and moderators component is configured to: identify statistical correlations and causal interactions in the explainable model, and store the statistical correlations and causal interactions as one or more of: transformations and mappings of subsets of data features; predictions from information embedded in a reconstructed state space, a latent space, and/or a phase space; co-occurrence statistics indicative of cause-and-effect; and estimator functions and estimands together with a set of corresponding resulting estimates(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation), wherein the system is configured to determine an estimate from an estimand using the estimator functions, and wherein the estimator functions comprise one or more of a point estimator or an interval estimator(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
12. The system of claim 5, wherein the counterfactuals component is integrated with a continuous or discrete dynamic systems model, phase space model, recurrent feedback control system model and is configured to identify one or more: one or more of: hypothetical adjustments; hypothetical interventions; deterministic and non-deterministic counterfactual determination methods; abduction estimation methods; action estimation methods; prediction estimation methods; consequence estimation methods; attribution of causation estimation methods; and direct and indirect effect estimation methods; causal inference obtained from using causal counterfactuals; answers to conditional probability sentences of the form P(yxl x', y') = p, provided that the probability of an event Y = y, had X been x, given that X was observed to be x' and Y to be y', is equal to p; or answers to a scenario analysis(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
13. The system of claim 4, wherein the framing, protocol, and contextual component comprises an interpretation framing component, an interpretation rules and procedures component, a protocol context component, an interpretation brief component, an interpretation templates component, an interpreter domain knowledge component, an interpreter beliefs component in the plurality of interpreter beliefs components, and an interactive context component, wherein the interpretation framing component is configured to identify a framing of the interpretation using one or more models, representations, and/or simplifications to be applied by the interpretation component, the interpretation rules and procedures component is configured to apply one or more interpretation rules and procedures; the protocol context component comprises a protocol to be used when processing the explanation scaffolding; and the interactive context component comprises one or more interactive and iterative processes to be tracked by the interpretation component(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
14. The system of claim 13, wherein the interpretation model component comprises a scenario model, interpretation model, selection model, and conflict resolver component; wherein the scenario model comprises information specific to a scenario observed by the interpretation component; wherein the selection model identifies a selection process and method for ranking or scoring the interpretations resulting from the interpretation component; and wherein the conflict resolver component is configured to identify one or more conflicts relating to the interpretation component and action triggers configured to be activated when one or more of the conflicts cannot be resolved(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
15. The system of claim 1, further comprising a semiotics, taxonomical, and ontological component comprising a metrics and dimensions component, a taxonomies and ontologies component, a semiotics component, and a domain knowledge component, wherein: the metrics and dimensions component comprises information regarding different systems of measurement and one or more of: underlying units and dimensions of measurement comprising one or more of: a distance function; a differentiable manifold function; a translation, scale and rotational invariant metric function; a vector space metric; a multiset function; and a topological function; a relationship between the underlying units and dimensions; a relationship with a base standard topological space comprising at least one map, atlas, or transition map; a conversion relationship to a base standard metric system; and a translation process from a machine-readable format to a human readable format or from a human readable format to a machine-readable format(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
16. The system of claim 1, further comprising a scenarios, interactions, and presentation component comprising: a presentation data component comprising data used to present the explanation, a layouts and templates component comprising layout and format information, a presentation state component comprising information identifying a state and history of a presentation layer; a user model, wherein the user model (see above) provides a partial or full model of a plurality of users of the system and identifies whether users are human or automated, (mental process of modeling with assistance of pen and paper) and is configured to associate a user profile to one or more users of the system, and wherein the scenarios, interactions, and presentation component is configured to constantly update the user model based on new information regarding the users; an evaluation component configured to evaluate (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data)and log one or more of: a quality, accuracy, precision, complexity, usefulness, satisfaction, fairness, bias, authority, precedence, and effectiveness of the explanation; a goals component comprising one or more system user goals, a plans and questions component configured to represent and execute one or more plans oriented by the system user goals, and an actions component comprising an action selection policy and a set of allowed actions, wherein the actions component is configured to identify and select a next action for the explainable model to perform and/or output; and a world and environment model comprising a plurality of models of an interaction environment with which the explainable model is configured to interact and a plurality of models of an external environment beyond the explainable model, wherein one or more of the models of the interaction environment and models of the external environment comprise a behavioral model, a behavioral model hierarchy, an action trigger, and a feedback loop(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
17. The system of claim 1, wherein a filter is configured to selectively apply a specified amount of noise to the input query and/or the model output according to a predetermined noise factor(mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
18. The system of claim 1, wherein the explainable model utilizes secure multi-party computation; and wherein the system further comprises an access control component, wherein the explanation scaffolding is stored within the memory (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))as the plurality of components, and wherein the access control component is configured to authenticate a plurality of parties and selectively control access to the plurality of components by the plurality of parties, comprising enabling access to at least one component in the plurality of components by a first party in the plurality of parties and enabling access to at least one other component in the plurality of components but not to the at least one component in the plurality of components by a second party in the plurality of parties(encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data).
19. The system of claim 1, wherein the explainable model comprises a plurality of combined decentralized explainable models, wherein each of the plurality of explainable models comprises local samples unique to each explainable model(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
20. The system of claim 1, wherein at least one of the plurality of interpreters (humans) is configured to receive (data gathering) the explanation scaffolding by a process comprising filtering, with a filter component, deconstructed explanation scaffolding data, selectively removing data from the deconstructed explanation scaffolding data based on a user profile, and outputting filtered explanation scaffolding data to at least one of the interpretation components; and wherein the filter component comprises an additional explainable model, or wherein the filter component is configured to apply a learnt function for performing a domain-specific optimization of the explainable model(mental process of modeling with assistance of pen and paper).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Isahagian (US 2021/0142190) teaches using interpretation components (“An interpretation component (104) generates multiple explanations of a machine learning model prediction based on causal relationships determined between feature data of a set of feature data”, abstract).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123
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