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 .
This action is in response to communications filed on 05/11/2023. Claims 1-20 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement filed 05/11/2023 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed (a copy of WO2017212225 was not provided). It has been placed in the application file, but the information referred to therein has not been considered.
Information disclosure statements (IDS) submitted were filed on 08/24/2023, 11/03/2023, 01/19/2024, 05/24/2024, 09/19/2024, and 11/22/2024. Each submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, each information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
The use of a trade name or a mark used in commerce (e.g. JAVA, PYTHON, PERL, JAVSCRIPT, ACTIONSCRIPT, etc.) has been noted in this application. It should be capitalized (each letter) wherever it appears and be accompanied by the generic terminology or, where appropriate, include a proper symbol indicating use in commerce, such as ™, SM, or ® following the word. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
As per claim 1, the term “for” in line 5 raises question as to whether the features following are limiting or merely refer to intended use.
Appropriate correction is required.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) is/are: “a data repository storing…” in claim 1.
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 1-7, 13-14, and 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “optimal” in claims 6-7, 13-14, and 19-20 is a relative term which renders the claim indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. What is considered “optimal” varies depending on person, context, etc. As such, the claims are indefinite.
Claim limitation “a data repository storing…” in claim 1 invokes 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. For example, the specification actually describes “storing an entity data repository 120 and timing-prediction model code 130 to be processed by the development computing system” (e.g. in paragraph 67), which suggests that “data repository” refers to computer code, i.e. not structure. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Dependent claims 2-7 incorporate the features of claim 1, and thus are also indefinite.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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. The claims recite a system, method, and medium associated with storing, accessing, generating, computing and causing.
The limitations “accessing… generating… computing… causing…” as recited in claim 1 are each a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “data repository” and “one or more processors”, the limitation “accessing the predictor data samples” in the context of the claim encompasses the user making observations. The limitation “generating wavelet predictor variable data by, at least, applying a wavelet transform to the time-series values of the predictor variables in the predictor data samples, the wavelet predictor variable data comprising a first set of shift value input data for a first scale and a second set of shift value input data for a second scale” in the context of the claim encompasses the user making calculations. The limitation “computing a set of probabilities for a target event by applying a set of timing- prediction models associated with respective time windows to the first set of shift value input data and the second set of shift value input data, wherein each timing-prediction model of the set of timing-prediction models is configured to generate a respective probability of the set of probabilities indicating a probability of the target event occurring in a time window associated with the timing-prediction model” in the context of the claim encompasses the user making calculations (note: “timing-prediction model” broadly includes an equation or formula). The limitation “computing an event prediction from the set of probabilities” in the context of the claim encompasses the user making calculations. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements. The claim recites “a data repository” and “one or more processors configured for performing operations”. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Moreover, the limitation “storing predictor data samples including time-series values of predictor variables that respectively correspond to actions performed by an entity or observations of the entity” is considered as insignificant extra-solution activity (see MPEP 2106.05(g)). The limitation “causing a host system operation to be modified based on the computed event prediction” amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are no more than a generic computer component and/or field of use. With respect to “storing predictor data samples including time-series values of predictor variables that respectively correspond to actions performed by an entity or observations of the entity” considered as insignificant extra-solution activity, MPEP 2106.05(d)(II) indicates that mere storing of information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here; note e.g. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Therefore, the claims are not patent eligible.
Claims 8 and 15 also recite similar claim language as claim 1, and thus have the same issues. It is noted, with respect to claim 8, that the claim recites “a computing device” to perform the method. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). It is noted, with respect to claim 15, that the claim further recites a “non-transitory computer-readable medium, comprising computer-executable program instructions that, when executed by a processor, cause the processor to perform operations” to perform the method. The elements are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
Regarding claim 2, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes determining, setting, generating, and calculating, which are mental steps (amounting to a user making observations, evaluations, and calculations) and does not include any additional elements. This similarly applies to claims 9 and 16.
Regarding claim 3, the claim does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes generating explanatory data, which is a mental step (amounting to a user making evaluations) and does not include any additional elements. This similarly applies to claims 10 and 17.
Regarding claim 4, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes determining wavelet values, computing points lost and generating explanatory data which are mental steps (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claim 11.
Regarding claim 5, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the wavelet transform, the wavelet predictor variable data, and the wavelet values, which is part of the mental steps (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claims 12 and 18.
Regarding claim 6, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further describes determining values, evaluating a calculation, determining an allocation of change, and selecting one or more allocations, which are mental steps (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claims 13 and 19.
Regarding claim 7, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further includes determining values, calculating a contribution, associating the contribution, and selecting one or more behaviors, which are mental steps (amounting to a user making evaluations and calculations) and does not include any additional elements. This similarly applies to claims 14 and 20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Litt et al. (US 6658287 B1) in view of Echauz et al. (US 20020103512 A1).
As per independent claim 1, Litt teaches a computing system comprising:
a data repository storing predictor data samples including time-series values of predictor variables that respectively correspond to actions performed by an entity or observations of the entity (e.g. in column 5 lines 14-48, column 7 lines 56, column 11 lines 45-48, column 25 lines 44-64, and column 28 lines 15-23, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data…for each individual patient… memory… features are extracted… "feature library" is a collection of features which are extracted by algorithms from raw brain activity data… feature behavior… a processor readable memory medium”); and one or more processors configured for performing operations (e.g. in column 7 lines 21-47 and column 28 lines 15-23, “software programs(s) executed by a processor”) comprising:
accessing the predictor data samples in the data repository (e.g. in column 5 lines 14-48, column 11 lines 45-48, and column 28 lines 15-23, “large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data…for each individual patient… features are extracted… "feature library" is a collection of features which are extracted by algorithms from raw brain activity data”);
generating wavelet predictor variable data by, at least, applying a wavelet transform to the time-series values of the predictor variables in the predictor data samples, the wavelet predictor variable data comprising a first set of value input data for a first scale and a second set of value input data for a second scale (e.g. in column 12 lines 40-44 and column 25 lines 44-59, “a window length of 30 seconds [i.e. first scale]… feature vector for a particular patient is generated that contains windowed (i.e. calculated over a particular time window, such as 1.25 seconds [i.e. second scale]) features such as…a single scale of the wavelet transform”);
computing a set of probabilities for a target event by applying a set of timing-prediction models associated with respective time windows to the first set of value input data and the second set of value input data (e.g. in column 17 lines 38-49 and column 18 lines 25-35, “the WNN module is effectively 4 separate WNNs, each trained on a corresponding prediction horizon. The number of prediction horizons and their corresponding time interval may vary” and figure 7), wherein each timing-prediction model of the set of timing-prediction models is configured to generate a respective probability of the set of probabilities indicating a probability of the target event occurring in a time window associated with the timing-prediction model (e.g. in column 18 lines 25-35 and column 21 lines 32-43, “number of prediction horizons and their corresponding time interval… providing as output a time-based probability measure, a patient or physician may set thresholds for the probability of a seizure over a prediction horizon”);
computing an event prediction from the set of probabilities (e.g. in column 10 lines 4-21, “provide…several continuous outputs representing probabilities for multiple time horizons”); and
causing a host system operation to be modified based on the computed event prediction (e.g. in column 10 lines 4-21, “system is programmable to respond to the output of the intelligent prediction subsystem to take one or more actions… activate the audible alert device 156, the visible alert device 157, the vibration alert device 158 and/or display a suitable warning… warning can also be issued to others external to the individual patient”),
but does not specifically teach wherein a first/second set of value input data includes shift value input data.
However, Echauz teaches a first/second set of value input data including shift value input data (e.g. in paragraphs 80, 203, 209, 242-244, and 251, “in the wavelet analysis different window lengths are used (i.e, different scales)… the wavelet transform breaks the data signal into shifted and scaled versions of the mother wavelet used… wavelet transform is run over the data for four or more different scales… allow the creation of feature vectors from features extracted with different sliding window sizes and sometimes also with different window shiftings”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Litt to include the teachings of Echauz because one of ordinary skill in the art would have recognized the benefit of incorporating relevant wavelet properties.
Claim 8 is the method claim corresponding to system claim 1, and is rejected under the same reasons set forth.
Claim 15 is the medium claim corresponding to system claim 1, and is rejected under the same reasons set forth and the combination further teaches non-transitory computer-readable medium, comprising computer-executable program instructions that, when executed by a processor, cause the processor to perform operations (e.g. Litt, in column 28 lines 15-23, “a processor readable memory medium storing instructions, which when executed by a processor, perform”).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Litt et al. (US 6658287 B1) in view of Echauz et al. (US 20020103512 A1) and further in view of Leppanen et al. (US 20140357291 A1).
As per claim 2, the rejection of claim 1 is incorporated, but the combination does not specifically teach determining that the time series values of the predictor data samples are missing a time series value for at least one time instance of a time series; setting the time series value for the at least one time instance to zero; generating a missing value indicator for the time series, the missing value indicator having a value of zero for the at least one time instance and a value of one for other time instances of the time series; and based on the missing value indicator and the wavelet predictor variable data, calculating confidence values that correspond to wavelet coefficients for the time series data, wherein the wavelet variable predictor data further comprise the confidence values. However, the combination teaches datum including time series associated with wavelet predictor variable data/coefficients (e.g. Litt, in column 5 lines 14-48, column 18 lines 25-35, and column 25 lines 44-64, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient… feature vector for a particular patient is generated… wavelet transform”, i.e. wavelet coefficients; Echauz, in paragraph 209, “wavelet transform is run over the data for four or more different scales”) and Leppanen teaches determining that datum values of data samples are missing a datum value for at least one datum instance of a datum (e.g. in paragraph 58, “If any measurements are missing”); setting the value for the at least one instance to zero (e.g. in paragraph 58, “If any measurements are missing, the corresponding weights in matrix W may be set to zero”); generating a missing value indicator for the datum, the missing value indicator having a value of zero for the at least one datum instance and a value of one for other datum instances of the datum (e.g. in paragraph 58, “the weights can be all set to 1. If any measurements are missing, the corresponding weights in matrix W may be set to zero”); and based on the missing value indicator and variable data, calculating confidence values that correspond to coefficients for the datum data, wherein the variable data further comprise the confidence values (e.g. in paragraph 58, “Scaling… a matrix W of weights is generated, such as by the processor 104, the elements of which define how each of the distances in matrix D is weighted in the calculations… the weights can be all set to 1. If any measurements are missing, the corresponding weights [i.e. coefficients] in matrix W may be set to zero. In addition, if there is not confidence in some of the distance estimates, this can be reflected in the weight matrix W. For example, if the signal strengths of device A, as recorded by device B, differ significantly from the signal strengths of device B, as recorded by device A, a lower weight may be used in conjunction with the distance estimate”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Leppanen because one of ordinary skill in the art would have recognized the benefit of accounting for relevant aspects of datum.
Claim 9 is the method claim corresponding to system claim 2 and is rejected under the same reasons set forth.
Claim 16 is the medium claim corresponding to system claim 2 and is rejected under the same reasons set forth.
Claims 3-6, 10-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Litt et al. (US 6658287 B1) in view of Echauz et al. (US 20020103512 A1) and further in view of Merrill et al. (US 20200265336 A1).
As per claim 3, the rejection of claim 1 is incorporated, but the combination does not specifically teach generate explanatory data for the event prediction. However, Merrill teaches generate explanatory data for an event prediction (e.g. in paragraphs 22, 29-30, 37-38, 65, and 125, “contribution values for a feature…are computed using a specific method as described in Merrill, et al., “Generalized Integrated Gradients: A practical method for explaining diverse ensemble… produce a…prediction… models to be used in applications that require transparency and explanations, such as in financial services, where regulation and prudence require model-based decisions be explained to consumers, risk managers, and regulators… output explanation module 124 functions to generate information based on the influence of features”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Merrill because one of ordinary skill in the art would have recognized the benefit of facilitating transparency and/or meeting regulations.
As per claim 4, the rejection of claim 3 is incorporated and the combination further teaches generate the explanatory data by: determining a set of wavelet values of the wavelet predictor variable data that, when the set of timing-prediction models is applied to the wavelet values, result in a maximum value for the event prediction (e.g. Merrill, in paragraphs 19, 22, 35, 37, and claim 16, “reference data set is a set of healthy patients, a set of defendants found innocent, a set of signals from sensors indicating a safe lane change, a set of admitted students [i.e. a maximum value]… Adverse Action information is comprised of input variables and their contribution to the difference in score… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”); computing, for a wavelet of the wavelet predictor variable data, a points lost value as a difference between the maximum value and a value of the event prediction generated by replacing the wavelet in the set of wavelet values with a current value of the wavelet (e.g. Merrill, in paragraphs 19, 22, 35, 37, and claim 16, “reference data set is a set of healthy patients, a set of defendants found innocent, a set of signals from sensors indicating a safe lane change, a set of admitted students… Adverse Action information is comprised of input variables and their contribution to the difference in score… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”, i.e. points lost); and generating explanatory data for the prediction based, at least in part, upon the points lost value for the wavelet prediction (e.g. Merrill, in paragraph 37 and claim 16, “generated information includes Adverse Action information. In some variations, the Adverse Action information is comprised of input variables and their contribution to the difference… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”).
As per claim 5, the rejection of claim 4 is incorporated and the combination further teaches wherein the wavelet transform comprises a set of wavelets, wherein the wavelet predictor variable data is generated by, at least, applying the set of wavelets of the wavelet transform to the predictor data samples, and wherein the wavelet values of the wavelet predictor variable data correspond to the set of wavelets (e.g. Litt, in column 5 lines 14-48, column 18 lines 25-35, and column 25 lines 44-64, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient… feature vector for a particular patient is generated… wavelet transform”; Echauz, in paragraph 209, “wavelet transform is run over the data for four or more different scales”).
As per claim 6, the rejection of claim 3 is incorporated and the combination further teaches generate the explanatory data by: determining an optimal set of time-series values associated with an optimum event prediction (e.g. Litt, in column 5 lines 14-48, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient”; Merrill, in paragraphs 19, 22, 35, 37, and claim 16, “reference data set is a set of healthy patients, a set of defendants found innocent, a set of signals from sensors indicating a safe lane change, a set of admitted students… Adverse Action information is comprised of input variables and their contribution to the difference in score… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”); evaluating an integrated gradients calculation along a path in attribute space from the optimal set of time-series values to the set of time series values (e.g. Litt, in column 5 lines 14-48, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient”; Merrill, in paragraph 35, “performing an integrated gradients process to compute the feature contributions on segments of a path or plurality of paths between each element of an evaluation input data set and each element of a reference input data set”); determining an allocation of change between the event prediction and the optimum event prediction for each of the set of time-series values by summing integrated gradients for each of the set of time-series values (e.g. Litt, in column 5 lines 14-48, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient”; Merrill, in paragraphs 37 and 125 and claim 16, “Adverse Action information is comprised of input variables and their contribution to the difference in score… decompositions for each segment are summed (sum of segment decompositions) together to produce a sum of the segment decompositions to determine the contribution… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”); and selecting one or more of the determined allocations as an explanation for the event prediction (e.g. Merrill, in paragraph 37, “generated information includes Adverse Action information. In some variations, the Adverse Action information is comprised of input variables and their contribution to the difference”).
Claims 10-13 are the method claims corresponding to system claims 3-6, and are rejected under the same reasons set forth.
Claims 17-19 are the medium claims corresponding to system claims 4-6 and are rejected under the same reasons set forth.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Litt et al. (US 6658287 B1) in view of Echauz et al. (US 20020103512 A1) and Merrill et al. (US 20200265336 A1) and further in view of Chintalapati et al. (US 20200104775 A1).
As per claim 7, the rejection of claim 3 is incorporated and the combination further teaches wherein the one or more processors are configured to generate explanatory data (e.g. Merrill, in paragraphs 22, 29-30, 37-38, 65, and 125, “contribution values for a feature…are computed using a specific method as described in Merrill, et al., “Generalized Integrated Gradients: A practical method for explaining diverse ensemble… produce a…prediction… models to be used in applications that require transparency and explanations, such as in financial services, where regulation and prudence require model-based decisions be explained to consumers, risk managers, and regulators… output explanation module 124 functions to generate information based on the influence of features”) by: training the set of timing-prediction models using a data set of entity behaviors (e.g. Litt, in column 2 lines 1-11, column 5 lines 14-48, column 17 lines 38-49, column 18 lines 25-35, and column 25 lines 44-64, “analyzing the feature vector with a trainable algorithm implemented by, for example, a wavelet neural network… observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient… the WNN module is effectively 4 separate WNNs, each trained on a corresponding prediction horizon. The number of prediction horizons and their corresponding time interval may vary… based on feature behavior” and figure 7); determining an optimal set of time-series values associated with an optimum event prediction prediction (e.g. Litt, in column 5 lines 14-48, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient”; Merrill, in paragraphs 19, 22, 35, 37, and claim 16, “reference data set is a set of healthy patients, a set of defendants found innocent, a set of signals from sensors indicating a safe lane change, a set of admitted students… Adverse Action information is comprised of input variables and their contribution to the difference in score… wherein the model explanation information identifies model disparity between the evaluation input data set and the reference input data set”); calculating, for each of the values of the set of time-series values, a Shapley value contribution (e.g. Litt, in column 5 lines 14-48, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data… for each individual patient”; Merrill, in paragraphs 21 and 24, “the model explanation system is based on measure-theoretic methods that extend Aumann-Shapley as described herein… produces a specific quantification of the importance of each input variable to a model-based decision, such as, e.g., a decision to deny a credit application. This quantification can be used to power explanations that enable model users to understand why a model made a given decision and what to do to change the model-based decision outcome… the componentwise integral includes contribution values {c.sub.1, c.sub.2, c.sub.3} which correspond to the feature contribution of features {x.sub.1, x.sub.2, x.sub.3}, respectively”); associating the Shapley value contributions with entity behaviors by combining, for each entity behavior, individual contributions of attributes upon which the entity behavior is dependent prediction (e.g. Litt, in column 5 lines 14-48 and column 25 lines 44-64, “observation window during which time processing of the brain activity signal is continuous… pre-ictal time frame for seizure prediction… large set of independent, instantaneous and historical features are extracted from the intracranial EEG, real-time brain activity data and/or other physiologic data…for each individual patient… based on feature behavior”; Merrill, in paragraphs 21 and 24, “the model explanation system is based on measure-theoretic methods that extend Aumann-Shapley as described herein… produces a specific quantification of the importance of each input variable to a model-based decision, such as, e.g., a decision to deny a credit application. This quantification can be used to power explanations that enable model users to understand why a model made a given decision and what to do to change the model-based decision outcome… the componentwise integral includes contribution values {c.sub.1, c.sub.2, c.sub.3} which correspond to the feature contribution of features {x.sub.1, x.sub.2, x.sub.3}, respectively”),
but does not specifically teach selecting one or more entity behaviors having a greatest Shapley value contribution as an explanation of the event prediction.
However, Chintalapati teaches selecting one or more entity attributes having a greatest Shapley value contribution as an explanation of an event prediction (e.g. in paragraphs 4, 27, and 122, “an explanation of the metrics with the greatest entropy change for the subset of the plurality of metric indicators is generated… using computational techniques including but not limited to…Shapley Additive Explanation”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Chintalapati because one of ordinary skill in the art would have recognized the benefit of providing explanations for attributes that have the most impact.
Claim 14 is the method claim corresponding to system claim 7 and is rejected under the same reasons set forth.
Claim 20 is the medium claim corresponding to system claim 7 and is rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Khorrami et al. (US 20190340392 A1) teaches “multiple temporal lengths provides a multi-resolution approach that facilitates learning of temporal patterns that are apparent over different time scales… the time series signals in the specific application” (e.g. in paragraph 73).
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/W.W/Examiner, Art Unit 2144 03/07/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144