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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/02/2026 has been entered.
Response to Arguments
Applicant's arguments, see pages 7-8, filed 04/02/2026, with respect to the rejection of claims 1-2, 4-14, and 16-20 under 35 U.S.C. § 112(a) have been fully considered but they are not persuasive.
Applicant argues that the limitation “and at least one of: issue a notification and execute a corrective action in response to the determination the source of the first data distribution is under attack or the source of the second data distribution is under attack” is adequately supported by paragraphs [0056] and [0069] of the originally filed disclosure.
The Examiner respectfully disagrees.
For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. The critical inquiry is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.
As in MPEP 2161.01 (I), "The description requirement of the patent statute requires a description of an invention, not an indication of a result that one might achieve if one made that invention." It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Applicant is reminded that support should be found in the originally filed specification and not the publication. Here, paragraph [0056] of corresponding U.S. Publication No. US 2023/0136071 (“Applicant’s specification”) recites, “The attack detection module 900 detects the attack 908 at t99 in response to the inversion of the causality and may at least one of issue a notification and execute a corrective action”; paragraph [0069] recites a table, and paragraph [0070] recites “Note that the embodiments described herein may be implemented using any number of different hardware configurations. For example, FIG. 13 is a block diagram of a causality platform 1300 that may be, for example, associated with the system 100 of FIG. 1, and/or any other system described herein. The causality platform 1300 comprises a processor 1310, such as one or more commercially available Central Processing Units (“CPUs”) in the form of one-chip microprocessors, coupled to a communication device 1320 configured to communicate via a communication network (not shown in FIG. 13). The communication device 1320 may be used to communicate, for example, with one or more remote monitoring nodes, user platforms, digital twins, etc. The causality platform 1300 further includes an input device 1340 (e.g., a computer mouse and/or keyboard to input causality parameters and/or modeling information) and/an output device 1350 (e.g., a computer monitor to render a display, provide alerts, transmit recommendations, and/or create reports). According to some embodiments, a mobile device, monitoring physical system, and/or PC may be used to exchange information with the causality platform 1300”.
U.S. Publication No. US 2023/0136071 Figure 13
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Disclosure of repeated claim language, combined with a generic “block diagram of a causality platform” does not meet the requirements of 35 U.S.C. § 112(a) written description. When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). The claim requires “at least one of: issue a notification and execute a corrective action in response to the determination the source of the first data distribution is under attack or the source of the second data distribution is under attack” and the originally filed disclosure only provides a single, high-level sentence, without any further elaboration of how the notification or corrective action are implemented in the claimed system and method, nor any explanation as to the interrelationship between detection, notification, and corrective action. Mere recitation of a feature is insufficient under MPEP § 2163 and the requirements of 35 U.S.C. § 112(a) written description, which requires the originally filed disclosure to contain enough details to demonstrate that the inventors had possession of the claimed invention at the effective filing date.
Original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2161.01, 2163.02, and 2181, subsection IV.
Applicant's arguments, see pages 8-11, filed 04/02/2026, with respect to the rejection of claims 1-2, 4-14 and 16-20 under 35 U.S.C. § 103 have been fully considered but they are not persuasive.
Applicant first argues that the previous combination of references does not teach a smallest grammar generated via compression of a sequence.
The Examiner respectfully disagrees.
Applicant’s own specification discloses at paragraph [0031] that the Minimum Description Length (MDL) Compression algorithm can achieve generating a “smallest grammar” and that “the term ‘grammars’ refers to a set of rules and relationships that are associated with particular data sequences”. At least the previously presented Goldfarb reference teaches using MDL for compression at pages 1-3.
Applicant then argues that the previously presented combination of references do not teach a repetition of the full claim amendments, and alleges that the claims are allowable.
The Examiner respectfully disagrees.
Since applicant does not give any further explanation as to how the previously cited art differentiates from the claimed invention other than repeating the amendments made to the claim, the examiner defers to the rejection below as a response to this argument.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2,4-8,11-14,16-17 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claims 1 and 14:
Independent claims 1 and 14 recite “issue a notification and execute a corrective action”, the specification is devoid of adequate description of such “issue a notification and execute a corrective action”. The limitations in question do not satisfy the written description requirement under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph. The specification does not describe the limitation in sufficient detail so that one of ordinary skill in the art would recognize that the applicant had possession of the claimed invention. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Dependent claims fall together accordingly.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-8, 11-14, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over "Causal Inference via Conditional Kolmogorov Complexity using MDL Binning" by Goldfarb et al. (hereinafter Goldfarb) in view of “Data-Independent Space Partitionings for Summaries” by Graham et al. (hereinafter Graham), and in further view of Bell; David Gordon, US Patent Application Publication No. 10,110,976 B2 hereinafter Bell.
Regarding Claims 1 and 14:
Claim 1. Goldfarb discloses a system comprising :a memory storing processor-executable steps; and a processor to execute the processor-executable steps to cause the system to: receive a first data distribution for a first variable (Goldfarb pg. 2, Col. 1, lines 16-17, e.g., distribution X); determine … a first data optimum number of bins (Goldfarb pg. 2, Col. 1, lines 13-15, e.g., optimal bins) for the first data distribution; create a first model for the first data distribution using the first data optimum number of bins (Goldfarb pg. 1, Col. 1, lines 30-33, e.g., treat the binning techniques as the model); … generate a first smallest grammar model via compression of the first sequence (Goldfarb, pg. 1, Col 2, lines 21-22, e.g., Minimum Description Length); receive a second data distribution for a second variable (Goldfarb pg. 4, Col. 1, lines 11-13, e.g., variable Y); determine a second data optimum number of bins (Goldfarb pg. 2, Col. 1, lines 13-15, e.g., optimal bins) for the second data distribution; create a second model for the second data distribution using the second data optimum number of bins (Goldfarb pg. 1, Col. 1, lines 30-33, e.g., treat the binning techniques as the model); … generate a second smallest grammar model via compression of the second sequence (Goldfarb, pg. 1, Col 2, lines 21-22, e.g., Minimum Description Length); apply the first model to the second data distribution to calculate a smallest descriptive size (Goldfarb pg. 1, Col. 2, lines 21-22, e.g., Minimum Description Length; pg. 4, Section IV, e.g., use the optimal bin number for Y to compute K(X) and then iteratively balance out code lengths of bins to better fit the distribution of X) of the second data distribution given the first model, wherein application of the first model further comprises application of the first smallest grammar to the second model non-numeric characters to calculate the smallest descriptive size of the second data distribution given the first smallest grammar (Goldfarb, pg. 4, Section IV, e.g., use the optimal bin number for Y to compute K(X) and then iteratively balance out code lengths of bins to better fit the distribution of X); apply the second model to the first data distribution to calculate a smallest descriptive size of the first data distribution given the second model (Goldfarb pg. 1, Col. 2, lines 21-22, e.g., Minimum Description Length; pg. 4, Section IV, e.g., use the optimal bin number for Y to compute K(X) and then iteratively balance out code lengths of bins to better fit the distribution of X); and determine a causal direction between the first variable and the second variable based on the application of the first model and the second model (Goldfarb pg. 1, Col. 2, lines 15-17, e.g., predicting causal direction).
Goldfarb does not explicitly disclose via a dyadic formulation of a binning algorithm, … map each bin of the first data optimum number of bins to a non-numeric character; encode the first data distribution into a first sequence by mapping the first data distribution to the non-numeric characters for the first data optimum number of bins; … map each bin of the second data optimum number of bins to a non-numeric character; encode the second data distribution into a second sequence by mapping the second data distribution to the non-numeric characters for the second data optimum number of bins; … determine a source of the first data distribution is under attack or a source of the second data distribution is under attack based on the determined causal direction; and at least one of: issue a notification and execute a corrective action in response to the determination the source of the first data distribution is under attack or the source of the second data distribution is under attack.
However, determining the optimal number of bins by a dyadic binning algorithm is a well notoriously known concept in the art, as evidenced by Graham.
Graham discloses determine, via a dyadic formulation of a binning algorithm, optimum number of bins (Graham pg. 3, Col. 2, lines 6-11, e.g., The complete dyadic binning
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Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system and method disclosed by Goldfarb to include the dyadic binning algorithm of Graham. One of ordinary skill in the art would have been motivated to achieve more precision using fewer bins, as suggested by Graham (pg. 10, Col. 2, lines 1-3).
Goldfarb and Graham do not explicitly teach map each bin of the first data optimum number of bins to a non-numeric character; encode the first data distribution into a first sequence by mapping the first data distribution to the non-numeric characters for the first data optimum number of bins; … map each bin of the second data optimum number of bins to a non-numeric character; encode the second data distribution into a second sequence by mapping the second data distribution to the non-numeric characters for the second data optimum number of bins; … determine a source of the first data distribution is under attack or a source of the second data distribution is under attack based on the determined causal direction; and at least one of: issue a notification and execute a corrective action in response to the determination the source of the first data distribution is under attack or the source of the second data distribution is under attack.
Bell teaches map each bin of the first data optimum number of bins to a non-numeric character (Bell, Col. 18, line 67 through Col. 19, line 20, e.g., use symbolic representations of aspects of the sampled time series to determine patterns in the signal samples; Col. 13 lines 25-53 binning); encode the first data distribution into a first sequence by mapping the first data distribution to the non-numeric characters for the first data optimum number of bins (Bell, Col. 18, line 67 through Col. 19, line 20, e.g., use symbolic representations of aspects of the sampled time series to determine patterns in the signal samples; Col. 13 lines 25-53 binning); … map each bin of the second data optimum number of bins to a non-numeric character (Bell, Col. 18, line 67 through Col. 19, line 20, e.g., use symbolic representations of aspects of the sampled time series to determine patterns in the signal samples; Col. 13 lines 25-53 binning); encode the second data distribution into a second sequence by mapping the second data distribution to the non-numeric characters for the second data optimum number of bins (Bell, Col. 18, line 67 through Col. 19, line 20, e.g., use symbolic representations of aspects of the sampled time series to determine patterns in the signal samples; Col. 13 lines 25-53 binning); … determine a source of the first data distribution is under attack or a source of the second data distribution is under attack based on the determined causal direction (Bell Col. 5, lines 43-44, e.g., degree of causal dependence; Col. 28, lines 14-16, e.g., responsive to the alert indicating that a digital computation device has been affected by an attack that causes an anomaly); and at least one of: issue a notification and execute a corrective action in response to the determination the source of the first data distribution is under attack or the source of the second data distribution is under attack (Bell Col. 5, lines 43-44, e.g., degree of causal dependence); and at least one of: issue a notification and execute a corrective action (Col. 25, lines 37-59, e.g., The alert can include a report, notification, sound, light, signal, message, SMS message, text message, electronic mail, prompt, or other indicator. The alert module 140 can generate an alert including instructions. The instructions can include a command, control parameter, or configuration. The instructions can include an instruction to disable, reset, turn off, or turn on a device or component of the distribution grid 105; Col. 20, lines 25-35, e.g., The NCSD 155 can analyze the dependence of feature properties on time shift. The NCSD 155 can learn the nominal range of feature properties using known good sample data. The NCSD 155 can detect deviations from these nominal metrics, and generate an alert indicating a forgery or deviation from nominal).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system and method disclosed by Goldfarb and Graham to include the detection of attack and error handling of Bell. One of ordinary skill in the art would have been motivated to make this modification to protect data integrity as emphasized by Bell (Bell Col. 3 line 62 through Col. 4 line 17).
Claim 14 contains substantially the same content and is therefore rejected under the same rationales. Goldfarb further discloses a computer implemented method (Goldfarb pg. 1 Section I).
Regarding Claim 2:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein the optimum number of bins includes a model cost (Goldfarb pg. 2, Col. 1, lines 44-45, e.g., Model Cost), a code length (Goldfarb pg. 2, Col. 1, lines 46-47, e.g., Code Length Cost) and an error cost (Goldfarb pg. 2, Col. 2, lines 1-2, e.g., Error Cost).
Regarding Claims 4 and 16:
Claim 4. The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein application of the first model to the second data distribution and application of the second model to the first data distribution is via a Kolmogorov complexity algorithm (Goldfarb pg. 2, Col. 1, lines 13-15, e.g., Kolmogorov complexity estimation).
Claim 16 contains substantially the same content and is therefore rejected under the same rationales.
Regarding Claim 5:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 4, wherein: the application of the first model to the second data distribution fits the first model to the second data distribution; and the application of the second model to the first data distribution fits the second model to the first data distribution (Goldfarb pg. 4, Col. 1, lines 3-5, e.g., compute K(X|Y) and K(Y|X)).
Regarding Claims 6 and 20:
Claim 6. The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein the direction of causality is determined to be: the first variable causes the second variable in a case that:
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the second variable causes the first data variable in a case that:
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(Goldfarb pg. 4, Col. 1, lines 2-3, e.g., Recall that if K(X) + K(Y|X) < K(Y) + K(X|Y) then it is most likely that X → Y .) wherein K(X) is the first data optimum number of bins (Goldfarb pg. 2, Col. 1, lines 16-17, e.g., Kolmogorov complexity for sampled distribution X : K(X)), K(Y|X) is an output of the application of the first model to the second data distribution, K(Y) is the second data optimum number of bins (Goldfarb pg. 4, Col. 1, lines 3-5, e.g., K(Y)), and K(X|Y) is an output of the application of the second model to the first data distribution (Goldfarb pg. 4, Col. 1, lines 3-5, e.g., K(X|Y)).
Claim 20 contains substantially the same content and is therefore rejected under the same rationales.
Regarding Claim 7:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1 wherein the first data distribution and the second data distribution are provided by a cyber-physical system (Goldfarb Col. 3, lines 21-23, e.g., utility distribution systems).
Regarding Claims 8 and 17:
Claim 8. The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein the first data distribution is a time series and the second data distribution is a time series (Goldfarb pg. 4, Col. 2, lines 24-26, e.g., perform causal inference on continuous data).
Claim 17 contains substantially the same content and is therefore rejected under the same rationales.
Regarding Claim 11:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein the compression is via a grammar-based minimum description length (MDL) algorithm (Goldfarb, pg. 1, Col 2, lines 21-22, e.g., Minimum Description Length).
Regarding Claim 12:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein application of the first smallest grammar model to the second model non-numeric characters further comprises processor-executable steps to cause the system to: search, in real-time (Bell, Col. 17, lines 44-50, e.g., testing for increased bias on new sample data acquired in real time), of the second model non-numeric characters for MDL compression phrases from the first model smallest grammar (Goldfarb, pg. 4, Col. 1, lines 38-40, e.g., use the optimal bin number for Y to compute K(X) and then iteratively balance out code lengths of bins to better fit the distribution of X).
Regarding Claim 13:
The combination of Goldfarb, Graham, and Bell further teaches the system of claim 1, wherein the determination of a direction of causality (Goldfarb, pg. 1, Col. 2, lines 15-17, e.g., predicting causal direction) further comprises processor-executable steps to cause the system to: determine whether the first data distribution causes a delay in the second data distribution or the second data distribution causes a delay in the first data distribution (Bell, Col. 20, lines 30-31, e.g., can analyze the dependence of feature properties on time shift), wherein: the first data distribution causes the delay in the second data distribution in a case that the first data distribution causes the second data distribution, and the second data distribution causes the delay in the first data distribution in a case that the second data distribution causes the first data distribution (Bell, Col. 15, lines 51-53, e.g., determine a bi-directional shift along the extent of the sampled time series of the signal).
Conclusion
The prior art made of record in the submitted PTO-892 Notice of References Cited and not relied upon is considered pertinent to applicant’s disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIGUEL A LOPEZ whose telephone number is (703)756-1241. The examiner can normally be reached 8:00AM-5:00PM.
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/M.A.L./ Examiner, Art Unit 2496
/JORGE L ORTIZ CRIADO/Supervisory Patent Examiner, Art Unit 2496
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