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 .
Non-Compliant Amendments
Independent Claims 1, 10, and 16 and dependent Claims 7 and 15 have changed the language from the claimed pages (i.e., including a second data page) having/including “a plurality of data tokens” to “a plurality of identifiers”. See the 6 June 2022 claim set compared to the present claims filed on 14 August 2025.
However, Applicant did not strike through the original language of “data tokens” and underline “identifier” in accordance with 37 CFR 1.121 (“Manner of making amendments in application”), where “The text of any added subject matter must be shown by underlining the added text. The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters”.
Therefore, Applicants amendments are non-compliant with respect to 37 CFR 1.121. However, this modified language, despite not being in compliance with 37 CFR 1.121, has been treated as an amendment, and the claims have been examined on the merits. See the Final Rejection below.
Introductory Remarks
In response to communications filed on 14 August 2025, claims 1, 3-10, 12-16, and 18-20 are amended per Applicant's request. Claims 2, 11, and 17 are cancelled. No claims were withdrawn. No new claims were added. Therefore, claims 1, 3-10, 12-16, and 18-20 are presently pending in the application, of which claims 1, 10, and 16 are presented in independent form.
The previously raised 112 rejection of the pending claims is withdrawn in view of the amendments to the claims. A new ground(s) of rejection has been issued. See the 112 rejection below for further detail.
The previously raised 101 rejection of the pending claims is maintained.
The previously raised 103 rejection of the pending claims is withdrawn in view of the amendments to the claims. A new ground(s) of rejection has been issued.
Response to Arguments
Applicant’s arguments filed 14 August 2025 with respect to the rejection of the claims under 35 U.S.C. 112 (see Remarks, p. 7-8) have been fully considered but are not persuasive. The amendments raise new issues. See the 112 rejections below for further details.
Applicant’s arguments filed 14 August 2025 with respect to the rejection of the claims under 35 U.S.C. 101 (see Remarks, p. 8-13) have been fully considered but are not persuasive.
Applicant argues that the claimed problem being solved “cannot be resolved in abstract manners as suggested by the rejection” (see Remarks, p. 8). This is unpersuasive. The resolution of problems is in the realm of enablement (112(a)), not in the realm of 101. Thus, this point is not relevant to patent eligibility considerations, as the question is not whether the claimed invention could resolve the claimed problem, but whether there is a concrete solution to the problem.
Applicant argues that the claimed invention “provides significant improvement over the state-of-the-art systems…the [claimed] process determines and uses a set of identifiers that are specific to each dump and cannot be performed abstractly” (see Remarks, p. 9) and similarly argues “Each page is different in its order of execution and cannot be designed abstractly” (see Remarks, p. 9). These are not persuasive, as patent eligibility is not based on whether something can or cannot be performed abstractly, but whether the claimed invention recites and ultimately is directed to an abstract idea, which has been found to be the case. See the 101 rejection below for further detail.
Applicant’s arguments that the claims do not fall within the category of “Certain Methods of Organizing Human Activity” and explain a background with regards to healthcare and NLP technologies, as well as why the claims render an improvement to the underlying technology with respect to medical data and in view of McRo (see Remarks, p. 10-12) are moot, as the claims were found to be directed to the “Mental Processes” category, and do not pertain to healthcare and NLP technologies. Applicant’s arguments are not addressing the specific claimed invention or claimed technology in the instant application, and thus are moot.
Applicant’s arguments with respect to Step 2B (see Remarks, p. 13) are unpersuasive, as Applicant solely argues that the claimed invention amounts to significantly more than the judicial exception under this step. However, for at least the same reasons set forth in the 101 rejection below, this argument is unpersuasive.
For at least the aforementioned reasons and those set forth in the 101 rejection below, the 101 rejection has been maintained.
Applicant’s arguments filed 14 August 2025 with respect to the rejection of the claims under 35 U.S.C. 103 (see Remarks, p. 14-15) have been fully considered but are not persuasive.
Applicant argues that primary reference Kesarwani does not disclose the claimed machine learning techniques (see e.g., Remarks, p. 14). However, this is moot, as Kesarwani was not used to disclose these limitations.
Applicant argues that secondary reference Qiu optimizes genetic algorithms for finding solutions, stating “This very different in problem and solution provided by the currency amended claims that has to do with recovery after failure” (see Remarks, p. 14-15).
However, firstly, Kesarwani discloses the claimed context. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references.
Secondly, a reference is analogous art to the claimed invention if (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). Applicant appears to focus arguments on the former (the reference being from the same field of endeavor). However, Qiu’s disclosure is reasonably pertinent to the problem faced by the inventor, as both Qiu and the claimed invention pertain to determining an order of choices, via the application of the multi-armed bandit probability distribution function. Thus, whether those order of choices pertain to, e.g., the claimed identifiers or some other data (such as that disclosed by Qiu), both Qiu and the claimed invention apply the same machine learning algorithm in the same way, just applied to different types of data. Therefore, the combination of Kesarwani and Qiu result in the claimed invention, and therefore disclose, suggest, or otherwise render obvious the combination of the claimed limitations.
The 103 rejection has been modified to conform to the current claim language. See the 103 rejection below for further detail.
Claim Objections
Claims 1, 10, and 16 are objected to because of the following informalities: the claims contain an extra space after “using a multi-armed bandit probability distribution function”. Appropriate correction is required.
Claims 1, 10, and 16 are objected to because of the following informalities: the claims contain a semicolon instead of a comma after “using a multi-armed bandit probability distribution function ;”. This should be a comma, given that the following limitation is “wherein” and is in the same paragraph as this limitation. Appropriate correction is required.
Claims 3, 12, and 18 are objected to because of the following informalities: the claims recite “said probability distribution function”. This should be “said multi-bandit probability distribution function” to be consistent with their respective independent claims. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: the claim should be “wherein an exploration factor and an exploitation factor are provided for calculating rewards”. Appropriate correction is required.
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 1-20 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.
Independent Claims 1, 10, and 16 recite “receiving at least one page of a computer system dump, wherein said page includes a plurality of identifiers”.1 Similarly, dependent Claims 7 and 15 recite “receiving a new second page for processing having a plurality of identifiers”.
However, there appears to be a lack of written description for such a limitation. Rather, the Specification only appears to state “data token” with respect to the pages. See, e.g., Specification, [0026].
The dependent claims are rejected for at least by virtue of their dependency on their respective independent claims, and for failing to cure the deficiencies of their respective independent claims.
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 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.
Independent Claims 1, 10, and 16 recite “receiving at least one page of a computer system dump, wherein said page includes a plurality of identifiers”.2 Similarly, dependent Claims 7 and 15 recite “receiving a new second page for processing having a plurality of identifiers”.
The Specification only appears to state “data token” with respect to the pages. See, e.g., Specification, [0026].
Thus, the distinction between “data token” and “identifier” is unclear (i.e., as the data token is extracted from the page in the second step, but there was no mention of the data token being in the first step; therefore, it is unclear whether “data token” and ‘Identifier” are meant to be utilized interchangeably, as the first step refers to “a” plurality of identifiers, and the second step refers to “a” plurality of data tokens; or whether the data tokens and identifiers are meant to be distinct, in which case the first step makes no mention of the data tokens being present).
The dependent claims are rejected for at least by virtue of their dependency on their respective independent claims, and for failing to cure the deficiencies of their respective independent claims.
Claims 3, 12, and 18 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 claims recite “wherein every identifier is considered as an arm of said multi-armed bandit for said probability distribution function”. There is no previous mention of “a” bandit in the respective independent claims, as “multi-armed bandit probability distribution function” refers to the probability distribution function, not elements within the multi-armed bandit probability distribution function (i.e., the independent claims do not themselves mention “bandit”).
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, 3-10, 12-16, and 18-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent Claims 1, 10, and 16 recite parsing a page that includes a plurality of data tokens to extract the plurality of data tokens; determining an order of identifiers for processing the page; calculating a plurality of reward based weights for each identifier using the tokens; processing the page using the determined order of identifier; and updating the reward based weights after the page is processed (i.e., recalculating the reward based weights) by determining frequency of each identifier detected during page processing. Similarly, dependent claims 7 and 15 recite performing the same steps for a second page; and dependent claim 9 recites performing the same steps as claim 7 for each page in the (computer) dump. These encompass an evaluation, observation, and/or judgment, as well as mathematical operations, all of which fall under the “Mental Processes” grouping of abstract ideas.
Dependent Claims 4-5, 13, and 19 recite calculating certain factors (exploration and exploitation factors) for determining the reward based weights. Similarly to the independent claims above, this encompasses an evaluation, observation, and/or judgment, as well as mathematical operations, all of which fall under the “Mental Processes” grouping of abstract ideas.
Because the claims recite limitations that fall under the “Mental Processes” grouping of abstract ideas, accordingly the claims recite an abstract idea.
The judicial exception is not integrated into a practical application of the idea. The claims recite various computing hardware components and computing elements, which are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Independent Claims 1, 10, and 16 recite the use of a “probability sampling model” to be utilized in the determination of an order of identifiers for processing the page, i.e., the probability sampling model is utilized in calculating a plurality of reward based weights for each identifier using the tokens. However, this does nothing more than recite the abstract idea while adding the words “apply it” with a probability sampling model, i.e., a computer. This does not amount to significantly more, as it only attempts to limit the claims to a particular technological environment—namely, implementation via computers. In other words, such a limitation does nothing more than describe a context rather than a particular manner of achieving the result, i.e., an insignificant field-of-use limitation.
Similarly, attempting to limit this probability sampling model to a “multi-armed bandit probability function” simply provide further narrowing of what are still mathematical operations, adding nothing outside the abstract realm. Because these claims only state the context rather than a particular manner of achieving the result, such limitations amount to nothing more than insignificant field-of-use limitations. See, e.g., BSG Tech3 at p. 17-18 (“As a matter of law, narrowing or reformulating an abstract idea does not add ‘significantly more’ to it”, citing SAP America v. InvestPic4, (“What is needed is an inventive concept in the non-abstract application realm…[L]imitation of the claims to a particular field of information…does not move the claims out of the realm of abstract ideas”)). As seen, this interpretation also extends to the claims’ attempts to narrow the claimed invention to slightly more specific/narrower forms of probability distribution functions, e.g., the probability distribution function being of a narrower type of probability distribution function.5
The claims recite various limitations that attempt to narrow the claims to particular forms of data or formulas. In particular, independent claims 1, 10, and 16 and dependent claims 7, 9, and 15 recite the type of data involved pertaining to at least one page of a computer system dump, wherein said page includes a plurality of data tokens, and that the method pertains to page processing. Dependent claims 3, 12, and 18 with respect to every identifier being an arm of said bandit for said probability distribution. Dependent claims 4-5, 13, and 19 with respect to calculating “exploration and exploitation” factors using “the multi-arm bandit probability distribution”, which is also an insignificant extra-solution activity. Dependent claims 6, 14, and 20 with respect to every identifier being an equally likely arm of a bandit to be selected. Dependent claim 8 with respect to the (first) and second pages sharing at least some of the set of identifiers.
The claims also variously recite insignificant extra-solution activities, which are tangential and/or nominal additions to the claims. In particular, independent claims 1, 10, and 16 and similarly, dependent claims 7, 9, and 15 recite receiving data and updating data to new calculated weights. Furthermore, as noted above, calculating “exploration and exploitation” factors using “the multi-[arm] bandit probability distribution” is an insignificant extra-solution activity (i.e., for being a nominal addition to the claims, as further explained in Step 2B below).
Accordingly, the judicial exception is not integrated into a practical application of the idea.
With respect to analyzing the claims under Step 2B, the claims recite various computing hardware components, which are recited at a high level of generality and recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
The claims recite receiving data, which is a well-understood, routine, and conventional activity. See MPEP 2106.05(d)(II) (“Receiving or transmitting data over a network, e.g., using the Internet to gather data”).
Furthermore, updating data is also directed to well-understood, routine, and conventional activities within the realm of computers. See MPEP 2106.05(d)(II) (“Performing repetitive calculations”, citing Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values)).
Calculating exploration and exploitation factors using the multi-arm bandit algorithm is also well-understood, routine, and conventional, as seen in the prior art examples below:
Liu et al. (US 2023/0214670 A1) (disclosing in the background that a multi-arm bandit algorithm evaluates a context vector, in which this context vector, along with historical actions and rewards, can be used by a policy to choose the best arm to play/choose (Liu et al., [0004]), and that a neural network can explore and exploit any number of actions to process the input data and in an attempt to maximize a reward (Liu et al., [0017]));
Yan et al. (US 2023/0059115 A1) (disclosing that a reinforcement learning problem exemplifies the exploration-exploitation tradeoff dilemma which was derived from the bandits problem; see Yan et al., [0109-0111]);
Dutt et al. (US 2022/0414099 A1) (disclosing an approach that focuses on a balance between exploration of new choices and exploitation of knowledge already gained; see, e.g., Dutt et al., [0074]);
Mukul (US 2022/0405124 A1) (disclosing a bandit model that employs a multi-arm bandit technique that determines whether to continue with one choice (exploitation) or try a different choice (exploration), and providing a reward based on a probability distribution specific to that choice; see, e.g., Mukul, [0021-0022], [0028], and [0076-0082]);
Kratzer et al. (US 2022/0292999 A1) (disclosing a multi-arm bandit (MAB) model used to resolve a conflict between exploiting choices that are known to produce good results versus exploring the effects of choices with unknown effectiveness; see, e.g., Kratzer et al., [0096-0098], [0103], [0123-0129], and [0132-0137]);
Mehrotra et al. (US 2022/0019922 A1) (disclosing the trade-off exploitation of known arms with exploration of potentially useful arms in a multi-arm bandit model which calculates and/or updates a probability function based on objectives and context; see Mehrotra et al., [0044-0045], [0087-0091]);
Tomoda (US 2021/0303044 A1) (disclosing that the Bandit algorithm is an unsupervised machine learning method also referred to as a reinforcement learning, which maximizes a reward for a certain period by performing the exploration that calculates the reward and searches for the optimal solution for all the options; see Tomoda, [0071]);
Banis et al. (US 2020/0210867 A1) (disclosing using a multi-armed bandit approach for selecting an inference outcome which may explore other choices, such as by iterating on those values using one or more machine learning models and evaluating the results of the iteration; the multi-arm bandit approach, after a number of further trials are completed, may indicate to exploit the highest peak of the distribution, so as to select a choice corresponding to that highest peak; see, e.g., Banis et al., [0129]);
Grosso (US 2016/0232548 A1) (disclosing multi-arm bandit algorithm to gather information, cycling through exploring or exploiting behavior across many alternatives; see, e.g., Grosso, [0233]); and
Qiu et al. (US 2019/0244110 A1) (disclosing implementing a multi-arm bandit algorithm in which the goal of classical multi-arm bandit problems, the goal is to maximize the cumulative sum of rewards over the n rounds, where since the agent has no prior knowledge about the reward distributions, it needs to explore the different arms and, at the same time, exploit the seemingly most rewarding arms, resulting in a delicate trade-off between exploration and exploitation; see, e.g., Qiu et al., [0218-0225]).
Lastly, updating reward weights within the context of both reinforcement learning and multi-arm bandit problems, is also well-understood, routine, and conventional. See, e.g., Liu et al., [0004] and [Claim 1]; Yan et al., [0109-0111]; Dutt et al., [0074] (with respect to multi-arm bandit problem and using the reward to influence future actions for similar contexts, which is how the claimed weights are utilized); Mukul et al., [0021-0022], [0028], and [0076-0082], particularly [0082] with respect to reward based weights for choosing arms and corresponding choices; Kratzer et al., [0035], [0102-0105], and [0123-0129]; Mehrotra et al., [0020] and [0083-0091]; Tomoda, [0071]; and Qiu et al., [0218-0249].
Thus, even when considered as an ordered combination, the claimed elements do not add anything that is not already present when the steps are considered separately. The claims recite a series of abstract steps at a high level of generality, with the most specificity being found in attempts to narrow abstract ideas to particular types or fields of information, or certain pre-existing algorithms utilized in their well-understood, routine, and conventional manner. In other words, the claims recite mental tasks or processes, while stating the word “apply it” using pre-existing algorithms claimed using conventional elements within that pre-existing algorithm.
At this level of generality of the claims, the claims do no more than describe a desired function or outcome, and without providing any limiting detail that confines the claims to a particular solution to an identified problem. The purely functional nature of the claims confirm that they are directed to an abstract idea, not to a concrete embodiment of the idea.
A desired goal (i.e., result or effect), absent of structural or procedural means for achieving that goal, is an abstract idea. In this case, the claims are directed to an abstract idea for failing to describe how—by what particular process or structure—the goal is accomplished. Even with the additional elements, the claimed limitations fail to restrict how the goal is accomplished.
Thus, for at least the aforementioned reasons, the claims are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., an abstract idea) without significantly more.
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, 3-10, 12-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kesarwani et al. (“Kesarwani”) (US 2021/0109802 A1), in view of Qiu et al. (“Qiu”) (US 2019/0244110 A1).
Regarding claim 1: Kesarwani teaches A method for dynamically processing a computer dump, comprising:
receiving at least one page of a computer system dump, wherein said page includes a plurality of identifiers (Kesarwani, [0016], where an input file, which can include diagnostic dumps, is provided to and/or obtained by an input parser. See Kesarwani, [0012], where the disclosed system processes every token in a page, and Kesarwani, [0021], where the disclosed system identifies tokens in each page individually, “wherein said page includes a plurality of [data tokens]”. Note that Kesarwani discloses grouping multiple identifiers within a record and prioritizing an execution, implying that the page includes a plurality of identifiers (Kesarwani, [0025]));
parsing said page to extract a plurality of data tokens (Kesarwani, [0016], where the input parser 108 parses the input file 102 to recognize portions of parsed data 110 derived from input file 102. See Kesarwani, [0012], where the disclosed system processes every token in a page, and Kesarwani, [0021], where the disclosed system identifies tokens in each page individually, implying that the extracted portions correspond to a “plurality of data tokens”, as claimed);
determining an order of identifiers for processing said page …; … [and] processing said page using said determined order of identifier (Kesarwani, [0025], where the system can group multiple identifiers within a record, and prioritize their execution. Once a pattern is identified, the same identifiers can be run in the same order. See Kesarwani, [0026], where with respect to the identifier ordering component 140, various different types of identifiers derived from machine learning techniques, can be run in series until a match is found) … .
Kesawarni does not appear to explicitly teach that the order of identifiers is determined by using said data tokens and a probability sampling model using a multi-armed bandit probability distribution function; wherein said probability sampling model calculates a plurality of reward based weights for each identifier; [and] updating said reward based weights after said page is processed by determining frequency of each identifier detected during page processing.
Qin teaches using said data tokens and a probability sampling model using a multi-armed bandit probability distribution function [to determine an order of choices]; wherein said probability sampling model calculates a plurality of reward based weights for each identifier; [and] updating said reward based weights after said page is processed by determining frequency of each identifier detected during page processing (Qiu, [Claim 1], [Claim 4], and [0227], where the disclosed system that implements a multi-armed bandit (MAB) algorithm (i.e., “probability sampling model”). See Qiu, [0218], where the MAB problem is based on the premise that a slot machine with multiple arms is given and the gambler has to decide which arms to pull, how many times to pull each arm, and in which order to pull them (i.e., an order of which choice to select). A common MAB problem is parameterized by the number of arms K, the number of rounds n, and K fixed but unknown reward distributions v1, v2, …, vk associated with arm 1, arm 2, …, arm K, respectively. For t=1, 2, …, n, at round t, the agent chooses an arm from the set of arms {1, 2, …, K} to pull, and observes a reward sampled from vlt (i.e., “probability sampling model”). Similarly, the Thompson Sampling for Bernoulli Bandits may sample from updated reward distributions and select the next arm to pull according to the sampled reward (see Qiu, [0229]).
See Qiu, [0221], where in [ALGORITHM 1] on line 7, a score is calculated for each arm i, and the arm having the highest score is represented as imax, where the x-hat component of the score represents exploitation and the “log” component of the score represents exploration, such that the exploitation terms and the exploration terms are calculated for every arm, and then the arm having the highest score is selected as imax, and at lines 8 and 9, the average reward is updated for arm imax, and the number of visits for arm imax is also updated (i.e., “number of visits” corresponding to “frequency of each [item] detected during [processing]”.
See Kesarwani, [0012] and [0024-0025] above with regards to page processing and identifiers detected during page processing, e.g., where each identifier can be executed for all tokens one by one (or each token is tested against all identifiers), which may prioritize identifiers after entity types are determined for multiple identifiers, and the system subsequently groups these identifiers and prioritizes their execution (i.e., “each identifier”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kesarwani and Qiu (hereinafter “Kesarwani as modified”), as Qiu’s multi-armed bandit algorithm is applied with the intention of determining the order of choices to select, which overlaps with Kesarwani’s disclosure of determining an order of identifiers. Therefore, one of ordinary skill in the art would have found it obvious to have applied Qiu’s multi-armed bandit algorithm to Kesarwani’s disclosure with the motivation of balancing exploration and exploitation (e.g., Qiu, [0223]), which allows for fast(er) learning of patterns and learning optimization of choices.
Regarding claim 3: Kesarwani as modified teaches The method of claim 1, wherein every identifier is considered as an arm of said multi-armed bandit for said probability distribution function (Qiu, [0218], where a fixed limited set of resources (e.g., limited number of pulls on the arm), must be allocated between competing (alternative) choices in a way that maximizes their gain, when each choice’s properties are only partially known at the time of allocation. This implies that each arm corresponds to each available choice. See Kesarwani, [0024-0025], with respect to each identifier being checked against tokens one by one (or each token checked against all identifiers)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kesarwani as modified and Qiu with the motivation of ensuring that all available choices are possible to be selected from.
Regarding claim 4: Kesarwani as modified teaches The method of claim 3, The method of claim 3, wherein an exploration factor and an exploitation factor provided for calculating rewards (Qiu, [0221], where in [ALGORITHM 1] on line 7, a score is calculated for each arm i, and the arm having the highest score is represented as imax, where the x-hat component of the score represents exploitation and the “log” component of the score represents exploration, such that the exploitation terms and the exploration terms are calculated for every arm, and then the arm having the highest score is selected as imax, and at lines 8 and 9, the average reward is updated for arm imax, and the number of visits for arm imax is also updated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kesarwani as modified and Qiu with the motivation of balancing between two goals of playing good arms while also learning which arms are good (see, e.g., Qiu, [0270]).
Regarding claim 5: Kesarwani as modified teaches The method of claim 3, wherein an exploration factor and an exploitation factor is calculated using the multi-armed bandit probability distribution function for determining reward based weights (Qiu, [0221], where in [ALGORITHM 1] on line 7, a score is calculated for each arm i, and the arm having the highest score is represented as imax, where the x-hat component of the score represents exploitation and the “log” component of the score represents exploration, such that the exploitation terms and the exploration terms are calculated for every arm, and then the arm having the highest score is selected as imax, and at lines 8 and 9, the average reward is updated for arm imax, and the number of visits for arm imax is also updated).
Regarding claim 6: Kesarwani as modified teaches The method of claim 3, wherein every identifier is considered as an equally likely arm of a bandit to be selected at least initially (Qiu, [0228], where the success scores and fail scores for every arm is set (initially) to equal 0 (i.e., since all scores are initially 0, this means that they are equally likely to be selected). See also, e.g., Qiu, [0246], where MAB algorithms initialize the statistics of all the arms as 0. See also Qiu, [0270], where initially, all of the reward estimates are equal and all of the certainties are identical. See Kesarwani, [0024-0025], with respect to each identifier being checked against tokens one by one (or each token checked against all identifiers)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kesarwani as modified and Qiu with the motivation of selecting each arm with equal probability, as the system does not yet know which is yet the best arm and thus should treat them all with equal probability to converge to the correct best arm more quickly, i.e., thus arriving at the correct choice/answer with more certainty as more data is collected to estimate rewards (see, e.g., Qiu, [0270]).
Regarding claim 7: Kesarwani as modified teaches The method of claim 3, further comprising:
receiving a new second page for processing having a plurality of identifiers; parsing said second page to extract a plurality of new data tokens; determining a new order of identifiers for processing by using said new data tokens from said second page and said probability sampling model; wherein said probability sampling model calculates a plurality of new reward based weights for each identifier by also using said updated weights from a first page; processing said second page using said new determined order of identifiers; updating said reward based weights after said second page is processed by determining frequency of each identifier detected during second page processing (see claim 1 above. See also Kesarwani, [0025], where identifiers which have a positive match in a current page will have more priority in the subsequent pages, implying that at least one “second page” was also received and processed. See also, e.g., Kesarwani, [0015], where a new diagnostic dump can be searched based on knowledge learned from a previous diagnostic dump. See also Kesarwani, [0028], where patterns identified with respect to a specific application can be utilized in future runs to improve performance, and application-specific tag information can be identified to help in fetching data quickly from diagnostic dump pages).
Regarding claim 8: Kesarwani as modified teaches The method of claim 7, wherein said page and said second page share at least a set of identifiers (Kesarwani, [0015], where a new diagnostic dump can be searched based on knowledge learned from a previous diagnostic dump. See also Kesarwani, [0025], where identifiers which have a positive match in a current page will have more priority in the subsequent pages, implying that there are shared sets of identifiers. Note that Kesarwani discloses grouping multiple identifiers within a record and prioritizing an execution, implying that the page includes a plurality of identifiers (Kesarwani, [0025])).
Regarding claim 9: Kesarwani as modified teaches The method of claim 7, wherein additional pages are received one after another until the dump is completed after said second page is completed; and a same process as in method of claim 7 is repeated for each page (see claim 1 above. See also Kesarwani, [0025], where identifiers which have a positive match in a current page will have more priority in the subsequent pages, implying that at least one “second page” was also received and processed. See also, e.g., Kesarwani, [0015], where a new diagnostic dump can be searched based on knowledge learned from a previous diagnostic dump (implying that a diagnostic dump was previously “completed”). See also Kesarwani, [0022], where the diagnostic dump processing has to finish in a given time limit, also implying that a dump is “completed”, i.e., finished, but within a certain time frame. Note that one of ordinary skill in the art would have recognized that the completion of a dump implies no more data, i.e., pages, to process).
Regarding claim 10: Claim 10 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Kesarwani teaches A computer system, comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising [the claimed steps] (Kesarwani, [0037] and [0046-0053], where the disclosed system may include software modules, i.e., program instructions, being embodied on a tangible computer-readable storage medium which can run on a hardware processor, where the method steps are then carried out using the software modules of the system, executing on a hardware processor).
Regarding claim 12: Claim 12 recites substantially the same claim limitations as claim 3, and is rejected for the same reasons.
Regarding claim 13: Claim 13 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 14: Claim 14 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Regarding claim 15: Claim 15 recites substantially the same claim limitations as claim 7, and is rejected for the same reasons.
Regarding claim 16: Claim 16 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Kesarwaki teaches A computer program product, comprising: one or more computer-readable storage medium and program instructions stored on at least one or more tangible storage medium, the program instructions executable by a processor, the program instructions comprising [the claimed steps] (Kesarwaki, [0037-0038] and [0046], where the disclosed system may be implemented via a computer program product that includes computer useable program code stored in a computer readable storage medium, having instructions stored thereon for causing a processor to carry out the disclosed methods).
Regarding claim 18: Claim 18 recites substantially the same claim limitations as claim 3, and is rejected for the same reasons.
Regarding claim 19: Claim 19 recites substantially the same claim limitations as claim 5, and is rejected for the same reasons.
Regarding claim 20: Claim 20 recites substantially the same claim limitations as claim 6, and is rejected for the same reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IRENE BAKER/Primary Examiner, Art Unit 2152
29 August 2025
1 As noted in the section “Non-Compliant Amendments” above, this language was amended from the originally presented claim language of “data tokens”. Therefore, the 112 rejections are new ground(s) of rejections necessitated by Applicant’s amendment.
2 As noted in the section “Non-compliance” above, this language was amended from the originally presented claim language of “data tokens”. Therefore, the 112 rejections are new ground(s) of rejections necessitated by Applicant’s amendment.
3 BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281 (Fed. Cir. 2018)
4 SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018)
5 See, e.g., SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) at p. 12 (“Dependent method claims 2-7 and 10 add ‘limitations…[that] require[] the resampling method to be a bootstrap method.’ SAP, 260 F. Supp. 3d at 715. Likewise, ‘[c]laims 8 and 9 add limitations that the statistical method is a jackknife method and a cross validation method.’ Id. at 716. Because bootstrap, jackknife, and cross-validation methods are all “particular methods of resampling,” those features simply provide further narrowing of what are still mathematical operations. They add nothing outside the abstract realm. See Mayo, 566 U.S. at 88-89 (stating that narrow embodiments of ineligible matter, citing mathematical ideas as an example, are still ineligible); buySAFE, 765 F.3d at 1353 (same)”).