DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the RCE filed on 5/01/2026.
Claims 1, 3-4, 15, 17-18, and 20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/24/2026 has been entered.
Response to Applicant’s Arguments
Claim Interpretation
The present amendments to the claims obviate the previous 112(f) interpretations of Claims 15 and 18; therefore, these interpretations are withdrawn.
Regarding the previous interpretations of Claim 1 as including contingent limitations of a method claim, while the present amendments delete the language called out in the Final Rejection of 2/24/2026 as creating an optional condition which renders the limitations which are effectuated based on the occurrence of that optional condition, extremely similar language is presently amended into Claim 1, having essentially identical effect.
Applicant’s argument that “[e]very claim limitation of every claim positively recites an active step (or component) which is performed (or configured) to make a certain determination and/or achieve certain results and, thus, clearly define the metes and bounds of the claim scope” appears to entirely misapprehend the standards of contingent limitations of method claims, as Applicant’s assertion is not dispositive of and really has nothing to do with whether such contingencies are recited. Rather, Claim 1 (a method claim) recites the limitation “executing an artificial intelligence recognition process to recognize a format of the request,” then recites further limitations which only execute based on the optional occurrence of a particular result of this above-quoted limitation (ie: “in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format…”). As presently claimed, nowhere in Claim 1 or elsewhere is the recognition process required to produce a result of identifying the request as either an unstructured format or an invalid structured schema format (e.g., this limitation might instead result in a valid structured schema format); instead, the claims merely describe what happens if one of those two named results occur. As such, Claim 1 as drafted contains contingent limitations of a method claim, as described in MPEP 2111.04, wherein Claim 1 may be practiced without any of the “executing…,” “applying…,” and “establishing…” steps which follow the optional condition described above; hence, these limitations do not receive patentable weight. In other words, in situations where the request format is recognized as anything other than either an unstructured format or an invalid structured schema format, the subsequently claimed steps do not occur.
The language of Claims 3-4 previously identified as also reciting contingent limitations of a method claim continues to do so in the same ways as previously described. See the Advisory Action of 5/01/2026 for more information.
Objections
The present amendments to the claims obviate the previous objections to Claims 15 and 18; therefore, these objections are withdrawn.
Claim Rejections – 35 USC § 112
The present amendments to Claims 17 and 20 obviate the previous 112(b) rejections thereto; therefore, these rejections are withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant’s arguments regarding 101, largely copied and pasted from the Remarks of 1/21/2026 and modified to update the claim language to be in line with the present claim amendments, are unpersuasive for the same reasons set forth in the Final Rejection of 2/24/2026 (e.g., they contain the same misapprehensions and conflations of standards of distinct 101 analysis steps, flawed legal underpinnings, misapprehension and misapplication of the content of the referenced Kim Memorandum and various sections of the MPEP, presentation of improper/conclusory/unsupported arguments, and presentation of insufficiently supported arguments despite such deficiencies being specifically identified in the Final Rejection of 2/24/2026). Further, the present 101 arguments make no response to or acknowledgement of the responses to the near-identical arguments in the Final Rejection of 2/24/2026, nor to either the present arguments or claim amendments overcome the shortcomings noted therein. Examiner recommends a thorough review of the Response to Applicant’s Arguments section of the Final Rejection of 2/24/2026, and the re-working of claim amendments and arguments in light of the content thereof.
Regarding the slight supplementing of Prong One recitation of mental process arguments in the present iteration of these arguments, specifically by the conclusory and unexplained “[t]here is simply no reasonable or logical basis to find otherwise,” adds nothing meaningful to these arguments. Further, this is untrue, at least based on the content of Examiner’s response to the near-identical arguments in the previous Office Action (which apply essentially in the same way to the presently amended but highly similar claim language) as well as the content of the previously pointed out Examples 47-49 of the July 2024 PEG Update (which illustrates several AI- and machine learning-based limitations which do indeed recite mental processes).
The present failure to acknowledge or respond to Examiner’s answers to each of the near-identical arguments in the Final Rejection of 2/24/2026 is particularly egregious in relation to Applicant’s repeated assertion of embodied improvements to technology. Examiner pointed out the shortcomings of both the arguments (particularly, the insufficient citations to the original disclosure) and the claims (which, even as presently amended, continue to “recite[] only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished” as in the previously identified language of MPEP 2106.05(f)). Despite this, as well as Examiner’s noting that what Applicant is arguing is really two separate improvements to technology (one approach related to treatment of unstructured data and another approach related to structured data with an invalid schema, wherein these approaches must necessarily be different based on which format the request takes), the claims continue to recite no more than a black box-type claiming of this solution (as in the language of MPEP 2106.05(f) cited above and previously) and the arguments fail to supplement the previously provided citations to the original disclosure at all, thus failing to claim or explain how either of these potential improvements to a technology is accomplished. If the original disclosure does no more than recite the idea of these solutions (e.g., as in the claims and previously/presently cited Paragraphs 0031-0035), failing to provide sufficient details as to how either of these potential solutions are accomplished, then the present invention does not provide proper evidence of these purported improvements to a technology necessary for recognition by one of ordinary skill in the art at the time of filing (see, e.g., MPEP 2106.04(d)(1) and 2106.05(a)), and thus these arguments should be abandoned in favor of other potential arguments which do have sufficient support in the original disclosure.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Applicant’s 103 arguments are entirely based upon presently amended claim language, and as such need not be addressed here. Further, these arguments are moot in view of the updated 103 rejections below.
As a general observation, the vague format and content of these arguments does not meaningfully explain Applicant’s contentions. Specifically, Applicant presents a vague summary of part of the functionality of Zeng, presents an unexplained conclusory statement that Zeng does not disclose or suggest various limitations as presently amended, then presents an equally unexplained conclusory statement that Amamou fails to cure the purported deficiencies of Zeng. Given the citations on record in the several rounds of prosecution thus far, this gives Examiner no understanding of precisely what distinctions Applicant believes exist between the claim limitations and the content of Zeng and Amamou, and therefore nothing particular to which he may respond.
Claim Interpretation
Claim 1 contains the following limitation: “executing an artificial intelligence recognition process to recognize a format of the request.” In conjunction with this determination, every limitation which follows stems from the following root: “in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format.” This root represents conditional language, as Claim 1 does not require a determination that the request comprises an unstructured request or a structured request having an invalid schema format (ie: this root merely identifies what happens if the limitation “executing an artificial intelligence recognition process to recognize a format of the request” results in a determination that the request comprises an unstructured request or a structured request having an invalid schema format). As such, each limitation following from the above-quoted root constitutes a contingent limitation of a method claim, and as such is not given patentable weight. For the purposes of this examination, these limitations will be treated as if it had patentable weight.
Claim 3 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model when the at least one entity is an unstructured entity.” This constitutes a contingent limitation of a method claim, particularly due to the conditional language “when the at least one entity is an unstructured entity” (an outcome of a Claim 1 limitation which is not required to occur), and as such is not given patentable weight. As per the present 112(b) interpretation of Claim 3 (see below), this also constitutes a contingent limitation of a method claim as the limitation of Claim 1 which this limitation further narrows is itself a contingent limitation of a method claim. For the purposes of this examination, this limitation will be treated as if it had patentable weight.
Claim 4 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is a structured entity having an invalid schema format that is not recognized.” This constitutes a contingent limitation of a method claim, particularly due to the conditional language “when the at least one entity is a structured entity having an invalid schema format that is not recognized” (an outcome of a Claim 1 limitation which is not required to occur), and as such is not given patentable weight. As per the present 112(b) interpretation of Claim 4 (see below), this also constitutes a contingent limitation of a method claim as the limitation of Claim 1 which this limitation further narrows is itself a contingent limitation of a method claim. For the purposes of this examination, this limitation will be treated as if it had patentable weight.
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 3-4, 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.
Claim 3 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model when the at least one entity is an unstructured entity.” The original disclosure does not include proper support for this limitation as presently drafted, and thus this limitation constitutes new matter. Specifically, the utilizing of a named entity recognition model to recognize at least one entity in the request is not disclosed as occurring as (and, as described in the original disclosure, cannot function as) part of “executing the artificial intelligence recognition process to recognize the format of the request” as claimed. Rather, as is also drafted into this limitation (ie: “when the at least one entity is an unstructured entity”), this functionality is described as occurring and indeed must occur subsequent to recognition of the format of the request. Paragraphs 0040-0046, discussing the use of an AI-based entity recognizer (e.g., Named Entity Recognition (NER) model, such as Spacy) to identify at least one entity in the request, makes clear that this is to occur (ie: Steps 4-5 of the exemplary algorithm found in these paragraphs) after determination that the incoming request is structured (e.g., Step 1 of said exemplary algorithm). These paragraphs also make clear that the particular NER model utilized to recognize and extract entities differ based on whether the request is structured or unstructured (ie: “…module 204 is configured with a structured request entity recognizer configured to validate each individual format type using pre-defined schemas to identify the entities, as well as an AI-based entity recognizer to recognize the entities when not in a pre-defined schema or format); thus, to apply the correct model, the structured or unstructured nature of the request must necessarily already have been determined (which is in keeping with the content of the rest of these paragraphs). Similar discussion of this functionality (e.g., Paragraphs 0050-0067, 0133-0134) comports with the above-cited paragraphs, reinforcing that the utilized named entity recognition model is utilized to recognize an entity based on whether the request is structured or unstructured. As such, the original disclosure fails to support this limitation as presently drafted.
Claim 4 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is a structured entity having an invalid schema format that is not recognized.” The original disclosure does not include proper support for this limitation as presently drafted, and thus this limitation constitutes new matter. Specifically, the utilizing of a named entity recognition model to recognize at least one entity in the request is not disclosed as occurring as (and, as described in the original disclosure, cannot function as) part of “executing the artificial intelligence recognition process to recognize the format of the request” as claimed. Rather, as is also drafted into this limitation (ie: “when the at least one entity is a structured entity having an invalid schema format that is not recognized”), this functionality is described as occurring and indeed must occur subsequent to recognition of the format of the request. Paragraphs 0040-0046, discussing the use of an AI-based entity recognizer (e.g., Named Entity Recognition (NER) model, such as Spacy) to identify at least one entity in the request, makes clear that this is to occur (ie: Steps 4-5 of the exemplary algorithm found in these paragraphs) after determination that the incoming request is structured (e.g., Step 1 of said exemplary algorithm). These paragraphs also make clear that the particular NER model utilized to recognize and extract entities differ based on whether the request is structured or unstructured (ie: “…module 204 is configured with a structured request entity recognizer configured to validate each individual format type using pre-defined schemas to identify the entities, as well as an AI-based entity recognizer to recognize the entities when not in a pre-defined schema or format); thus, to apply the correct model, the structured or unstructured nature of the request must necessarily already have been determined (which is in keeping with the content of the rest of these paragraphs). Similar discussion of this functionality (e.g., Paragraphs 0050-0067, 0133-0134) comports with the above-cited paragraphs, reinforcing that the utilized named entity recognition model is utilized to recognize an entity based on whether the request is structured or unstructured. As such, the original disclosure fails to support this limitation as presently drafted.
Claims 17 and 20, containing limitations found in Claims 3-4, constitute new matter in the same ways as discussed above in relation to Claims 3-4.
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.
Claim 1 contains the following language: “A method comprising: executing an autonomous usage and permissions system on a computing platform to perform a method for processing usage and permission requests for products received from heterogeneous entities, wherein the method comprises: …” (Examiner’s emphases). This language is indefinite, as it is unclear as drafted to which of the preceding instances of “a method” the subsequently drafted “the method” is to relate back. For the purposes of this examination, and in light of the previous drafting of Claim 1, this language will be interpreted as “A method comprising: executing an autonomous usage and permissions system on a computing platform for processing usage and permission requests for products received from heterogeneous entities, wherein processing usage and permission requests for products comprises: …” Claims 2-14 are rejected due to their dependence upon Claim 1. Examiner notes for clarity that Claims 15 and 18 avoid this same issue, as each of these claims only recite a singular instance of “a method,” making the subsequent antecedent basis of “the method” clear.
Claims 1, 15, and 18 contain the following language, which ends with a semicolon: “in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format.” As drafted, this language is an incomplete thought, indicating that something is to occur in response to the condition set forth therein, but failing to specify what that something is. As such, it is unclear as drafted how this language is to function in relation to the claims in which it is found. For the purposes of this examination, and in light of both presently deleted claim language which this claim language appears intended to replace, as well as the extra indentation of the limitations which follow it, the above-quoted language is interpreted as ending with a colon rather than a semi-colon, thereby this language sets forth a condition precedent to the limitations which follow. Claims 2-14, 16-17, and 19-20 are rejected due to their dependence upon Claims 1, 15, and 18 respectively.
Claim 3 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model when the at least one entity is an unstructured entity.” As drafted, this limitation is paradoxical and nonsensical. This limitation is explicitly drafted as a narrowing of “executing the artificial intelligence recognition process to recognize the format of the request” (ie: the limitation “executing an artificial intelligence recognition process to recognize a format of the request” of Claim 1), yet this limitation also recites action which is to occur based on a particular outcome of this same limitation of Claim 1 (ie: “when the at least one entity is an unstructured entity”). A step cannot both be part of the recognition process and based on a result of said recognition process; both of these are not possible simultaneously. The claimed functionality here being part of the recognition process is also not supported in the original disclosure (see 112(a) new matter rejections). For the purposes of this examination, and in light of the original disclosure and previous draftings of Claim 3, this limitation will be interpreted as “wherein the executing of the one or more trained machine learning algorithms comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model when the at least one entity is an unstructured entity.”
Claim 4 contains the following limitation: “executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is a structured entity having an invalid schema format that is not recognized.” As drafted, this limitation is paradoxical and nonsensical. This limitation is explicitly drafted as a narrowing of “executing the artificial intelligence recognition process to recognize the format of the request” (ie: the limitation “executing an artificial intelligence recognition process to recognize a format of the request” of Claim 1), yet this limitation also recites action which is to occur based on a particular outcome of this same limitation of Claim 1 (ie: “when the at least one entity is a structured entity having an invalid schema format that is not recognized”). A step cannot both be part of the recognition process and based on a result of said recognition process; both of these are not possible simultaneously. The claimed functionality here being part of the recognition process is also not supported in the original disclosure (see 112(a) new matter rejections). For the purposes of this examination, and in light of the original disclosure and previous draftings of Claim 4, this limitation will be interpreted as “wherein the executing of the one or more trained machine learning algorithms comprises recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is a structured entity having an invalid schema format that is not recognized.”
Claims 17 and 20, containing limitations found in Claims 3-4, are indefinite as paradoxical and nonsensical for the same reasons explained above in relation to Claims 3-4, and are interpreted in similar manner for the purposes of this examination.
Claims 7 and 9 contain the following language: “wherein processing the request using the one or more trained machine learning algorithms comprises…” This language is indefinite as lacking antecedent basis, as the claim strings as presently amended do not previously claim the processing of the request using one or more trained machine learning algorithms. For the purposes of this examination, “wherein processing the request using the one or more trained machine learning algorithms comprises…” will be interpreted as “wherein the executing of the one or more trained machine learning algorithms comprises…” Claims 8 and 10 are rejected due to their dependence upon Claims 7 and 9 respectively.
Each of Claims 11-14 contain the following language: “wherein generating the one or more usage and permission parameters further comprises…” This language is indefinite as lacking antecedent basis, as the claim strings as presently amended do not previously claim the generating of the one or more usage and permission parameters. For the purposes of this examination, the above-quoted language in each of Claims 11-14 will be interpreted as “wherein establishing the one or more usage and permission parameters further comprises…” in light of the present drafting of Claim 1 (upon which each of Claims 11-14 eventually depend).
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.
Regarding Claims 1, 15, and 18, the limitations of executing an autonomous usage and permissions process for processing usage and permission requests for products received from heterogeneous entities; obtaining a request to establish one or more usage and permission parameters associated with a product; executing a recognition process to recognize a format of the request; in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format: executing one or more algorithms to analyze content of the request and generate data for use in establishing the one or more usage and permission parameters associated with the product; applying at least a portion of the generated data to an approval feedback process; and establishing the one or more usage and permission parameters based on the data generated by the one or more trained machine learning algorithms and the approval feedback process, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of executing an autonomous usage and permissions process for processing usage and permission requests for products received from heterogeneous entities; obtaining a request to establish one or more usage and permission parameters associated with a product; executing a recognition process to recognize a format of the request; in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format: executing one or more algorithms to analyze content of the request and generate data for use in establishing the one or more usage and permission parameters associated with the product; applying at least a portion of the generated data to an approval feedback process; and establishing the one or more usage and permission parameters based on the data generated by the one or more trained machine learning algorithms and the approval feedback process, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an autonomous usage and permission system, at least one computing platform comprising at least one processor device coupled to at least one memory that stores computer program instructions executable by the at least one processor device on the at least one computing platform, a computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs executable by at least one processing device, an artificial intelligence recognition process, and one or more trained machine learning algorithms. These, in the context of the claims as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claims are therefore directed to an abstract idea.
The claims 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 judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.
Claims 2-14, 16-17, and 19-20, describing various additional limitations to the method of Claim 1, the system of Claim 15, or the product of Claim 18, amount to substantially the same unintegrated abstract idea as Claims 1, 15, and 18 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claims 2, 16, and 18 disclose wherein the request comprises one or more entities, the one or more entities being at least one of structured and unstructured (further defining the abstract idea already set forth in Claims 1, 15, and 18), which does not integrate the claims into a practical application.
Claim 3 discloses wherein executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model (mere instructions to apply a judicial exception) when the at least one entity is an unstructured entity (an abstract idea in the form of a certain method of organizing human activity and a mental process); and the named entity recognition model is trained with unstructured training data (mere instructions to apply a judicial exception), which do not integrate the claim into a practical application.
Claim 4 discloses wherein executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities utilizing a named entity recognition model (mere instructions to apply a judicial exception) when the at least one entity is a structured entity having an invalid schema format that is not recognized (an abstract idea in the form of a certain method of organizing human activity and a mental process); and the named entity recognition model is trained with structured training data (mere instructions to apply a judicial exception), which do not integrate the claim into a practical application.
Claim 5 discloses further comprising updating an entity recognition rule set to include previously recognized entities (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 6 discloses further comprising updating an entity recognition rule set to include a recognition rule for unrecognized entities (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 7 discloses wherein processing the request using the one or more trained machine learning algorithms comprises determining one or more dependency relationships between the one or more entities in the request (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 8 discloses wherein determining one or more dependency relationships between the one or more entities in the request utilizes a named entity recognition model (mere instructions to apply a judicial exception) to perform a dependency parsing process to determine the one or more dependency relationships (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 9 discloses wherein processing the request using the one or more trained machine learning algorithms comprises augmenting one or more recognized entities from the request with one or more additional entities derived from one or more historical data sources (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 10 discloses wherein augmenting one or more recognized entities from the request with one or more additional entities derived from one or more historical data sources utilizes a decision tree model to derive the one or more additional entities (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 11 discloses wherein generating the one or more usage and permission parameters further comprises classifying the one or more entities using a regression classification model (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 12 discloses wherein generating the one or more usage and permission parameters further comprises generating at least a portion of the one or more usage and permission parameters in an unstructured format (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 13 discloses wherein generating the one or more usage and permission parameters further comprises generating at least a portion of the one or more usage and permission parameters in a structured format (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 14 discloses wherein generating the one or more usage and permission parameters further comprises utilizing an online prompt driven analytical processing model (mere instructions to apply a judicial exception), which does not integrate the claim into a practical application.
Claims 17 and 20 disclose the limitations of Claims 3-6, and do not integrate the claims into a practical application for the same reasons expressed above regarding these claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-3, 5-9, 12-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng et al (PGPub 20160191535) (hereafter, “Zeng”) in view of Amamou (PGPub 20240127617) (hereafter, “Amamou”) and Poirier et al (PGPub 20240202539) (hereafter, “Poirier”).
Regarding Claims 1, 15, and 18, Zeng discloses:
implementing an autonomous usage and permissions system on at least one computing platform comprising at least one processor device coupled to at least one memory that stores computer program instructions which are executed by the at least one processor device to implement an autonomous usage and permissions system that executes on the at least one computing platform to process usage and permission requests for products received from heterogeneous entities (Abstract; ¶ 0024, 0038, 0064-0068; methods and apparatus for controlling data permission; receiving a request to access an entity object, and rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; the permission information may be personalized according to the needs of users and there are no limitations on such permission information according to embodiments of the present disclosure; there may be multiple access requests for access to each of the entity objects; in a typical configuration, the computing system includes one or more central processing units (CPUs), an input/output port, an Internet port and a memory; a memory is an example of computer readable medium; the information can be computer readable commands, data structures, programming modules and other data; it should be understood that the embodiments of each step/block in a flow/block diagram and the combinations of each step/block in a flow/block diagram can be accomplished by executing commands or instructions of a computer program);
a computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code is executed by at least one processing device (¶ 0064-0068; it is appreciated that those skilled in the art understand the present disclosure can take the form of methods, apparatus and computing programming products; embodiments of the present disclosure can use a non-transitory computer readable storage medium or other programmable data terminal equipment having embedded therein program instructions (e.g., a magnetic storage disk, a CD-ROM or an optical storage device)); and
wherein processing usage and permission requests for products comprises: obtaining a request to establish one or more usage and permission parameters associated with a product (Abstract; ¶ 0009, 0037; Fig. 1; at step 120, a request to access an entity object is received from a user (e.g., an “accessing user”); the access request includes an identification of an accessing user and as an access timestamp; a method of controlling data permissions is disclosed and includes receiving a request to access a first entity object, where the access request comprises an identification of an accessing user and an access timestamp, and creating a first permission information for the first entity object in accordance with the accessing request).
Zeng does not explicitly disclose but Amamou does disclose applying at least a portion of the generated data to an approval feedback process (¶ 0006, 0084-0086; Fig. 3; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; additionally, each of the steps involved in training the ML model, including defining the entities of interest, manually curating instructions and example text to submit to the LLM, processing an output of the LLM using various algorithms, and manually verifying and when necessary, correcting the output of the LLM to increase a quality of the output).
Zeng additionally discloses executing one or more algorithms to analyze content of the request to generate data for use in establishing the one or more usage and permission parameters associated with the product (Abstract; ¶ 0008-0009, 0017-0018, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; if the identification of the accessing user is not substantially similar to the corresponding identification of the entity object, the permission information of corresponding entity object will fail). Zeng does not explicitly disclose but Amamou does disclose wherein the one or more algorithms are one or more trained machine learning algorithms; wherein processing the request using one or more trained machine learning algorithms occurs in response to recognizing the format of the request as being at least one of an unstructured format and an invalid structured schema format (Abstract; ¶ 0001, 0006, 0028, 0031, 0033-0034, 0056, 0059-0061, 0090; Figs. 1A, 3; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; text annotation system may be configured to receive unstructured text data (e.g., a set of text documents), process unstructured text data based on input from a user of text annotation system, and output a set of structured data; method 300 begins at 302, where method 300 includes receiving a text dataset (e.g., unstructured text data, a text document in a first format, a text document including various elements in various formats, etc.); after text documents of unstructured text have been uploaded to text annotation system, a pre-annotation step may be carried out where an initial annotation of the uploaded (and/or processed via OCR) text documents may be performed in an automated manner based on one or more rule-based algorithms (e.g., regular expressions, dependency matching, phone numbers, emails, etc.), which may be configurable and/or customizable by a user of text annotation system; annotation of unstructured text necessitates recognition of the text as unstructured).
Zeng additionally discloses establishing the one or more usage and permission parameters based on the data generated by one or more algorithms (Abstract; ¶ 0008-0009, 0024, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; creating a first permission information for the first entity object in accordance with the accessing request when an identification of the accessing user is substantially similar to the user identification information of the entity object and the accessing timestamp is within a first time interval of the entity object; a set of permission information corresponding to an entity object is saved with the entity objects in a database of related permissions and includes the rights that allow targeting, opening, modifying, and/or accessing the entity object; the rights may include permissions to access the entity object in a limited manner, permission to freely use the entity object, and/or other specific permissions; the permission information may be personalized according to the needs of the users). Zeng does not explicitly disclose but Amamou does disclose wherein the one or more algorithms are the one or more trained machine learning algorithms; wherein the system output is also based on the approval feedback process (¶ 0006, 0084-0086; Fig. 3; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; additionally, each of the steps involved in training the ML model, including defining the entities of interest, manually curating instructions and example text to submit to the LLM, processing an output of the LLM using various algorithms, and manually verifying and when necessary, correcting the output of the LLM to increase a quality of the output).
Zeng does not explicitly disclose but Poitier does disclose executing an artificial intelligence recognition process to recognize a format of the request (¶ 0029, 0057-0058, 0074, 0157, 0184; Fig. 7; orchestrators can include one or more machine learning models and can execute supervisory functions, such as routing inputs (e.g., queries, instruction sets, natural language inputs or other human-readable inputs, machine-readable inputs) to specific agents to accomplish a set of prescribed tasks (e.g., retrieval requests prescribed by the orchestrator to answer a query; different agents can use various tools to execute and process unstructured data retrieval requests, structured data retrieval requests, API calls (e.g., for accessing artificial intelligence application insights), and the like; the orchestrator may use one or more multimodal models (e.g., language, video, audio, statistical models, etc.), and/or other machine learning models, to interpret the input to select appropriate agents and appropriate tools; in some implementations, the orchestrator includes one or more large language models; the orchestrator can interpret inputs, select appropriate agents for handling queries and other inputs, and route the interpreted input to the selected agents; an orchestrator module receives an input (e.g., a complex input) and generates a plan and a corresponding set of prescribed tasks; the orchestrator generates several sub-queries from the input, the plan, and/or the prescribed set of tasks; the orchestrator coordinates an unstructured data agent (e.g., unstructured data retriever agent module) to handle a first sub-query which may be based on a first portion of the prescribed set of tasks, a structured data agent (e.g., structured data retriever agent module) to handle a second sub-query which may be based on a second portion of the prescribed set of tasks, etc.).
One of ordinary skill in the art would have been motivated to include the entity recognition, labeling, and verification techniques of Amamou with the entity identification and licensing system of Zeng because the use of ML models including LLMs can be leveraged to perform labeling of entities in unstructured text, leading to advancements in automation of unsupervised document classification, sentiment analysis, question answering, and other tasks such as the entity comparison/identification of Zeng (see at least Paragraph 0004-0006 and 0021-0022 of Amamou). It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the format determination and filtering techniques of Poirier with the entity identification and licensing system of Zeng and Amamou because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Poirier are applicable to the base device (Zeng and Amamou), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claims 2, 16, and 19, Zeng in view of Amamou and Poirier discloses the limitations of Claims 1, 15, and 18. Zeng does not explicitly disclose but Amamou does disclose wherein the request comprises one or more entities, the one or more entities being at least one of structured and unstructured (¶ 0028, 0059; text annotation system may be configured to receive unstructured text data (e.g., a set of text documents); the method includes receiving a text dataset (e.g., the unstructured text data); in various embodiments, the text dataset may include one or more text documents; the text dataset may include text in one or more languages, and may be structured in various formats).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 3, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng does not explicitly disclose but Amamou does disclose wherein executing the artificial intelligence recognition process to recognize the format of the request comprises recognizing at least one entity of the one or more entities in the request utilizing a named entity recognition model when the at least one entity is an unstructured entity; and the named entity recognition model is trained with unstructured training data (Abstract; ¶ 0001, 0006, 0033-0034, 0047-0048, 0056, 0060, 0090; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; deploying ML models for entity recognition, relation extraction, and document classification; the user may select an ML model via a menu of a GUI of text annotation system to train to identify a desired set of entities in first portion of the unstructured text data; when performance of the model during training is satisfactory, the user may deploy the trained model on a second or subsequent portions of unstructured text data uploaded to text annotation system, to generate structured data).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 5, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses further comprising updating an entity recognition rule set to include previously recognized entities (Abstract; ¶ 0014, 0027-0029, 0036; Fig. 1; a relational database is pre-generated and coupled to entity objects, corresponding user identifications, and obligatory relationships of corresponding permission information; at step S13, an identification of a user is determined, and at step S14, an obligatory relation is generated resulting from the association between the first identification code and the determined user identification to create a relational database; once the generation of the obligatory relationships/connections has been substantially completed, the relationships will be organized in the form of a relational database).
Regarding Claim 6, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses further comprising updating an entity recognition rule set to include a recognition rule for unrecognized entities (¶ 0017-0018; in step S1, initially a module includes a rule for calculating an index and determining conditions; the module can be initialized by calculating an index and determining conditions associated with the calculations using predetermined rules; rules for calculating an index are used to calculate index data for a specific entity object; by forming relationships between variables and using the rules associated with calculating the index and further defining additional rules for individual scenarios, different kinds of index data can be calculated and synergies can be determined when generating data models).
Regarding Claim 7, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng does not explicitly disclose but Amamou does disclose wherein processing the request using the one or more trained machine learning algorithms comprises determining one or more dependency relationships between the one or more entities in the request (Abstract; ¶ 0001, 0006, 0031, 0056, 0060, 0090; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; deploying ML models for entity recognition, relation extraction, and document classification; after text documents of unstructured text have been uploaded to text annotation system, a pre-annotation step may be carried out where an initial annotation of the uploaded (and/or processed via OCR) text documents may be performed in an automated manner based on one or more rule-based algorithms (e.g., regular expressions, dependency matching, phone numbers, emails, etc.), which may be configurable and/or customizable by a user of text annotation system).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 8, Zeng in view of Amamou and Poirier discloses the limitations of Claim 7. Zeng does not explicitly disclose but Amamou does disclose wherein determining one or more dependency relationships between the one or more entities in the request utilizes a named entity recognition model to perform a dependency parsing process to determine the one or more dependency relationships (Abstract; ¶ 0001, 0006, 0031, 0056, 0060, 0090; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; deploying ML models for entity recognition, relation extraction, and document classification; after text documents of unstructured text have been uploaded to text annotation system, a pre-annotation step may be carried out where an initial annotation of the uploaded (and/or processed via OCR) text documents may be performed in an automated manner based on one or more rule-based algorithms (e.g., regular expressions, dependency matching, phone numbers, emails, etc.), which may be configurable and/or customizable by a user of text annotation system).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 9, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng does not explicitly disclose but Amamou does disclose wherein processing the request using the one or more trained machine learning algorithms comprises augmenting one or more recognized entities from the request with one or more additional entities derived from one or more historical data sources (¶ 0090, 0095; the manual annotation task may include, for example, entity recognition, named-entity recognition (NER), relation extraction, document classification, or a different type of annotation; defining the set of manual annotation guidelines may also include defining a scope of one or more entities; for some entity extraction tasks, a user may wish to include words adjacent to an entity, such as descriptors; for example, instances of an entity “pizza” may be labeled in the text data; however, the user may wish to view types of pizza mentioned in the text data, without defining different entity types for different types of pizza; the user may expand a scope of the entity “pizza” to encompass words found prior to instances of the entity “pizza”, whereby expressions such as “cheese pizza”, “pepperoni pizza”, etc. may be included within entity labels assigned to the instances).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 12, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses wherein generating the one or more usage and permission parameters further comprises generating data including at least a portion of the one or more usage and permission parameters (Abstract; ¶ 0008-0009, 0024, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; creating a first permission information for the first entity object in accordance with the accessing request when an identification of the accessing user is substantially similar to the user identification information of the entity object and the accessing timestamp is within a first time interval of the entity object; a set of permission information corresponding to an entity object is saved with the entity objects in a database of related permissions and includes the rights that allow targeting, opening, modifying, and/or accessing the entity object; the rights may include permissions to access the entity object in a limited manner, permission to freely use the entity object, and/or other specific permissions; the permission information may be personalized according to the needs of the users). Zeng does not explicitly disclose but Amamou does disclose wherein data may be in an unstructured format (¶ 0021; text documents (e.g., unstructured text)).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 13, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses wherein generating the one or more usage and permission parameters comprises outputting at least a portion of the one or more usage and permission parameters (Abstract; ¶ 0008-0009, 0024, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; creating a first permission information for the first entity object in accordance with the accessing request when an identification of the accessing user is substantially similar to the user identification information of the entity object and the accessing timestamp is within a first time interval of the entity object; a set of permission information corresponding to an entity object is saved with the entity objects in a database of related permissions and includes the rights that allow targeting, opening, modifying, and/or accessing the entity object; the rights may include permissions to access the entity object in a limited manner, permission to freely use the entity object, and/or other specific permissions; the permission information may be personalized according to the needs of the users). Zeng does not explicitly disclose but Amamou does disclose wherein the output is in a structured format (Abstract; ¶ 0001, 0006, 0033-0034, 0056, 0060; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; the user may select an ML model via a menu of a GUI of text annotation system to train to identify a desired set of entities in first portion of the unstructured text data; when performance of the model during training is satisfactory, the user may deploy the trained model on a second or subsequent portions of unstructured text data uploaded to text annotation system, to generate structured data).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 14, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses wherein generating the one or more usage and permission parameters further comprises utilizing an algorithm (Abstract; ¶ 0008-0009, 0024, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; creating a first permission information for the first entity object in accordance with the accessing request when an identification of the accessing user is substantially similar to the user identification information of the entity object and the accessing timestamp is within a first time interval of the entity object; a set of permission information corresponding to an entity object is saved with the entity objects in a database of related permissions and includes the rights that allow targeting, opening, modifying, and/or accessing the entity object; the rights may include permissions to access the entity object in a limited manner, permission to freely use the entity object, and/or other specific permissions; the permission information may be personalized according to the needs of the users). Zeng does not explicitly disclose but Amamou does disclose wherein the algorithm comprises utilizing an online prompt driven analytical processing model (¶ 0001, 0006, 0056, 0060, 0066-0067, 0081, 0090; Fig. 3; automated processes for labeling text data, and more specifically, to generating labeled datasets for supervised learning; leveraging natural language processing (NLP) capabilities of the LLM to label instances of entities; trained neural network may subsequently be used to label new text data similar to text data, for example, as part of an entity extraction process carried out via the text annotation system; at step 308, the method includes generating prompts from the labeled text data, where each prompt may include an instruction to be provided to an LLM, to facilitate labeling of a second, remaining portion of the text dataset; at step 310, the method includes submitting the prompts to the LLM, along with the text dataset; the LLM may process the prompts, and generate an output in a format corresponding to the prompts).
The rationale to combine remains the same as for Claim 1.
Claims 4, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou, Poirier, and McLean (PGPub 20190130009) (hereafter, “McLean”).
Regarding Claim 4, Zeng in view of Amamou discloses the limitations of Claim 2. Zeng additionally discloses wherein executing the one or more trained machine learning algorithms comprises recognizing at least one entity of the one or more entities when the at least one entity is a structured entity that is not recognized (Abstract; ¶ 0008-0009, 0017-0018, 0040-0041, 0049-0050; Fig. 1; the method further includes rendering the permission information of the corresponding entity object if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the accessing timestamp is within the time interval in response to the accessing request; permission information of a corresponding entity object is opened in accordance with the access request if the identification of the accessing user is substantially similar to the corresponding identification of the entity object and the access timestamp is within the time interval; if the identification of the accessing user is not substantially similar to the corresponding identification of the entity object, the permission information of corresponding entity object will fail). Zeng does not explicitly disclose but Amamou does disclose wherein the one or more algorithms are one or more trained machine learning algorithms; wherein recognizing at least one entity of the one or more entities comprises utilizing a named entity recognition model when the at least one entity is a structured entity having an invalid schema format that is not recognized; wherein the named entity recognition model is trained with structured training data (¶ 0005-0007, 0026, 0030; one or more engines for statistical normalization of unstructured logs and/or unrecognized formatted logs; the one or more engines can include a trained statistical entity tagger; the statistical entity tagger will be trained and operable to identify or tag one or more entities in the unstructured log data based at least in part on one or more attributes or commonalities of the entities; the engine may use probabilistic modeling, such as using Named Entity Recognition (NER) applied as part of Natural Language Processing (NLP) of the incoming logs, to determine whether specific attributes or commonalities, or sequences thereof, in the unstructured log data are indicative of one or more identifiable entities; other suitable models are also possible with the present disclosure, such as neural networks, fuzzy logic, or other statistical models; the training data set thereafter can be used to dynamically train and/or update the one or more engines, e.g., the engine may correlate tagged entities from the structured SIEM event data with their corresponding unstructured entities and build a probability model for the unstructured entities; based on development of the one or more engines ability to recognize and extract commonalities/identifiable features or entities across different log formats, the engine(s) can thereafter receive, analyze incoming logs in newer, different or unrecognized formats, and identify, parse and/or normalize selected key attributes or features thereof based on a prescribed; as a result, even in the event of new or unrecognized format logs coming in, the security system may still be enabled to automatically analyze or otherwise monitor the normalized event/security log data; when a new client provides unstructured or unrecognized format logs for security monitoring, or when an existing client makes changes or updates to their systems or software, resulting in a change to their logs, a large number scripts will not have to be manually updated or generated to normalize the provided unstructured log data).
The rationale to combine Zeng, Amamou, and Poirier remains the same as for Claim 1. One of ordinary skill in the art would further have been motivated to include the machine learning-based NER entity processing techniques of McLean with the entity identification and licensing system of Zeng, Amamou, and Poirier to enable the system to substantially automatically/dynamically identify and extract attributes, entities, or common sequences or patterns in incoming unstructured data or structured data with unrecognized formats (see at least Paragraphs 0005-0006 of McLean).
Regarding Claims 17 and 20, Zeng in view of Amamou and Poirier discloses the limitations of Claims 16 and 19. The additional limitations of Claims 17 and 20 are obvious in view of Zeng in view of Amamou, Poirier, and McLean in the same manner as described above in relation to Claims 3-6.
The rationale to combine remains the same as for Claim 4.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou, Poirier, and Ren et al (CN 113254641) (hereafter, “Ren”).
Regarding Claim 10, Zeng in view of Amamou and Poirier discloses the limitations of Claim 9. Zeng does not explicitly disclose but Amamou does disclose wherein augmenting one or more recognized entities from the request with one or more additional entities derived from one or more historical data sources utilizes decisions to derive the one or more additional entities (¶ 0090, 0095; the manual annotation task may include, for example, entity recognition, named-entity recognition (NER), relation extraction, document classification, or a different type of annotation; defining the set of manual annotation guidelines may also include defining a scope of one or more entities; for some entity extraction tasks, a user may wish to include words adjacent to an entity, such as descriptors; for example, instances of an entity “pizza” may be labeled in the text data; however, the user may wish to view types of pizza mentioned in the text data, without defining different entity types for different types of pizza; the user may expand a scope of the entity “pizza” to encompass words found prior to instances of the entity “pizza”, whereby expressions such as “cheese pizza”, “pepperoni pizza”, etc. may be included within entity labels assigned to the instances). Zeng does not explicitly disclose but Ren does disclose wherein the decisions take the form of a decision tree model (Abstract; pg. 3; Claim 5; the using decision tree ID3 classification algorithm for training is as follows: step one: calculating and obtaining the current information entropy of the training data; calculating the branch information entropy under each of the n entity attributes; calculating the condition entropy according to the branch information entropy; respectively, calculating the information gain of n attributes; selecting the attribute with the maximum information gain as the decision point and adding the decision tree; step two: removing the attribute column data with the maximum information gain from the training data; repeating the step one for the current training data, until all the entity attributes are added with decision tree).
The rationale to combine Zeng, Amamou, and Poirier remains the same as for Claim 1. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the entity extraction-based decision tree functionality of Ren with the entity identification and licensing system of Zeng, Amamou, and Poirier because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Ren are applicable to the base device (Zeng, Amamou, and Poirier), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou, Poirier, and Tran (PGPub 20220237368) (hereafter, “Tran”).
Regarding Claim 11, Zeng in view of Amamou and Poirier discloses the limitations of Claim 2. Zeng additionally discloses wherein generating the one or more usage and permission parameters further comprises classifying the one or more entities (Abstract; ¶ 0008-0009, 0024, 0040-0041, 0049-0050; Fig. 1; creating a first permission information for the first entity object in accordance with the accessing request when an identification of the accessing user is substantially similar to the user identification information of the entity object and the accessing timestamp is within a first time interval of the entity object; a set of permission information corresponding to an entity object is saved with the entity objects in a database of related permissions and includes the rights that allow targeting, opening, modifying, and/or accessing the entity object; the rights may include permissions to access the entity object in a limited manner, permission to freely use the entity object, and/or other specific permissions; the permission information may be personalized according to the needs of the users; each entity object can be also described using various types of attribute information; when an entity object is a person, the typical attribute information used to describe the person may include the person's age, height, weight and/or ethnicity; if the entity object is a product, the typical attribute information used to describe the product may be the product's price, color and/or material). Zeng does not explicitly disclose but Tran does disclose wherein the classifying the one or more entities uses a regression classification model (¶ 0096, 0279, 0415; Logistic Regression to classify the data within the embedding; the text generation generates ontological markups or schema markups for entities on web page content, relationships to other entities, their connected relationships to attributes (properties) about those entities and the relationships to entity classifications).
The rationale to combine Zeng, Amamou, and Poirier remains the same as for Claim 1. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the entity classification techniques of Tran with the entity identification and licensing system of Zeng, Amamou, and Poirier because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Tran are applicable to the base device (Zeng, Amamou, and Poirier), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20180288616 – “Predictive Permissioning for Mobile Devices,” Knox, disclosing a system for utilizing machine learning to predict mobile device permissions in response to a request
PGPub 20230216887 – “Forecast-Based Permissions Recommendations,” Strong et al, disclosing a system for storing and analyzing permissions, as well as forecasting prediction recommendations based on historical information and usage pattern data
US 11868492 – “Systems And Methods For Mediating Permissions,” Watson et al, disclosing a system for generating a predictive machine learning model and using it to respond to permission requests
Conclusion
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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