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 amendment filed on 1/21/2026.
Claims 1, 3-4, 7, 9, 13, 15, 17-18, and 20 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Response to Applicant’s Arguments
Claim Interpretation
The present Remarks treat the 112(f) interpretation of a term of Claim 18 and the interpretations of Claims 3-4 as containing contingent limitations of a method claim collectively, failing to provide any substantive argument against either. Examiner notes for clarity that these are two distinct issues relating to distinct standards.
Regarding the 112(f) interpretation of a “processing device” in Claim 18, Examiner disagrees with Applicant’s assertion of this 112(f) interpretation being rendered moot by the present amendments. Indeed, the present amendments expand this issue such that Claim 15 now contains the term “processor device” which now requires similar 112(f) interpretation, where the previous form of “processor” did not. While the present amendments to Claim 1 remove the language “a processing device comprising a processor” referenced in the previous claims, Claim 1 as originally drafted continues to constitute part of the original disclosure which these terms “processing device” and “processor device” must be considered in relation to. Further, pg. 19 of the specification as filed comports with this language of Claim 1 (e.g., “The processing device 1802-1 in the processing platform 1800 comprises a processor 1810 coupled to a memory 1812,” making clear that the processing device and the processor itself are distinct elements). While Examiner reminds Applicant that interpretation under 112(f) is not a rejection and does not in and of itself preclude patentability, if Applicant wishes to avoid such interpretations, Examiner recommends replacing all instances of “processing device” and “processor device” with “processor.”
Regarding the interpretation of limitations of Claims 3-4 as contingent limitations of a method claim, Applicant’s attempts to avoid such interpretations, similar to the 112(f) interpretations addressed above, expand this issue rather than avoid it. Specifically, similar conditional language as found previously in Claims 3-4 is presently amended into Claim 1. More particularly, the limitation “in response to determining that the request comprises an unstructured request or a structured request having an invalid schema format: …” discloses a condition tied to one potential result of the limitation “determining whether the request comprises an unstructured request or a structured request having an invalid schema format” claimed earlier in Claim 1, rendering several limitations thereof as contingent as well (and thus lacking in patentable weight). Claim 1 as currently drafted does not require that a determination that the request comprises an unstructured request or a structured request having an invalid schema format, hence the interpretation as a contingency. The narrowing of one of these now-contingent limitations of Claim 1 in Claims 3-4 does not correct this contingency, and thus these limitations remain contingent based on their own merits (as before) as well as based on the contingency of the base limitation of Claim 1. Based on the present claim drafting, Examiner recommends amending “determining whether the request comprises an unstructured request or a structured request having an invalid schema format” to “determining whether the request comprises an unstructured request or a structured request having an invalid schema format” to avoid these interpretations.
Objections
The present amendments to Claims 12-13 obviate the previous objections thereto; therefore, these objections are withdrawn.
Claim Rejections – 35 USC § 112
The present amendments to Claim 1 obviate the previous 112(b) rejection thereof; therefore, this rejection is withdrawn.
The issues noted in the previous 112(b) rejection of Claims 17 and 20 are not addressed by the present amendments thereto. As such, these rejections are modified in view of the presently amended language and maintained.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant first presents 101 subject matter eligibility arguments related to the standards of Step 2A, Prong One. Before addressing the substance of these arguments as relates to the present invention, Examiner notes that these arguments contain numerous misapprehensions of the standards of Prong One, most commonly improperly conflating the standards of the Prong One analysis (ie: whether a claim recites an abstract idea) with those of Prong Two (ie: whether any recited abstract ideas are integrated into a practical application, or the determination of what the claim as a whole is “directed to”). Relatedly, Applicant uses the language “directed to,” which has specific meaning within the 101 subject matter eligibility analysis, in relation to multiple Prong One arguments. This is inappropriate, as the “directed to” inquiry is not a concern of Prong One but rather Prong Two. As illustrated in Applicant’s reproduced flowchart of MPEP 2106.04 and as articulated in the various subsections of MPEP 2106.04, Step 2A, Prong One and Step 2A, Prong Two are two distinct inquiries with unique standards which should not be confused and conflated as in the present Remarks. These misapprehensions cause foundational failings in these arguments, resulting in erroneous and unpersuasive conclusions as Applicant attempts to apply these misunderstood standards to the present invention.
For clarity, while the overall conclusion of Step 2A (ie: the result of Prong Two thereof) must consider the claim “as a whole,” the Prong One analysis is performed on a limitation-by-limitation basis. See, e.g., MPEP 2106.04(a)(2) and the various subsections thereof, which contain numerous examples of claim limitations which recite various forms of abstract ideas. See also the July 2024 PEG and the various Examples set forth and carried therein, which likewise illustrate the Prong One analysis identifying particular limitations which recite abstract ideas.
Indeed, Examples 47-49 of the July 2024 PEG are particularly useful in refuting an implied and unsupported argument in the present Remarks regarding recitation of abstract ideas in relation to artificial intelligence and machine learning applications. Particularly, Applicant invokes the Kim Memorandum of August 4, 2025, generally correctly paraphrasing the content thereof as indicating that, as Applicant puts it, “claim limitations which encompass artificial intelligent [sic] and machine learning in a way that cannot be practically performed in the human mind do not fall within the ‘mental process’ grouping” (Applicant’s emphasis); however, despite this generally correct assertion of Prong One standards as relates to AI-based functions, Applicant makes no attempt to support the implied notion that the machine learning-based functions recited in the claims cannot be practically performed in the human mind. If Applicant seemingly believes that this is so due to the bare usage of a trained machine learning algorithm, Applicant is mistaken as illustrated in the analyses of various claims of Examples 47-49 of the July 2024 PEG (as well as the content of said PEG). Regarding the limitation “processing the request using one or more trained machine learning algorithms to generate data for use in generating the one or more usage and permission parameters,” other than the non-abstract machine learning model itself, Examiner sees nothing in this vague and high-level limitation, lacking in any technical details as to how this machine learning application might function which might differentiate its performance from how a human mind might achieve the claimed result, which could not be readily understood in the human mind and achieved mentally or with the aid of pen and paper (e.g., analyzing the request content to generate data for use in generating one or more usage and permission parameters).
More generally, the idea that an abstract process executed by way of computers and programming thereof (such as the aforementioned machine learning) somehow removes that process from reciting a judicial exception (and in particular, the argued abstract category of mental processes) is refuted in the holding of the seminal Alice as well as in MPEP 2106.04(a)(2)(III)(C) (entitled “A Claim That Requires a Computer May Still Recite a Mental Process”), which is entirely dedicated to refuting this seemingly implied notion.
Regarding recitation of the separate abstract category of certain methods of organizing human activity, Applicant presents a non-substantive and wholly unsupported conclusory statement that “the claims do not include fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people.” Examiner disagrees. As Applicant fails to support this conclusory statement in any way, Examiner has nothing particular to which he may respond in relation to this argument. Nonetheless, Examiner does his best to guess at Applicant’s thought process. If, similar to the present mental processes arguments, Applicant believes this to be the case due to the usage of machine learning in a single limitation and the computer implementation of the claimed limitations more broadly, Applicant is mistaken. As stated in o MPEP 2106.04(a)(2)(II): “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the ‘certain methods of organizing human activity’ grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.” Particularly regarding the presently amended limitations, the claims relate to the receipt and processing of usage and permissions requests (e.g., as explicitly and repeatedly stated in the original disclosure, in relation to licensing agreements such as for software products). Given this context, Examiner is at a loss as to how Applicant could reasonably conclude that such limitations do not at least recite commercial or legal interactions. Further, as is also explicitly made clear in the original disclosure, the claimed feedback approval process in the limitation “applying at least a portion of the generated data to an approval feedback process” is performed manually by a human, such as a subject matter expert (SME). Given this, limitations of the present invention certainly fall within the bounds of certain methods of organizing human activity.
Applicant next moves to a Step 2A, Prong Two argument, particularly asserting that the claims embody an improvement to a technology. Applicant cites to Paragraphs 0031-0035 of the specification as published as identifying and supporting this purported technological improvement, essentially: allowing a system to process request data when such data is either unstructured or using an unrecognized schema format. Examiner notes that this purported improvement is really two separate improvements requiring two separate approaches, neither of which are sufficiently reflected in either the cited passages (which largely use conclusory statements of what particular “[i]llustrative embodiments” of the present invention might be capable of, and some vague and incomplete descriptions of how such improvements might be achieved by such embodiments of the present invention). Particularly, an improvement to “the automated processing and management of usage and permission requests associated with products (e.g., software programs),” as articulated by Applicant, may or may not constitute an improvement to a technology depending upon whether the specific improvement (assuming that improvement is actually achieved – an important consideration discussed in more detail below) is an improvement to a technical aspect of the computer system performing such automation, or whether the improvement is instead directed to the abstract concept of processing and management of usage and permission requests associated with products. Applicant’s referring to this as a technology or technical field is not dispositive as to whether this is so.
The consideration of improvements to technologies or technical field is discussed in MPEP 2106.05(a), which in part states that “[a]fter the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016)” … “[t]hat is, the claim must include the components or steps of the invention that provide the improvement described in the specification.” In view of this, even if Examiner is to take Applicant at their word that the improvement set forth in the cited Paragraphs 0031-0035 constitutes an improvement to a technology, the content of the claims fails to embody such an improvement. Specifically, while the claims set forth several steps to occur in response to determining that the request includes data which is either unstructured or structured but having an invalid schema format, the content of these reactionary steps are claimed in a vague, high-level, and results-based manner, lacking in nearly any details in how these steps are to be accomplished. In other words, the claims fail to provide sufficient details as to how this argued improvement to a technology would be achieved. These high-level limitations, both as claimed in the independent claims and as in part further narrowed in various dependent claims, do not provide sufficient details such that one of ordinary skill in the art would recognize the claims as providing an improvement, e.g., the argued ability to automatically process usage and permissions requests when said requests contain either unstructured data or structured data having an unfamiliar data format (e.g., a format other than the exemplary “JavaScript object notation (Json) schema, an extensible Markup Language (XML) format, a natural language (NL) format, etc.” of the cited language, or some unrecognized modification of such formats).
As set forth in MPEP 2106.05(f), one consideration in distinguishing from improvements to technology and mere instructions to apply a judicial exception is “[w]hether the claim recites 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.” Here, as noted above, the claims set forth several steps to occur in response to determining that the request includes data which is either unstructured or structured but having an invalid schema format, but the content of these reactionary steps are claimed in a vague, high-level, and results-based manner, lacking in nearly any details in how these steps are to be accomplished. This, in conjunction with the standards discussed above, results in the categorization of the use of the machine learning algorithm as presently claimed as constituting mere instructions to apply a judicial exception in the 101 rejections below (similar to its categorization in the previous Office Action).
To be clear, the purported technological improvement set forth in the cited Paragraphs 0031-0035 may indeed be a technological improvement, but there is not enough regarding how these results are achieved in these paragraphs to make that determination. Such details may very well be present elsewhere in the original disclosure (and Examiner again stresses that approaches allowing for automation of processing unstructured requests would necessarily differ from those for processing structured requests having an unrecognized data format, thus providing two potential avenues for the present invention to embody an improvement to a technology here), but they are not present in either the passages cited by Applicant in the present Remarks or the claims themselves. Examiner recommends, should such details be present in the original disclosure, that they be amended into the claims in a future round of prosecution.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
The present 103 arguments are based entirely on presently amended claim language, and further are moot in view of the updated 103 rejections below. Examiner notes that these arguments are non-substantive regarding the previously cited combinations of references, merely setting forth several conclusory statements that various previously cited secondary references fail to cure purported deficiencies of the primary Zeng reference.
Claim Objections
Claims 15 and 18 are objected to because of the following informalities: In Claims 15 and 18, “the one or more trained machine learning algorithms and the the approval feedback process” should read “the one or more trained machine learning algorithms and the approval feedback process.” Appropriate correction is required.
Claim Interpretation
Claim 1 contains the following limitation: “determining whether the request comprises an unstructured request or a structured request having an invalid schema format.” In conjunction with this determination, every limitation which follows stems from the following root: “in response to determining that the request comprises an unstructured request or a structured request having an invalid 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 “determining whether the request comprises an unstructured request or a structured request having an invalid schema format” 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: “wherein processing the request using 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 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,” and as such is not given patentable weight. 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: “wherein processing the request using the applying 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.” 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,” and as such is not given patentable weight. 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.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“…at least one processor device…executed by the at least one processor device…” of Claim 15; and
“…executed by at least one processing device…” of Claim 18.
Examiner notes that the original disclosure does not create a special definition of “processing device,” and the language of original Claim 1 makes clear that this term is not analogous to a processor (ie: as the processing device of Claim 1 comprises a processor).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. For the purposes of this examination, these terms will be interpreted in light of pg. 19 of the specification as filed and original Claim 1.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17 and 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.
Claims 17 and 20 each contain the following limitations: “recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is an unstructured entity, wherein the named entity recognition model is trained with unstructured training data” and “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, wherein the named entity recognition model is trained with structured training data.” These limitations, in combination, cause multiple indefiniteness issues. Firstly, it is unclear whether the second instance of “a named entity recognition model” (found in the second above-quoted limitation) is intended to relate back to the previously disclosed “a/the named entity recognition model” (found in the first above-quoted limitation). Secondly and similarly, it is unclear to which of the previously disclosed first “a named entity recognition model,” second “a named entity recognition model,” or “the named entity recognition model” (if, indeed, these are distinct) the second instance of “the named entity recognition model” (found in the second above-quoted limitation) is intended to relate back. Thirdly, if these disparate instances of “a/the named entity recognition model” are intended to indicate the same model, it is unclear as drafted how this model can be simultaneously “trained with unstructured training data” and “trained with structured training data.” For the purposes of this examination, these limitations will be respectively interpreted as “recognizing at least one entity of the one or more entities utilizing a first named entity recognition model when the at least one entity is an unstructured entity, wherein the first named entity recognition model is trained with unstructured training data” and “recognizing at least one entity of the one or more entities utilizing a second 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 second named entity recognition model is trained with structured training data.”
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 implementing an autonomous usage and permissions process for processing usage and permission requests for products received from heterogeneous entities; obtaining a request for generating one or more usage and permission parameters associated with a product; determining whether the request comprises an unstructured request or a structured request having an invalid schema format; in response to determining that the request comprises an unstructured request or a structured request having an invalid schema format: processing the request using one or more algorithms to generate data for use in generating one or more usage and permission parameters; applying at least a portion of the generated data to an approval feedback process; and generating the one or more usage and permission parameters based on the data generated by the one or more 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 implementing an autonomous usage and permissions process for processing usage and permission requests for products received from heterogeneous entities; obtaining a request for generating one or more usage and permission parameters associated with a product; determining whether the request comprises an unstructured request or a structured request having an invalid schema format; in response to determining that the request comprises an unstructured request or a structured request having an invalid schema format: processing the request using one or more algorithms to generate data for use in generating one or more usage and permission parameters; applying at least a portion of the generated data to an approval feedback process; and generating the one or more usage and permission parameters based on the data generated by the one or more 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, 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 processing the request using 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 (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 wherein 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 processing the request using 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 (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 wherein 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 wherein in processing the request using the one or more trained machine learning algorithms, the autonomous suage and permissions system operates to perform a process which comprises one or more of: 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 an unstructured entity, wherein the named entity recognition model is trained with unstructured training data (mere instructions to apply a judicial exception); 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, wherein the named entity recognition model is trained with structured training data (mere instructions to apply a judicial exception); updating an entity recognition rule set to include previously recognized entities; and 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 claims into a practical application.
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, and 12-20 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”).
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 for generating one or more usage and permission parameters associated with a product (Abstract; ¶ 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).
Zeng does not explicitly disclose but Amamou does disclose:
determining whether the request comprises an unstructured request or a structured request having an invalid schema format (¶ 0028, 0031, 0043-0044, 0059-0061; Figs. 1A, 3; 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); and
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 processing the request using one or more algorithms to the request to generate data for use in generating the one or more usage and permission parameters (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 determining that the request comprises an unstructured request or a structured request having an invalid 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 generating 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).
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).
Regarding Claims 2, 16, and 19, Zeng in view of Amamou 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 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 recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is an unstructured entity, wherein 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 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 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 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 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 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 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 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 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.
Regarding Claims 17 and 20, Zeng in view of Amamou discloses the limitations of Claims 16 and 19. Zeng additionally discloses wherein processing the request using the one or more trained machine learning algorithms, the autonomous usage and permissions system operates to perform a process which comprises one or more of: recognizing at least one entity of the one or more entities utilizing a named entity recognition model when the at least one entity is an unstructured entity, wherein the named entity recognition model is trained with unstructured training data; 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, wherein the named entity recognition model is trained with structured training data; updating an entity recognition rule set to include previously recognized entities; and updating an entity recognition rule set to include a recognition rule for unrecognized 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).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou and McLean (PGPub 20190130009) (hereafter, “McLean”).
Regarding Claim 4, Zeng in view of Amamou discloses the limitations of Claim 2. Zeng additionally discloses wherein processing the request using the one or more 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 and Amamou 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 and Amamou 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).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou and Ren et al (CN 113254641) (hereafter, “Ren”).
Regarding Claim 10, Zeng in view of Amamou 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 and Amamou 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 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 Ren 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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zeng in view of Amamou and Tran (PGPub 20220237368) (hereafter, “Tran”).
Regarding Claim 11, Zeng in view of Amamou 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 and Amamou 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 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 Tran 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.
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
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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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628