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 9/08/2026.
Claims 1, 3-15, 17-18, and 20 have been amended and are hereby entered.
Claims 21-23 have been added.
Claims 2, 16, and 19 have been canceled.
Claims 1, 3-15, 17-18, and 20-23 are currently pending and have been examined.
This action is made FINAL.
Response to Applicant’s Arguments
Claim Interpretation
Applicant first presents arguments under the heading of “Claim Interpretations - 35 U.S.C. 112(f),” yet there are no pending 112(f)-based interpretations and the content of these arguments relate to the separate issue of contingent limitations of method claims. While such contingencies are a matter of claim interpretation, they have nothing to do with the standards of 112(f).
Despite extensive discussion of the argued contingency issue of the method of Claim 1 in the two most recent Office Actions, and despite such explanations containing a roadmap to an easy fix for this issue, both the present claim amendments and the present Remarks continue to ignore the root cause of this contingency. Instead, Applicant takes the bizarre approach of attempting to create a hybrid claim, seemingly on the theory that limitations of a method claim would somehow be interpreted as if the claim belonged to the separate statutory category of a machine/system instead. Applicant’s attempts to this end are unsuccessful: Claim 1 is and remains a method claim, and the high-level claiming of the method thereof being executed by “an autonomous usage and permissions system on a computing platform” does not make this otherwise. As such, the recited steps of this method are interpreted accordingly as method steps, rather than as limiting the structure and functionalities of a system claim. Indeed, it is fortunate that Applicant’s attempt to create a hybrid claim is unsuccessful, as such hybrid claims are improper and would require rejection under both 112(b) and 101 Step 1 grounds, the hypothetical future correction of which would revive this contingency issue.
Examiner reiterates this root cause in relation to Claim 1, as explained in the Non-Final Rejection of 6/08/2026: “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 […] 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.”
Examiner again provides suggestion of a simple solution to this issue: based on the present claim drafting, Examiner recommends amending “executing an artificial intelligence recognition process to recognize a format of the given request” to “using an artificial intelligence recognition process, determine a format of the given request to be one of an unstructured format or a structured format having an invalid schema” or similar. By requiring only one of these two potential results to occur (ie: avoiding the possibility of other results such as a structured format having a valid schema), with subsequent method steps provided for analysis of each of these two potential results (ie: as presently amended), these functions avoid categorization of contingent limitations of a method claim as the claim would then address the entire scope of potential outcomes (see MPEP 2111.04). Examiner also suggests caution regarding the drafting of any dependent claim limitations which might no longer properly relate back to this limitation once amended.
Examiner does agree with Applicant that the present amendments to Claims 3-4 obviate the previous contingencies thereof.
Claim Rejections – 35 USC § 112
The present amendments to the claims obviate the previous 112(a) and 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 on the topic of Step 2A, Prong One, in significant part merely modifying arguments of previous Remarks in view of the amended claim language, continue to improperly conflate the distinct steps and standards of Step 2A, Prongs One and Two in the same manner as has now been explained in multiple previous Office Actions. These arguments further contain the same misapprehensions of these individual standards likewise explained in multiple previous Office Actions.
These arguments do not address these previously provided explanations, instead seemingly ignoring them such that the present arguments continue to contain the same foundational flaws which render these arguments unpersuasive before even addressing the specific merits thereof. For example, these arguments continue to demonstrate lack of comprehension of the distinction between recitation of abstract ideas under the Prong One analysis (e.g., which is performed on a limitation-by-limitation basis, contrary to Applicant’s various assertions of recitation based on a claim “as a whole,” “when viewed in its entirety,” etc.) and whether a claim is directed to a recited judicial exception under the Prong Two analysis (e.g., which, in contrast to Prong One, is performed based on the claim as a whole; and for which the multiple abstract ideas recited under the Prong One analysis are not parsed based on the categories recited but rather taken together as a singular abstract idea – see MPEP 2106.04(II)(B)).
In view of the above, Applicant is strongly encouraged to review the explanations provided in the two most recent Office Actions in order to better understand the distinct steps and standards of the 101 subject matter eligibility analysis, and to draft any future arguments from scratch to avoid the continued incorporation of these foundational flaws (resulting in continued misapplication of these standards to the claim language).
Regarding recitation of mental processes under Prong One, even ignoring the foundational flaws noted above and previously, Applicant’s conclusory assertions that “a human mind cannot execute an autonomous usage and permissions system on a computing platform” and “a human mind cannot execute artificial intelligence processes using trained artificial intelligence models to perform the claimed functions” are unpersuasive, demonstrating a misapprehension of how the Prong One inquiry is performed and a misreading of the language of the previous 101 rejections themselves.
Firstly, while Examiner generally agrees that the listed elements of “an autonomous usage permission system on a computing platform” and “artificial intelligence processes using trained artificial intelligence models” themselves constitute non-abstract additional elements (and indeed, the various forms in which these elements have been claimed in the prosecution of this application have always been treated thusly), these elements are nowhere to be found in the lists of recited abstract ideas in the previous 101 rejections. As these elements are not part of the recited abstract ideas identified in the previous rejections, these arguments fail to address the actual content of said rejections. Relatedly, that one claim element does not recite a judicial exception (in this case, an abstract idea) under the Prong One analysis in no way prevents any other claim element from doing so. Indeed, were this the case, this would render Steps 2A, Prong Two and 2B entirely superfluous, which is clearly not the case.
Secondly, it has been well-settled for well over a decade at this point that computer-based execution of an abstract step does not prevent that step from reciting abstract ideas. See the Final Rejection of 2/24/2026 for more information, wherein Examiner explained the erroneous nature of this notion in response to highly similar argument. Specifically regarding the artificial intelligence-based argument, as in the Final Rejection of 2/24/2026, Applicant is again directed to the July 2024 PEG Update and the additional Examples thereof, which demonstrate Applicant’s flawed application of these standards to AI-based functions. Such AI-based functions, despite the AI models themselves constituting non-abstract additional elements, may nonetheless recite abstract ideas of any of the three enumerated categories depending upon what these functions actually are/do. Merely continuing to argue AI-based effectuation does not presently and will not in the future (barring a significant change in the law) prevent recitation of abstract ideas.
Regarding the separate abstract category of certain methods of organizing human activity, Applicant continues to misapprehend the standards of Prong One in the multiple ways addressed above and previously (e.g., failing to distinguish between “recited” under Prong One and “directed to” under Prong Two, seemingly erroneously using these standards interchangeably; considering the claim as a whole, which is not how Prong One is applied), and consequently misapply these standards to the claim language. These arguments do not address the actual content identified as reciting certain methods of organizing human activity identified in the previous 101 rejections, but instead makes similar AI-based arguments and unsupported subjective assertions as to what the present invention is “directed to” (again, a concern of Prong Two, for which the standards for recitation of the individual category of certain methods of organizing human activity is not relevant, rather than Prong One). Again, see the Final Rejection of 2/24/2026 for more information on this standard, as well as the various citations provided in relation thereto.
Regarding Step 2A, Prong Two, Applicant again argues the same purported improvement to a technology argued and refuted in multiple previous rounds of prosecution. Even as presently amended, these arguments contain essentially the same flaws and shortcomings explained in multiple previous Office Actions. While the claims as presently amended now acknowledge the necessarily different processes which must occur in analyzing unstructured request data versus structured data with an invalid schema, which to be clear is a move in the right direction in terms of the claims actually reflecting the argued improvement to a technology, each of these distinct processes continues to recite merely the idea of a solution rather than actually reflecting an improvement to a technology (see MPEP 2106.05(a) and the previous Office Actions for more information).
Even with these two processes bisected in the claims as presently amended, each is still drafted as “no more than a black box-type claiming of this solution” as explained in the Non-Final Rejection of 6/08/2026. Rather than properly evidencing and reflecting an improvement to a technology, both the presently updated arguments (including the citations of the original disclosure therein) and presently amended claims boil down to little more than a nebulous statement to do it with AI. The claims merely recite the application of an AI process using a trained AI model to the request data, and provide some description of the results of this process (e.g., “first data including entity-value pairs”), while providing no technical details as to how this AI process and model would function to accomplish this result. The present arguments and cited passages of the original disclosure are little better, making various assertions of the present invention’s ability to interpret such unstructured or structured but invalid formats (which would purportedly cause prior art systems to crash/fail) without any explanation as to how the AI/machine learning processes of the present invention function to do so. Without such technical details, this argued improvement to a technology continues to represent merely the idea of a solution, and as such could not reasonably be recognized by one of ordinary skill in the art as an improvement to a technology. Applicant is again encouraged to review the previous Office Actions for more information on the shortcomings of Applicant’s continued approach here, and to claim and argue such technical details of how the claimed models achieve this result should the original disclosure actually contain them. If the original disclosure does not contain such technical details, then the present application fails to meet the evidentiary standards for this purported improvement (see, e.g., MPEP 2106.04(d)(1)).
On this same topic, Examiner further discusses the limited value of the passages of the original disclosure provided in these arguments, as (1) they discuss the improvement of computer interpretation of unrecognized/invalid data formatting as an idea, failing to provide the requisite technical details to actually achieve the asserted outcome (ie: as explained above and previously), and (2) the discussion of the prior art and problems thereof in these passages appears reductive and incomplete, at least in view of various previously recited references which indicate the prior art does in fact contain similar AI-based attempts to solve the very problems asserted by Applicant. For clarity regarding point (2), subject matter eligibility under 101 is distinct from novelty under 102 and obviousness under 103, but such references are relevant under the Step 2A, Prong Two analysis in establishing whether asserted technological problems were actually problems of the prior art at an application’s date of filing and similarly whether the asserted solution to this problem was already established in the prior art at this date of filing, thus indicating whether an argued technological improvement would actually be considered such at this date of filing or whether it would merely be considered within the state of the art at the time.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are in part persuasive and in part unpersuasive.
Regarding independent Claims 15 and 18, Examiner finds the claims as presently amended to be novel and non-obvious, as the number of references and manner of combination of these references required to disclose every element thereof would be unreasonable. As such, the 103 rejections are withdrawn for these claims.
Claim 1, being a method claim, does not require this same unreasonable combination of references. The contingency issues thereof, discussed previously, above in this same Response section, and below in the Claim Interpretation section, renders much of Claim 1 (and limitations of several dependent claims by extension) lacking in patentable weight. As noted below in the Claim Interpretation section, Claim 1 is being interpreted for the purposes of this examination as if these limitations did have patentable weight. This is a courtesy in the interests of compact prosecution.
However, even in light of this interpretation (ie: as if the “executing…” limitation were amended in line with the exemplary language provided above to cure this issue), the requirements for 103 obviousness of method Claim 1 are not identical to the requirements for system Claim 15 or product Claim 18. Specifically, in a method of Claim 1, even when this contingency issue is cured, the two roots “in response to recognizing the format of the given request as having an unstructured format” and “in response to recognizing the format of the given request as having a structured schema format which is invalid” form a Markush Group, as a method does not and cannot require both approaches (ie: the given request cannot both have an unstructured format and a structured format with an invalid schema; these are mutually exclusive). As both of these paths are not required in a singular embodiment of the claimed method, art need only be cited for one of these paths to render the claim obvious. This differs for system and product claims, wherein the claimed system or product must be capable of addressing both of these claimed approaches (see Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016)). Applicant may wish to review at least MPEP 2117, 2173.05(h), and 2111.04 regarding these standards and the intersection thereof.
Claim Interpretation
Claim 1 contains the following limitation: “executing an artificial intelligence recognition process to recognize a format of the given request.” In conjunction with this determination, every limitation which follows stems from one of the following roots: “in response to recognizing the format of the given request as having an unstructured format” or “in response to recognizing the format of the given request as having a structured schema format which is invalid.” These roots represent conditional language, as Claim 1 does not require a determination that the request comprises either 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 roots constitutes a contingent limitation of a method claim, and as such is not given patentable weight. For the purposes of this examination, these limitations are interpreted as if they were did have patentable weight, ie: as though the two roots above form a Markush Group.
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 8-10 and 21-22 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 8 and 21 each contain the term “a third named entity recognition model.” The presence of this term in these claims is indefinite, as no first or second named entity recognition models are disclosed in these respective claim strings, thus making it unclear as drafted whether this “third” named entity recognition model is intended to necessarily imply the unclaimed presence of first and second named entity recognition models in these claims, and if so, it is further unclear as drafted what these first and second named entity recognition models are or how they function within these claims. To further explain this issue, Claims 8 and 21 do not depend upon Claims 3-4 and 20; thus the various named entity recognition models thereof do not operate to clarify the meaning of “third” in “a third named entity recognition model.” For the purposes of this examination, “a third named entity recognition model” in these claims will be interpreted as “a named entity recognition model.” Should future amendments affect the dependencies of these claims and/or antecedent basis of these terms, Applicant is encouraged to further modify this language accordingly to avoid similar issues of indefiniteness.
Claims 9 and 22 contain the terms “a fourth artificial intelligence process” and “a fourth trained artificial intelligence model.” These terms are indefinite for similar reasons as “a third named entity recognition model” in Claims 8 and 21 above. Particularly, the presence of these terms in these claims is indefinite as, while Claims 1 and 18 each disclose first and second AI processes and models respectively, no third artificial intelligence process or third trained model are disclosed in these respective claim strings, thus making it unclear as drafted whether these “fourth” process and model are intended to necessarily imply the unclaimed presence of a third process and third model in these claims, and if so, it is further unclear as drafted what these third process and third model are or how they function within these claims. For the purposes of this examination, “a fourth artificial intelligence process” and “a fourth trained artificial intelligence model” will be interpreted as “an additional artificial intelligence process” and “an additional trained artificial intelligence model” respectively. Claim 10 is rejected due to its dependence upon Claim 9 and its inclusion of the term “the fourth trained artificial intelligence model.” Should future amendments affect the dependencies of these claims and/or antecedent basis of these terms, Applicant is encouraged to further modify this language accordingly to avoid similar issues of indefiniteness.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-15, 17-18, and 20-23 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 given request to establish one or more usage and permission parameters associated with a given product; executing a recognition process to recognize a format of the given request; in response to recognizing the format of the request as having an unstructured format, executing a first process using at least a first model to analyze content of the given request and generate first data including entity-value pairs for use in establishing the one or more usage and permission parameters associated with the given product; in response to recognizing the format of the request as having a structured schema format which is invalid, executing a second process using at least a second model associated with the structured schema format to analyze content of the given request to identify and extract one or more entities and associated values in the given request, which correspond to entities of a valid format of the structured schema, to generate second data including entity-value pairs for use in establishing the one or more usage and permission parameters associated with the given product; applying at least a portion of the generated first data or the generated second data to an approval feedback process; and generating the one or more usage and permission parameters based on the generated first data or the generated second data 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 given request to establish one or more usage and permission parameters associated with a given product; executing a recognition process to recognize a format of the given request; in response to recognizing the format of the request as having an unstructured format, executing a first process using at least a first model to analyze content of the given request and generate first data including entity-value pairs for use in establishing the one or more usage and permission parameters associated with the given product; in response to recognizing the format of the request as having a structured schema format which is invalid, executing a second process using at least a second model associated with the structured schema format to analyze content of the given request to identify and extract one or more entities and associated values in the given request, which correspond to entities of a valid format of the structured schema, to generate second data including entity-value pairs for use in establishing the one or more usage and permission parameters associated with the given product; applying at least a portion of the generated first data or the generated second data to an approval feedback process; and generating the one or more usage and permission parameters based on the generated first data or the generated second data 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 permissions 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; wherein the given product is a software product or a hardware product; an artificial intelligence recognition process; a first artificial intelligence process using at least a first trained machine learning model; and a second artificial intelligence process using at least a second trained machine learning model. An autonomous usage and permissions 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; a first artificial intelligence process using at least a first trained machine learning model; and a second artificial intelligence process using at least a second trained machine learning model, 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)). Wherein the given product is a software product or a hardware product, in the context of the claims as a whole, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). 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, and generally linking the use of a judicial exception to a particular technological environment or field of use 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 3-14, 17, and 20-23, 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.
Claim 3 discloses wherein the first trained artificial intelligence model comprises a first named entity recognition model (mere instructions to apply a judicial exception); and the first 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 the second trained artificial intelligence model comprises a second named entity recognition model (mere instructions to apply a judicial exception); 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 executing a third artificial intelligence process using at least a third trained artificial intelligence model (mere instructions to apply a judicial exception) to determine one or more dependency relationships between the one or more entities in the given 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 the third trained artificial intelligence model comprises a third named entity recognition model that is trained (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 executing a fourth artificial intelligence process using at least a fourth trained artificial intelligence model (mere instructions to apply a judicial exception) to augment one or more recognized entities from the given 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 the fourth trained artificial intelligence model comprises 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.
Claims 11 and 23 disclose 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 and 5-6, and do not integrate the claims into a practical application for the same reasons expressed above regarding these claims.
Claim 21 discloses the limitations of Claims 7-8, and does not integrate the claims into a practical application for the same reasons expressed above regarding these claims.
Claim 22 discloses the limitations of Claims 9-10, and does 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, 3-9, and 12-14 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 Claim 1, Zeng discloses:
Implementing/executing 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 for managing 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 managing usage and permission requests for products comprises: obtaining a given request to establish one or more usage and permission parameters associated with a product including a software product or a hardware product (Abstract; ¶ 0009, 0037; Fig. 1; methods and apparatus for controlling data permission; 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 Poitier does disclose executing an artificial intelligence recognition process to recognize a format of the given 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.).
Zeng additionally discloses executing a first process to analyze content of the given request and generate first data for use in establishing the one or more usage and permission parameters associated with the given 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 first process is a first artificial intelligence process using at least a first trained artificial intelligence model; wherein processing the request using a first artificial intelligence process occurs in response to recognizing the format of the given request having an unstructured format; wherein the first data includes entity-value pairs (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).
As this is a method claim, no art need be mapped to the limitation “in response to recognizing the format of the given request as having a structured schema format which is invalid, executing a second artificial intelligence process using at least a second trained artificial intelligence model associated with the structured schema format to analyze content of the given request to identify and extract one or more entities and associated values in the given request, which correspond to entities of a valid format of the structured schema, to generate second data including entity-value pairs for use in establishing the one or more usage and permission parameters associated with the given product” as this is an alternative method step to the limitation addressed immediately above (see Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016)).
Zeng does not explicitly disclose but Amamou does disclose applying at least a portion of the generated first data or the generated second 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 generating the one or more usage and permission parameters based on the generated first data or the generated second data generated by the first process (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 first process is the first artificial intelligence process; 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). 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 Claim 3, Zeng in view of Amamou and Poirier discloses the limitations of Claim 1. Zeng does not explicitly disclose but Amamou does disclose the first trained artificial intelligence model comprises a first named entity recognition model; and the first 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 4, Zeng in view of Amamou and Poirier discloses the limitations of Claim 1. The method of Claim 4 is disclosed by Zeng in view of Amamou and Poirier as set forth above in Claim 1, as the additional limitations of Claim 4 relate to the method step alternative to the “in response to recognizing the format of the given request having an unstructured format…” step selected in the rejection of Claim 1 above (see Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016)).
Regarding Claim 5, Zeng in view of Amamou and Poirier discloses the limitations of Claim 1. 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 1. 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 1. Zeng does not explicitly disclose but Amamou does disclose executing a third artificial intelligence process using at least a third trained artificial intelligence model to determine one or more dependency relationships between the one or more entities in the given 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 the third trained artificial intelligence model comprises a third named entity recognition model that is trained 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 1. Zeng does not explicitly disclose but Amamou does disclose further comprising executing a fourth artificial intelligence process using at least a fourth trained artificial intelligence model to augment one or more recognized entities from the given 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 1. 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 1. 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 1. 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.
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 the fourth trained artificial intelligence model 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 1. 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.
Novel/Non-Obvious Subject Matter
Claims 15 and 18 contain novel and non-obvious subject matter. The following is a statement of reasons for the indication of novel and non-obvious subject matter: while various references (such as those previously cited) teach the elements of these claims in isolation, none teach or suggest the respective specific sequences of logical steps and logical operations in the context of the systems and methods recited for permission management. Further, combining these references with the references presently cited as disclosing the limitations of the independent claims to reach these respective specific sequences of logical steps of logical operations would be unreasonable based on the number of and the manner of combining said references that would be required to do so.
Claims 17-18 and 20-23 contain novel and non-obvious subject matter based on their dependence upon Claims 15 and 18 respectively.
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
