70Notice 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 .
Response to Amendment
The office action is responsive to the amendment filed on 01/27/2026. As directed by the amendments Claims 2, 4-7, 9, 12, 14-17, 19, 22-23 and 25-26 were previously cancelled without prejudice, and claims 21-32 were previously added. Claims 1, 3, 8, 10-11, 18, 20-21, 24 and 27-32 remain pending. Claims 1, 11 and 20 are amended herein to facilitate expeditious prosecution of the application.
Response to Arguments
Regarding the 35 U.S.C § 101 Rejection:
Applicant's further arguments see pg. 9-13 filed 01/27/2026, have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues the rejection of claims 1, 3-5, 8-11, 13-15 and 18-26 under §101. Specifically applicant argues, the claims are not directed to an abstract idea and “clearly integrate any such abstract idea into practical applications that provide improvements in computer technology”.
Further “applicant traverses the conclusory allegation that the limitations in question - nearly *all* limitations of the independent claims - are "insignificant extra solution activity." [And] applicant questions (and the Office Action provides no support evidence or explanation of) the plausibility of executing the claimed method *without* such required "activity." In support of Applicant's traversal of this particularly general and non-specific rationale, Applicant reiterates that Ex Parte Desjardins et al. expressly instructs away from such generalities on the part of Examiners”. In addition applicant submits that the amended independent claims expressly recite the technological benefit of improving predictions generated by graph node label prediction models, and explicitly generating such improvements to the specific machine learning technology in question by reducing group-dependent errors and enhancing error distributions across groups for the corresponding machine learning models, as required and recited in the independent claims”.
Accordingly, Applicant requests withdrawal of the rejection under § 101.
EXAMINER RESPONSE: Examiner respectfully disagree, applicant argument is not persuasive. For example, amended claim 1 as presented is directed toward an abstract idea such as a mental process of predicting labels associated with graphs, determining fairness based constrained based on the centrality measurement associated with the node in the graph, and a mathematical concept of generating an updated version of model by integrating the group fairness-based constraints in conjunction with at least one divergence measure as at least one regularization term into loss function of the model can be all performed in the human mind with the aid of pen and paper (see MPEP 2106.04(a)(2)). Further, amended claim 1 as presented does not integrate into a practical application under the second prong of the two-prong analysis since the claimed invention do not improves the functioning of a computer or improves another technology or technical field. Rather the claim recites additional element of:
obtaining at least one input graph, wherein obtaining at least one input graph comprises obtaining at least one of a directed graph, an undirected graph, an unweighted graph, and a weighted graph;
...by processing at least a portion of the at least one input graph using a graph node label prediction model, wherein the graph node label prediction model includes at least one loss function;
performing one or more automated actions using the updated version of the graph node label prediction model ...by processing at least a portion of the one or more additional graphs using the updated version of the graph node label prediction model, and automatically training the updated version of the graph node label prediction model based at least in part on the one or more predicted node labels associated with the one or more additional graphs;
wherein the method is carried out by at least one computing device.
That merely recites the words "apply it" (or an equivalent) with the judicial exception, as discussed in MPEP § 2106.05(f) and adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) which the courts have identified such limitations do not integrate a judicial exception into a practical application (see MPEP 2106.04(d)(I)).
Further, the claims of the Ex Parte Desjardins et al. were found to be an improvement since when evaluating the claim as a whole with the disclosure the claims “allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." In the instant application, claim 1 and similar independent claims 11 and 20 where amended to recite “improving predictions generated by the graph node label prediction model via reducing group-dependent errors and enhancing error distributions across groups, wherein improving the predictions comprises..”, however, examiner will like to emphasize such improvement is not disclosed in the specification such that the improvement would be apparent to one of ordinary skill in the art. The applicant is reminded that the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement and that the specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. (See MPEP 2106.05 (a)). While, applicant states “support for the amendment can be found, for example, on pages 4-5, paragraphs [0015-0017] and pages 5-6, paragraphs [0018]-[0020] of the specification”, the cited portions of the specification and the specification as whole when evaluated does not provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field (MPEP § 2106.04(d)(1)). In particular, paragraphs [0015-0017] and [0018]-[0020] does not teach or suggest any reduction of group dependent error and any enhancement of error distribution across groups and does not teach or suggest how the graph node label prediction model predictions are being improved via reducing group-dependent errors and enhancing error distributions across groups. Rather the cited paragraphs teach how the training objective (i.e., loss function) of the graph node label prediction model is being modify with a regularization term such as a Kullback-Leibler (KL) divergence term by degree term to enable fairness prediction. Therefore, amended indepepnt claims 1, 11 and 20 when view as whole do not improves technology.
Accordingly, claims 1, 3, 8, 10-11, 13, 18, 20-21, 24, 27-32 are not patent eligible under 35 U.S.C § 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 8, 10-11, 13, 18, 20-21, 24, 27-32 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 1 recites “improving predictions generated by the graph node label prediction model via reducing group-dependent errors and enhancing error distributions across groups, wherein improving the predictions comprises..”, however, the specification does not teach or suggest the newly added limitation. In particular, paragraphs [0015-0017] and [0018]-[0020] does not teach or suggest any reduction of group dependent error and any enhancement of error distribution across groups and does not teach or suggest how the graph node label prediction model predictions are being improved via reducing group-dependent errors and enhancing error distributions across groups. Rather the cited paragraphs teach how the training objective (i.e., loss function) of the graph node label prediction model is being modify with a regularization term such as a Kullback-Leibler (KL) divergence term by degree term to enable fairness prediction. Thus, the specification does not show any reduction in group-dependent errors, any enhancing of error distribution across groups and any improvement to the graph node label prediction model predictions via reducing group-dependent errors and enhancing error distributions across groups as recited in amended claim 1.
Independent claims 11 and 20 recites limitations similar to those of claim 1, therefore are rejected under the same rationale of claim 1.
Claims 3, 8, 10, 13, 18, 21, 24, 27-32 are dependent claims 1,11 and 20, thus are rejected for reasons set forth in the rejection of claims 1,11, and 20.
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, 8, 10-11, 13, 18, 20-21, 24, 27-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1, 3, 10, and 27-28 are a method type claim. Claims 11,13,18 and 29-30, are a computer program product claim. Claims 20-21,24, and 31-32 are a system type claim. Therefore, claims 1, 3, 8, 10-11, 13, 18, 20-21, 24, 27-32 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: 2A Prong 1:
predicting one or more node labels associated with the at least one input graph... ( mental process – of predicting the labels associated with an input graph can be performed by the human mind with the help of pen and paper ( e.g., judgement)).
determining one or more group fairness-based constraints relevant to the at least one input graph based at least in part on one or more centrality measures associated with at least a portion of nodes in the at least one input graph,... ( mental process of - determining at least a portion of the one or more group fairness can be performed by the human mind with the help pf pen and paper (e.g., evaluation)).
...wherein determining the one or more group fairness-based constraints comprises determining, based at least in part on the one or more centrality measures, one or more group fairness-based constraints such that a penalty for incorrect predictions of labels for one or more low-degree nodes in the at least one input graph is more significant than a penalty for incorrect predictions of labels for one or more high-degree nodes in the at least one input graph; ( mental process – of determining at least a portion of the one or more group fairness-based constraints can be performed by the human mind with the help pf pen and paper (e.g., judgement)).
improving predictions generated by the graph node label prediction model via reducing group-dependent errors and enhancing error distributions across groups, wherein improving the predictions comprises generating an updated version of the graph node label prediction model based at least in part on the one or more predicted node labels and the one or more group fairness-based constraints relevant to the at least one input graph, wherein generating the updated version of the graph node label prediction model comprises integrating, into the at least one loss function of the graph node label prediction model, the one or more group fairness-based constraints as at least one regularization term; and (mathematical concept – of generating an updated version of model by integrating the group fairness-based constraints in conjunction with at least one divergence measure as at least one regularization term into loss function of the model can by performed with the help of pen and paper. For example, applicant specification paragraphs [0015], [0019] and [0023] teaches the mathematical functions being used to integrate the regularization term into the loss function of the model (e.g., mathematical calculation)).
...predicting one or more node labels associated with one or more additional graphs... ( mental process – of predicting the labels associated with graphs can be performed by the human mind with the help of pen and paper ( e.g., judgement)).
2A Prong 2: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining at least one input graph, wherein obtaining at least one input graph comprises obtaining at least one of a directed graph, an undirected graph, an unweighted graph, and a weighted graph; (This is understood to be insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g)).
...by processing at least a portion of the at least one input graph using a graph node label prediction model, wherein the graph node label prediction model includes at least one loss function; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
performing one or more automated actions using the updated version of the graph node label prediction model ...by processing at least a portion of the one or more additional graphs using the updated version of the graph node label prediction model, and automatically training the updated version of the graph node label prediction model based at least in part on the one or more predicted node labels associated with the one or more additional graphs; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
wherein the method is carried out by at least one computing device. (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining at least one input graph, wherein obtaining at least one input graph comprises obtaining at least one of a directed graph, an undirected graph, an unweighted graph, and a weighted graph; (This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II).
...by processing at least a portion of the at least one input graph using a graph node label prediction model, wherein the graph node label prediction model includes at least one loss function; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
performing one or more automated actions using the updated version of the graph node label prediction model...by processing at least a portion of the one or more additional graphs using the updated version of the graph node label prediction model, and automatically training the updated version of the graph node label prediction model based at least in part on the one or more predicted node labels associated with the one or more additional graphs; (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
wherein the method is carried out by at least one computing device. (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 11: is rejected under the same rational of claim 1. Claim 11 only recites the additional elements of A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 20: is rejected under the same rational of claim 1. Claim 20 only recites the additional elements of A system comprising: a memory configured to store program instructions; and a processor operatively coupled to the memory to execute the program instructions to... which is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f).
Regarding claim 3: Depends on claim 1, thus the rejection of claim 1 is incorporated.2A Prong 1:
learning at least one classifier associated with the one or more group fairness-based constraints ( mental process of – learning classifier associated with group fairness-based constraints can be performed by the human mind with the help of pen and paper (e.g., observation)).
2A Prong 2 & 2B: None.
Regarding claim 8: Depends on claim 1, thus the rejection of claim 1 is incorporated.2A Prong 1: None.
2A Prong 2 & 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
Additional elements:
wherein performing one or more automated actions comprises outputting the updated version of the graph node label prediction model to at least one user (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 10: Depends on claim 1, thus the rejection of claim 1 is incorporated.2A Prong 1: None.
2A Prong 2 & 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
Additional elements:
wherein software implementing the method is provided as a service in a cloud environment (This is directed to restricting the abstract idea to a Particular Technological Environment. See MPEP 2106.05(h)).
Regarding claim 13 and 21: See rejection of claim 3, same rational applies.
Regarding claim 18 and 24: See rejection of claim 8, same rational applies.
Regarding claim 27: Depends on claim 1, thus the rejection of claim 1 is incorporated.2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the one or more centrality measures associated with the at least a portion of nodes in the at least one input graph comprises at least one of a degree centrality measure and a betweenness centrality measure (This is directed to restricting the abstract idea to a particular technological environment. See MPEP 2106.05(h)).
Regarding claim 28: Depends on claim 1, thus the rejection of claim 1 is incorporated.2A Prong 1:
wherein determining the one or more group fairness-based constraints comprises implementing a divergence term by degree term operation (mathematical concept – of determining the one or more group fairness-based constraints by implementing divergence term by degree term operation such as
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(see applicant specification para. [0015]) (e.g., mathematical calculation)).
2A Prong 2 and 2B: None.
Regarding claim 29 and 31: See rejection of claim 27, same rational applies.
Regarding claim 30 and 32: See rejection of claim 28, same rational applies.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GISEL G FACCENDA whose telephone number is (703)756-1919. The examiner can normally be reached Monday - Friday 8:00 am - 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.G.F./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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