Final Rejection
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 .
Claims 5, 7, 10-17, and 19 are rejected under 35 U.S.C. 101
Claims 1-3, 5-17, and 19 are rejected under 35 U.S.C. 103
Claims 4, 18, and 20 have been cancelled by Applicant
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 5, 7, 10-17, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite mathematical relationships, concepts, and equations, as well as mental processes. This judicial exception is not integrated into a practical application because it generally links the abstract ideas to a particular technology/field of use. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they include mere instructions to perform abstract ideas on a generic computer and mere data gathering.
These claims involve predictions, which are considered judgments, and specify that the training comprises mathematics. Please note that, “The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application… even if an independent claim is determined to be eligible, a dependent claim may be ineligible because it adds a judicial exception without also adding limitations that integrate the judicial exception or provide significantly more. Thus, each claim in an application should be considered separately based on the particular elements recited therein” (MPEP 2106.07) and that only one abstract idea is needed to trigger the analysis (MPEP 2106.04(II)(A)).
Claim 5
Step 2A Prong 1: Identification of Abstract Ideas
Claim 5 recites:
further comprising adjusting the confidence score (MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas; MPEP 2106.04(a)(2)(III)(A), a judgment based on an observation is a mental process; MPEP 2106.04(a)(2)(I)(C), “A mathematical calculation is … an act of calculating using mathematical methods to determine a variable or number.” Note that “There is no particular word or set of words that indicates a claim recites a mathematical calculation.” “A step of ‘determining’ a variable … may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”) by comparing the response team prediction object with (MPEP 2106.04(a)(2)(III)(A), a judgment based on an observation is a mental process) at least one of the user input (MPEP 2106.04(a)(2)(III)(A), a user input is considered a mental process of a judgment; MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity) …
Step 2A Prong 2: Identification of Additional Elements
Claim 5 recites:
… or a closing alert (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Step 2B: Significantly More Analysis
The additional elements of the claim do not integrate the abstract ideas into a practical application. The claims simply state mental processes and mathematics and link the judicial exception to a particular technology (MPEP 2106.05(h)) via the selection of data related to said technology (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Claim 7
Step 2A Prong 1: Identification of Abstract Ideas
Claim 1 recites:
wherein the confidence score (MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas) … to determine one or more future response team predictions (MPEP 2106.04(a)(2)(III)(A), “evaluations, judgments, and opinions,” are mental processes).
Step 2A Prong 2: Identification of Additional Elements
Claim 1 recites:
… is applied to the responder prediction machine learning model (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) …
Step 2B: Significantly More Analysis
The additional elements of the claim do not integrate the abstract ideas into a practical application. The claims simply state mental processes and mathematics with mere instructions to perform these abstract ideas on a generic computer (MPEP 2106.05(f)(3)). The computer is cited at such a high level of generality that it cannot be determined to be a particular machine (MPEP 2106.05(b)) and is simply linking the judicial exception to a particular technology (MPEP 2106.05(h)). The claim specifies that the training process contains math, and that the output of the model is a determined prediction.
Claim 10
Step 2A Prong 1: Identification of Abstract Ideas
… determine a response team prediction object (MPEP 2106.04(a)(2)(III)(A), “observations, evaluations, judgments, and opinions,” are mental processes) wherein each response team prediction object comprises a confidence score (MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas; MPEP 2106.04(a)(2)(I)(C), “A mathematical calculation is … an act of calculating using mathematical methods to determine a variable or number.” Note that “There is no particular word or set of words that indicates a claim recites a mathematical calculation.” “A step of ‘determining’ a variable … may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”) …
… generate user feedback data based on a user input (MPEP 2106.04(a)(2)(III)(A), a human opinion or judgment is a mental process) …
Step 2A Prong 2: Identification of Additional Elements
Claim 10 recites:
An apparatus for generating a response team prediction associated with one or more alerts (MPEP 2106.05(h)(vi), limiting the data collection and analysis to a particular field of use does not integrate the abstract idea into a practical application; MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application), the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and program code configured to, with the processor, cause the apparatus to at least (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application):
receive the one or more alerts from an alert monitoring service tool (MPEP 2106.05(g), mere data gathering is considered insignificant extra-solution activity; MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity; MPEP 2106.05(f)(2), “using a computer in its ordinary capacity … e.g. to receive, store, or transmit data … does not integrate a judicial exception into a practical application”);
for each of the one or more alerts, apply a responder prediction machine learning model to (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) … indicating a confidence level attributed to a predicted response team for an incident alert (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
cause rendering of a response team suggestion interface based on the response team prediction object (MPEP 2106.05(g), the display of data is considered insignificant extra-solution activity);
… via the response team suggestion interface associated with a responder prediction data object generated by the responder prediction machine learning model (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer are not enough to integrate the claim into a practical application; MPEP 2106.05(g), the display and output of data is considered insignificant extra-solution activity);
and transmit the user feedback data to the responder prediction machine learning model to train the responder prediction machine learning model based on the user feedback data (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer are not enough to integrate the claim into a practical application; MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity; MPEP 2106.05(d), well-understood, routine, and conventional activities do not favor eligibility).
Retraining a machine learning model based off feedback, particularly that of a user, is well-understood, routine, and conventional, as shown by:
Stack Overflow, “Incorporating User Feedback in a ML Model,” 2017
Data Camp, “Feedback Loop, Re-Training, and Labeling”
Chen et al., “Perspectives on Incorporating Expert Feedback into Model Updates,” Patterns, Vol. 4, Issue 7, 2023
Step 2B: Significantly More Analysis
The additional elements of the claim do not integrate the abstract ideas into a practical application. The claims simply state mental processes and mathematics with mere instructions to perform these abstract ideas on a generic computer (MPEP 2106.05(f)(3)). The computer is cited at such a high level of generality that it cannot be determined to be a particular machine (MPEP 2106.05(b)) and is simply linking the judicial exception to a particular technology (MPEP 2106.05(h)). The claim recites only the idea of a solution but fails to recite details as to how the solution to the problem is accomplished, because it leaves a majority of the analysis to the generic computer (MPEP 2106.05(f)(1)). Certain limitations, such as incorporating user feedback, are well-understood, routine, and conventional, and therefore do not amount to an inventive concept (MPEP 2106.05(d)).
Claim 11
Claim 11 recites:
wherein the response team prediction object is transmitted to a prediction service API that is configured to indicate an alert notification (MPEP 2106.05(g), the display of data is considered insignificant extra-solution activity) comprising at least one of the response team prediction, a dataset of routing information associated with at least a client identifier set for the response team prediction, or an alert associated with the response team prediction (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Claim 12
Claim 12 recites:
wherein the responder prediction machine learning model comprises a pre-training with an extracted alert related dataset associated with a complex platform (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application; MPEP 2106.05(g), mere data gathering is considered insignificant extra-solution activity).
Claim 13
wherein the extracted related dataset comprises data extracted from a predetermined time period (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Claim 14
Claim 14 recites:
for each of the one or more alerts, apply a prioritization machine learning model to determine a prioritization weight for each alert (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application; MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas; MPEP 2106.04(a)(2)(I)(C), “A mathematical calculation is … an act of calculating using mathematical methods to determine a variable or number.” Note that “There is no particular word or set of words that indicates a claim recites a mathematical calculation.” “A step of ‘determining’ a variable … may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”).
Claim 15
Claim 15 recites:
wherein an operation sequence of processing for the responder prediction machine learning model is applied to the one or more alerts based on the prioritization weight for each of the one or more alerts (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity; MPEP 2106.04(a)(2)(III)(A), a judgment of how to organize data is a mental process).
Claim 16
Claim 16 recites:
wherein an operation sequence for determining the response team prediction object is applied to the one or more alerts based on the prioritization weight (MPEP 2106.04(a)(2)(III)(A), a judgment based on an observation is a mental process) for each alert (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Claim 17
Claim 17 recites:
wherein an operation sequence for the rendering of the response team suggestion interface based on the response team prediction object is based on the prioritization weight (MPEP 2106.04(a)(2)(III)(A), a judgment of how to order a process based on an observation is a mental process; MPEP 2106.05(g), the display of data is considered insignificant extra-solution activity) for each of the one or more alerts used to generate the response team prediction object (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity).
Claim 19
Claim 19 recites:
wherein the confidence score (MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas) is applied to the responder prediction machine learning model (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) to determine one or more future response team predictions (MPEP 2106.04(a)(2)(III)(A), “evaluations, judgments, and opinions,” are mental processes).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-7, 10-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Arzani et al. (U.S. Publication No. 2021/0224676 A1), hereinafter referred to as Arzani, in view of Espinosa Godinez et al. (U.S. Publication No. 2022/0198223 A1), hereinafter referred to as Espinosa Godinez.
With regards to Claim 1, Arzani teaches:
A computer-implemented method of training a responder prediction machine learning model for generating response team predictions (Paragraph 0111; Fig. 8B) comprising:
collecting alert related datasets originating from one or more alert monitoring service tools over a predetermined time period (Paragraphs 0039-0041 and 0094, routinely collecting monitoring data through various components);
extracting alert attributes from the alert related datasets to create a responder prediction training corpus (Fig. 2 and Paragraph 0065), wherein the alert attributes comprise an alert identifier (Paragraphs 0087, 0049, 0090, and 0062, labels or classifications used can be input when the model is retrained), a tag identifier (Paragraphs 0041-0042 and 0090-0091, multiple types of annotations), a log identifier (Paragraphs 0090 and 0041, multiple types of annotations), a description identifier (Paragraph 0024, incident description), and a responder team identifier (Fig. 7; Paragraphs 0092-0093, 0063, and 0081, team);
training the responder prediction machine learning model using the responder prediction training corpus (Paragraphs 0063 and 0090-0093; Fig. 7) to generate response team prediction objects, wherein each response team prediction object comprises a confidence score indicating a confidence level attributed to a predicted response team for an incident alert (Paragraphs 0051, 0081, and 0045, routing to teams based off a model and containing a confidence score);
storing the responder prediction machine learning model following training to a responder prediction model repository, wherein the responder prediction model repository is accessible by a responder prediction service (Paragraphs 0046, 0048, and 0092-0093; Fig. 7 and 8B);
… response team suggestion interface associated with a responder prediction data object generated by the responder prediction machine learning model (Paragraph 0054, output of likely responsible team and other information as a result of the model);
and transmitting … feedback data to the responder prediction machine learning model to re-train the responder prediction machine learning model (Paragraphs 0093 and 0063 and Fig. 7, retraining the model, such as due to low accuracy) ...
Espinosa Godinez teaches the following limitations not explicitly taught by Arzani:
generating user feedback data based on a user input via a (Paragraphs 0066-0068, receiving user feedback) …
user … based on the user feedback data (Paragraphs 0066-0068, retraining the model based on user feedback data) …
Therefore, it would have been obvious to one of ordinary skill in the art in which said subject matter pertains to, prior to the effective filing date of the claimed invention, allow for user feedback, as taught by Espinosa Godinez, in the method of Arzani, in order to allow for better retraining that improves the model’s accuracy over time (Espinosa Godinez, Paragraph 0068).
With regards to Claim 2, Arzani in view of Espinosa Godinez teaches the method of Claim 1 as cited above. Claim 2 describes a second iteration of the process of Claim 1. Iteration is taught in Arzani in Paragraphs 0063 and 0092-0093, and Fig. 7. Please see the above rejection for further details.
With regards to Claim 3, Arzani in view of Espinosa Godinez teaches the method of Claim 1 as cited above. Arzani in view of Espinosa Godinez further teaches:
receiving one or more alerts from an alert monitoring service tool (Arzani, Paragraphs 0087 and 0094);
and applying, for each of the one or more alerts, the responder prediction machine learning model to determine a corresponding response team prediction object for each alert (Arzani, Paragraph 0097).
With regards to Claim 5, Arzani in view of Espinosa Godinez teaches the method of Claim 1 as cited above. Arzani in view of Espinosa Godinez further teaches:
further comprising adjusting the confidence score (Arazani, Paragraph 0081, score) by comparing the response team prediction object with at least one of the user input or a closing alert (Arzani, Paragraphs 0063 and 0092-0093 and Fig. 7, retraining due to low confidence; Arzani, Fig. 7; Espinosa Godinez, Paragraphs 0066-0068, retraining based off user feedback). Please note that this limitation is being interpreted as a training process that would iteratively change a score through feedback and more training, as opposed to directly changing the score, as described in Paragraph 0109 of Applicant’s specification.
With regards to Claim 6, Arzani in view of Espinosa Godinez teaches the method of Claim 1 as cited above. Arzani in view of Espinosa Godinez further teaches:
further comprising training the responder prediction machine learning model in a subsequent stage using the confidence score associated with each response team prediction object of the one or more alerts (Arzani, Paragraphs 0092-0093, retraining due to low confidence; Arzani, Paragraphs 0083 and 0097-0098, score and confidence as results of the model; Arzani, Paragraphs 0063 and 0105, retraining using past results; Arzani, Paragraph 0050, predicting the team with the model; Arzani, Fig. 7 and 8B).
With regards to Claim 7, Arzani in view of Espinosa Godinez teaches the method of Claim 6 as cited above. Arzani in view of Espinosa Godinez further teaches:
wherein the confidence score is applied to the responder prediction machine learning model to determine one or more future response team prediction objects (Arzani, Paragraphs 0092-0093, retraining due to low confidence; Arzani, Paragraphs 0083 and 0097-0098, score and confidence as results of the model; Arzani, Paragraphs 0063 and 0105, retraining using past results; Arzani, Paragraph 0050, predicting the team with the model; Arzani, Fig. 7 and 8B).
With regards to Claim 10, Arzani teaches:
An apparatus for generating a response team prediction associated with one or more alerts, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least (Paragraphs 0110-0112; Fig. 8B):
receive the one or more alerts from an alert monitoring service tool (Paragraphs 0087 and 0094, collecting monitoring data from various sources);
for each of the one or more alerts, apply a responder prediction machine learning model to determine a response team prediction object (Paragraph 0097), wherein each response team prediction object comprises a confidence score indicating a confidence level attributed to a predicted response team for an incident alert (Paragraphs 0051, 0081, and 0045);
and cause rendering of a response team suggestion interface based on the response team prediction object (Paragraph 0054); …
Arzani in view of Espinosa Godinez teaches the remaining limitations of Claim 10. Please see the above rejection of Claim 1 for citations of these limitations, as well as the motivation to combine references in accordance with 35 U.S.C. 103.
With regards to Claim 11, Arzani in view of Espinosa Godinez teaches the apparatus of Claim 10 as cited above. Arzani in view of Espinosa Godinez further teaches:
wherein the response team prediction object is transmitted to a prediction service API that is configured to indicate an alert notification comprising at least one of the response team prediction, a dataset of routing information associated with at least a client identifier set for the response team prediction, or an alert associated with the response team prediction (Arzani, Paragraphs 0054, 0048, 0081, and 0099, displaying various information related to the team and routing).
With regards to Claim 12, Arzani in view of Espinosa Godinez teaches the apparatus of Claim 10 as cited above. Arzani in view of Espinosa Godinez further teaches:
wherein the responder prediction machine learning model comprises a pre-training with an extracted alert related dataset associated with a complex platform (Arzani, Paragraph 0096, preprocessing; Arzani, Paragraph 0024, processing of data prior to analyzation).
With regards to Claim 13, Arzani in view of Espinosa Godinez teaches the apparatus of Claim 12 as cited above. Arzani in view of Espinosa Godinez further teaches:
wherein the extracted alert related dataset comprises data extracted from a predetermined time period (Arzani, Paragraphs 0039-0041 and 0094, routine monitoring).
With regards to Claim 19, Arzani in view of Espinosa Godinez teaches the apparatus of Claim 10 as cited above. Arzani in view of Espinosa Godinez further teaches:
wherein the confidence score is applied to the responder prediction machine learning model to determine one or more future response team predictions (Arzani, Paragraphs 0092-0093, retraining due to low confidence; Arzani, Paragraphs 0083 and 0097-0098, score and confidence as results of the model; Arzani, Paragraphs 0063 and 0105, retraining using past results; Arzani, Paragraph 0050, predicting the team with the model; Arzani, Fig. 7 and 8B).
Claims 8-9 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Arzani in view of Espinosa Godinez, in further view of Renners et al. (Renners et al., "A Feedback-Based Evaluation Approach for the Continuous Adjustment of Incident Prioritization," 2018). Hereinafter referred to as Renners.
With regards to Claim 8, Arzani in view of Espinosa Godinez teaches the method of Claim 1 as referenced above. Arzani in view of Espinosa Godinez further teaches:
training a … machine learning model using the responder prediction training corpus (Arzani, Paragraphs 0063 and 0091-0093; Arzani, Fig. 7), the alert attributes of the responder prediction training corpus further comprising a prioritization … (Arzani, Paragraph 0098);
and storing the … machine learning model following training to the responder prediction model repository, wherein the responder prediction model repository is accessible by the responder prediction service (Arzani, Paragraphs 0048, 0046, and 0092-0093, storing of models, training, and retraining; Arzani, Fig. 7 and 8B, order of the method).
Arzani in view of Espinosa Godinez does not explicitly teach:
… prioritization …
… weight identifier …
However, Renners teaches:
training a prioritization machine learning model using the responder prediction training corpus (Page 179, training and the careful selection for incidents for the model to be trained on), the alert attributes of the responder prediction training corpus further comprising a prioritization weight identifier (Page 178, Section 3.1, Prioritization, “prioritization itself, i.e. evaluating some calculation/model to derive the numeric priority by which the incidents can be ordered”);
and storing the prioritization machine learning model following training to the responder prediction model repository (Page 181, Model Update, the model can be stored and continuously updated).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to add the prioritization machine learning model as taught by Renners to the method of Arzani in view of Espinosa Godinez that already contains priority, in order to make the priority calculations more accurate and flexible (Pages 176-177, Contribution).
With regards to Claim 9, Arzani in view of Espinosa Godinez in further view of Renners teaches the method of Claim 8 as referenced above. The analyses of Claims 2 and 8 teach the limitations of Claim 9. Please see the above citations for further details.
With regards to Claim 14, Arzani in view of Espinosa Godinez teaches the apparatus of Claim 10 as referenced above. Arzani in view of Espinosa Godinez further teaches:
for each of the one or more alerts, apply/determine a prioritization for each alert (Arzani, Paragraph 0098, prioritization).
Arzani in view of Espinosa Godinez does not explicitly teach:
apply a prioritization machine learning model to determine a prioritization weight
However, Renners teaches:
and for each of the one or more alerts, apply a prioritization machine learning model to determine a prioritization weight for each alert (Page 178, Section 3.1, Prioritization “prioritization itself, i.e. evaluating some calculation/model to derive the numeric priority by which the incidents can be ordered”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to add the prioritization machine learning model as taught by Renners to the apparatus of Arzani in view of Espinosa Godinez that already contains priority, in order to make the priority calculations more accurate and flexible (Renners, Pages 176-177, Contribution).
With regards to Claim 15, Arzani in view of Espinosa Godinez in further view of Renners teaches the apparatus of Claim 14 as referenced above. Arzani in view of Espinosa Godinez in further view of Renners further teaches:
wherein an operation sequence of processing for the responder prediction machine learning model is applied to the one or more alerts based on the prioritization weight for each of the one or more alerts (Renners, Page 182, “the analyst starts to respond to the incidents in the order of that list”; Arzani, Paragraph 0098). Please note that “processing” is interpreted as the processing of the output as in Paragraph 0043 of the applicant’s specification.
With regards to Claim 16, Arzani in view of Espinosa Godinez in further view of Renners teaches the apparatus of Claim 14 as referenced above. Arzani in view of Espinosa Godinez in further view of Renners further teaches:
wherein an operation sequence for determining the response team prediction object is applied to the one or more alerts based on the prioritization weight for each alert (Renners, Page 179, Section 3.1, priority is taken into account for the incident selection for the training).
With regards to Claim 17, Arzani in view of Espinosa Godinez in further view of Renners teaches the apparatus of Claim 14 as referenced above. Arzani in view of Espinosa Godinez in further view of Renners further teaches:
wherein an operation sequence for the rendering of the response team suggestion interface based on the response team prediction object is based on the prioritization weight for each of the one or more alerts used to generate the response team prediction object (Page 179, Section 3.1, “the effective priority determines the final order in which the analyst is then confronted with the incidents”; Page 182, regarding a printed list of incident results in order; Arzani, Paragraph 0054, display; Arzani, Paragraph 0098, prioritization).
Response to Arguments
Applicant's arguments filed on August 6h, 2026, have been fully considered but they are not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Espinosa Godinez teaches user feedback and retraining. Arzani teaches using machine learning to predict response teams. In view of the combination of these references, the limitations of Claims 1 and 10 would have been obvious. Please see the above rejections for further details.
Applicant argues that the claims should not be rejected under 35 U.S.C. 101. Examiner respectfully disagrees.
In regards to arguments that the rejection oversimplifies the claims, Examiner respectfully disagrees. Examiner follows MPEP 2106 in establishing the broadest reasonable interpretation and analyzing the claims under 35 U.S.C. 101. Applicant further argues that the interface of Claim 10 is an improvement to technology. Examiner respectfully disagrees. The interface is not described with sufficient detail to be considered more than a mere output of data and thus is considered insignificant extra-solution activity (MPEP 2106.05(a); MPEP 2106.05(g)). Paragraphs 0027-0028 of the specification describe the invention as determining a team, which is a mental process with respect to MPEP 2106.04(a)(2)(III)(A), and an improvement to a mental process is still an abstract idea (MPEP 2106.04(a)(II)).
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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/G.S./Examiner, Art Unit 2113
/MARC DUNCAN/Primary Examiner, Art Unit 2113
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