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 .
DETAILED ACTION
The instant application having Application No. 18370619 has a total of 26 claims pending in the application, of which claims 2-3, 13-14, and 18-19 have been cancelled.
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, 3-12, 15-17, and 20-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 12 is a manufacture type claim, and claim 17 is a machine type claim. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“detecting… one or more patterns indicative of one or more task performance issues by processing at least a portion of the obtained data” A manager mentally or with pencil and paper looks for patterns in the task performance of their team and detects patterns.
“Generating … at least one recommendation in response to at least one of the one or more detected patterns” The manager mentally or with pencil and paper puts together a recommendation to improve the performance of his team.
“performing one or more automated actions based at least in part on the at least one recommendation” The manager mentally or with pencil and paper prepares a notification and plans to implement their recommendation.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“Computer implemented”, ”processor based” “one processing device comprising a processor coupled to memory” (mere instructions to apply the exception using a generic computer component);
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtaining data associated with at least one task and related to one or more task performance related metrics” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“Computer implemented”, ”processor based” “one processing device comprising a processor coupled to memory” (mere instructions to apply the exception using a generic computer component)
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtaining data associated with at least one task and related to one or more task performance related metrics” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 4-5, 7, and 9-11 contain similar mental steps and generic artificial intelligence aspects similar to claim 1, and are rejected for similar reasons.
As per claim 6, this claim contains similar mental steps to claim 1 and is rejected for similar reasons.
As per claim 12,
2A Prong 1:
“detect … one or more patterns indicative of one or more task performance issues by processing at least a portion of the obtained data” A manager mentally or with pencil and paper looks for patterns in the task performance of their team and detects patterns.
“Generate … at least one recommendation in response to at least one of the one or more detected patterns” The manager mentally or with pencil and paper puts together a recommendation to improve the performance of his team.
“perform one or more automated actions based at least in part on the at least one recommendation” The manager mentally or with pencil and paper prepares a notification and plans to implement their recommendation.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“non-transitory processor readable storage medium”, “at least one processing device” (mere instructions to apply the exception using a generic computer component);
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtain data associated with at least one task and related to one or more task performance related metrics” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“non-transitory processor readable storage medium”, “at least one processing device” (mere instructions to apply the exception using a generic computer component)
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtain data associated with at least one task and related to one or more task performance related metrics” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 15 contains similar mental steps and generic artificial intelligence aspects similar to claim 12, and is rejected for similar reasons.
As per claim 16, this claim contains similar mental steps to claim 12 and is rejected for similar reasons.
As per claim 17,
2A Prong 1:
“detect one or more patterns indicative of one or more task performance issues by processing at least a portion of the obtained data” A manager mentally or with pencil and paper looks for patterns in the task performance of their team and detects patterns.
“Generate … at least one recommendation in response to at least one of the one or more detected patterns” The manager mentally or with pencil and paper puts together a recommendation to improve the performance of his team.
“perform one or more automated actions based at least in part on the at least one recommendation” The manager mentally or with pencil and paper prepares a notification and plans to implement their recommendation.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“an apparatus”, “at least one processing device comprising a processor coupled to a memory”, (mere instructions to apply the exception using a generic computer component);
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtain data associated with at least one task and related to one or more task performance related metrics” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“an apparatus”, “at least one processing device comprising a processor coupled to a memory”, (mere instructions to apply the exception using a generic computer component)
“a decision tree model”, “machine learning system”, “a classification model, wherein the decision tree model and the classification model are interconnected in a series with one another in a processing pipeline” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional details or limitations beyond generic artificial intelligence algorithms making them nothing more than generic, off the shelf algorithms.
“obtain data associated with at least one task and related to one or more task performance related metrics” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 20-26 contain similar mental steps and generic artificial intelligence aspects similar to claim 17, and are rejected for similar reasons.
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, 4-12, 15-17, and 20-26 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.
As per claims 1, 12, and 17, these claims call for “the processor-based machine learning system further comprising a classification model, wherein the decision tree model and the classification model are interconnected in series with one another in a processing pipeline of the processor based machine learning system.” This limitation is not supported by the specification.
First, “classification” is used once in the entire specification, and that is as an option among several other techniques including decision trees and unsupervised learning. (See instant specification, pg.10, L3-14). At no time is there any discussion of a classification model working with a decision tree, being connected to a decision tree, let alone any discussion about being interconnected in series with one another in a processing pipeline.
At no time in the specification is there any discussion of a pipeline, let alone a processing pipeline, no discussion of models being connected in series, and no discussion of any particular interaction between a decision tree and a classification model.
This causes this limitation to be unsupported by the specification and therefore considered new matter and rejected under U.S.C. 112(a).
As per claims 4-11, 15-16, and 20-26, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
As per claims 1, 12, and 17, these claims also call for “generating, in the classification model… at least one recommendation in response to at least one of the one or more detected patterns.” This limitation is not supported by the specification. As stated above, the term “classification” is only used one in the specification. (See instant specification, pg.10, L3-14). This paragraph does not disclose providing a recommendation. Recommendations are discussed in pgs.1, 6, 9-11, 14-15, and 17, but at best these paragraphs describe recommendations via the decision tree or a generic “artificial intelligence based bot” but does not disclose these actions being performed by a classification model. This causes the limitation to be new matter and therefore rejected under U.S.C. 112(a).
As per claims 4-11, 15-16, and 20-26, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
As per claims 7, 9-10, 22, and 24-25, these claims have similar issues about interaction with the classification model, none of which are discussed in the specification, and are rejected for similar reasons for the classification model described in the independent claims. They are therefore rejected under U.S.C. 112(a) for new matter for the use of a classification model which is not disclosed in the specification performing the various actions of 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.
Claims 1, 3-12, 15-17, and 20-26 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava et al (US 20170192778 A1) in view of Apps (US 20070094060 A1) and Zhou (Ensemble Methods: Foundations and Algorithms”).
As per claims 1, 12, and 17, Srivastava discloses, “A computer implemented method” (Pg.1, particularly paragraph 0003; EN: This denotes the system being associated with processors and the like).
“obtaining data associated with at least one task and related to one or more task performance metrics” (Pg.1-2, particularly paragraph 0012; EN: this denotes taking in data about projects including metrics (performance metrics) and requirements, change requests (tasks)).
“detecting, … of a processor-based machine learning system” (Pg.1, particularly paragraph 0003; EN: This denotes the system being associated with processors and the like). “one or more patterns” (Pg.5, particularly paragraph 0047; EN: this denotes using machine learning to make predictions from previous projects to current projects. Here the patterns are the data found in the previous projects that match the project being predicted for). “indicative of one or more task performance issues by processing at least a portion of the obtained data” (Pg.5, particularly paragraph 0048; EN: this denotes predicting alerts related to the project). “The processor-based machine learning system further comprising a classification model” (Pg.5, particularly paragraph 0044; EN: this denotes using machine learning to identify (i.e. classify) language that may be associated with a timeline, a work request, an incident, or the like).
“generating, … at least one recommendation in response to at least one of the one or more detected patterns” (pg.6, particularly paragraph 0059; EN: this denotes providing recommendations to fix the problem).
“performing one or more automated actions based at least in part on the at least one recommendation” (pg.6, particularly paragraph 0059; EN: this denotes sending emails or text messages relevant to the recommendation).
“wherein the method is performed by at least one processing device comprising a processors coupled to a memory” (Pg.1, particularly paragraph 0003; EN: This denotes the system being associated with processors and the like).
However, Srivastava fails to explicitly disclose, “detecting, in a decision tree model… one or more patterns…”, “Wherein the decision tree model and the classification model are interconnected in series with one another in a processing pipeline of the processor-based machine learning system”, “Generating, in the classification model of the processor-based machine learning system, at least one recommendation….”
Apps discloses, “detecting, in a decision tree model… one or more patterns…” (Pg.1, particularly paragraph 0004; EN: this denotes using decision trees to find patterns).
“Generating, … in the … model of the processor-based machine learning system, at least one recommendation…” (Abstract EN: this denotes using decision trees for business based responses (i.e. recommendations)).
Zhou discloses, “Wherein the decision tree model” (Pg.4-5, particularly section 1.2.2; EN: this denotes the use of decision trees). “and the classification model are interconnected in series with one another in a processing pipeline of the processor-based machine learning system” (Pg.15-16, particularly section 1.4; EN: this denotes the use of heterogeneous ensembles, which denote mixing different models such as decision trees with other models in order to create ensemble models that work together. This discusses boosting algorithms, which has series of models working together). “Generating, in the classification model of the processor-based machine learning system…” (Pg.15-16, particularly section 1.4; EN: this denotes the use of classifiers within ensemble learning).
Srivastava and Apps are analogous art because both involve business optimization.
Before the effective filing date it would have been obvious to one skilled in the art of business optimization to combine the work of Srivastava and Apps in order to use decision trees for pattern recognition and recommendation.
The motivation for doing so would be because “decision trees are used for analysis of data structures to reveal relationships and patterns of which to apply analytical techniques and statistical methods to reveal the relationships and patterns” (Apps, Pg.1, paragraph 0004) or in the case of Srivastava, use the decision tree to detect the patterns and provide data for recommendations as needed by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of business optimization to combine the work of Srivastava and Apps in order to use decision trees for pattern recognition and recommendation.
Srivastava and Zhou are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Srivastava and Zhou in order to use ensemble learning to improve machine learning performance.
The motivation for doing so would be because “ The generalization ability of an ensemble is often much stronger than that of base learners” (Zhou, Pg.15, last paragraph) or in the case of Srivastava, allow the machine learning process of Srivastava to make use of ensemble learning for improved performance.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Srivastava and Zhou in order to use ensemble learning to improve machine learning performance.
As per claims 4 and 21, Srivastava discloses, “at least one pattern indicative of one or more task performance issues” (Pg.5, particularly paragraph 0048; EN: this denotes predicting alerts related to the project).
Apps discloses, “wherein processing at least a portion of the obtained data tree comprises detecting and tagging … at each of two or more decision levels in the decision tree model” (Pg.4, particularly paragraph 0040; EN: this denotes performing calculations at each node of the tree, with nodes at each level of the tree).
As per claims 5, 15, and 20, Srivastava discloses, “wherein processing at least a portion of the obtained data comprises comparing, on a given temporal basis, the at least a portion of the obtained data against at least one predetermined pattern related to performing at least a portion of the at least one task” (Pg.5, particularly paragraph 0044; EN: this denotes the program taking in and accessing things like timelines and time estimates which denote temporal data around the decisions being made).
As per claims 6 and 16, Srivastava discloses, “wherein performing one or more automated actions comprises automatically initiating at least one operation, in connection with one or more systems associated with performing the at least one task, in furtherance of the at least one recommendation” (pg.6, particularly paragraph 0059; EN: this denotes sending emails or text messages relevant to the recommendation).
As per claims 7 and 22, Srivastava discloses, “wherein performing one or more automated actions comprises automatically … using feedback related to the at least one recommendation” (Pg.6, particularly paragraph 0059; EN: this denotes storing recommendations in order to use them to improve future recommendations).
Zhou discloses, “training at least a portion of the one or more of the decision tree model and the classification model” (pg.15, particularly section 1.4; EN: this denotes training the various models in the ensemble).
As per claims 8 and 23, Srivastava discloses, “wherein performing one or more automated actions comprises automatically outputting a description of the at least one recommendation to at least one user associated with performing the at least one task” (pg.6, particularly paragraph 0059; EN: this denotes sending emails or text messages relevant to the recommendation).
As per claims 9 and 24, Srivastava discloses, “Wherein at least a portion … is trained using data associated with one or more patterns indicative of satisfactory performance of the at least one task” (Pg.5, particularly paragraph 0047; EN: this denotes using previous projects to train the model, which will include successful projects).
Zhou discloses, “one or more of the decision tree model and the classification model is trained…” (pg.15, particularly section 1.4; EN: this denotes training the various models in the ensemble).
As per claims 10 and 25, Srivastava discloses, “Wherein at least a portion of … is trained using data pertaining to at least one user associated with performing the at least one task” (Pg.6, particularly paragraph 0054; EN: this denotes including employee skills and proficiency levels as data related to making predictions for the system).
Zhou discloses, “one or more of the decision tree model and the classification model is trained…” (pg.15, particularly section 1.4; EN: this denotes training the various models in the ensemble).
As per claims 11 and 26, Srivastava discloses, “Wherein obtaining data comprises obtaining data pertaining to one or more of at least one burndown chart associated with at least one task, at least one epic burndown report associated with at least one task, at least one control chart associated with at least one task, at least one cumulative flow diagram associated with the at least one task, lead time associated with the at least one task, throughput associated with at least one task, blocked time associated with at least one task, and one or more … defects associated with the at least one task” (Pg.2, particularly paragraph 0013; EN: this denotes monitoring for defects reported for the project).
While Srivastava fails to explicitly disclose “escaped defects” the Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art at the time of filing to make use of defects found internally as well as defects found externally (i.e. escaped defects) when dealing with design projects, as this would allow the system to solve defects/bugs found both by outside users as well as those found by inside users in order to improve the project.
As the Applicant has not argued or otherwise traversed the official notice in the response filed 7/7/26, this is now applicant admitted prior art as per MPEP 2144.03(C).
Response to Arguments
In pg.9-10, the Applicant argues in regards to the rejection under U.S.C. 101 of the independent claims,
Applicant respectfully traverse, for at least the reasons noted above pertaining to the particular multi-model architecture of the processor-based machine learning system, as well as the decision tree model and the classification model being explicitly interconnected in series with one another in a processing pipeline of the processor-based machine learning system, required by the amended independent claims. Additionally, as stated in Ex Parte Desjardins et al., No. 2024- Confirmation No.: 2129 000567 (PTAB Appeals Review Panel, September 26, 2025), "[c]ategorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology," and improvements to how a machine learning model itself operates represent improvements to computer functionality. Accordingly, even if one assumes for purposes of argument only that independent claims 1, 12 and 17 could somehow be construed as reciting an abstract idea, these claims are not directed to an abstract idea for reasons similar to those set forth in Ex Parte Desjardins et al., as independent claims 1, 12 and 17 clearly integrate any such abstract idea into practical applications that provide improvements in computer technology by, for example, improving the multiple machine learning models interconnected in series with one another to form targeted outputs within a pipeline of an encompassing processor-based machine learning system.
In response the Examiner maintains the rejection as shown above. Merely stating that well-known machine learning algorithms such as a generic “classification algorithm” and a “decision tree” are connected in series does not provide an improvement to a technology. This is no different than using multiple memories or multiple processors in order to implement an abstract idea. There is no additional details or improvements to the “classification model” or “decision tree” that amounts to anything more than generic, off the shelf models, and therefore does not provide significantly more than the abstract idea. Therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1, 4-12, 15-17, and 20-26 have been considered but are either moot in view of the new ground(s) of rejection or are similar to arguments given above and rejected for similar reasons.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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