CTNF 18/455,546 CTNF 94985 DETAILED ACTION Claims 1-20 are pending and have been examined. -- Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 1, 3, 5-6, 10, 12, 14-15 and 19 are objected to because of the following informalities: In claims 1, 10 and 19, “a predefined criteria” should be “a predefined criterion” (‘criterion’ is singular, ‘criteria’ is plural) In claims 3, 5-6, 12 and 14-15, “the MMTL model” should be “the MTML model” Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claims 1-20 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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 pre-AIA the applicant regards as the invention. Claims 1, 10 and 19 recite the limitation “the performance of the MTML model.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted “the performance of the MTML model” to be “a performance of the MTML model.” Claims 5 and 14 recite the limitation “the completion state of the each task with the overall completion state of the set of tasks.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted “ the completion state of the each task with the overall completion state of the set of tasks” to be “ a completion state of the each task with an overall completion state of the set of tasks.” Claims 8 and 17 recite the limitation “wherein, k is the training epoch, M is the total number of a set of task groups.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted “wherein, k is the training epoch , M is the total number of a set of task groups” to be “wherein, k is a training epoch , M is a total number of a set of task groups.” Claims 9 and 18 recite the limitation “N t is the number of tasks in the each task group i, and Nj is the remaining number of tasks in the set of tasks.” There is insufficient antecedent basis for this limitation in the claim. For examination purposes examiner has interpreted “N t is the number of tasks in the each task group i, and Nj is the remaining number of tasks in the set of tasks” to be “N t is a number of tasks in the each task group i, and Nj is a remaining number of tasks in the set of tasks.” Claims 2-4, 6-7, 11-13, 15-16 and 20 are also rejected due to their dependency on a rejected claim. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. - Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more Step 1 : Claims 1-9 recite a method. Claims 10-18 recite a system. Claims 19-20 a non-transitory storage medium. Therefore, claims 1-9 are directed to a process, claims 10-18 are directed to a machine, and claims 19-20 are directed to a manufacture. With respect to claims 1, 10 and 19: 2A Prong 1: The claim recites a judicial exception. initializing the MTML model based, at least in part, on one or more model parameters (mental process – evaluation or judgement, setting/determining the initial parameters) computing a task affinity metric for each task of the set of tasks based, at least in part, on determining an affinity between the each task and one or more tasks from the set of tasks; (mathematical concept - mathematical equation, in light of specification [0083] “the task affinity metric P(k,t)… can be defined as…” ) computing a task-specific activation probability for the each task of the set of tasks based, at least in part, on the task affinity metric corresponding to the each task; (mathematical concept - mathematical equation, in light of specification [0089] “the task-specific activation probability for each task of the set of tasks: P(k,t) = … Eqn. 4” ) activating a subset of tasks from the set of tasks based, at least in part, on the task-specific activation probability corresponding to each individual task from the subset of tasks being lower than a predefined threshold; (mathematical concept - mathematical equation, in light of specification [0072] “These metrics are combined to define a task-wise activation probability… Eqn. 1”) processing… the training dataset by performing the subset of tasks to compute a set of outputs; (mathematical concept - mathematical calculation, in light of specification [0085] “compute the set of probability metrics for each task of the set of tasks based, at least in part, on performing the set of tasks.” ) generating a set of task-specific losses for the subset of tasks based, at least in part, on the set of outputs and the training dataset; and (mathematical concept - mathematical equation; generating a set of loss functions, in light of specification [0037] “the set of loss functions may include…” ) optimizing the one or more model parameters based, at least in part, on back-propagating the set of task-specific losses. (mathematical concept - mathematical calculation, in light of specification [0064, 87-88, 0094], backpropagation is math ) 2A Prong 2: The judicial exception is not integrated into a practical application. (claim 10) a memory configured to store instructions; a communication interface; and a processor in communication with the memory and the communication interface, the processor configured to execute the instructions stored in the memory and thereby cause the server system to perform at least in part to: (claim 19) computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components ) accessing, by a server system, a training dataset for training a multi-task machine learning (MTML) model for a set of tasks from a database associated with the server system; and (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting) training, by the server system, the MTML model based, at least in part, on performing a set of operations for a plurality of iterations till the performance of the MTML model converges to a predefined criteria, the set of operations comprising (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a server to train the MTML model until convergence ) the MTML model comprising a set of shared layers and a set of task- specific heads, wherein each task-specific head of the set of task-specific heads comprises a set of task-specific layers corresponding to an individual task from the set of tasks; (insignificant extra-solution activity – MPEP 2106.05(g), (1) Whether the extra-solution limitation is well known) … via the MTML model… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the MTML model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. (claim 10) a memory configured to store instructions; a communication interface; and a processor in communication with the memory and the communication interface, the processor configured to execute the instructions stored in the memory and thereby cause the server system to perform at least in part to: (claim 19) computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising (mere instructions to apply an exception, (2) Whether the claim invokes computers - MPEP 2106.05(f); generic computer components ) accessing, by a server system, a training dataset for training a multi-task machine learning (MTML) model for a set of tasks from a database associated with the server system; and (insignificant extra-solution activity – MPEP 2106.05(g), (3) data gathering and outputting, and WURC: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 - MPEP 2106.05(d)(II)(i)) training, by the server system, the MTML model based, at least in part, on performing a set of operations for a plurality of iterations till the performance of the MTML model converges to a predefined criteria, the set of operations comprising (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a server to train the MTML model until convergence ) the MTML model comprising a set of shared layers and a set of task-specific heads, wherein each task-specific head of the set of task-specific heads comprises a set of task-specific layers corresponding to an individual task from the set of tasks; (insignificant extra-solution activity – MPEP 2106.05(g), (1) Whether the extra-solution limitation is well known; the architecture with a shared backbone and task-specific heads is well-known and in common use in muti-task learning ) Azorin ("'It’s a Match!' A Benchmark of Task Affinity Scores for Joint Learning" 20230107) teaches (Azorin, p. 3, Figure 3 "MTL model schematic architecture for two tasks t1 = a and t2 = b. θB denotes the common backbone weights. θHa and θHb denote the separate heads weights."; both Azorin and Malhotra use a shared backbone and PNG media_image1.png 93 374 media_image1.png Greyscale task-specific heads ) … via the MTML model… (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the MTML model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 2, 11 and 20: 2A Prong 1: The claim recites a judicial exception. wherein initializing the MTML model for a first iteration of the plurality of iterations comprises: (mental process – evaluation or judgement, setting/determining the initial parameters) initiating, the MTML model based, at least in part, on one or more initial model parameters; (mental process – evaluation or judgement, setting/determining the initial parameters) generating a set of task groups from the set of tasks based, at least in part, on the task affinity metric for each task of the set of tasks; (mental process – evaluation or judgement, generating a set of task groups based on the task affinity) computing a group activation metric for each task group from the set of task groups based, at least in part, on the task affinity metric corresponding to the each task; (mathematical concept - mathematical equation, in light of specification [0091] “the formulation for an inter-group affinity metric may be done using the following Eqn. 5:… where, A i is the group activation metric of the each task group…”) activating a weakest task group from the set of task groups based, at least in part, on the group activation metric corresponding to the weakest task group, the weakest task group selected from the set of task groups based on least group activation metric from the group activation metric corresponding to the each task group; and (mathematical concept - mathematical equation, in light of specification [0090]-[0091] “the formulation for an inter-group affinity metric may be done using the following Eqn. 5:… where, A i is the group activation metric of the each task group…”) 2A Prong 2: The judicial exception is not integrated into a practical application. processing the MTML model by performing the weakest task group to learn the one or more model parameters (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the model to perform the group ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. processing the MTML model by performing the weakest task group to learn the one or more model parameters (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using the model to perform the group ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 3 and 12: 2A Prong 1: The claim recites a judicial exception. wherein computing the task- specific activation probability further comprises: (mathematical concept - mathematical equation, in light of specification [0089] “the task-specific activation probability for each task of the set of tasks: P(k,t) = … Eqn. 4” ) computing… a set of probability metrics for the each task based, at least in part, on performing the set of tasks; and (mathematical concept - mathematical equation, in light of specification [0085] “for computing the task-specific activation probability of each task, apart from the task affinity metric corresponding to that task, a set of probability metrics for the each task may also be used.”) generating the task-specific activation probability for the each task based, at least in part, on aggregating the set of probability metrics and the task affinity metric. (mathematical concept - mathematical equation, in light of specification [0089] “computed by aggregating the set of probability metrics…the task-specific activation probability for each task of the set of tasks: P(k,t) = … Eqn. 4” ) 2A Prong 2: The judicial exception is not integrated into a practical application. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 4 and 13: 2A Prong 2: The judicial exception is not integrated into a practical application. wherein the set of probability metrics for the each task comprises at least one of a task completion metric, a task stagnancy metric, and a regularization metric. (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); not a meaningful limitation, no actual steps, merely additional details of the claim elements ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. wherein the set of probability metrics for the each task comprises at least one of a task completion metric, a task stagnancy metric, and a regularization metric. (whether additional elements meaningfully limit the judicial exception – MPEP 2106.05(e); not a meaningful limitation, no actual steps, merely additional details of the claim elements ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 5 and 14: 2A Prong 1: The claim recites a judicial exception. further comprising: determining… the task completion metric for each task based, at least in part, on comparing the completion state of the each task with the overall completion state of the set of tasks. (mathematical concept - mathematical equation; in light of specification [0086] “the following Eqn. 3 given below may be used to compute the task completion metric for each task from the set of tasks…” ) 2A Prong 2: The judicial exception is not integrated into a practical application. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 6 and 15: 2A Prong 1: The claim recites a judicial exception. further comprising: computing… the task stagnancy metric for the each task based, at least in part, on computing a number of iterations from the plurality of iterations where the each task has been stagnant. (mental process – evaluation or judgement,--- computing the task stagnancy metric, evaluating if the task is stagnant) 2A Prong 2: The judicial exception is not integrated into a practical application. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. via the MMTL model (mere instructions to apply an exception – MPEP 2106.05(f), (3) The particularity or generality of the application of the judicial exception; using a model ) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible. With respect to claims 7 and 16: 2A Prong 1: The claim recites a judicial exception. wherein the regularization metric for each task is set as unity for one iteration of the plurality of iterations. (mental process – evaluation or judgement,--- setting a parameter to 1) With respect to claims 8 and 17: 2A Prong 1: The claim recites a judicial exception. PNG media_image2.png 118 458 media_image2.png Greyscale wherein the task affinity metric for each task is computed as: wherein, k is the training epoch, M is the total number of a set of task groups, n is a series of non-zero natural numbers, wherein n is determined based on k. (mathematical concept - mathematical equation) With respect to claims 9 and 18: 2A Prong 1: The claim recites a judicial exception. PNG media_image3.png 82 208 media_image3.png Greyscale wherein the group activation metric for each task group from the set of task groups is computed as: wherein, Ai is the group activation metric of the each task group i, τt,j is the task affinity metric of a task t belonging to the each task group i while task j is outside the each task group i, Nt is the number of tasks in the each task group i, and Nj is the remaining number of tasks in the set of tasks. (mathematical concept - mathematical equation) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Liang ("Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving" 20220919) teaches: a server with access to a database processes inputs to perform multi-task learning. Kingma ("Adam: A method for stochastic optimization" 20170130) teaches gradient calculations, parameter updates until training converges. Azorin ("'It’s a Match!' A Benchmark of Task Affinity Scores for Joint Learning" 20230107) teaches affinity scores for 2-task MTL, but does not teach a probability based on affinity scores, and activating a subset of tasks based on the probability. Malhotra ("Dropped Scheduled Task: Mitigating Negative Transfer in Multi-task Learning using Dynamic Task Dropping" 20230127) teaches a shared backbone with task-specific heads, and further teaches a loss function including all the task losses, and using the gates to switch off tasks. However, the claim requires choosing/activating the subset of tasks first, and then computing a set of output and a set of losses for the subset of tasks. Combining Azorin and Malhotra does not teach: “ computing a task affinity metric for each task of the set of tasks based, at least in part, on determining an affinity between the each task and one or more tasks from the set of tasks; computing a task-specific activation probability for the each task of the set of tasks based, at least in part, on the task affinity metric corresponding to the each task; activating a subset of tasks from the set of tasks based, at least in part, on the task-specific activation probability corresponding to each individual task from the subset of tasks being lower than a predefined threshold; processing, via the MTML model, the training dataset by performing the subset of tasks to compute a set of outputs; generating a set of task-specific losses for the subset of tasks based, at least in part, on the set of outputs and the training dataset; ” in claims 1, 10 and 19. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SU-TING CHUANG whose telephone number is (408)918-7519. The examiner can normally be reached Monday - Thursday 8-5 PT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /S.C./Examiner, Art Unit 2146 /USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146 Application/Control Number: 18/455,546 Page 2 Art Unit: 2146 Application/Control Number: 18/455,546 Page 3 Art Unit: 2146 Application/Control Number: 18/455,546 Page 4 Art Unit: 2146 Application/Control Number: 18/455,546 Page 5 Art Unit: 2146 Application/Control Number: 18/455,546 Page 6 Art Unit: 2146 Application/Control Number: 18/455,546 Page 7 Art Unit: 2146 Application/Control Number: 18/455,546 Page 8 Art Unit: 2146 Application/Control Number: 18/455,546 Page 9 Art Unit: 2146 Application/Control Number: 18/455,546 Page 10 Art Unit: 2146 Application/Control Number: 18/455,546 Page 11 Art Unit: 2146 Application/Control Number: 18/455,546 Page 12 Art Unit: 2146 Application/Control Number: 18/455,546 Page 13 Art Unit: 2146 Application/Control Number: 18/455,546 Page 14 Art Unit: 2146 Application/Control Number: 18/455,546 Page 15 Art Unit: 2146
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