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 .
This action is responsive to the application filed 07/03/2023. Claims 1-30 are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted 10/06/2023, 10/06/2023, and 10/28/2023 have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitation(s) are: means for accessing, means for accessing, means for generating, means for generating, means for generating, means for updating, means for aggregating, means for generating, means for generating in claims 28-30.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: The claim recites “A processing system comprising”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
generate a combined loss based on the first and second datasets, wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to: generate a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset; and generate a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset; and update one or more parameters of a multitask machine learning model based on the combined loss: These limitations recite mathematical relationships similar to organizing information and manipulating information through mathematical correlations per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
A processing system comprising: a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to: These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception. See MPEP § 2106.05(f).
access a first dataset comprising one or more labeled exemplars for a first machine learning task; access a second dataset comprising one or more labeled exemplars for a second machine learning task: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception and insignificant extra-solution activity of data gathering recited by “access a first dataset comprising one or more labeled exemplars for a first machine learning task; access a second dataset comprising one or more labeled exemplars for a second machine learning task” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1 as well as, inter alia:
aggregate the first supervised loss and the first self-supervised loss based at least in part on a first weight for the first self-supervised loss: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to aggregate a first supervised loss and first self-supervised loss based at least in part on a first weight for the first self-supervised loss.
the first weight being determined based on a current epoch of training the multitask machine learning model: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to determine the first weight based on a current epoch of training the multitask machine learning model.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to: These additional elements recite only the idea of the one or more processors being configured to execute the computer-executable instructions and attempts to cover any implementation without any restriction as to how the one or more processors are configured to execute the computer-executable instructions. Thus, these additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 2 as well as, inter alia:
wherein the first weight is assigned a relatively lower value during relatively earlier epochs of training the multitask machine learning model, as compared to relatively later epochs of training the multitask machine learning model: These limitations recite mathematical relationships of assigning a lower value to the first weight in comparison to later epochs similar to the relationship between variables and numbers per MPEP 2106.04(a)(2)(I)(A).
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a
practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 2 as well as, inter alia:
wherein the first supervised loss and the first self-supervised loss are aggregated based further on a second weight for the first machine learning task: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to aggregate a loss for the first supervised loss and first self-supervised loss based further on a second weight.
the second weight having a constant value during training of the multitask machine learning model: These limitations recite mathematical relationships of the second weight having a constant value similar to the relationship between variables and numbers per MPEP 2106.04(a)(2)(I)(A).
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a
practical application and is not considered to be significantly more. As such, the claim is patent
ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1 as well as, inter alia:
generate a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to generate a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to: These additional elements recite only the idea of the one or more processors being configured to execute the computer-executable instructions and attempts to cover any implementation without any restriction as to how the one or more processors are configured to execute the computer-executable instructions. Thus, these additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1 as well as, inter alia:
generate a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to generate a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to: These additional elements recite only the idea of the one or more processors being configured to execute the computer-executable instructions and attempts to cover any implementation without any restriction as to how the one or more processors are configured to execute the computer-executable instructions. Thus, these additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1 as well as:
generate a first output based on the first labeled exemplar augmented according to a first set of augmentations; generate a second output based on the first labeled exemplar augmented according to a second set of augmentations; generate a pseudo-label based on modifying the first output using the first and second sets of augmentations; and compare the pseudo-label and the second output: These limitations recite mathematical relationships similar to organizing information and manipulating information through mathematical correlations per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein to generate the first self-supervised loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to, for a first labeled exemplar from the second dataset: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. to generate the first self-supervised loss, to a particular technological environment or field of use, e.g. the one or more processors are configured to execute the computer-executable instructions to cause the processing system to, for a first labeled exemplar from the second dataset. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application. Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the multitask machine learning model comprises an encoder component shared by both the first and second machine learning tasks, a first decoder component for the first machine learning task, and a second decoder component for the second machine learning task: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. to generate a supervised loss and self-supervised loss for the first and second machine learning tasks, to a particular technological environment or field of use, e.g. a first decoder component for the first machine learning task, and a second decoder component for the second machine learning task. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application. Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1:
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the first and second machine learning tasks are computer vision tasks and comprise at least one of: monocular depth estimation, semantic segmentation, object detection, surface normal estimation, or edge detection: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. to generate a supervised loss and self-supervised loss for the first and second machine learning tasks, to a particular technological environment or field of use, e.g. the first and second machine learning tasks are computer vision tasks and comprise at least one of: monocular depth estimation, semantic segmentation, object detection, surface normal estimation, or edge detection. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application. Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claims 10-18
Step 1: These claims are directed to “A processor-implemented method, comprising:”; therefore, it is directed the statutory category of a process.
Step 2A Prong 1: Claims 10-18 recite the same judicial exception as Claims 1-9, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The analysis at this step for 10-18 mirrors that of Claims 1-9, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The analysis at this step for Claims 10-18 mirrors that of Claims 1-9, respectively.
Claims 19-27
Step 1: This claim recites "A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to:"; therefore, it is directed to the statutory category of an article of manufacture.
Step 2A Prong 1: Claims 19-27 recite the same judicial exception as Claims 1-9, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The only difference between Claims 19-27 and Claims 1-9, is that Claims 19-27 are directed to "A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step for Claims 19-27 mirrors that of Claims 1-9, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The only difference between Claims 19-27 and Claims 1-9, is that Claims 19-27 are directed to "A non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to”. However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a non-transitory computer-readable medium comprising computer-executable instructions that, when executed by one or more processors of a processing system, cause the processing system to, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step for Claims 19-27 mirrors that of Claims 1-9, respectively.
Claim 28
Step 1: The claim recites “A processing system comprising”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
means for generating a combined loss based on the first and second datasets, comprising: means for generating a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset; and means for generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset; and means for updating one or more parameters of a multitask machine learning model based on the combined loss: These limitations recite mathematical relationships similar to organizing information and manipulating information through mathematical correlations per MPEP 2106.04(a)(2)(I)(A)(iv).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
A processing system, comprising: means for accessing a first dataset comprising one or more labeled exemplars for a first machine learning task; means for accessing a second dataset comprising one or more labeled exemplars for a second machine learning task: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “means for accessing a first dataset comprising one or more labeled exemplars for a first machine learning task; means for accessing a second dataset comprising one or more labeled exemplars for a second machine learning task” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 29
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 28 as well as, inter alia:
means for aggregating the first supervised loss and the first self-supervised loss based at least in part on a first weight for the first self-supervised loss: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to aggregate a first supervised loss and first self-supervised loss based at least in part on a first weight for the first self-supervised loss.
the first weight being determined based on a current epoch of training the multitask machine learning model: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to determine the first weight based on a current epoch of training the multitask machine learning model.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the means for generating the combined loss comprises: These additional elements recite only the idea of generating a combined loss based on supervised and self-supervised losses for the first and second datasets, respectively, and attempts to cover any implementation of task operation order without any restriction as to the order in which supervised and self-supervised losses should be generated for the first and second datasets, respectively, or if they should be generated simultaneously. Thus, these additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 30
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 28 as well as, inter alia:
means for generating a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to generate a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset.
means for generating a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the first dataset: These limitations recite a mentally performable process with the aid of pen and paper of using evaluation to generate a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the means for generating the combined loss comprise: These additional elements recite only the idea of generating a combined loss based on supervised and self-supervised losses for the first and second datasets, respectively, and attempts to cover any implementation of task operation order without any restriction as to the order in which supervised and self-supervised losses should be generated for the first and second datasets, respectively, or if they should be generated simultaneously. Thus, these additional elements do not meaningfully limit the claim and does not integrate the judicial exception into a practical application because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include adding words equivalent to "apply it" with the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 10, 14-15, 17, 19, 23-24, and 26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hong et al. ("BEYOND WITHOUT FORGETTING: MULTI-TASK LEARNING FOR CLASSIFICATION WITH DISJOINT DATASETS", arXiv) (Year: 2020), hereafter Hong.
Regarding independent claim 10, Hong teaches a computer-implemented method comprising:
accessing a first dataset comprising one or more labeled exemplars for a first machine learning task ([Sec. 3.1] discusses accessing a first dataset comprised of labeled exemplars for task A);
accessing a second dataset comprising one or more labeled exemplars for a second machine learning task ([Sec. 3.1] discusses accessing a second dataset comprised of labeled exemplars for task B);
generating a combined loss based on the first and second datasets, comprising: generating a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset ([Sec. 3.1-3.2 & Fig. 1] discusses generating a supervised loss using cross-entropy based on the first dataset);
and generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset ([Sec. 3.2, 4 & Fig. 1] discusses generating a self-supervised loss using cross-entropy by using the second dataset to obtain soft label vectors);
and updating one or more parameters of a multitask machine learning model based on the combined loss ([Algorithm 1] discusses updating parameters based on the loss function calculated).
Regarding dependent claim 14, Hong further teaches wherein generating the combined loss further comprises generating a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset ([Sec. 3.2 & Equation 2 & 5] discusses generating a second supervised loss which is based off of DB and thus, represents the one or more labeled exemplars from the second dataset).
Regarding dependent claim 15, Hong further teaches wherein generating the combined loss further comprises generating a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the first dataset ([Sec. 1, Pg. 1] discusses that once the self-supervised loss is generated based on the second dataset, the same method is applied to generate a second self-supervised loss based on the first dataset).
Regarding dependent claim 17, Hong further teaches wherein the multitask machine learning model comprises an encoder component shared by both the first and second machine learning tasks, a first decoder component for the first machine learning task, and a second decoder component for the second machine learning task ([Sec. 3.1 & 5.2] discusses the use of convolutional layers of VGG as shared layers and two FC layers as task-specific layers and thus, represents an encoder shared between the two “layers” or first and second machine learning tasks as well as a separate encoder for each layer or machine learning task).
Regarding claims 19, 23-24, & 26, claims 19, 23-24, & 26 are non-transitory computer-readable storage medium claims that are substantially the same as the method of claims 10, 14-15, & 17. Therefore, claims 19, 23-24, & 26 are rejected for the same reasons as claims 10, 14-15, & 17.
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, 5-6, & 8 are rejected under 35 U.S.C. 103 as being unpatentable over Hong.
Regarding independent claim 1, Hong teaches a processing system comprising:
access a first dataset comprising one or more labeled exemplars for a first machine learning task ([Sec. 3.1] discusses accessing a first dataset comprised of labeled exemplars for task A);
access a second dataset comprising one or more labeled exemplars for a second machine learning task ([Sec. 3.1] discusses accessing a second dataset comprised of labeled exemplars for task B);
generate a combined loss based on the first and second datasets, wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to: generate a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset ([Sec. 3.1-3.2 & Fig. 1] discusses generating a supervised loss using cross-entropy based on the first dataset);
and generate a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset ([Sec. 3.2, 4 & Fig. 1] discusses generating a self-supervised loss using cross-entropy by using the second dataset to obtain soft label vectors);
and update one or more parameters of a multitask machine learning model based on the combined loss ([Algorithm 1] discusses updating parameters based on the loss function calculated).
Hong does not explicitly teach a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform the processes as recited in claim 1:
However, Hong teaches that the system operates on a computer or in a computer environment to perform the processes of claim 1 ([Sec. 3.1] discusses the multi-task network consisting of convolutional layers and thus, represents a machine learning architecture which operates in a computer environment). Therefore, it would have been obvious to one of an ordinary skill in the arts, before the effective filling date, to have recognized that the computer environment would have included a memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions in order to perform the processes as recited in claim 1.
Regarding dependent claim 5, Hong teaches the claimed invention as claimed in claim 5, including wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to generate a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset ([Sec. 3.2 & Equation 2 & 5] discusses generating a second supervised loss which is based off of DB and thus, represents the one or more labeled exemplars from the second dataset).
Regarding dependent claim 6, Hong teaches the claimed invention as claimed in claim 6, including wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to generate a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the first dataset ([Sec. 1, Pg. 1] discusses that once the first self-supervised loss is generated based on the second dataset, the same method is applied to generate a second self-supervised loss based on the first dataset).
Regarding dependent claim 8, Hong teaches the claimed invention as claimed in claim 8, including wherein the multitask machine learning model comprises an encoder component shared by both the first and second machine learning tasks, a first decoder component for the first machine learning task, and a second decoder component for the second machine learning task ([Sec. 3.1 & 5.2] discusses the use of convolutional layers of VGG as shared layers and two FC layers as task-specific layers and thus, represents an encoder shared between the two “layers” or first and second machine learning tasks as well as a separate encoder for each layer or machine learning task).
Claims 2-4, 11-13, 20-22, 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Hong as applied in claims 1, 10, and 19 above, and in view of Tarvainen et al. ("Mean Teachers are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-Supervised Deep Learning Results", arXiv:1703.01780v6 [cs.NE], 16 April 2018, pp.1-16), hereafter Tarvainen.
Tarvainen was cited in IDS filed 10/06/2023.
Regarding dependent claim 2, Hong teaches a processing system of claim 1 including wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to aggregate the first supervised loss and the first self-supervised loss based at least in part on a first weight for the first self-supervised loss ([Sec. 4] discusses assigning a weight to each training sample to be used in loss calculations for the supervised and self-supervised loss and thus, the first supervised loss and first self-supervised loss are aggregated based in part on the weight for the first self-supervised loss).
Hong does not explicitly teach the first weight being determined based on a current epoch of training the multitask machine learning model.
However, in the same field of endeavor, Tarvainen teaches aggregating the first supervised loss and first self-supervised loss based on a first weight and that weight is determined based on a current epoch of training ([Sec. 3] discusses assigning a mean squared error as the consistency cost for training and the weight value ramps up from 0 to its final value based on each epoch).
Because Hong teaches aggregating losses based in part on an assigned weight, and Tarvainen teaches aggregating losses based in part on a first weight and the weight being determined based on a current epoch of training, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate determining the weight based on the current epoch of training as taught by Tarvainen into Hong’s computer-implemented system, with a reasonable expectation of success, to teach wherein to generate the combined loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to aggregate the first supervised loss and the first self-supervised loss based at least in part on a first weight for the first self-supervised loss, the first weight being determined based on a current epoch of training the multitask machine learning model. This combination would have been motivated by the desire to have a more accurate model and aggregate information after every step (Tarvainen [Sec. 2]).
Regarding dependent claim 3, the combination of Hong and Tarvainen teaches the claimed invention as claimed in claim 3, including wherein the first weight is assigned a relatively lower value during relatively earlier epochs of training the multitask machine learning model, as compared to relatively later epochs of training the multitask machine learning model (Tarvainen [Sec. 3] discusses the weight (mean squared error) being ramped up from 0 during the first 80 epochs and thus, the weight is assigned a relatively lower value during earlier epochs as compared to relatively later epochs).
Regarding dependent claim 4, the combination of Hong and Tarvainen teaches the claimed invention as claimed in claim 4, including wherein the first supervised loss and the first self-supervised loss are aggregated based further on a second weight for the first machine learning task, the second weight having a constant value during training of the multitask machine learning model (Hong [Sec. 3.2-4 & 5.4] discusses assigning a weight and that weight remaining constant throughout training and thus, represents aggregating losses based further on a second weight, that weight having a constant value during training
Regarding claims 11-13, claims 11-13 are method claims that are substantially the same as the process of claims 2-4. Therefore, claims 11-13 are rejected for the same reasons as claims 2-4.
Regarding claims 20-22, claims 20-22 are non-transitory computer-readable storage medium claims that are substantially the same as the process of claims 2-4. Therefore, claims 20-22 are rejected for the same reasons as claims 2-4.
Regarding independent claim 28, Hong teaches a processing system comprising:
means for accessing a first dataset comprising one or more labeled exemplars for a first machine learning task; means for accessing a second dataset comprising one or more labeled exemplars for a second machine learning task (Hong [Sec. 3.1] discusses the system obtaining and accessing a first dataset comprised of labeled exemplars for task A, and the system obtaining and accessing a second dataset comprised of labeled exemplars for task B);
means for generating a combined loss based on the first and second datasets, comprising: means for generating a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset (Hong [Sec. 3.1-3.2 & Fig. 1] discusses the system generating a supervised loss using cross-entropy based on the first dataset);
generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset (Hong [Sec. 3.2, 4 & Fig. 1] discusses the system generating a self-supervised loss using cross-entropy by using the second dataset to obtain soft label vectors);
and means for updating one or more parameters of a multitask machine learning model based on the combined loss (Hong [Algorithm 1] discusses the system updating parameters based on the loss function calculated).
Hong does not explicitly teach means for generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset.
However, in the same field of endeavor, Tarvainen teaches the necessary structure for generating a first self-supervised loss (Tarvainen [Sec. 2] discusses the structure of a teacher and student model to generate a self-supervised loss using EMA of weights, per the specification, and thus, teaches a means for generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset).
Because Hong teaches means for accessing a first and second dataset for a first and second machine learning task, means for generating a combined loss comprising means for generating a first supervised loss and generating a first self-supervised loss, and means for updating one or more parameters based on the combined loss, and Tarvainen teaches means for generating a first self-supervised loss for the first machine learning task, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teacher and student model using EMA for weights as taught by Tarvainen into Hong’s computer-implemented system, with a reasonable expectation of success, to teach means for accessing a first dataset comprising one or more labeled exemplars for a first machine learning task; means for accessing a second dataset comprising one or more labeled exemplars for a second machine learning task; means for generating a combined loss based on the first and second datasets, comprising: means for generating a first supervised loss for the first machine learning task based on the one or more labeled exemplars from the first dataset; and means for generating a first self-supervised loss for the first machine learning task based on the one or more labeled exemplars from the second dataset; and means for updating one or more parameters of a multitask machine learning model based on the combined loss. This combination would have been motivated by the desire to aggregate information after every step and update weights with EMA to reduce noise (Tarvainen [Sec. 2-3]).
Regarding dependent claim 29, the combination of Hong and Tarvainen teaches the claimed invention as claimed in claim 29, including wherein the means for generating the combined loss comprises means for aggregating the first supervised loss and the first self-supervised loss based at least in part on a first weight for the first self-supervised loss, the first weight being determined based on a current epoch of training the multitask machine learning model (Hong [Sec. 4] discusses the system assigning a weight to each training sample to be used in loss calculations for the supervised and self-supervised loss and thus, the first supervised loss and first self-supervised loss are aggregated based in part on the weight for the first self-supervised loss; Tarvainen [Sec. 2-3] discusses assigning a mean squared error as the consistency cost for training and the weight value ramps up from 0 to its final value based on each epoch and thus, the first weight is being determined based on a current epoch of training).
Regarding dependent claim 30, the combination of Hong and Tarvainen teaches the claimed invention as claimed in claim 30, including
wherein the means for generating the combined loss comprise: means for generating a second supervised loss for the second machine learning task based on the one or more labeled exemplars from the second dataset (Hong [Sec. 3.2] discusses the system generating a second supervised loss using cross-entropy for dataset B and the second machine learning task);
and means for generating a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the first dataset (Hong [Sec. 4 & Eq. 5] discusses the system generating a second self-supervised loss based on the first dataset; and Tarvainen [Sec. 2] discusses the structure of a teacher and student model to generate a self-supervised loss using EMA of weights, per the specification, and thus, the combination teaches a means for generating a second self-supervised loss for the second machine learning task based on the one or more labeled exemplars from the first dataset).
Claims 7, 16, & 25 are rejected under 35 U.S.C. 103 as being unpatentable over Hong as applied in claims 1, 10, and 19 above, and in view of Rabadán et al. ("Dense FixMatch: A Simple Semi-Supervised Learning Method for Pixel-wise Prediction Tasks," 18 October 2022, pp.1-12, [Pages 2, 22, and 34.]), hereafter Rabadán.
Rabadán was cited in IDS filed 10/06/2023.
Regarding dependent claim 7, Hong teaches a processing system comprising:
generate a first output based on the first labeled exemplar augmented according to a first set of augmentations ([Sec. 1] discusses uses dataset B with pseudo labels to augment task A, and generate a first output based on that set of augmentations);
generate a second output based on the first labeled exemplar augmented according to a second set of augmentations ([Sec. 1] discusses generating output based on augmentations to task A; correspondingly, a second output is generated based on using dataset A with pseudo labels to augment task B, and generate a second output based on the second set of augmentations);
generate a pseudo-label ([Sec. 4] discusses generating a pseudo-label to supervise and augment each task
and compare the pseudo-label and the second output ([Sec. 4, Eq. 4, 5] discusses comparing the pseudo-label to the second output in loss functions to take the minimum cross-entropy loss).
Hong does not explicitly teach generate a pseudo-label based on modifying the first output using the first and second sets of augmentations.
However, in the same field of endeavor, Rabadán teaches a system for a semi-supervised learning method including generating a pseudo label based on modifying the first output using the first and second sets of augmentations ([Sec. 3, Pg. 4-5, Eq. 5] discusses generating a pseudo-label by modifying the first output, using the first and second sets of augmentations).
Because Hong teaches generating a first output based on a first set of augmentations, generating a second output based on a second set of augmentations, generating a pseudo-label, and comparing the pseudo-label and the second output; and Rabadán teaches generating a pseudo-label based on modifying the first output using the first and second sets of augmentation, accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to incorporate generating the pseudo label based on modifying the first output as taught by Rabadán into Hong’s computer-implemented system, with a reasonable expectation of success, to teach wherein to generate the first self-supervised loss, the one or more processors are configured to execute the computer-executable instructions to cause the processing system to, for a first labeled exemplar from the second dataset: generate a first output based on the first labeled exemplar augmented according to a first set of augmentations; generate a second output based on the first labeled exemplar augmented according to a second set of augmentations; generate a pseudo-label based on modifying the first output using the first and second sets of augmentations; and compare the pseudo-label and the second output. This combination would have been motivated by the desire to define a consistency objective between the two views for any dense or structured task, including semantic segmentation, object detection, and instance segmentation, while still being able to use different geometric transformations in both augmentation pipelines (Rabadán [Pg. 4]).
Regarding claim 16, claim 16 is a method claim that is substantially the same as the process of claim 7. Therefore, claim 16 is rejected for the same reasons as claim 7.
Regarding claim 25, claim 25 is a non-transitory computer-readable storage medium claim that are substantially the same as the process of claim 7. Therefore, claim 25 is rejected for the same reasons as claim 7.
Claims 9, 18, & 27 are rejected under 35 U.S.C. 103 as being unpatentable over Hong as applied in claims 1, 10, and 19 above, and in view of Ghiasi et al. ("Multi-Task Self-Training for Learning General Representations", arXiv) (Year: 2021), hereafter Ghiasi.
Regarding dependent claim 9, Hong teaches a processing system comprising first and second machine learning tasks ([Sec. 1] discusses taking two datasets corresponding to two machine learning tasks).
Hong does not explicitly disclose wherein the first and second machine learning tasks are computer vision tasks and comprise at least one of: monocular depth estimation, semantic segmentation, object detection, surface normal estimation, or edge detection.
However, in the same field of endeavor, Ghiasi teaches a multi-task training for learning general representations where the teacher models include four important tasks ([Sec. 1 & 3.1] discusses the four machine learning tasks being classification, object detection, semantic segmentation, and depth estimation).
Because Hong teaches the use of two machine learning tasks, and Ghiasi teaches using machine learning tasks comprising object detection, semantic segmentation, and depth estimation, accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date, to incorporate machine learning tasks comprising either object detection, semantic segmentation, and depth estimation as taught by Ghiasi into Hong’s processing system, with a reasonable expectation of success, to teach wherein the first and second machine learning tasks are computer vision tasks and comprise at least one of: monocular depth estimation, semantic segmentation, object detection, surface normal estimation, or edge detection. This combination would have been motivated by the desire to learn from a set of teachers that provide rich training signals with their pseudo labels (Ghiasi [3.1]).
Regarding claim 18, claim 18 is a method claim that is substantially the same as the process of claim 9. Therefore, claim 18 is rejected for the same reasons as claim 9.
Regarding claim 27, claim 27 is a non-transitory computer-readable storage medium claim that are substantially the same as the process of claim 9. Therefore, claim 27 is rejected for the same reasons as claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Augenstein et al. ("Multi-task learning of pairwise sequence classification tasks over disparate label spaces", arXiv) (Year: 2018) ([Abstract] We combine multi-task learning and semi supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets.)
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/RILEY S ACOSTA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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