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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2023 204 753.5, filed on 05/22/2023.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-7 recite a process (“A method for determining an architecture of a multitasking model with a number of L layers Li with i=1 to L for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the method comprising the following steps”), one of the four statutory categories of patentable subject matter. Claim 9 recite an article of manufacture (“A non-transitory computer-readable medium on which is stored a computer program for determining an architecture of a multitasking model with a number of L layers Li with i=1 to L for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the computer program, when executed by a computer, causing the computer to perform the following steps”), one of the four statutory categories of patentable subject matter.
Claim 8 does not recites one of the four statutory categories of patentable subject matter. Claim 8 recites “A multitasking model with a number of L layers for achieving a plurality of mutually different tasks, the multitasking model having an architecture determined for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the architecture being determined by” wherein the claimed model is software per se. The claim recites a model and the claim elements are not specifically disclosed in the specification as being software or hardware elements. Under the broadest reasonable interpretation, these claim elements can be software elements. Therefore, the claimed model under the broadest reasonable interpretation can include only software elements and therefore software per se.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“for the set of tasks to be achieved, estimating pairwise affinities between the tasks to be achieved of the set” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“assigning the tasks to be achieved to a number of N groups gi with i=1 to N based on the pairwise affinities” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“determining a branching depth of the multitasking model with layers, wherein the branching depth specifies at what depth of the layers of the multitasking model a base network of the multitasking model that is shared by the tasks to be achieved branches into a number of N branch networks Zi with i=1 to N” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
There are no additional elements in the claim that integrate the judicial exception into practical application. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
There are no additional elements in the claim that recite significantly more than the abstract idea itself. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein each pairwise affinity specifies a value of how well a respective pair of the tasks can be trained in a shared layer network” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the tasks to be achieved are assigned to the groups in such a way that an average pairwise affinity value of a respective group is maximized” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“selecting an optimal branching depth based on a predictive accuracy of the networks” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“training a plurality of networks with mutually different branching depths” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein, for a respective group including two or more of the tasks, it is checked whether assigning the tasks to be achieved of the respective group to a number of M subgroups ugi with i=1 to M increases an average pairwise affinity value of the respective group” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“assigning the tasks to be achieved of the respective group to a number of M subgroups ugi with i=1 to M based on the pairwise affinities” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“determining a subbranching depth in a respective branch network, wherein the subbranching depth specifies at what depth of the layers of a branch branches into a number of M subbranch networks UZi with i=1 to M” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the steps of checking, assigning to subgroups, and determining the subbranching are performed iteratively for each respective subgroup ugi” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 8:
The claim performs the method described in claim 1. Therefore, claim 8 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 8 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A multitasking model with a number of L layers for achieving a plurality of mutually different tasks, the multitasking model having an architecture determined for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the architecture being determined by” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 9:
The claim recites an article of manufacture that performs the method as described in claim 1. Therefore, claim 9 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 9 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A non-transitory computer-readable medium on which is stored a computer program for determining an architecture of a multitasking model with a number of L layers Li with i=1 to L for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the computer program, when executed by a computer, causing the computer to perform the following steps” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vandenhende “Branched Multi-Task Networks: Deciding What Layers To Share”.
Regarding claim 1, Vandenhende teaches:
“A method for determining an architecture of a multitasking model with a number of L layers Li with i=1 to L for achieving a set of a plurality of mutually different tasks Ti with i = ℕ ≥ 2, the method comprising the following steps” (abstract, pg. 4, Section 3, par. 1, The proposed approach automatically construct branched multi-task networks by leveraging the employed tasks’ affinities. The method aims to find an effective task grouping of the plurality of visual recognition tasks for the sharable layers of the encoder.)
“for the set of tasks to be achieved, estimating pairwise affinities between the tasks to be achieved of the set” ([pg. 5-6, Section 3.1, par. 2-5], The task affinity scores are calculated and the tasks are assigned in the same or different branches of a branched multi-task network based on the measured levels of task affinity.)
“assigning the tasks to be achieved to a number of N groups gi with i=1 to N based on the pairwise affinities” ([pg. 6-7, Section 3.2, par. 1-2], Algorithm1, In the branched multi-task network, each node is responsible for solving a unique subset of tasks. Dissimilar tasks are assigned to separate branches and the dissimilarity score is defined by the task affinity tensor between two tasks.)
“determining a branching depth of the multitasking model with layers, wherein the branching depth specifies at what depth of the layers of the multitasking model a base network of the multitasking model that is shared by the tasks to be achieved branches into a number of N branch networks Zi with i=1 to N” ([pg. 6, Section 3.2, par. 1, pg. 12, Figure S4], The root node contains the first layer and the nodes at depth l are split into different number of branches. Figure S4 shows the different branched multi-task networks for the same set of tasks applied to the Cityscapes dataset.)
Regarding claim 2, Vandenhende teaches:
“wherein each pairwise affinity specifies a value of how well a respective pair of the tasks can be trained in a shared layer network” ([pg. 4, Section 3, par. 1, pg. 5-6, Section 3.1, par. 2-3], Each task is used to train a single-task model that uses the same encoder and decoder architecture as the multi-task network. The dissimilarity score between two tasks is calculated at specific locations in the sharable encoder. When two tasks are strongly related, their single-task models rely on a similar feature set.)
Regarding claim 3, Vandenhende teaches:
“wherein the tasks to be achieved are assigned to the groups in such a way that an average pairwise affinity value of a respective group is maximized” ([pg. 6-7, Section 3.2, par. 2], The task dissimilarity score of a tree is determined by the summation of the averages of the maximum distance between the dissimilarity scores of the elements in every cluster. The use of the maximum distance encourages the separation of dissimilar tasks.)
Regarding claim 4, Vandenhende teaches:
“training a plurality of networks with mutually different branching depths, and selecting an optimal branching depth based on a predictive accuracy of the networks” ([pg. 7, Section 4.1, par. 1-3, pg. 11, Section A.1, par. 3, pg. 12, Figure S4], In the experiment using the Cityscapes dataset, all possible task groupings of the last three ResNet blocks are trained and the performance of the trained architectures are determined. For a fixed budget, the proposed method is capable of selecting the best performing task grouping.)
Regarding claim 5, Vandenhende teaches:
“wherein, for a respective group including two or more of the tasks, it is checked whether assigning the tasks to be achieved of the respective group to a number of M subgroups ugi with i=1 to M increases an average pairwise affinity value of the respective group” ([pg. 6-7, Section 3.2, par. 1-2], The granularity of the layers fl corresponds to the intervals at which we measure the task affinity in the sharable encoder at D locations. The encoder is split into bl branches at depth l. All possible trees that fall within the given computational budget is enumerated and the tree that minimizes the task dissimilarity score is selected. Dissimilar tasks are branched into different nodes and similar tasks are grouped together in the same branches of the tree.)
Regarding claim 6, Vandenhende teaches:
“assigning the tasks to be achieved of the respective group to a number of M subgroups ugi with i=1 to M based on the pairwise affinities, and determining a subbranching depth in a respective branch network, wherein the subbranching depth specifies at what depth of the layers of a branch branches into a number of M subbranch networks UZi with i=1 to M” ([pg. 6-7, Section 3.2, par. 1-2, pg. 12, Figure S4], The granularity of the layers fl corresponds to the intervals at which we measure the task affinity in the sharable encoder at D locations. The computation of task affinity helps to determine how layers in the sharable encoder should be shared among the tasks. In Figure S1, all the tasks are processed by Blocks 1 through 3 before the tasks are branched out into sub-branches as shown in Block 4. In Figure S3, only Block 1 processes all the tasks before it is separated into different sub-branches.)
Regarding claim 7, Vandenhende teaches:
“wherein the steps of checking, assigning to subgroups, and determining the subbranching are performed iteratively for each respective subgroup ugi” ([pg. 6-7, Section 3.2, par. 1-2], The branched multi-task network is found by minimizing the sum of the task dissimilarity scores at every location in the sharable encoder. By taking into account the clustering cost at all depths, the procedure can find a task grouping that is considered optimal in a global sense. The dissimilarity score between two tasks is determine for all locations and the dissimilarity score is used to determine if tasks need to be assigned to separate branches.)
Regarding claim 8:
Claim 8 recites a multitasking model that performs the same process as described in Claim 1. Therefore claim 8 is rejected under the same reasons mention for claim 1.
Regarding claim 9:
Claim 9 recites an article of manufacture that performs the same process as described in Claim 1. Therefore claim 9 is rejected under the same reasons mention for claim 1. The additional elements of claim 9 is addressed below:
“A non-transitory computer-readable medium on which is stored a computer program ..., the computer program, when executed by a computer, causing the computer to perform the following steps” ([pg. 7, Section 4, par. 1-3], A ResNet-50 encoder is used in the experiments. It is implied that the model to executed on a computer with a processor and memory to perform the proposed methods.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY MAC whose telephone number is (703)756-1517. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM.
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/GARY MAC/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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