DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/16/2025 has been entered.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claims 1, 8, and 15,
Step 2A, Prong One
The claim recites in part:
creating an initial expert layer of an expert hierarchy with a plurality of initial experts trained for prediction;
generating, by each of the initial and augmented experts in the expert hierarchy, a respective expert prediction based on the input.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example: (1) A human can create an initial expert hierarchy by mentally organizing knowledge, ranking areas of expertise, and deciding which “experts” (skills, perspectives, or strategies) should take priority in different situations. (2) A human expert can generate prediction based on their expertise.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving an input provided to the expert hierarchy for a prediction;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s)of the expert hierarchy so that an expert of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites a processor, a memory, communication platform, and machine readable and non-transitory medium which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of user segment, initial expert layer, expert hierarchy, augmented expert layer, machine learning, input, expert hierarchy, and expert prediction amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving an input provided to the expert hierarchy for a prediction;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s)of the expert hierarchy so that an expert of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The processor, a memory, communication platform, and machine readable and non-transitory medium are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of user segment, initial expert layer, expert hierarchy, augmented expert layer, machine learning, input, expert hierarchy, and expert prediction amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2, 9, and 16, the recitation of “wherein the plurality of initial experts are heterogeneous experts” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 3, 10, and 17, the recitation of “wherein when the expert hierarch has multiple augmented expert layers, each augmented expert at an augmented expert layer higher than a first augmented expert layer additionally augments any augmented expert at a lower augmented expert layer” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claims 4, 11, and 18, the limitations “generating an augmented expert at a first augmented expert layer based on first training data and a plurality of predictions generated by the plurality of initial experts based on the first training data; and generating an augmented expert at an augmented expert layer above the first augmented expert layer based on second training data, a plurality of predictions generated by the plurality of initial experts based on the second training data, and one or more predictions generated by respective one or more augmented experts at any lower augmented expert layer based on the second training data” are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
As to claims 5, 12, and 19, the limitations “wherein the step of generating an augmented expert at a first augmented expert layer comprises: accessing the first training data having input features and ground truth labels; sending the input features to the plurality of initial experts; receiving expert predictions from the respective plurality of initial experts; and iteratively learning the augmented expert at the first augmented expert layer based on the input features, the expert predictions from the respective plurality of initial experts, and the ground truth labels” which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 6 and 13, the limitations “wherein the step of generating an augmented expert at an augmented expert layer above the first augmented expert layer comprises: accessing the second training data having input features and ground truth labels; sending the input features to the plurality of initial experts and one or more augmented experts at each lower augmented expert layer; receiving both initial expert predictions from the respective plurality of initial experts and augmented expert predictions from respective previously trained augmented experts at each lower augmented expert layer; and iteratively learning the augmented expert based on the input features, the initial expert predictions, the augmented expert predictions, and the ground truth labels” which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As to claims 7, 14, and 20, the limitations “accessing a nonlinear integration model provided for integrating different expert predictions; combining, in accordance with the nonlinear integration model, expert predictions from the initial and augmented experts in the expert hierarchy generated based on the input; and generating an integrated expert prediction based on a result of the combining” are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas.
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.
Claim(s) 1 -3, 7 - 10, 14 - 17, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jordan et al ("Hierarchical of Experts and the EM algorithm").
As to claim 1, Jordan et al figure 1 shows and teaches a method implemented on at least one processor, a memory, and a communication platform for predicting user segment via machine learning (page 4, paragraph 5, line 3 "parameters are synchronized through a set of parameter servers" and page 16, paragraph 4, lines 1-2 "Models are trained on a cluster of 32 Tesla K40 GPUs, except for the last two models, which are trained on clusters of 64 and 128 GPUs" reads on at least one processors a memory, and a communication platforms), comprising:
creating an initial expert layer of an expert hierarchy with a plurality of initial experts trained for prediction (page, 2, paragraph 5, line 4, “expert networks” reads on “initial expert layer an expert hierachyr” ; page 13, paragraph 4, line 1, “HME architecture was trained Algorithms 1 and 2” reads on “plurality of initial experts trained for prediction” );
deriving at least one augmented expert layer (page 2, paragraph 5, line 2 “gating network reads on “at least one augmented expert layer”) for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer trained, via machine learning for the prediction (Abstract, line 1, “tree-structured architecture for supervised learning” reads on “ machine learning”), using based on training data directed thereto and expert outputs from all of experts from a-all lower expert layer(s)layer of the expert hierarchy so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy (page 4, paragraph 2, “the output vector at each nonterminal of the tree is the weighted output of the experts below that nonterminal” reads on “an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy”); receiving an input provided to the expert hierarchy for a prediction (page 4, paragraph 3, “”Note that both the g’s and µ’s depend on the input x” reads on “generated by each of the experts from the lower expert layer based on the training data”);
generating, by each of the initial and augmented experts in the expert hierarchy, a respective expert prediction based on the input (page 4, paragraph 3, “”Note that both the g’s and µ’s depend on the input x” reads on “a respective expert prediction”).
As to claim 2, Jordan et al teaches the method where the plurality of initial experts are heterogenous experts (page 2, paragraph 6, lines 1 - 5 “ll of the expert networks in the tree are lincar with a single output nonlincarity. We will refer to such a network as "generalized linear," borrowing the terminology from statistics (McCullagh & Nelder, 1983). Expert network (i,j) produces its output µᵢⱼ as a generalized linear function of the input x: µᵢⱼ = f(Uᵢⱼx))
As to claim 3, Jordan et al teaches the method, wherein when the expert hierarch has multiple augmented expert layers, each augmented expert at an augmented expert layer higher than a first augmented expert layer additionally augments any augmented expert at a lower augmented expert layer (page 4, paragraph 2, “the output vector at each nonterminal of the tree is the weighted output of the experts below that nonterminal”
Claim 8 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 9 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 10 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over S Jordan et al ("Hierarchical of Experts and the EM algorithm") in view of DUBE et al (US 2022/0414428).
As to claim 7, Shazeer et al shows the expert hierarchy making predictions.
Jordan et al fails to explicitly/show teach accessing a nonlinear integration model provided for integrating different expert predictions; combining, in accordance with the nonlinear integration model, expert predictions from the initial and augmented experts in the expert hierarchy generated based on the input; and generating an integrated expert prediction based on a result of the combining.
However, DUBE et al teaches accessing a nonlinear integration model provided for integrating different expert predictions (paragraph [0170] FIG. 11A illustrates expert layers employed in the AI model 1100 located between the common layer. The components (e.g., experts 1106, common layers 1104, argmin operation 1118, reinforcement learning module 1140, and selector 1120), the inputs (e.g., training sample 1102 and ground truth 1110) and the outputs (e.g., outputs 1108, errors 1112 and output 1122) may be the same structure and perform the same function as those components, inputs and outputs as described in FIG. 7);
combining, in accordance with the nonlinear integration model, expert predictions
from the initial and augmented experts in the expert hierarchy generated based on the
input predictions (paragraph [0170] FIG. 11A illustrates expert layers employed in the AI model 1100 located between the common layer. The components (e.g., experts 1106, common layers 1104, argmin operation 1118, reinforcement learning module 1140, and selector 1120), the inputs (e.g., training sample 1102 and ground truth 1110) and the outputs (e.g., outputs 1108, errors 1112 and output 1122) may be the same structure and perform the same function as those components, inputs and outputs as described
in FIG. 7); and generating an integrated expert prediction based on a result of the combining (paragraph [0164]...906m-1 to experts 906m-n may have the same architecture or different architecture (or some combination). Further, experts 906a-1 to experts 906a-n may be the same architecture or different architecture (or some combination) as experts 906m-1 to experts 906m-n. In some configurations, each of the experts 906 in the layers of experts may be trained to segment different portions of the training sample 902. Additionally, or alternatively, each of the experts 906 in the layers of experts may be trained to further refine the segments determined by the experts in the preceding expert layers).
Therefore it would have been obvious for one having ordinary skill in the art, at the time the invention was made, for the purpose of Jordan et al accessing a nonlinear integration model provided for integrating different expert predictions; combining, in accordance with the nonlinear integration model, expert predictions from the initial and augmented experts in the expert hierarchy generated based on the input; and generating an integrated expert prediction based on a result of the combining, as in DUBE et al, for the purpose of match preferences to experts and/or mixtures of experts, enabling an artificial intelligence model to learn preferences.
Claim 14 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 12/16/205 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 112
The newly added limitations overcome the 112 Rejection and the 112 Rejection has been withdrawn.
Claim Rejections - 35 USC § 102
However, Applicant’s arguments with respect to claim(s) 1-3, 7 - 10, 14 - 17, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
The Office Action alleges that claim 1 falls under the "Mental Processes." Office Action, pages 3-4. Applicant respectfully disagrees. Initially, claim 1 as a whole is related to "machine learning" (see the preamble), which cannot be performed by the human mind.
Particularly, claim 1 recites "deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy." These claim features are related to machine learning via an expert hierarchy, wherein an expert at any augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy. The above-quoted claim features cannot be performed by a human mind or a human with pen/paper at least because a human mind cannot perform machine learning via an expert hierarchy, wherein an expert at any augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy. Thus, claim 1 does not fall within the “mental processes” grouping of abstract ideas.
The Office Action on page 17 alleges that "Applicant argues that machine learning cannot be performed in the human mind but the claims are written with a level [of] generality that encompasses abstract manipulation of data which fails to transform an otherwise patent-ineligible concept into an eligible one." Applicant respectfully disagrees at least because "machine learning" is not recited in a level of generality that encompasses abstract manipulation of data but is recited in details - "based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy so that an expert at any of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy." Human minds cannot machine train an augmented expert in an expert hierarchy based on training data and expert outputs from all of experts from all lower expert layer(s) of the expert hierarchy.
The examiner disagrees. As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example: a human can create an initial expert hierarchy by mentally organizing knowledge, ranking areas of expertise, and deciding which “experts” (skills, perspectives, or strategies) should take priority in different situations and said human expert can generate prediction based on their own expertise. Further, the limitations constitute data analysis and decision-making that fall within abstract mental processes.
As claimed the “machine learning” is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The applicant argues:
Initially, as mentioned previously, the Office Action has improperly analyzed claim 1 when determining whether claim 1 recites a judicial exception because claim 1 does not fall into any of the abstract idea exceptions - mathematical concepts, certain methods of organizing human activity, or mental processes.
Even assuming, for the sake of argument, that claim 1 does recite an abstract idea (which the Applicant disagrees), Applicant respectfully submits that claim 1 is patent eligible under
Prong Two of the Step 2A Analysis.
The claims provide an improvement in technical fields of ensemble learning. As a known problem of the traditional ensemble learning, "due to the complexity of inter-relationships among data and different data sources, it is not possible to capture such inter-relationships via linear models" (para. [0004]), and "linearly combining their outputs using a linear combination cannot capture the actuality of the world" (para. [0005]).
"The present teaching discloses solutions that address challenges in the art. To resolve the issues associated with task heterogeneity, data long-tailness, and data availability in predicting based on online data, the present teaching presents a scheme of augmenting experts at one or more levels to not only leverage the learned expertise from original experts but also expand the expertise in terms of aspects of knowledge not yet learned by the existing experts including inter-relationships among existing experts that the traditional systems completely ignore. To achieve that, in deriving a new augmented expert, in addition to training data, the outputs from previously trained experts (including original and previously augmented experts) are also used to train the new augmented expert, where the outputs from the previously trained experts are generated by these experts based on the same training data. The disclosed expert augmentation scheme yields heterogeneous experts which form an expert hierarchy. This expert hierarchy provides an expanded range of knowledge learned by different experts so that their respective expertise on the same task may be integrated to enhance the quality of the prediction as compared with the traditional systems." (Para. [0031]) "The present teaching also discloses a nonlinear framework for integrating outputs from different experts to overcome the deficiencies of the traditional approaches that use linear weighted sum in integrating different experts. The present teaching presents a scheme of combining multiple experts in a nonlinear manner via learning. The multiple experts being combined using the scheme as disclosed herein may include homogeneous and/or heterogeneous experts. In some embodiments, the experts being combined may include conventional experts and/or augmented experts created based on some given existing experts using the augmentation scheme as disclosed herein. In some embodiments, an artificial neural network (ANN) is employed for integration so that embeddings of the ANN may be learned to capture the nonlinear complex relationships and serve as a nonlinear integration function for combining multiple expert outputs. Such a trained ANN with learned embeddings, when receiving outputs from multiple experts as input, yields an integrated expert via complex non-linear function learned and implicitly specified via the parameterized ANN." (Para. [0032]). "As discussed herein, in some embodiments, experts in the hierarchy are trained one layer at a time. That is, the initial experts may be trained first. When the initial experts are trained, they are used in training augmented experts at the next layer. For example, in Fig. 3C, when training augmented expert 21 320-1, the trained initial expert 1 310-1 takes the same training data used for training the augmented expert 21 320-1 as input and produces its expert prediction which is provided to the augmented expert 21 320-1 as input to facilitate the learning. Once the augmented expert 21 is trained, both the initial expert 11 310-1 and augmented expert 21 320-1 are used in training augmented expert 31 340-1 by providing expert outputs thereto based on the training data used to train augmented expert 31 340-1, etc. So, the training of augmented expert N1 350-1 use training data as well as outputs from all experts, whether initial or augmented, from lower layers. In this manner, an augmented expert created at a certain layer not only learns from the training data used but also leverages the learned expertise from all lower-level experts." (Para. [0048]). Also, Applicant respectfully submits that the recited" deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data directed thereto and an expert output from an expert at any lower layer of the expert hierarchy so that an expert at any of the at least one augmented expert augments the expertise of experts at any lower layer of the expert hierarchy" is not recited at a high level of generality at least because the claim recites details about how the step of deriving is performed, e.g., training augmented experts via machine learning based on training data directed thereto and expert outputs from all experts at all lower expert layer(s) of the expert hierarchy.
The recited claim features are clearly tied to a practical application i.e., machine learning. Thus, Applicant respectfully submits that, under the Prong Two of the Step 2A Analysis from the Guidance, the claimed concept is integrated into a practical application and therefore is not directed to a judicial exception. Therefore, Applicant respectfully submits that the claims are directed to patent eligible subject matter.
The examiner disagrees.
The limitations “deriving at least one augmented expert layer for the expert hierarchy with one or more augmented experts at each of the at least one augmented expert layer, wherein each augmented expert at any of the at least one augmented expert layer is trained via machine learning for the prediction, based on training data directed thereto and expert outputs from all of experts from all lower expert layer(s)of the expert hierarchy so that an expert of the at least one augmented expert layer augments the expertise of all experts at all lower layer(s) of the expert hierarchy” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The “expert hierarchy and machine learning model” are recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites “any one of the at least one augmented expert layer is trained, via machine learning for the prediction, based on training data”. No detail is given as to how the training is performed or the task that it is trained to perform. Consequently, this limitation merely appears to be a generic training process performed on the general purpose computer to apply the abstract idea and is not sufficient to integrate the abstract idea into a practical application or amount to significantly more (MPEP 2106.05(f)).
The applicant argues:
Further, claim 1 amounts to significantly more than the judicial exception. The BerkheimerV. HP Inc, No. 2017-1437 (Fed. Cir. Feb. 8, 2018)¹ ("Berkheimer") decision re-emphasized that,"[a]t step two, we consider the elements of each claim both individually and 'as an ordered combination' to determine whether the additional elements 'transform the nature of the claim' into a patent eligible application." Berkheimer, pages 11 and 12. Berkheimer resolved that the "inventive concept" is not restricted to only the additional elements, but may include one or more allegedly abstract elements that, in combination with the additional elements, form the claim's inventive concept. See Id., page 12 (stating, without reference to an "additional" element, that "[t]he question of whether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact"). For example, while the Berkheimer Court held independent claim 1 to be directed to "the abstract idea of parsing and comparing data with conventional computer components, the Berkheimer Court nevertheless concluded that dependent claim 4 (which depended on claim 1) was potentially patent-eligible. In concluding that claim 4 could be patent-eligible, the Berkheimer Court did not narrow the inventive concept to merely the additional limitation of "storing a reconciled object structure in the archive without substantial redundancy." Indeed, the general operation of storing data/object structures in some archive without substantial redundancy by itself would clearly have been found to be well- understood, routine, and conventional. Despite this, however, the Berkheimer Court concluded that the claimed invention of claim 4 could be patent-eligible.
In the instant application, the Office Action appears to allege that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Applicant respectfully disagrees with the contentions, and further submits that claim 1 is patent eligible under Step 2B Analysis from the Guidance. Berkheimer showed that it is not merely the additional elements that are to be viewed for eligibility, but the claimed concept described by the additional elements in conjunction with the non-additional elements. Furthermore, even assuming arguendo that each of the claim limitations individually is abstract, or is performed by or is a generic computer, SO too are the BASCOM claim limitations (e.g., BASCOM Global Internet V. AT& Mobility LLC, No. 2015-1763 (Fed. Cir. Jun. 27, 2016)² ("BASCOM")).
In addition to failing to consider Applicant's claims as an ordered combination and as a whole, the Office Action has improperly analyzed the claims without considering the "additional element(s)" in combination with the non-additional elements. As a result, the Office Action has also incorrectly and improperly identified that the alleged "additional elements" do not amount to significantly more than the alleged judicial exception.
Accordingly, Applicant respectfully submits that claim 1 is patent eligible under the Step 2B Analysis of the Guidance.
Applicant respectfully submits that claim 1 is directed to patent eligible subject matter.
Accordingly, no further analysis is necessary to find claim 1 patent eligible under 35 U.S.C.§ 101.
Claims 8 and 15 recite features similar to the features of claim 1 discussed above and thus are directed to patent eligible subject matter for the same reasons as discussed above with respect to claim 1.
The examiner disagrees. Applicant has not provided sufficient evidence or persuasive reasoning demonstrating that the additional elements, whether considered individually or as an ordered combination, amount to significantly more than the abstract idea.
While, Berkheimer recognizes that whether certain elements are well-understood, routine, and conventional may present a factual issue, the present record supports the determination that the additional elements, whether considered individually or as an ordered combination, amount to significantly more than abstract.
While Berkheimer recognizes that whether certain elements are well-understood, routine, and conventional may present a factual issue, the present set of claims are generic computer components performing routine and conventional functions. The claim does not recite any specific technologically improvement or unconventional arrangement that transforms the nature of the claim into patent-eligible subject matter.
Applicant’s reliance on BASCOM is also un persuasive, Unlike in BASCOM, where the claims recited a specific, non-conventional arrangement of known components, the instant claims merely implement the abstract idea using generic computer elements without any meaningful limitation.
Furthermore, the Examiner has considered the claims both individually and as an ordered combination. The combination of elements does not add any inventive concept, as the elements operate in their expected and conventional manner. Claim 1 does not included additional elements sufficient to amount to significantly more than the judicial exception.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128