DETAILED ACTION
This action is in response to claims filed 07 April 2023 for application 18131903 filed 07 April 2023. Currently claims 1-14 are pending.
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 .
Claim Objections
Claim 1 is objected to because of the following informalities: using the ranked list). The parenthesis appears to be a typo. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: the user to ex-tend the partial complex system. The hyphen appears to be a typo.
Claim Rejections - 35 USC § 112
Claims 6-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the embedding matrix" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the preference query" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the Gini index G" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation " the Gini index " in line 1. There is insufficient antecedent basis for this limitation in the claim. It appears that claim 9 should depend on claim 8.
Correction or clarification is required.
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 and 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 1, 12 and 13 are are directed to the statutory category of a method, an article of manufacture and a system.
In step 2a prong 1, claims 1, 12 and 13 recite, in part, generating a recommender system based on a model and a graph neural network, the model has nodes and edges, a design process includes steps items are added to a source node, calculating a ranked list of candidate items and providing a user a subset of ranked items as recommendation, determining a calibrated score, applying a threshold, determining if a score is above a threshold, using information of a property, forming a subset of items having the property, performing a computation of the calibrated scores, applying the threshold, using the ranked list and providing the it to the user. The limitations of generating a recommender system based on a model and a graph neural network, the model has nodes and edges, a design process includes steps items are added to a source node, calculating a ranked list of candidate items and providing a user a subset of ranked items as recommendation, determining a calibrated score, applying a threshold, determining if a score is above a threshold, using information of a property, forming a subset of items having the property, performing a computation of the calibrated scores, applying the threshold, using the ranked list and providing the it to the user are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “computer”, “computer readable hardware storage device”, “processor”, “computer program”, and “interface device” in the context of the claims, the limitations encompass a person analyzing and modeling a complex system, determining scores and ranking potential additions to the system model and recommending the additions in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “computer”, “computer readable hardware storage device”, “processor”, “computer program”, and “interface device”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer”, “computer readable hardware storage device”, “processor”, “computer program”, and “interface device” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-9 and 14 recite further limitations of repeating the steps, keeping the threshold constant or changing it, methods for score calibration, methods of choosing the threshold, the score calculation, requesting a preference query answer, choosing a subset using a Gini index, calculation for the Gini index, and a display with a menu.
These limitations amount to the same abstract idea identified above. Further additional elements are a display with a menu. This amount to a generic computer component and therefore does not amount to a practical application or significantly more than the abstract idea itself.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rocco et al. (A GNN-based Recommender System to Assist the Specification of Metamodels and Models) in view of Creed et al. (US 20210081717 A1).
Regarding claim 1, Rocco discloses: A computer implemented method for providing a recommender system for use in the design process of a complex system
whereby the recommender system is generated by use of a model of the complex system, the model based on a graph neural network (Fig 3 GNN and graph model the complex system Rocco),
for the model, the complex system being described by nodes representing components of the complex system and edges representing connections between the components (“Furthermore, this component employs NetworkX,7 a Python library that creates nodes and edges considering the structure of the parsed model. According to the format shown in Equation 4, each model is represented by a list of connected graphs in which each class is linked with corresponding elements.” P73 §III.B, see also p73 §III.A Metamodel parsing),
wherein the design process includes design steps in each of which at least one item is added to a source node of a partial design (“By comparing the two sub-figures, we see that some assistances are needed to enrich the partial metamodel shown in Fig.1(a), e.g., by adding new classes, attributes or relations. Moreover, it is also necessary to suggest new metaclasses including structural features, i.e., attributes and references.” P71 §II.A metamodeling assistant),
whereby the recommender system calculates a ranked list of all candidate items out of a variety of items and provides to a user a subset of the ranked list as recommendation which item to add to the source node in a subsequent design step (“Since the employed kernel algorithm is a pairwise operator, MORGAN retrieves an ordered list of classes stored in the training set ranked by similarity scores. The top-5 similar elements are extracted from the whole training set to support two kinds of recommendation: (i) specification of missing structural features; and (ii) generation of (meta)classes that can be used to enhance the artifact under specification with further concepts.” P74 §III.C ¶1, see also p76 §IV.D),
the providing of the recommendation comprising:
a) determining for each item a calibrated score indicating a likelihood that the specific item will be added in the subsequent design step (P74 §III.C ¶1 similarity);
b) applying a threshold with regard to the score P74 §III.C ¶1 top-5,
c) determining whether at least one item has a score above the applied threshold P74 §III.C ¶1 top-5;
h) using the ranked list) of the subset as ranked list if at least one item thereof has a calibrated score above the threshold applied for the subset and providing to the user the part of the ranked list above the threshold as recommendation of items to be added in the next design step (“Since the employed kernel algorithm is a pairwise operator, MORGAN retrieves an ordered list of classes stored in the training set ranked by similarity scores. The top-5 similar elements are extracted from the whole training set to support two kinds of recommendation: (i) specification of missing structural features; and (ii) generation of (meta)classes that can be used to enhance the artifact under specification with further concepts.” P74 §III.C ¶1).
Rocco does not explicitly disclose: d) if there is no item with a calibrated score above the threshold, then using information whether at least one property of the items is desired
e) forming a subset of items possessing the desired property
f) performing, by the recommender system, on the subset a computation of the calibrated scores of the items contained in the subset;
g) applying a threshold for the subset.
Creed teaches: d) if there is no item with a calibrated score above the threshold, then using information whether at least one property of the items is desired (“Filtering the entity-entity graph based on the trained attention weights may include removing those relationship edge(s) from the entity-entity graph or portion thereof having a corresponding trained attention weight that is below or equal to an attention relevancy threshold. Alternatively, filtering the entity-entity graph may include removing those relationship edge(s) from the entity-entity graph or portion thereof having a corresponding trained attention weight that is above or equal to another attention relevancy threshold. In step 136, the filtered entity-entity graph may be output. Additionally or alternatively, step 134 may further include identifying uncertain relationship edges in the entity-entity graph or portion thereof based on a set of attention weights retrieved from the trained GNN model associated with the entity-entity graph and an attention relevancy threshold. The identified uncertain relationship edges may be culled, removed, and/or rules may be derived defining actions for excluding the relationship edges, where the rules for part of the data representative of the entity-entity graph. Once a filtered entity-entity graph has been output, it may be used to update the GNN model based on the filtered entity-entity graph. Alternatively or additionally, the output filtered entity-entity graph may be used by a graph-based technique to generate or train another GNN model based on the filtered entity-entity graph.” [0114]);
f) performing, by the recommender system, on the subset a computation of the calibrated scores of the items contained in the subset [0114];
g) applying a threshold for the subset [0114].
Rocco and Creed are in the same field of endeavor of graph neural networks and are analogous. Rocco discloses a system that uses a GNN to implement recommendation for system models. Creed teaches calibrated scores and threshold for determining subsets of items in a graph neural network. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the GNN based recommender having a ranked list as disclosed by Rocco to utilize the known filtering of elements in the GNN and forming subsets of items below a threshold to find relevant ones having certain properties as taught by Creed to yield predictable results of improving the model and GNN through retraining.
Regarding claim 2, Rocco discloses: The method according to claim 1, wherein steps d through g are repeated until at least one item has a score above the threshold for the respective subset (p74 §III.C process is iterative).
Regarding claim 3, Rocco discloses: The method according to claim 1, wherein the threshold is either kept constant or configured when applied to a subset P74 §III.C ¶1 top-5.
Regarding claim 4, Rocco does not explicitly disclose:
The method according to claim 1, wherein for the calibration of the score at least one of the following methods is applied:
temperature scaling;
histogram binning; and
isotonic regression.
Creed teaches: wherein for the calibration of the score at least one of the following methods is applied:
temperature scaling;
histogram binning (“The first group corresponds to edges that exist in the Open Targets platform, but have a score below 0.1, indicating low confidence. The second group corresponds to edges that have an Open Targets score above 0.9, indicating a high confidence. After organizing the weights in histograms of 30 bins, the likelihood of a weight at a given magnitude belonging to a low or high Open Targets score was empirically estimated. As FIG. 3e illustrates, a low-weighted edge is 4 times more likely to be a low-scoring Open Targets edge than a high-weighted one. At higher weights, this ratio drops to nearly parity, making high-scoring Open Targets edges much more likely. A 2-sample Kolmogorv-Smirnov test yields p=6e−28, strongly rejecting the hypothesis that these two weight distributions arise from the same underlying distribution and suggesting that the weights have an interpretable meaning related to their correctness.” [0169]); and
isotonic regression.
Regarding claim 5, Rocco discloses: The method according to claim 1, wherein for choosing the threshold at least one of is performed:
choosing a predetermined number and taking it as threshold P74 §III.C ¶1 top-5;
calculating an F1 value as threshold; and
determining the threshold as a function of the calibrated score such, that a predetermined number of items is provided to the user
Regarding claim 6, Rocco does not explicitly disclose, however, Creed teaches: The method according to claim 1, wherein the score is calculated by
s=Z*h SN
wherein
s denotes the score,
Z denotes the embedding matrix obtained by calculating for all items out of the variety of items a probability that the item is connected to each other components (“Preferably, the computer-implemented method further comprising: generating one or more relationship scores based on data representative of one or more embedding(s) associated with the portion of entity-entity graph; where updating the GNN model further includes updating the GNN model including the attention weights by minimising the loss function based on at least the embedding and the relationship scores.” [0010])
Regarding claim 7, Rocco discloses: The method according to claim 1, wherein the preference query comprises:
i) requesting, from the user, information whether property is consistent with the design goals and/or requirements;
ii) if the received answer is yes, requesting, from the user, the desired value of the property (“In this way, the assistant should be able to provide the modeler with useful recommendations to help her finalize the metamodeling activities.” P71 §II.A Metamodeling assistant).
Regarding claim 12, Rocco discloses: A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method comprising program instructions that cause, when the program is executed by a computer, the computer to carry out a method according to claim 1 (Fig 5 A, see rejection of claim 1).
Regarding claim 13, Rocco discloses: A recommendation device, wherein the recommendation device stores and/or provides and/or accesses the computer program according to claim 12, the recommendation device having a communication interface via which entries used in the program can be made or information being retrieved and/or by which access to a platform is granted on which the computer program is performed (“In this way, the assistant should be able to provide the modeler with useful recommendations to help her finalize the metamodeling activities.” P71 §II.A Metamodeling assistant),
the recommendation device for use in an engineering tool for the design of a complex system comprising a variety of items proposing a selection of items to a specific user which are used at a design step, the selection being part of the subset having yielded a score above a predetermined threshold (“In this way, the assistant should be able to provide the modeler with useful recommendations to help her finalize the metamodeling activities.” P71 §II.A Metamodeling assistant).
Regarding claim 14, Rocco discloses: The recommendation device according to claim 13, which is used for an engineering tool, which recommends items to be added in a step in the design process, the recommending being realized by a menu in which only a subset of items with a score over a predefined threshold is displayed at each design step P74 §III.C ¶1 top-5.
Conclusion
No art has been found for claims 8-11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Ojo et al. (US 20230297625 A1) discloses the use of GNNs to implement recommendation for users in a data network; Mai et al. (Deep Learning to Predict the Feasibility of Priority-Based Ethernet Network Configurations) discloses the use of GNNs for feasibility analysis of an ethernet network configuration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trujillo can be reached at (571)-272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
/James Trujillo/ Supervisory Patent Examiner, Art Unit 2151
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