DETAILED ACTION
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 in response to communications filed on 01/26/2024. Claims 1-15 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 04/24/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 10, and 15 are objected to because of the following informalities:
As per claim 1, the term “for” in lines 7, 8, and 9 raises question as to whether the features following are limiting, or merely refer to intended use. The term “aims at” in lines 7 and 9 raises question as to whether the features following are limiting, or merely refer to intended use. This similarly applies to claims 10 and 15.
Appropriate correction is required.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “labelling function selection module” in claim 13 and “uncertainty estimation module” in claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 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.
The phrase “as many… as possible…” in claim 1 is a relative term which renders the claim indefinite. The phrase “as many… as possible” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. “as many… as possible…” varies depending on situation, etc. As such, the claim is indefinite. This similarly applies to claims 10 and 15. Due at least to their dependency upon claims 1 or 10, dependent claims 2-9, and 11-14 also fail to comply with the written description requirement.
Further as per claim 4, there is lack of antecedent basis for “the machine learning model”. This similarly applies to claims 5 and 13.
Further as per claim 8, there is lack of antecedent basis for “the final predicted matches”.
Further, claim limitations “labelling function selection module” in claim 13 and “uncertainty estimation module” in claim 14 each invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification only generally describes a labelling function selection module (e.g. in page 7) and a uncertainty estimation module (e.g. in page 12), but does not clearly link the modules to any structure (e.g. such modules may refer to merely software, which is not structure). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
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.
Claims 1-4, 9-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Thirumuruganathan et al. (“Deep Learning for Blocking in Entity Matching: A Design Space Exploration”, Proceedings of the VLDB Endowment, Volume 14, Issue 11, doi.org/10.14778/3476249.3476294, July 1 2021, pages 2459-2472) in view of Castelli et al. (US 20140163962 A1).
As per independent claim 1, Thirumuruganathan teaches a method of identifying entities from different data sources as matching entity pairs that refer to a same real-world object, the method comprising:
providing a set of labelling entities to determine matching entities and non-matching entities of a source data set and a least one target data set (e.g. in pages 2459-2460 and 2462, “Entity matching (EM) finds data instances that refer to the same real-world entity… a matcher to the remaining tuple pairs to predict match/no-match… each component can be instantiated with many possible DL models, e.g., LSTM, transformer, etc., resulting in many self-supervised solution choices… automatically derive labeled training data” and table 2);
selecting, from the provided set of labelling entities, a subset of labelling entities for training machine learning models (e.g. in pages 2460 and 2462, “each component can be instantiated with many possible DL models, e.g., LSTM, transformer, etc., resulting in many self-supervised solution choices… automatically derive labeled training data” and table 2) for a blocking module that aims at filtering out as many unmatched entity pairs as possible without missing any true matches (e.g. in pages 2460-2461, “The blocking step uses heuristics to quickly remove pairs (𝑎, 𝑏) judged unlikely to match… seek to develop solutions that maximizes the recall |𝐶 ∩ 𝐺|/|𝐺|, where 𝐺 is the set of (unknown) true matches, while minimizing |𝐶| and the time taken for blocking”) and for a matching module that aims at predicting matching results for remaining entity pairs not filtered out by the blocking module (e.g. in page 2460, “matching step applies a matcher to predict match/nomatch for each remaining pair”); and
jointly learning both a blocking model for the blocking module and a matching model for the matching module based on available unlabeled entity pairs and the labelling entities of the selected subset of labelling entities (e.g. in pages 2459-2460, “applied deep learning (DL) to matching… applying DL to blocking for EM… do not require labeled training data… applies a matcher to the remaining tuple pairs to predict match/no-match… each component can be instantiated with many possible DL models, e.g., LSTM, transformer, etc., resulting in many self-supervised solution choices”),
but does not specifically teach wherein the entities include functions.
However, Castelli teaches providing a set of entities including functions and selecting, from the provided set of entities, a subset of functions (e.g. in paragraphs 21, 24, 63, and 75, “observed examples for a specific problem and domain… Given a collection of feature vectors with their labels, a learning algorithm selects a function that predicts the label given the feature vector… choosing an objective function that computes how well a specific prediction function fits the training set… optimization…to select a prediction function”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Thirumuruganathan to include the teachings of Castelli because one of ordinary skill in the art would have recognized the benefit of facilitating optimization.
As per claim 2, the rejection of claim 1 is incorporated and the combination further teaches applying the learned blocking model to filter out unmatched entity pairs and passing the remaining entity pairs to the matching module (e.g. Thirumuruganathan, in page 2459, “EM solutions perform blocking then matching. Given two tables 𝐴 and 𝐵 to match, the blocking step uses heuristics to quickly remove tuple pairs (𝑎 ∈ 𝐴, 𝑏 ∈ 𝐵) judged unlikely to match. The matching step then applies a matcher to the remaining tuple pairs to predict match/no-match”).
As per claim 3, the rejection of claim 2 is incorporated and the combination further teaches applying the learned matching model to predict the matching results of the entity pairs received from the blocking module (e.g. Thirumuruganathan, in page 2459, “The matching step then applies a matcher to the remaining tuple pairs to predict match/no-match”).
As per claim 4, the rejection of claim 1 is incorporated and the combination further teaches wherein the selection of the subset of labelling functions is based on performance characteristics of the machine learning model achieved over a subset of entity pairs annotated with labels based on domain knowledge (e.g. Castelli, in paragraphs 21 and 24, “observed examples for a specific problem and domain… Given a collection of feature vectors with their labels, a learning algorithm selects a function that predicts the label given the feature vector… choosing an objective function that computes how well a specific prediction function fits the training set…to select a prediction function”).
As per claim 9, the rejection of claim 1 is incorporated and the combination further teaches saving all available labelling functions into a repository (e.g. Castelli, in paragraphs 24 and 75, “defining a priori a…set of prediction functions… These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner”).
Claims 10-13 are the system claims corresponding to method claims 1-4, and are rejected under the same reasons set forth and the combination further teaches one or more processors that, alone or in combination, are configured to provide for execution of a method (e.g. Castelli, in paragraph 63, “one or more processors… processor 12 may include a module 10 that performs the methods”).
Claim 15 is the medium claim corresponding to method claim 1, and is rejected under the same reasons set forth and the combination further teaches a tangible, non-transitory computer-readable medium having instructions stored thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method (e.g. Castelli, in paragraph 63, “one or more processors… processor 12 may include a module 10 that performs the methods… module 10 may be programmed into the integrated circuits of the processor 12, or loaded from memory 16, storage device 18”).
Claims 5 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Thirumuruganathan et al. (“Deep Learning for Blocking in Entity Matching: A Design Space Exploration”, Proceedings of the VLDB Endowment, Volume 14, Issue 11, doi.org/10.14778/3476249.3476294, July 1 2021, pages 2459-2472) in view of Castelli et al. (US 20140163962 A1) as applied above, and further in view of Borthwick et al. (US 20120278263 A1).
As per claim 5, the rejection of claim 4 is incorporated and the combination further teaches the performance characteristics used for selecting the subset of labelling functions (e.g. Thirumuruganathan, in pages 2460 and 2462, “each component can be instantiated with many possible DL models, e.g., LSTM, transformer, etc., resulting in many self-supervised solution choices… automatically derive labeled training data” and table 2; in paragraphs 21 and 24, “Given a collection of feature vectors with their labels, a learning algorithm selects a function that predicts the label given the feature vector… choosing an objective function that computes how well a specific prediction function fits the training set…to select a prediction function”),
but does not specifically teach wherein an achieved F1 score of the machine learning model is taken as the performance characteristics.
However, Borthwick teaches taking an achieved F1 score of a machine learning model is taken as performance characteristics (e.g. in paragraphs 97-98, “metrics for evaluating a classifier's performance on record linkage problem are precision, recall and their harmonic mean, f-measure”, i.e. F1 score). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Borthwick because one of ordinary skill in the art would have recognized the benefit of facilitating evaluation of performance (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
As per claim 8, the rejection of claim 1 is incorporated, but the combination does not specifically teach interacting with a domain expert to add new labelling functions, to annotate selected entity pairs, and/or to display the final predicted matches. However, Borthwick teaches interacting with a domain expert to add new labelling functions, to annotate selected entity pairs, and/or to display final predicted matches (e.g. in paragraph 57, “we identify pairs we are unsure of (200), send pairs to human annotators ("mechanical turk") (202), have the annotators label the pairs as match/no-match” and figure 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Borthwick because one of ordinary skill in the art would have recognized the benefit of improving learning.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Thirumuruganathan et al. (“Deep Learning for Blocking in Entity Matching: A Design Space Exploration”, Proceedings of the VLDB Endowment, Volume 14, Issue 11, doi.org/10.14778/3476249.3476294, July 1 2021, pages 2459-2472) in view of Castelli et al. (US 20140163962 A1) as applied above, and further in view of Gordo Soldevila et al. (US 20180260415 A1) .
As per claim 6, the rejection of claim 1 is incorporated and the combination further teaches wherein the provided set of labelling functions comprises at least two types of labelling functions, comprising a pair-wise labelling functions determining a matching status of individual entity pairs (e.g. Thirumuruganathan, in pages 2460 and 2462-2463, “each component can be instantiated with many possible DL models, e.g., LSTM, transformer, etc., resulting in many self-supervised solution choices… automatically derive labeled training data… a set of tuple pairs (𝑡𝑖 , 𝑡 𝑗 ) with match/no-match labels”),
but does not specifically teach a set-wise labelling functions determining a matching status of all entity pairs of a given data set.
However, Gordo Soldevila teaches set-wise functions determining a matching status of all entity pairs of a given data set (e.g. in paragraph 33, “Once a set of pairwise scores between all image pairs was obtained, a graph was constructed whose nodes are the images and edges are pairwise matches”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Gordo Soldevila because one of ordinary skill in the art would have recognized the benefit of constructing a graph.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Thirumuruganathan et al. (“Deep Learning for Blocking in Entity Matching: A Design Space Exploration”, Proceedings of the VLDB Endowment, Volume 14, Issue 11, doi.org/10.14778/3476249.3476294, July 1 2021, pages 2459-2472) in view of Castelli et al. (US 20140163962 A1) as applied above, and further in view of Borthwick et al. (US 20120278263 A1) and Chen et al. (US 20070294221 A1).
As per claim 7, the rejection of claim 1 is incorporated and the combination further teaches performing actions in both a learning phase and a prediction phase and jointly from both a model generation phase and a prediction phase and from both the blocking module and the matching module (e.g. Thirumuruganathan, in pages 2459 and 2464, “applied deep learning (DL) to matching… applying DL to blocking for EM… train a classifier to predict for each pair in 𝐶 the correct label… predicts the correct label”),
but does not specifically teach estimating an uncertainty of all entity pairs; selecting a number of entity pairs with an uncertainty exceeding a predefined threshold; and requesting user annotations for selected entity pairs from a domain expert.
However, Borthwick teaches estimating an uncertainty of all entity pairs, selecting a number of entity pairs with an uncertainty, and requesting user annotations for selected entity pairs from a domain expert (e.g. in paragraph 57, “we identify pairs we are unsure of (200), send pairs to human annotators ("mechanical turk") (202), have the annotators label the pairs as match/no-match” and figure 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Borthwick because one of ordinary skill in the art would have recognized the benefit of improving learning,
but does not specifically teach exceeding a predefined threshold.
However, Chen teaches selecting based on a determination exceeding a predefined threshold (e.g. in paragraph 37, “selection operator… measured by a specific function, is less (or greater) than a given threshold”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Chen because one of ordinary skill in the art would have recognized the benefit of facilitating selection determination.
Claim 14 is the system claim corresponding to method claim 7 and is rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Way et al. (US 10958784 B1) teaches “select the hyperparameter set 340 with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) overall cross-validation score for training the machine learning model. The machine learning system may then train the machine learning model using the selected hyperparameter set 340, without cross-validation (e.g., using all of data in the training set 320 without any hold-out groups), to generate a single machine learning model for a particular machine learning algorithm” (e.g. in column 26 lines 31-64).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144 09/05/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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