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 .
Remarks
Claims 1-20 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12499132. Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the claims in the current application are broader than those in the allowed patent.
The table below shows how the claims in the instant application are anticipated by U.S. Patent No. 12499132:
Patent 12499132
Instant Application
1. A method for generating training data for an entity matching classifier, the method comprising:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records; generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels; generating a training dataset based on the feature signatures and determined match levels;
training a classifier using the training dataset, wherein the classifier comprises an ordinal, regression-based classifier;
and applying the trained classifier to new client data to merge database records identified as matching entities, the merging comprising removing original data records and inserting a new database record.
1. A method for generating training data for an entity matching classifier, the method comprising:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records; generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels; generating a training dataset based on the feature signatures and determined match levels;
and training a classifier using the training dataset.
2. The method of claim 1, wherein generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key.
2. The method of claim 1, wherein generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key.
3. The method of claim 1, wherein performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds.
3. The method of claim 1, wherein performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds.
4. The method of claim 1, further comprising: computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system.
4. The method of claim 1, further comprising: computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system.
5. The method of claim 1, further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
5. The method of claim 1, further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
6. The method of claim 1, further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
6. The method of claim 1, further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
7. The method of claim 1, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
7. The method of claim 1, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
8. The method of claim 1, further comprising: calculating an entropy-based quality score for each client key system; weighting each client key system in the aggregate voting process based on its quality score; and flagging feature signatures with consistently high entropy across client key systems for further feature engineering.
8. The method of claim 1, further comprising: calculating an entropy-based quality score for each client key system; weighting each client key system in the aggregate voting process based on its quality score; and flagging feature signatures with consistently high entropy across client key systems for further feature engineering.
9. A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records;
generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels;
generating a training dataset based on the feature signatures and determined match levels;
training a classifier using the training dataset, wherein the classifier comprises an ordinal, regression-based classifier;
and applying the trained classifier to new client data to merge database records identified as matching entities, the merging comprising removing original data records and inserting a new database record.
9. A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records;
generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels;
generating a training dataset based on the feature signatures and determined match levels;
and training a classifier using the training dataset.
10. The non-transitory computer-readable storage medium of claim 9, wherein generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key.
10. The non-transitory computer-readable storage medium of claim 9, wherein generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key.
11. The non-transitory computer-readable storage medium of claim 9, wherein performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds.
11. The non-transitory computer-readable storage medium of claim 9, wherein performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds.
12. The non-transitory computer-readable storage medium of claim 9, further comprising: computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system.
12. The non-transitory computer-readable storage medium of claim 9, further comprising: computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system.
13. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
13. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
14. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
14. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
15. The non-transitory computer-readable storage medium of claim 9, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
15. The non-transitory computer-readable storage medium of claim 9, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
16. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: calculating an entropy-based quality score for each client key system; weighting each client key system in the aggregate voting process based on its quality score; and flagging feature signatures with consistently high entropy across client key systems for further feature engineering.
16. The non-transitory computer-readable storage medium of claim 9, the steps further comprising: calculating an entropy-based quality score for each client key system; weighting each client key system in the aggregate voting process based on its quality score; and flagging feature signatures with consistently high entropy across client key systems for further feature engineering.
17. A device comprising:
a processor; a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records;
generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels;
generating a training dataset based on the feature signatures and determined match levels;
training a classifier using the training dataset, wherein the classifier comprises an ordinal, regression-based classifier;
and applying the trained classifier to new client data to merge database records identified as matching entities, the merging comprising removing original data records and inserting a new database record.
17. A device comprising:
a processor; a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for:
receiving a set of client-provided data, the set of client-provided data including records with client keys;
concatenating the set of client-provided data and grouping records by client keys to generate grouped records;
generating pairwise features for record pairs in the grouped records;
generating inferred labels for the record pairs based on their corresponding client keys;
performing an aggregate voting process on feature signatures across multiple clients to determine match levels;
generating a training dataset based on the feature signatures and determined match levels;
and training a classifier using the training dataset.
18. The device of claim 17, the steps further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
18. The device of claim 17, the steps further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks.
19. The device of claim 17, the steps further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
19. The device of claim 17, the steps further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset.
20. The device of claim 17, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
20. The device of claim 17, wherein generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “A method for generating training data for an entity matching classifier, the method comprising: receiving a set of client-provided data, the set of client-provided data including records with client keys; concatenating the set of client-provided data and grouping records by client keys to generate grouped records; generating pairwise features for record pairs in the grouped records; generating inferred labels for the record pairs based on their corresponding client keys; performing an aggregate voting process on feature signatures across multiple clients to determine match levels; generating a training dataset based on the feature signatures and determined match levels; and training a classifier using the training dataset”.
The limitations of “A method for generating training data for an entity matching classifier, the method comprising: determined match levels; and
This judicial exception is not integrated into a practical application. The claim recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, wherein generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, wherein performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, further comprising: computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, further comprising: sampling record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, further comprising: for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, wherein generating inferred labels comprises: the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim also recites the additional elements of “obtaining labels from multiple sources including client keys and large language models” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “The method of claim 1, further comprising: calculating an entropy-based quality score for each client key system; weighting each client key system in the aggregate voting process based on its quality score; and flagging feature signatures with consistently high entropy across client key systems for further feature engineering”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of: receiving a set of client-provided data, the set of client-provided data including records with client keys; concatenating the set of client-provided data and grouping records by client keys to generate grouped records; generating pairwise features for record pairs in the grouped records; generating inferred labels for the record pairs based on their corresponding client keys; performing an aggregate voting process on feature signatures across multiple clients to determine match levels; generating a training dataset based on the feature signatures and determined match levels; and training a classifier using the training dataset”.
The limitations of “abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2).
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using “a non-transitory computer-readable storage medium” and “a computer processor” to perform the claimed steps. The “non-transitory computer-readable storage medium” and “computer processor” in these steps is recited at a high-level of generality (i.e., as “a non-transitory computer-readable storage medium” and “a computer processor” performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “a non-transitory computer-readable storage medium” and “a computer processor” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “record pairs based on client key matches; creating artificial non-matches by pairing records from different client key matches; and filtering the client key matches and artificial non-matches based on density and similarity checks”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources”. 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 claim recites an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong One. See also MPEP 2106.04(II)(A)(1), 2106.04(a)(2). This judicial exception is not integrated into a practical application. The claim also recites the additional elements of “obtaining labels from multiple sources including client keys and large language models” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, any 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim also recites the additional elements of “obtaining labels from multiple sources including client keys and large language models” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “A device comprising: a processor; a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for: receiving a set of client-provided data, the set of client-provided data including records with client keys; concatenating the set of client-provided data and grouping records by client keys to generate grouped records; generating pairwise features for record pairs in the grouped records; generating inferred labels for the record pairs based on their corresponding client keys; performing an aggregate voting process on feature signatures across multiple clients to determine match levels; generating a training dataset based on the feature signatures and determined match levels; and training a classifier using the training dataset”.
The limitations of “training dataset based on the feature signatures and determined match levels; and
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using “a device comprising: a processor; a storage medium” to perform the claimed steps. The “device comprising: a processor; a storage medium” in these steps is recited at a high-level of generality (i.e., as “a device comprising: a processor; a storage medium” performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See 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. The claim is directed to an abstract idea. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “a device comprising: a processor; a storage medium” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional elements of “receiving a set of client-provided data, the set of client-provided data including records with client keys” and “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). The claim also recites the additional element of “training a classifier using the training dataset” that is mere instructions to apply an exception. A recitation of the words "apply it" (or an equivalent) are mere instructions to implement an abstract idea or other exception on a computer. (See MPEP 2106.05(f)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In addition to any abstract ideas and additional elements in the parent claim(s), the claim recites “Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim also recites the additional elements of “obtaining labels from multiple sources including client keys and large language models” that are the insignificant extra-solution activity of data gathering and/or output, and can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). These additional elements cannot provide an inventive concept. The claim is not patent eligible. See 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
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.
Claims 1-3, 9-11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (‘Wu’ hereinafter) (Publication Number 20240427775) in view of Heckler et al. (‘Heckler’ hereinafter) (Publication Number 20150221045).
As per claim 1, Wu teaches
A method for generating training data for an entity matching classifier, the method comprising: (see abstract and background)
receiving a set of client-provided data, the set of client-provided data including records with client keys; (customer database records where customer identifier is assigned as a key to each record, paragraph [0026])
concatenating the set of client-provided data and grouping records by client keys to generate grouped records; (grouping similar records together based on specific key, paragraph [0027])
generating pairwise features for record pairs in the grouped records; (match vectors for each resulting record pair, where vectors measure the degree of similarity of several record attributes for a given pair, paragraph [0026])
generating inferred labels for the record pairs based on their corresponding client keys; (individual record pairs will be positively classified as matching or negatively classified as non-matching, paragraphs [0028]-[0042],[0062])
performing an aggregate voting process on feature signatures across multiple clients to determine match levels;
generating a training dataset based on the feature signatures and determined match levels; (training data obtained where data selected based on plurality of matched vectors in pairs of records, paragraphs [0063]-[0067], where matched vectors reads on feature signatures and determined match levels)
and training a classifier using the training dataset. (train the artificial intelligence model to classify, paragraphs [0050],[0064])
Wu does not explicitly indicate “performing an aggregate voting process on feature signatures across multiple clients to determine match levels”.
However, Heckler discloses “performing an aggregate voting process on feature signatures across multiple clients to determine match levels” (votes aggregated to determine degree of matching for vendor identifiers and/or transactions from multiple vendors, paragraphs [0006]-[0009],[0028], where vendor identifiers/transactions reads on feature signatures and multiple vendors reads on multiple clients).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu and Heckler because using the steps claimed would have given those skilled in the art the tools to improve the invention by more efficiently grouping, normalizing, and efficiently analyzing the services provided by vendors for more appropriate payment/billing (see Heckler, paragraphs [0024]-[0025]). This gives the user the advantage of ensuring appropriate pricing for services rendered.
As per claim 2, Wu teaches
generating inferred labels comprises: assigning a match label and a value of one to record pairs with matching client keys; assigning a non-match label and a value of zero to record pairs with conflicting client keys; and assigning an unknown label and a null value to record pairs where at least one record has a null client key (individual record pairs will be positively classified as matching or negatively classified as non-matching, paragraphs [0028]-[0042],[0062]).
As per claim 3,
Wu does not explicitly indicate “performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds”.
However, Heckler discloses “performing aggregate voting comprises: computing a match rate for each feature signature for each client; calculating an average match rate across all clients for each feature signature; and assigning a match level to each feature signature based on the average match rate and predefined thresholds” (weighted average of votes, paragraph [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu and Heckler because using the steps claimed would have given those skilled in the art the tools to improve the invention by more efficiently grouping, normalizing, and efficiently analyzing the services provided by vendors for more appropriate payment/billing (see Heckler, paragraphs [0024]-[0025]). This gives the user the advantage of ensuring appropriate pricing for services rendered.
As per claims 9-11,
These claims are rejected on grounds corresponding to the reasons given above for rejected claims 1-3, respectively, and are similarly rejected.
As per claim 17,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 1 and is similarly rejected.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (‘Wu’ hereinafter) (Publication Number 20240427775) in view of Heckler et al. (‘Heckler’ hereinafter) (Publication Number 20150221045) and further in view of Tsou et al. (‘Tsou’ hereinafter) (Publication Number 20180189667).
As per claim 4,
Neither Wu nor Heckler explicitly indicate “computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system.”
However, Tsou discloses “computing entropy-based weights for client key systems; and applying the entropy-based weights to refine the aggregate voting process, wherein the entropy-based weights are calculated as a function of entropies of feature signatures associated with each client key system” (paragraph [0051]; note that Wu and Heckler teach feature signatures as shown previously).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu, Heckler and Tsou because using the steps claimed would have given those skilled in the art the tools to improve the invention by providing for better precision and trustworthiness of decisions generated using voting systems (see Tsou, paragraph [0049]). This gives the user the advantage of having more reliable aggregated information from IoT devices where there could be unpredictable or anomalous sensor readings (see Tsou, paragraphs [0044]-[0046]).
As per claim 12,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 4 and is similarly rejected.
Claims 6, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (‘Wu’ hereinafter) (Publication Number 20240427775) in view of Heckler et al. (‘Heckler’ hereinafter) (Publication Number 20150221045) and further in view of Raghani et al. (‘Raghani’ hereinafter) (Publication Number 20220245657).
As per claim 6,
Neither Wu nor Heckler explicitly indicate “for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset”.
However, Raghani discloses “for each group of records sharing a client key: limiting a group size to a predetermined maximum; identifying and removing outlier records based on pairwise comparison distances; and generating pairwise record pairs from remaining records for inclusion in the training dataset” (paragraph [0029]; note that Wu teaches grouping pairs of records as shown previously).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu, Heckler and Raghani because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to more accurately identify fraudulent sellers (see paragraphs [0002]). This gives the user the advantage of reducing negative customer experiences and costs to retailers/resellers.
As per claim 14,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 6 and is similarly rejected.
As per claim 19,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 6 and is similarly rejected.
Claims 7, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (‘Wu’ hereinafter) (Publication Number 20240427775) in view of Heckler et al. (‘Heckler’ hereinafter) (Publication Number 20150221045) and further in view of Liu et al. (‘Liu’ hereinafter) (Publication Number 20240320492).
As per claim 7,
Neither Wu nor Heckler explicitly indicate “generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources”.
However, Liu discloses “generating inferred labels comprises: obtaining labels from multiple sources including client keys and large language models; assigning confidence scores to labels from each source; and computing a weighted label using ensemble voting based on the labels and confidence scores from the multiple sources” (paragraph [0058]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wu, Heckler and Liu because using the steps claimed would have given those skilled in the art the tools to improve the invention by being able to identify when additional processing of data for classification is warranted (see Liu, paragraph [0019]. This gives the user the advantage of more efficient use of expensive resources.
As per claim 15,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 7 and is similarly rejected.
As per claim 20,
This claim is rejected on grounds corresponding to the reasons given above for rejected claim 7 and is similarly rejected.
Allowable Subject Matter
Claims 5, 8, 13, 16 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims. However, these claims have outstanding 35 USC 101 rejections that must be resolved.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY A MORRISON whose telephone number is (571)272-7112. The examiner can normally be reached on Monday - Friday, 8:00 am - 4:00 pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Trujillo K James, can be reached at telephone number (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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/JAY A MORRISON/Primary Examiner, Art Unit 2151
