CTFR 18/978,199 CTFR 87609 Detailed Action Applicant amended claims 2, 10 and 11, added claim 20 and presented claims 1-20 for reconsideration on 03/30/2026. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 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. 07-07 AIA 07-07-aia The following is a quotation of 35 U.S.C. 102 that forms the basis for all the rejections under this section made in this Office Action: A person shall be entitled to a patent unless— 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Borthwick et al., Patent No.: US 11,514,054 B1 (Borthwick) . Borthwick teaches: Claim 1. A method comprising: receiving a dataset of records as an input; (4:41-59: “Records 102 for matching may be database or other records that can be compared to determine equivalent records”) executing a labeling procedure that outputs a matched dataset of the received dataset, the matched dataset including functionally matched pairs of records, (a record is represented as a node and a pair of nodes is labeled as similar when there exist an edge between them: 2:65-3:13: “supervised partitioning of a similarity graph to perform record matching may be understood as given D records as vertices with e computed edges, e.g. by a similarity function s (d i ,d j ), with possible errors in e, and a partitioning of approximately equivalent records such that the confidence of the resulting partitioned portions according to the partitioning, as determined by a model M, is maximized…M may be built from training data such that the resulting confidence values (e.g., scores or other indicators of a probability of the equivalence classification indicated by the partitioning) may reflect human labeling decisions”; 4:60-5:11: “Similarity graph of records 110 may be a weighted graph representation of the records for which matching may be performed…multiple graphs and/or sub-graphs of records may be generated before performing a final partitioning to determine equivalent records, in some embodiments…a coarse partitioning to split up a larger graph into sub-graphs (e.g., problem instances) that are of a manageable size so as to be computationally tractable”; 9:51-63: “Data access 310 may provide the records 318 to generate a classification model at classification model creation 330…users may interactively train or supply labeled data…as indicated by classification training prompts 332 and training input 334. At training time, the same techniques to generate proposals and feature vectors, as discussed below with regard to FIGS. 4, 5, 6, and 7, may be performed”) where a matching function determines that each functionally matched pair of records represent a respective same entity; (record similarity is performed for detecting records corresponding to same real-world entity: 2:25-58, “Computing a partitioning of a weighted graph that indicates similarity between records represented by nodes of the weighted graph may be performed to determine matching, similarities, associations, or other equivalence relationships between records ( e.g., in a database), in some embodiments. One example application is the detection of duplicate records corresponding to the same real-world item in a database of structured records… the partitioning of a similarity graph may group all records detected to be equivalent (e.g., be associated, linked, similar, match or be the same, such as records that refer to the same real-world entity) into a partitioned portion of the graph (e.g., which may be identified or described as the same equivalence class)”) determining transitive pairs of records including a first record and a second record that belong to a same entity based on the first record and a third record being one of the functionally matched pairs of records representing the same entity, and (as noted about, “a similarity function s (d i ,d j )” is used for identifying a similarity e between two records/nodes. Identifying a transitive relationship requires finding at least two pairs of nodes e.g., a, b and b, c and then relating a and c: 2: 59-64: “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c”; 4:41-59: “Equivalent records may be records that satisfy a similarity, link, association, or other threshold which can render the records identifying or pointing to a same item ( e.g., physical or virtual)…The thresholds for equivalent records may differ according to the application upon which the equivalence determinations may depend…( e.g., matching records may be records that belong to a matching group, like books all in a book series, or a single entity, such as records that refer to a same movie)”, 10:8-40: “A thresholded transitive closure clustering algorithm may be used to perform ᵠ”, 11:31-33: “Transitive Closure: given a threshold t select only edges with weight greater than t in a given sub-graph. Then return all connected components in the modified sub-graph”, 15:13-17: “The partitioning of the graph representation may be performed according various partition techniques, such as…Transitive Closure”, 15:53-63, ) the second record and the third record being another of the functionally matched pairs of records representing the same entity; (see above) labeling the transitive pairs of records and functionally matched pairs of records as matched pairs of records based on the records belonging to the same respective entity; and (pair of similar records identified as matched are labeled as matched because they are used for determining a transitive closure: 2:65-3:13: “M may be built from training data such that the resulting confidence values (e.g., scores or other indicators of a probability of the equivalence classification indicated by the partitioning) may reflect human labeling decisions”; 9:51-63: “Data access 310 may provide the records 318 to generate a classification model at classification model creation 330…users may interactively train or supply labeled data…as indicated by classification training prompts 332 and training input 334. At training time, the same techniques to generate proposals and feature vectors, as discussed below with regard to FIGS. 4, 5, 6, and 7, may be performed”) outputting the labeled matched pairs of records . (the output of a pairwise similarity function is a pair of records labeled as match; output of pairs of matched records further evaluated for outputting the transitive pairs of records in a partition of a graph; all records in a cluster of equivalent records can be interpreted as desired based on a need of an application. For example, equivalent records in a cluster can be seen as pairs of matched records or a list of records that are matched according to the definition provided for equivalence relationship: 2: 59-64: “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c”; 14: 36-39, “A match completion indication 444 may be sent to a requesting client, in some embodiments, which may indicate where and how to retrieve the results (e.g., how to locate, access, or view equivalence data)”; 15:27-37: “an indication of the equivalent records in the group of records according to the partitioning and the confidence in the partitioning of the graph representation…For example, a confidence value may be returned along with a link, pointer, or a file that indicates the equivalent records. As discussed above with regard to FIG. 4, in some embodiments, an update may be made to the records to add or annotate equivalent records ( e.g., equivalence class ids), partitionings ( e.g., a sub-graph), and/or confidence values”) Claims 10 and 19 are rejected under the same rationale as above. Claim 2. The method of claim 1, further comprising: storing maximum number of hops value; and limiting the transitive pairs of records to pairs of records that are connected by a number of matched pairs of records that is at most the maximum number of hops value. (a max number of hops for determining a transitive closure is a criterion or threshold that satisfies similarity; for example, “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c” as in 2: 59-64, simply shows number of hops among 3 nodes by comparing a and b, b and c, and then comparing a and c which can be limited to any number of pair of nodes comparison by user as in 14: 59-67: “the request may allow for the inclusion of match criteria , thresholds , or other information to determine the link, association, or other indication of when records match ”; 4:41-59: “Equivalent records may be records that satisfy a similarity, link, association, or other threshold which can render the records identifying or pointing to a same item (e.g., physical or virtual), in some embodiments”; 13: 8-18: “the diameter of a graph is the longest shortest path distance between any two nodes in the graph. In the context of a partitioned, thresholded proposal, each partitioned portion has a possibly different diameter. One feature value can be emitted for various diameters, d. For each d, the maximum edge threshold w may be computed such that every partitioned portion has diameter≤d. For example, d ∈ [1, 2, 3, 4] can yield four different features. Diameter characterizes the density of partitions and captures whether many low weight edges affect shortest paths”) Claim 2 is rejected under the same rationale. Claim 3. The method of claim 1, further comprising: determining a confidence level for pairs of records in the labeled matched pairs of records; and outputting the confidence level for pairs of records in the labeled matched pairs of records. (confidence value is provided for each labeled matched record: 12:12-25: “a confidence value may be returned along with a link, pointer, or a file that indicates the equivalent records. As discussed above with regard to FIG. 4, in some embodiments, an update may be made to the records to add or annotate equivalent records ( e.g., equivalence class ids), partitionings ( e.g., a sub-graph), and/or confidence values”) Claim 12 is rejected under the same rationale. Claim 4. The method of claim 1, further comprising: providing an option for selection of the matching function; and receiving an input selecting the matching function. (user is able to select matching criteria: 14:59-15:4: “the request may allow for the inclusion of match criteria, thresholds, or other information to determine the link, association, or other indication of when records match”) Claim 13 is rejected under the same rationale. Claim 5. The method of claim 1, wherein the labeling comprises producing a ground truth labeling with a labeling that prioritizes minimizing false positive pairs of records over a complete labeling of all pairs of records. (2:65-3:16, wherein “M may be built from training data such that the resulting confidence values (e.g., scores or other indicators of a probability of the equivalence classification indicated by the partitioning) may reflect human labeling decisions”, 3:48-61, “in some embodiments, given a modest corpus of ground truth partitionings over a weighted graph, a machine-learned model may be trained to recognize characteristics of a good partitioning… This approach can produce a learned probability for a given partitioning and a set of alternative partitionings with potentially higher probabilities”; 9:51-63, wherein “users may interactively train or supply labeled data…as indicated by classification training prompts 332 and training input 334. At training time, the same techniques to generate proposals and feature vectors, as discussed below with regard to FIGS. 4, 5, 6, and 7, may be performed” and 12:8-25, wherein “for each candidate partition, the confidence value, which may, in some embodiments be the probability that the candidate partitioning includes the ground truth clustering, may be modeled…Since P i , may be unique proposals, zero or one true labels may be returned for each sub-graph G', and all other proposals may be labeled false” suggest that a ground truth labeling is produces at training time by minimizing false positive) Claim 14 is rejected under the same rationale. Claim 6. The method of claim 1, further comprising estimating an accuracy of a machine learning entity resolution algorithm, where the estimated the accuracy is based on the labeled matched pairs of records. (7:29-49, wherein “Recording linking service 210 may provide many benefits to a user by providing both matching indications and probability of correctness. For example, very frequently in an industrial record linkage system users want to know the system's confidence in a matching. Thus, high confidence value matches can be automatically merged (as provided by record linking service 210), low confidence value matches can be disregarded ( as provided by record linking service 210), and intermediate confidence value of matches can be sent to clerical staff for human review ( as provided by record linking service 210).” indicates evaluating accuracy of matching algorithm based on the labeled matched pairs of records) Claim 15 is rejected under the same rationale. Claim 7. The method of claim 1, wherein executing the labeling procedure comprises: comparing, by the matching function, records in the received dataset; (similarity graph/subgraph includes groups of equivalent nodes, each of which belong to the same entity using a similarity threshold: 2:37-64, “the partitioning of a similarity graph may group all records detected to be equivalent (e.g., be associated, linked, similar, match or be the same, such as records that refer to the same real-world entity) into a partitioned portion of the graph (e.g., which may be identified or described as the same equivalence class)”; 9:39-50, “graph generation 320 may perform pairwise matching according to a similarity function for the pairs of records which may return higher values that correspond to higher similarity…From the pairwise similarity scores, a weighted graph may be generated that connects records represented as nodes in the weighted graph where the edges represent the similarity weights…only those edges with a value above a threshold may be considered (e.g., greater than 0.4)…Graph generation 320 may store 322 the graph 350 as part of graph model store 218”) providing, by the matching function, positive indications of pairs of records that belong to the same entity based on the comparing; and outputting the matched dataset including the functionally matched pairs of records based on the positive indications of pairs records that belong to the same entity. (see above) Claim 16 is rejected under the same rationale. Claim 8. The method of claim 1, wherein the transitive pairs of records include multiple-hop transitive pairs of records including a fourth pair of records determined to belong to the same entity based on multiple hops along a matched record chain including: matched pairs of records that belong to the same entity, and at least one transitive pair of records that belong to the same entity, wherein the at least one transitive pair of records is different from the fourth pair of records. (number of pair of record comparison for determining a transitive closure is a criterion or threshold that satisfies similarity determined by user entered criteria and thresholds; for example, “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c” as in 2: 59-64, simply shows number of hops among 3 nodes by comparing a and b, b and c, and then comparing a and c which can be limited/extended to any number of pair of nodes comparison by user as in 14: 59-67: “the request may allow for the inclusion of match criteria, thresholds, or other information to determine the link, association, or other indication of when records match”; 4:41-59: “Equivalent records may be records that satisfy a similarity, link, association, or other threshold which can render the records identifying or pointing to a same item (e.g., physical or virtual), in some embodiments”; 13: 8-18: “the diameter of a graph is the longest shortest path distance between any two nodes in the graph. In the context of a partitioned, thresholded proposal, each partitioned portion has a possibly different diameter. One feature value can be emitted for various diameters, d. For each d, the maximum edge threshold w may be computed such that every partitioned portion has diameter≤d. For example, d ∈ [1, 2, 3, 4] can yield four different features. Diameter characterizes the density of partitions and captures whether many low weight edges affect shortest paths”) Claim 17 is rejected under the same rationale. Claim 9. The method of claim 1, further comprising: selecting, from the dataset of records, pairs of records that are not matched in the transitive pairs of records and the functionally matched pairs of records; and assigning a negative label to the selected pairs records indicating the selected pairs of records are not matched pairs of records. (2:65-3:13, wherein “Given the variety of definitions of matching, similarity or other forms of equivalence, M may be built from training data such that the resulting confidence values (e.g., scores or other indicators of a probability of the equivalence classification indicated by the partitioning) may reflect human labeling decisions” suggests that a low confidence value is labeled as negative/not matched records) Claim 18 is rejected under the same rationale. Claim 20. The method of claim 1, wherein determining transitive pairs of records comprises determining transitive pairs of records including a first record and a second record that belong to a same entity by: identifying the first record and the third record of the functionally matched pairs of records as representing the same entity; and (as noted in claim 1, “a similarity function s (di,dj)” is used for identifying a similarity e between two records/nodes. Identifying a transitive relationship requires finding at least two pairs of nodes e.g., a, b and b, c and then relating a and c: 2: 59-64: “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c”; 4:41-59: “Equivalent records may be records that satisfy a similarity, link, association, or other threshold which can render the records identifying or pointing to a same item ( e.g., physical or virtual)…The thresholds for equivalent records may differ according to the application upon which the equivalence determinations may depend…( e.g., matching records may be records that belong to a matching group, like books all in a book series, or a single entity, such as records that refer to a same movie)”, 10:8-40: “A thresholded transitive closure clustering algorithm may be used to perform ᵠ”, 11:31-33: “Transitive Closure: given a threshold t select only edges with weight greater than t in a given sub-graph. Then return all connected components in the modified sub-graph”, 15:13-17: “The partitioning of the graph representation may be performed according various partition techniques, such as…Transitive Closure”, 15:53-63, )identifying the second record and the third record of the functionally matched pairs of records as representing the same entity, and (record similarity is performed for detecting records corresponding to same real-world entity: 2:25-58, “Computing a partitioning of a weighted graph that indicates similarity between records represented by nodes of the weighted graph may be performed to determine matching, similarities, associations, or other equivalence relationships between records ( e.g., in a database), in some embodiments. One example application is the detection of duplicate records corresponding to the same real-world item in a database of structured records… the partitioning of a similarity graph may group all records detected to be equivalent (e.g., be associated, linked, similar, match or be the same, such as records that refer to the same real-world entity) into a partitioned portion of the graph (e.g., which may be identified or described as the same equivalence class)”) wherein outputting the labeled matched pairs of records comprises outputting a list of matched pairs of records. (the output of a pairwise similarity function is a pair of records labeled as match; output of pairs of matched records further evaluated for outputting the transitive pairs of records in a partition of a graph; all records in a cluster of equivalent records can be interpreted as desired based on a need of an application. For example, equivalent records in a cluster can be seen as pairs of matched records or a list of records that are matched according to the definition provided for equivalence relationship: 2: 59-64: “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c”; 14: 36-39, “A match completion indication 444 may be sent to a requesting client, in some embodiments, which may indicate where and how to retrieve the results (e.g., how to locate, access, or view equivalence data)”; 15:27-37: “an indication of the equivalent records in the group of records according to the partitioning and the confidence in the partitioning of the graph representation…For example, a confidence value may be returned along with a link, pointer, or a file that indicates the equivalent records. As discussed above with regard to FIG. 4, in some embodiments, an update may be made to the records to add or annotate equivalent records ( e.g., equivalence class ids), partitionings ( e.g., a sub-graph), and/or confidence values”) Response to Amendment and Arguments In light of amendments, claim objections are withdrawn. The Examiner thanks Applicant for noting the portions in previous office action that are not related to Borthwick et al., Patent No.: US 11,514,054 B1. In fact, Borthwick’s disclosure of "clustering manager 130", "dynamic blocking manager 122", and "The clustering algorithm can partition this graph into non-overlapping clusters of records such that each partition corresponds to a real-world entity” are related to Borthwick et al., Patent No .: US 11,113,254 B1. The examiner deleted the portions from this office action with no impact on the prosecution. Applicant arguments with respect to Borthwick et al., Patent No.: US 11,514,054 B1 have been considered but are not persuasive for at least the following reasons: With respect to claim 1, Applicant argues that Borthwick does not disclose outputting labeled pairs because “Borthwick repeatedly emphasizes partitioning the similarity graph into equivalence classes: "the partitioning of a similarity graph may group all records detected to be equivalent into a partitioned portion of the graph ( ... an equivalence class)," (col. 2, lines 37-46). "In some embodiments, all records may be respectively assigned to one partitioned portion ... one and only one partitioned portion," (col. 2, lines 44-48), on output: "An indication of equivalent records according to the partitioning ... may be provided," (Abstract). This means that Borthwick outputs a set (e.g., {A,B,C}), not labeled pairs (A,B), (A,C), (B,C). Any partitioning of Borthwick produces clusters, not explicitly matched pairs. A cluster of {A, B, C} is not the same as outputting matched pairs (A,B), (A,C), (B,C)… Thus, Borthwick never determines the transitively matched pairs, even implicitly, because it does not compute or output any pair-level edges as output. Thus, Borthwick does not determine transitive pairs of records based on functionally matched pairs of records.”. Remarks, 10-11. In response, As noted in claim 1 above, records/nodes are compared, pairwise, using a similarity function. The output of a pairwise similarity function is a pair of matched records. Matched records are labeled as matched for further operation. Output of pairs of matched records further evaluated for outputting the transitive pairs of records in a partition of a graph. All records in a cluster of equivalent records are interpreted as desired based on a need of an application. For example, equivalent records in a cluster can be seen as pairs of matched records or a list of records that are matched according to the definition provided for equivalence relationship: 2: 59-64: “Equivalence relationships between records may be reflexive, symmetric, and transitive…Thus, equivalency may be such that a≡a, a≡b=>b≡a, and a≡b/\b≡c=>a≡c”. Therefore, for determining and outputting the pair of nodes a≡c, the pair a≡b and b≡c must have been identified and outputted otherwise a≡c cannot be determined. Even outputting a set of {A, B, C}, as concluded by Applicant, can be interpreted as labeled pairs of (A,B), (B,C), (A,C) because of pairwise comparison of records and equivalency definition provide for seeing {A, B, C} as “A≡A, A≡B=>B≡A, and A≡B/\B≡C=>A≡C”. Borthwick explicitly discloses that in response to the input records for identifying matched records “a confidence value may be returned along with a link, pointer, or a file that indicates the equivalent records…an update may be made to the records to add or annotate equivalent records (e.g., equivalence class ids), partitionings (e.g., a sub-graph), and/or confidence values”. Applicant argues “Borthwick indicates that the [transitive closure] algorithm is not performed on functional matched pairs. In particular, Borthwick describes the drawbacks of a transitive closure algorithm, which could put all nodes into a partition due to a single high probability edge between C and D, which may be erroneous, and later supervised partitioning is used to determine whether the technique is correct (col. 3, lines 27-47). Thus, if anything, Borthwick discloses using the transitive closure algorithm before the supervised partitioning of Borthwick is used on records, instead of determining transitive pairs on matched pairs of records after a labeling procedure that outputs the matched pairs of records” (emphasis original). Remarks, 11. In response, a graph representation of similarity comprises functional matched pairs. See, 2:65-3:13. The graph representation of similarity is partitioned for identifying equivalent record. “The partitioning of the graph representation may be performed according various partition techniques, such as those discussed above, like Transitive Closure”. A supervised machine learning model is used for determining “a confidence value in the partitioning of the graph representation”. Therefore, pairs of equivalent records are identified from functional matched pairs using a transitive closure algorithm and further a supervised machine learning model is used for evaluating “the partitioning in view of the ground truth training for correct matches reflected in the model”. See, Borthwick fig. 6 and associated description. with respect to claim 2, Applicant argues, “Borthwick does not disclose a maximum number of hops, much less limiting the transitive pairs of records to pairs of records that are connected by a number of matched pairs of records that is at most the maximum number of hops value” because “Borthwick only discloses a diameter, which corresponds to a distance. However, a diameter is not a maximum number of hops. For example, two nodes may be far apart with only one hop between them, whereas two other nodes may be closer in distance, but may include multiple short hops through other nodes. Furthermore, Borthwick expressly indicates the diameter is the path distance between any "two nodes" (col. 13, lines 8-9). The path between two nodes is direct and does not include a number of hops. Thus, Borthwick does not disclose a maximum number of hops.” Remarks, 12. In response, Borthwick, 13: 8-18, expressly discloses that “the diameter of a graph is the longest shortest path distance between any two nodes in the graph”. Longest shortest path distance between any two nodes in the graph represents the maximum number of hops for reaching from a node to another node: “Network diameter is the longest shortest path between any two nodes in a network graph, representing the maximum number of hops or links needed for information to travel from one point to another”. Retrieved from https://www.ituonline.com/tech-definitions/what-is-network-diameter on 6/10/2026. With respect to claim 5, Applicant argues: “Borthwick does not disclose producing a ground truth labeling with a labeling that prioritizes minimizing false positive pairs of records over a complete labeling of all pairs of records… because its objective is to optimize clustering quality, not to generate a partial ground-truth label set… Borthwick does not teach withholding labels to avoid false positives, nor does it teach intentionally producing an incomplete labeling; instead, Borthwick aims for a complete assignment of records to clusters using similarity scores and supervised optimization”. Remarks, 12. In response, it is not clear how the labeling in claim 5 “prioritizes minimizing false positive pairs of records over a complete labeling of all pairs of records” in claim 5 for “producing a ground truth labeling”. However, Borthwick discloses many features as shown in claim 5 above that suggest a ground truth labeling is produced at training time by minimizing errors, e.g., false positive) Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mohsen Almani whose telephone number is (571)270-7722. The examiner can normally be reached on M-F, 9 AM-5 PM, ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached on 571-272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHSEN ALMANI/Primary Examiner, Art Unit 2159 Application/Control Number: 18/978,199 Page 2 Art Unit: 2159 Application/Control Number: 18/978,199 Page 3 Art Unit: 2159 Application/Control Number: 18/978,199 Page 4 Art Unit: 2159 Application/Control Number: 18/978,199 Page 5 Art Unit: 2159 Application/Control Number: 18/978,199 Page 6 Art Unit: 2159 Application/Control Number: 18/978,199 Page 7 Art Unit: 2159 Application/Control Number: 18/978,199 Page 8 Art Unit: 2159 Application/Control Number: 18/978,199 Page 9 Art Unit: 2159 Application/Control Number: 18/978,199 Page 10 Art Unit: 2159 Application/Control Number: 18/978,199 Page 11 Art Unit: 2159 Application/Control Number: 18/978,199 Page 12 Art Unit: 2159 Application/Control Number: 18/978,199 Page 13 Art Unit: 2159 Application/Control Number: 18/978,199 Page 14 Art Unit: 2159 Application/Control Number: 18/978,199 Page 15 Art Unit: 2159 Application/Control Number: 18/978,199 Page 16 Art Unit: 2159 Application/Control Number: 18/978,199 Page 17 Art Unit: 2159
Prosecution Insights