DETAILED ACTION
This non-final rejection is responsive to communication filed October 22, 2024. Claims 11-18 are currently amended. Claims 1-19 are pending in this application.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/27/26, 2/24/25 and 12/22/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, and 19 recite: predicts, for each training sample, one or more generalizations that describe the training sample, each generalization of the one or more generalization being an n-gram; processing the queries stored in the query store to predict, for each query, one or more generalizations for the query; and for each query, determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, through observation and evaluation, a user can mentally predict generalizations for training samples, process queries to predict generalizations, and determine one or more retrieval token for the query based on generalizations.
This judicial exception is not integrated into a practical application. The limitations “obtaining a plurality of training samples, each training sample comprising one or more queries, each query of the one or more queries being a query selected from a query store” and “storing, in a data store and for each query, an association of the query with the one or more retrieval tokens determined for the query” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. The limitations reciting “training a language model to generate a trained language model, wherein the trained language model predicts” and “processing, by the trained language model…the queries…to predict” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). In claims 10 and 19, the claimed limitations are recited as being performed by “one or more computers in data communication” and “one or more non-transitory computer readable medium storing instructions”. These generic computer components are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “obtaining” and “storing” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and storing and retrieving information in memory and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed above, the recitation of computers and non-transitory media to perform limitations, training a language model, and processing by the trained language model amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claim 2 recites: predict a seed keyword that is a generalization of the training sample. This limitation represents a mental process because a user can mentally predict a seed keyword based on sample data. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because training the model to perform the predicting provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim 3 recites selecting, based on the one or more retrieval tokens and the seed keyword, one or more queries as a training sample. This limitation represents a mental process because a user can mentally select one or more queries based on retrieval tokens and seed keyword.
This judicial exception is not integrated into a practical application. The limitations “providing, as input to a retrieval process, a seed keyword; and accessing a data store that stores, for each query of a plurality of queries an association of the query with the one or more retrieval tokens” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “providing” and “accessing” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and storing and retrieving information in memory and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent insignificant extra-solution activity, which does not provide an inventive concept.
Claim 4 recites “predict the seed keyword that was provided as input to the retrieval process to obtain the one or more queries as the training sample.” This limitation represents a mental process because a user can mentally predict a seed keyword that was provided to obtain one or more queries. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because training the model to perform the predicting provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim 5 recites the additional element “wherein each query selected from the query store is a query that has been received as an input query to a search process and generated by a user of the search process.” This limitation further describes the obtained data. As stated above, the “obtaining” limitation is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitation of “obtaining” is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 6 recites wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining that each of the one or more generalizations is a retrieval token. This limitation represents a mental process because a user can mentally determine that each generalization is a retrieval token. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 7 recites wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining, from the one or more generalizations, one or more retrieval tokens, wherein the one or more retrieval tokens define a set of terms that are different from a set of terms defined by the one or more generalizations. This limitation represents a mental process because a user can mentally determine retrieval tokens that define a different set of terms. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 8 recites selecting one or more context queries for the query. This limitation represents a mental process because a user can mentally select context queries.
This judicial exception is not integrated into a practical application. The limitations “providing the query and the one or more context queries as an input to the language model; and receiving, from the language model, one or more generalizations predicted for the query based on the query and the one or more context queries” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “providing” and “receiving” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and storing and retrieving information in memory and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent insignificant extra-solution activity, which does not provide an inventive concept.
Claim 9 recites the additional element “wherein the language model is one of a multitask unified model, zero-shot model, domain specific model, or a language representation model.” This limitation further describes the language model that is trained and used to process data, and thus also provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim 11 recites: predict a seed keyword that is a generalization of the training sample. This limitation represents a mental process because a user can mentally predict a seed keyword based on sample data. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because training the model to perform the predicting provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim 12 recites selecting, based on the one or more retrieval tokens and the seed keyword, one or more queries as a training sample. This limitation represents a mental process because a user can mentally select one or more queries based on retrieval tokens and seed keyword.
This judicial exception is not integrated into a practical application. The limitations “providing, as input to a retrieval process, a seed keyword; and accessing a data store that stores, for each query of a plurality of queries an association of the query with the one or more retrieval tokens” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “providing” and “accessing” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and storing and retrieving information in memory and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent insignificant extra-solution activity, which does not provide an inventive concept.
Claim 13 recites “predict the seed keyword that was provided as input to the retrieval process to obtain the one or more queries as the training sample.” This limitation represents a mental process because a user can mentally predict a seed keyword that was provided to obtain one or more queries. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because training the model to perform the predicting provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim 14 recites the additional element “wherein each query selected from the query store is a query that has been received as an input query to a search process and generated by a user of the search process.” This limitation further describes the obtained data. As stated above, the “obtaining” limitation is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitation of “obtaining” is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 15 recites wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining that each of the one or more generalizations is a retrieval token. This limitation represents a mental process because a user can mentally determine that each generalization is a retrieval token. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 16 recites wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining, from the one or more generalizations, one or more retrieval tokens, wherein the one or more retrieval tokens define a set of terms that are different from a set of terms defined by the one or more generalizations. This limitation represents a mental process because a user can mentally determine retrieval tokens that define a different set of terms. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 17 recites selecting one or more context queries for the query. This limitation represents a mental process because a user can mentally select context queries.
This judicial exception is not integrated into a practical application. The limitations “providing the query and the one or more context queries as an input to the language model; and receiving, from the language model, one or more generalizations predicted for the query based on the query and the one or more context queries” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “providing” and “receiving” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and storing and retrieving information in memory and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, these additional elements represent insignificant extra-solution activity, which does not provide an inventive concept.
Claim 18 recites the additional element “wherein the language model is one of a multitask unified model, zero-shot model, domain specific model, or a language representation model.” This limitation further describes the language model that is trained and used to process data, and thus also provides nothing more than mere instructions to implement an abstract idea on a generic computer. Even when considered in combination, this additional element does not integrate the recited judicial exception into a practical application and represents mere instructions to implement an abstract idea or other exception on a computer, which does not provide an inventive concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sianez (US 11,481,388 B2) in view of Chakrapani et al. (US 9,244,972 B1) (‘Chakrapani’).
With respect to claim 1, Sianez teaches a computer-implemented method, comprising:
obtaining a plurality of training samples, each training sample comprising one or more queries, each query of the one or more queries being a query selected from a query store (col. 7 lines 44-58; col. 17 lines 40-63);
training a language model to generate a trained language model, wherein the trained language model predicts, for each training sample, one or more generalizations that describe the training sample, each generalization of the one or more generalization being an n-gram (i.e. machine learning model is trained to predict query term such as general and specific query terms) (col. 7 lines 27-58; col. 9 lines 14-32; col. 18 lines 16-27);
processing, by the trained language model, the queries stored in the query store to predict, for each query, one or more generalizations for the query (i.e. general topic/term) (col. 7 lines 18-26; col. 9 lines 33-44; col. 10 lines 1-19; col. 18 lines 49-63; col. 20 lines 32-40);
for each query, determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query (i.e. general term, specific term, and/or a set of words similar to and/or associated with the query term) (col. 9 lines 33-44; col. 10 lines 1-35; col. 12 lines 45-59; col. 20 lines 32-40); and
for each query, associating the query with the one or more retrieval tokens determined for the query (col. 12 lines 45-59; col. 16 lines 23-19; and col. 17 lines 1-5 and lines 8-10).
Although Sianez teaches associating queries with retrieval tokens and storing a history of queries and a history query terms, Sianez does not explicitly teach storing, in a data store, the association the query and retrieval tokens.
Chakrapani teaches storing, in a data store, an association of the query with one or more retrieval tokens (i.e. topic, verticals, terms of query) (col. 6 lines 25-38 and lines 51-59).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Sianez to store the association of the query with one or more retrieval tokens as taught by Chakrapani to enable improved search processing in which common keywords for a topic are used to enhance search results. Further, Sianez teaches an association between a query and retrieval token, and thus storing this determined information in a data store is merely incorporating a known element to achieve a predictable result of storage of association data that has been determined.
With respect to claims 2 and 11, Sianez in view of Chakrapani teaches wherein training the language model to generate the trained language model comprises, for each training sample, training the model to predict a seed keyword that is a generalization of the training sample (Sianez, col. 7 lines 27-58; col. 9 lines 14-32; col. 18 lines 16-27).
With respect to claims 3 and 12, Sianez in view of Chakrapani teaches wherein obtaining a plurality of training samples comprises, for each training sample:
providing, as input to a retrieval process, a seed keyword (Sianez, col. 7 lines 44-58; col. 17 lines 40-57);
accessing a data store that stores (Chakrapani, col. 6 lines 25-38 and lines 51-59), for each query of a plurality of queries an association of the query with the one or more retrieval tokens (Sianez, col. 7 lines 44-58; col. 17 lines 40-57); and
selecting, based on the one or more retrieval tokens and the seed keyword, one or more queries as a training sample (Sianez, col. 7 lines 44-58; col. 17 lines 40 – col. 18 line 15).
With respect to claims 4 and 13, Sianez in view of Chakrapani teaches wherein training the language model to generate the trained language model comprises, for each training sample: training the model to predict the seed keyword that was provided as input to the retrieval process to obtain the one or more queries as the training sample (Sianez, col. 7 lines 27-58; col. 9 lines 14-32; col. 18 lines 16-27).
With respect to claims 5 and 14, Sianez in view of Chakrapani teaches wherein each query selected from the query store is a query that has been received as an input query to a search process and generated by a user of the search process (Sianez, col. 7 lines 44-58; col. 17 lines 40-63; col. 22 lines 38-43).
With respect to claims 6 and 15, Sianez in view of Chakrapani teaches wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining that each of the one or more generalizations is a retrieval token (i.e. general topic/term) (Sianez, col. 9 lines 33-44; col. 10 lines 1-35; col. 12 lines 45-59; col. 20 lines 32-40).
With respect to claims 7 and 16, Sianez in view of Chakrapani teaches wherein determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises determining, from the one or more generalizations, one or more retrieval tokens, wherein the one or more retrieval tokens define a set of terms that are different from a set of terms defined by the one or more generalizations (i.e. specific term, and/or a set of words similar to and/or associated with the query term) (col. 9 lines 33-44; col. 10 lines 1-35; col. 12 lines 45-59; col. 20 lines 32-40).
With respect to claims 8 and 17, Sianez in view of Chakrapani teaches wherein for each query, determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query comprises:
selecting one or more context queries for the query (Sianez, col. 3 lines 27-34; col. 22 lines 8-12 and 31-34; claims 10 and 16);
providing the query and the one or more context queries as an input to the language model (Sianez, col. 3 lines 27-34; col. 22 lines 8-12 and 31-34; claims 10 and 16); and
receiving, from the language model, one or more generalizations predicted for the query based on the query and the one or more context queries (Sianez, col. 3 lines 27-34; col. 22 lines 8-12 and 31-34; claims 10 and 16).
With respect to claims 9 and 18, Sianez in view of Chakrapani teaches wherein the language model is one of a multitask unified model, zero-shot model, domain specific model, or a language representation model (Sianez, col. 6 lines 38-43; col. 7 lines 7-17 and 27-32; col. 28 lines 56-64).
With respect to claim 10, Sianez teaches a system, comprising:
one or more computers in data communication (Fig. 1); and
one or more non-transitory computer readable medium (col. 3 lines 24-41; col. 21 lines 41-53) storing instructions executable by the one or more computers that when executed by the one or more computers cause the one or more computers to perform operations comprising:
obtaining a plurality of training samples, each training sample comprising one or more queries, each query of the one or more queries being a query selected from a query store (col. 7 lines 44-58; col. 17 lines 40-63);
training a language model to generate a trained language model, wherein the trained language model predicts, for each training sample, one or more generalizations that describe the training sample, each generalization of the one or more generalization being an n-gram (i.e. machine learning model is trained to predict query term such as general and specific query terms) (col. 7 lines 27-58; col. 9 lines 14-32; col. 18 lines 16-27);
processing, by the trained language model, the queries stored in the query store to predict, for each query, one or more generalizations for the query (i.e. general topic/term) (col. 7 lines 18-26; col. 9 lines 33-44; col. 10 lines 1-19; col. 18 lines 49-63; col. 20 lines 32-40);
for each query, determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query (i.e. general term, specific term, and/or a set of words similar to and/or associated with the query term) (col. 9 lines 33-44; col. 10 lines 1-35; col. 12 lines 45-59; col. 20 lines 32-40); and
for each query, associating the query with the one or more retrieval tokens determined for the query (col. 12 liens 45-59; col. 16 lines 23-19; and col. 17 lines 1-5 and lines 8-10).
Although Sianez teaches associating queries with retrieval tokens and storing a history of queries and a history query terms, Sianez does not explicitly teach storing, in a data store, the association the query and retrieval tokens.
Chakrapani teaches storing, in a data store, an association of the query with one or more retrieval tokens (i.e. topic, verticals, terms of query) (col. 6 lines 25-38 and lines 51-59).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Sianez to store the association of the query with one or more retrieval tokens as taught by Chakrapani to enable improved search processing in which common keywords for a topic are used to enhance search results. Further, Sianez teaches an association between a query and retrieval token, and thus storing this determined information in a data store is merely incorporating a known element to achieve a predictable result of storage of association data that has been determined.
With respect to claim 19, Sianez teaches one or more non-transitory computer readable medium storing instructions, that when executed by an artificial intelligence system, causes the artificial intelligence system to perform operations (col. 3 lines 24-41; col. 21 lines 41-53) comprising:
obtaining a plurality of training samples, each training sample comprising one or more queries, each query of the one or more queries being a query selected from a query store (col. 7 lines 44-58; col. 17 lines 40-63);
training a language model to generate a trained language model, wherein the trained language model predicts, for each training sample, one or more generalizations that describe the training sample, each generalization of the one or more generalization being an n-gram (i.e. machine learning model is trained to predict query term such as general and specific query terms) (col. 7 lines 27-58; col. 9 lines 14-32; col. 18 lines 16-27);
processing, by the trained language model, the queries stored in the query store to predict, for each query, one or more generalizations for the query (i.e. general topic/term) (col. 7 lines 18-26; col. 9 lines 33-44; col. 10 lines 1-19; col. 18 lines 49-63; col. 20 lines 32-40);
for each query, determining, from the one or more generalizations predicted for the query, one or more retrieval tokens for the query (i.e. general term, specific term, and/or a set of words similar to and/or associated with the query term) (col. 9 lines 33-44; col. 10 lines 1-35; col. 12 lines 45-59; col. 20 lines 32-40); and
for each query, associating the query with the one or more retrieval tokens determined for the query (col. 12 liens 45-59; col. 16 lines 23-19; and col. 17 lines 1-5 and lines 8-10).
Although Sianez teaches associating queries with retrieval tokens and storing a history of queries and a history query terms, Sianez does not explicitly teach storing, in a data store, the association the query and retrieval tokens.
Chakrapani teaches storing, in a data store, an association of the query with one or more retrieval tokens (i.e. topic, verticals, terms of query) (col. 6 lines 25-38 and lines 51-59).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the invention to have modified Sianez to store the association of the query with one or more retrieval tokens as taught by Chakrapani to enable improved search processing in which common keywords for a topic are used to enhance search results. Further, Sianez teaches an association between a query and retrieval token, and thus storing this determined information in a data store is merely incorporating a known element to achieve a predictable result of storage of association data that has been determined.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 June 27, 2026
Prosecution Insights