CTFR 18/618,788 CTFR 92093 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Arguments Applicant's arguments with respect to 35 U.S.C. 101 Abstract Idea in regards to claims 1-20 have been considered, however are not found to be persuasive due to the following reasons. Examiner respectfully disagrees with Applicant’s arguments because the claims are directed to an abstract idea of receiving a text request, identifying the requested content, looking up a stored response, and presenting text. These are basic information processing steps that can be performed mentally or with a simple table. The claims use generic processors, machine learning models, and a data structure, but it does not recite a specific improvement to the machine learning model, the lookup structure, or computer technology. Instead, the computer is used as a tool to carry out the abstract idea. Therefore, the claims are directed to a judicial exception and are not integrated into a practical application. Under step 2B, the claims do not add significantly more than the abstract idea. The extra elements, “one or more processors,” “one or more machine learning models,” a “data structure,” a lookup, and presentation of natural language characters, are recited at a high level and perform ordinary computer functions. The claims do not require a special processor, a new ML architecture, an improved training technique, a new database structure, or any technical improvement in how content is retrieved or displayed. Taken individually or in combination, the elements merely implement the abstract information retrieval idea on generic computer components, so the claims lack an inventive concept. Applicant's arguments with respect to 35 U.S.C. 102 in regards to claims 1, 9 and 18 have been considered but are moot due to new grounds of rejection necessitated by amendments. See detailed rejection below. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 5 recites the limitation "the plurality of identifies". There is insufficient antecedent basis for this limitation in the claim. It should read “the plurality of identifiers”. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-20 are rejected under 35 U.S.C. 101. Claims 1, 9 and 18 are directed to an abstract idea. The claims mainly are about receiving a text request, predicting an identifier for referenced content, using that identifier to look up a stored response and presenting natural language characters. That is information classification, lookup, retrieval, and display. Those steps are a mental process or routine information handling task: a person could read a request, identify the requested item, look it up in a table, and provide the associated response. The USPTO guidance treats mental processes that can practically be performed in the human mind or with pen and paper as abstract idea. The extract computer language does not integrate the abstract idea into a practical application. The claims recite generic “one or more processors,” generic “machine learning models,” and generic “data structure,” but it does not recite a specific improvements to computer functionality, machine learning architecture, data structure operation, memory usage, latency, accuracy or another technical field. The claims also lack an inventive concept under Alice step 2. The ordered combination is still just: use ML to predict an identifier, perform a lookup and present text. The USPTO has held that merely applying generic machine learning to a data processing task without claiming an improvement to the ML model itself or another technical improvement, is patent ineligible. Here, the claims do not explain how the ML model is improved or how the lookup/presentation is technically improved, so the processor and ML limitations likely amount to generic computer implementation of the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device. Dependent claims 2-8, 10-17 and 19-20 further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is conventionally known. Claims 2, 10 and 19: just looking up and presenting information using generic computer components. Claims 3 and 12: not a technological improvement. Claims 4 and 13: just labeling or encoding information at a high level, without any specific technical improvement. Claims 5 and 14: routine data organization. Claims 6 and 15: no specific improved training technique or ML architecture. Claims 7 and 16: only describe the type of information being retrieved and presented. Claims 8, 17 and 20: does not recite how the systems are technically improved. Claim Rejections - 35 USC § 103 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 (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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1, 3, 6-9, 11-12, 15-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj et al. (US 2022/0247700) in view of Mingels (US 2022/0360544) . Claims 1, 9 and 18, Bhardwaj teaches one or more processors comprising: one or more processing units to ([Fig. 6] [0086] Bhardwaj teaches processor based implementation: “The components of computing system 600 may include but are not limited to, a network interface 610, a processor 620, an input/output 630 … an a storage 640.”) : generate, based at least on one or more machine learning models processing a text prompt comprising a reference to content, and indicative of a user request to retrieve the referenced content, a predicted identifier of the referenced content ([0051] [0058] [0077-0080] [Figs. 3A-B] [Fig. 5 step 520] Bhardwaj teaches receiving a text prompt/request about referenced content by stating that a chat message may include “text content with questions and other requests for information” from a buyer of an item; Bhardwaj further states that “the received chat message may include a request associated with an object,” and the response may be detected from “descriptive content extracted from a web page that describes the object.”; Bhardwaj teaches ML processing of the text prompt. It states that the method includes “determining, via a machine learning model, an intent of the received chat message.”; the ML mode may be “an intent classification model that comprises a bidirectional long short-term memory (Bi-LSTM) architecture.”; Bhardwaj teaches generating a predicted identifier. It states that the intent classification model “may receive a chat message as input and output an intent from among a plurality of possible intents.”; Bhardwaj further teaches that services may provide reply content by receiving “an identifier of the intent predicted by the intent classification model 300.”) ; The difference between the prior art and the claimed invention is that Bhardwaj does not explicitly teach retrieve, based at least on performing a lookup using a data structure that stores a plurality of associations between a plurality of identifiers and corresponding responses, a response comprising one or more natural language characters corresponding to the predicted identifier; and based at least on performing the lookup, cause presentation of the one or more natural language characters. Mingels teaches retrieve, based at least on performing a lookup using a data structure that stores a plurality of associations between a plurality of identifiers and corresponding responses, a response comprising one or more natural language characters corresponding to the predicted identifier ([0008] [0046-0047] [0088] [0090] [0116] Mingels teaches storing responses in a database and retrieving them using queries. Mingels states that responses may be “stored in a database format,” and that database storage allows the system “to use database queries to quickly access the responses.”; Mingels teaches the claimed data structure. It states that traversal paths through response graphs may be “stored in memory, such as database 140”, that “each response graph may be stored as a separate table,” that “each row of a table may correspond to a different traversal path,” and that columns may correspond to “a node or nodes within the traversal path.”; Mingels teaches lookup using identifier/associations. It states that the NLG module may perform “database queries, such as SQL queries,” to identify “a given response graph table, a given traversal path, a node within a traversal path, and/or associations between a graph table, traversal path, or node.”; Mingels further states that a traversal column may include “identifiers or each node” in a traversal path. Mingels teaches retrieving the response corresponding to the lookup result. It states that, based on dialog state, the NLG module “may query table 580 to determine a transversal path,” and if the query returns a row, “NLG module 138 may output a response associated with the nodes” in that row.; Mingels also states that “querying the table may cause a response to be returned from a database.”) ; and based at least on performing the lookup, cause presentation of the one or more natural language characters ([0042] [0117-0118] Mingels teaches outputting the response based on the database lookup; Mingels states: “Based on the determined response from the database, NLG module 138 may output a response to the user’s inquiry.”; Mingels further states that the computing device “may cause the chatbot to output the response,” including “sending the response to the client device.”; Mingels also teaches that the NLG module generates output “in the form of responses,” including “greetings, prompts, requests for information, alerts, notifications and/or responses to user input.”) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Bhardwaj with teachings of Mingels by modifying the interactive chatbot for multi-way communication as taught by Bhardwaj to include retrieve, based at least on performing a lookup using a data structure that stores a plurality of associations between a plurality of identifiers and corresponding responses, a response comprising one or more natural language characters corresponding to the predicted identifier; and based at least on performing the lookup, cause presentation of the one or more natural language characters as taught by Mingels for the benefit of being able to quickly access responses resulting in faster performance of dialog system and/or decrease resource utilization (Mingels [0008]) . Claims 3 and 12, Bhardwaj further taches the one or more processors of claim 1, wherein the associations include an association between the predicted identifier and the one or more second natural language characters stored using the data structure ([Figs. 3A-B] [0050] [0057-0058] Bhardwaj teaches associating predicted intent identifiers with reply messages; Bhardwaj states, “Each intent may be linked to a plurality of possible responses”; Bhardwaj further states that the chatbot service “may use the intent classification model 300 to predict an intent of the user within the message,” and that the chatbot service “may store a mapping of reply messages to intents within a file”; Bhardwaj then explains: “when an intent of the sender is found, the chatbot service 133 can map the intent to a small set of reply messages for the receiver to send to the sender.”; Bhardwaj also teaches of a predicted identifier: services may provide reply content by submitting “an identifier of the intent predicted by the intent classification model 300.”) , Mingels further teaches the data structure being implemented to include at least one of an index table, a hash table, a lookup table, or a pointer ([0008] [0046-0047] [0090] [0116] Mingels teaches storing chatbot response flows in database tables and querying those tables to retrieve responses; Mingels states: “A series of responses generated by a user with the GUI may be converted to, and stored in a database format,” and that storing responses in a database allows the system “to use database queries to quickly access the responses.”; Mingels further teaches that “each response graph may be stored as a separate table in database 140,” and that “each row of a table may correspond to a different traversal path through the response graph.”; Mingels also states that NLG module 138 may perform “database queries, such as SQL queries,” to identify “a given response graph table, a given traversal path, a node within a traversal path, and/or associations between a graph table, traversal path or node.”; Mingels gives a runtime lookup example: “NLG module 138 may query table 580 to determine a traversal path,” and “the query may return row 582, and NLG module 138 may output a response associated with the nodes in the traversal column of row 582.”; Mingels further states: “Querying the table may cause a response to be returned from a database.”) . Claims 6 and 15, Mingels further teaches the one or more processors of claim 1, wherein the one or more processing units are further to tune the one or more machine learning models ([0043] [0104] Mingels teaches tuning/retraining chatbot machine learning models; Mingels teaches that chatbot may be trained using machine learning techniques, such as a classifier, to determine associations between user input and one or more output responses. The chatbot learns such associations during a training phase, and Mingels further states that the chatbot may be retrained as additional user input is received to improve chatbot accuracy.; Mingels also teaches training a chatbot classifier to determine associations between input text and database tables/database rows corresponding to response nodes) . Bhardwaj further teaches based at least on learning a relationship between the text prompt and the predicted identifier ([0049-0054] [0058] [0073-0075] Bhardwaj teaches learning the relationship between a text prompt and an intent identifier; Bhardwaj teaches an intent classification model that receives a chat message and determines the intent of the message; Bhardwaj further teaches that historical messages may be labelled with identifiers of intents, e.g., the chat message “is the price negotiable” may be labelled with the intent “about-price-negotiable”; Bhardwaj also teaches inputting labelled training data into the intent classification model to train the model to assign intents to chat messages, and states that the training data may contain the buyer message and the associated target intent; In operation, Bhardwaj submits an identifier of the intent predicted by the intent classification model to other services to obtain reply content) . Claims 7 and 16, Bhardwaj further teaches the one or more processors of claim 1, wherein the one or more natural language characters represented by the predicted identifier and retrieved from the data structure ([0057-0061] Bhardwaj teaches that the chatbot predicts an intent for a received chat message and uses that predicted intent to obtain reply content; In particular, Bhardwaj teaches that the chatbot service may store a mapping to intents within a file, and when a intent is found, the chatbot maps the intent to a small set of reply messages; Bhardwaj further teaches submitting an identifier of the intent classification model to services that provide reply content; The reply content includes response messages/smart suggestions generated from sources such as website listings, historical user chat data, ad parameters, product titles/descriptions, and inspection reports) . Mingels further teaches include at least one of: a link, source code, predefined factual information, or predefined text ([0074] [0077] [0092-0095] Mingels teaches the predefined text alternative; Mingels teaches response graphs made of words nodes, where each word node corresponds to an output of one or more words or characters, including punctuation, used by the NLG module to generate an output response; Mingels further teaches that, rather than traversing the graph at runtime, the NLG module queries a database table to determine a response corresponding to a traversal path; Mingels’ database rows store node identifiers and node attributes, and those attributes indicate text associated with the word nodes; for example, nodes are associated with predefined text such as “I can’t”, “locate”, “that account” and “for you”) . Claims 8, 17 and 20, Bhardwaj further teaches the one or more processors of claim 1, wherein the one or more processors is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing real-time streaming; a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system for generating synthetic data using AI; a system incorporating one or more virtual machines (VMs);a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources ([Abstract] a chatbot trained via a machine learning model) . 07-21-aia AIA Claim (s) 2, 5, 10, 14 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj et al. (US 2022/0247700) in view of Mingels (US 2022/0360544) and further in view of Brown et al. (US 9,836,177) . Claims 2, 10 and 19, Bhardwaj further teaches the one or more processors of claim 1, wherein the content referenced in the text prompt identifies a website ([Fig. 3B] [Fig. 5] [0058] [0077] [0080] Bhardwaj teaches that the user’s chat message/text prompt concerns an item/object and that responsive content is taken from a website/web-page for that object; Bhardwaj states that the chat message may include “questions and other requests for information” about an item for sale.; Bhardwaj further states that “the received chat message may include a request associated with an object,” and recommended responses may be detected from “descriptive content extracted from a web page that describes the object.”; Bhardwaj also states that “reply message content may be developed using content from a website 310” that includes “web listing” for the product.) . The difference between the prior art and the claimed invention is that Bhardwaj does not explicitly teach the response retrieved from the data structure comprises a link to the website corresponding to the predicted identifier. Brown teaches the response retrieved from the data structure comprises a link to the website corresponding to the predicted identifier ([claim 1] [Figs. 4 & 5] [col. 6 lines 16-31] [col. 10 line 53 to col. 11 line 22] Brown teaches an intent to response data store structure; Brown teaches a virtual assistant service with “a datastore of one or more intents” and “a datastore of one or more responses”; Brown further teaches that the system maps the query to an intent and then maps the intent to a response; “each intent … may be associated with multiple different responses.”; also see claim 1; Brown teaches that the response can include links; Brown states that a response may include “one or more hyperlinks to pages” related to the query, and that returned content may include “text and one or more links”; Brown also teaches, in Fig. 2B, that “response 222 includes text 228, a hyperlink 230,” and that the device navigates to a new page of the site associated with the query; Brown’s claim 9 further recites “displaying … plain text and one or more hyperlinks related to the single query.”) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Bhardwaj and Mingels with teachings of Brown by modifying the interactive chatbot for multi-way communication as taught by Bhardwaj to include the response retrieved from the data structure comprises a link to the website corresponding to the predicted identifier as taught by Brown for the benefit of increasing the ability of virtual assistants to emulate human representatives (Brown [Background]) . Claims 5 and 14, Mingels further teaches the one or more processors of claim 1, wherein the one or more processing units are further to generate the data structure prior to receiving the text prompt ([Fig. 8] [0112] Mingels teaches generating the response table/data structure before receiving a runtime inquiry. In process 800, Mingels states: “At step 810, a computing device may generate a first graph,” and they may be generated through response editor 132 before runtime.; Mingels then states: “At step 820, the computing device … may generate a table comprising a plurality of responses for an automated response system, such as an AVR or chatbot. The compute device may generate the table based on the response graph.”; see Fig. 8; Mingels further states that the generated table includes row and columns; “each row corresponds to a different traversal path” and columns may contain “a sequence of nodes that corresponds to the traversal path of that row.”; Only after the graph/table are generated does Mingels receive the runtime inquiry: “At step 830, the computing device … may receive an inquiry from a client device,” where the inquiry is based on “user input from a user.”; see Fig. 8; Mingels’ claim 4 recites “generating, based on the first graph, a table comprising a plurality of responses for the chatbot; receiving, via the chatbot, a second inquiry; querying … the table.”) . The difference between the prior art and the claimed invention is that Bhardwaj nor Mingles explicitly teach the data structure stores the plurality of associations between the plurality of identifies and corresponding links. Brown teaches the data structure stores the plurality of associations between the plurality of identifies and corresponding links ([claims 24 and 29] [col. 6 lines 16-31] [col. 9 lines 26-49] [col. 10 line 53 to col. 12 line 3] Brown teaches a stored data structure containing associations between identifiers/intents and corresponding response content that includes links; Brown teaches memory/datastores for concepts, contexts, intents and responses.; Brown’s response mapping module maps an intent to a particular response, and each intent may be associated with multiple different responses.; Brown further teaches mapping intents 414(1)-414(N) to responses 416(1)-416(N)(E), which provides a plurality of stored associations between intent identifiers/indications and corresponding responses.; Brown also teaches that the response content may include links/hyperlinks, including the “Request Upgrade” hyperlink, and that response may include text, links, audio and actions) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Bhardwaj and Mingels with teachings of Brown by modifying the interactive chatbot for multi-way communication as taught by Bhardwaj to include the data structure stores the plurality of associations between the plurality of identifies and corresponding links as taught by Brown for the benefit of increasing the ability of virtual assistants to emulate human representatives (Brown [Background]) . 07-21-aia AIA Claim (s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj et al. (US 2022/0247700) in view of Mingels (US 2022/0360544) and further in view of Henry (US 10,109,275) . Claims 4 and 13, Bhardwaj and Mingels teaches all the limitations in claim 1. The difference between the prior art and the claimed invention is that Bhardwaj nor Mingels explicitly teach wherein the predicted identifier generated by the one or more machine learning models comprises a condensed representation of the one or more second natural language characters. Henry teaches wherein the predicted identifier generated by the one or more machine learning models comprises a condensed representation of the one or more second natural language characters ([col. 1 lines 53-67] [col. 5 lines 12-23] [col. 5 lines 40-49] [col. 8 lines 4-16] [col. 10 line 60 to col. 11 line 2] [col. 10 lines 38-48] [col. 12 lines 23-29] [Figs. 4B, 6 and 8] Henry teaches a compact neural language model output used as an identifier for natural language word text; Henry explains that existing language models may require large vocabularies and many parameters, but that its language model may “instead output a vector that describes words that are likely to follow an input sequence of words,” and that “the output vector may be a word embedding or a word hash vector.”; Henry further teaches explains the compression/condensing benefit: for a vocabulary of “around 50,000 words,” word embeddings may have length “around 500” and outputting the smaller word embedding vector “may be less computationally expensive” that outputting a probability vector for the whole vocabulary; Henry also teaches an even more compact hash-vector representation: “hash vectors may be used instead of word embeddings,” and “a hash vector may have only Boolean values.” Henry teaches the ML-generated predicted identifier. In Fig. 4B, “fixed-sized neural network LM 420 receives N hash vectors as input” and “outputs an output hash vector v.”; Henry further states: “Fixed-size neural network LM 420 outputs a hash vector v that describes words likely to follow the N words input into the language model. The output hash vector may then be used to select words.”; see Fig. 6; Henry teaches that compact/hash identifier corresponds to natural language characters stored in a data structure; Henry states that “word hash vectors are computed for each word for a vocabulary of words,” and the vocabulary may include “50,000 commonly used English words,”; Henry further teaches that the search component may “receive the output hash vector and select one or more words whose hash vectors are equal to or close to the output has vector,” and that the words data store stores “information about words in the vocabulary along with a hash vector computed for words in the vocabulary,”; see Fig. 4B; Henry states that the search component may retrieve words “by performing a query using the output hash vector.”; Henry also teaches that the words data store includes “the text of the words and hash vectors for the words.”; see Fig. 8) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Bhardwaj and Mingels with teachings of Henry by modifying the interactive chatbot for multi-way communication as taught by Bhardwaj to include wherein the predicted identifier generated by the one or more machine learning models comprises a condensed representation of the one or more second natural language characters as taught by Henry for the benefit of improving the computational efficiency of statistical language models (Henry [col. 1 lines 10-25]) . Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 SHREYANS A PATEL whose telephone number is (571)270-0689. The examiner can normally be reached Monday-Friday 8am-5pm PST. 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, Pierre Desir can be reached at 571-272-7799. 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. SHREYANS A. PATEL Primary Examiner Art Unit 2653 /SHREYANS A PATEL/Examiner, Art Unit 2659 Application/Control Number: 18/618,788 Page 2 Art Unit: 2659 Application/Control Number: 18/618,788 Page 3 Art Unit: 2659 Application/Control Number: 18/618,788 Page 4 Art Unit: 2659 Application/Control Number: 18/618,788 Page 5 Art Unit: 2659 Application/Control Number: 18/618,788 Page 6 Art Unit: 2659 Application/Control Number: 18/618,788 Page 7 Art Unit: 2659 Application/Control Number: 18/618,788 Page 8 Art Unit: 2659
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