DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to the RCE/amendments, arguments and remarks, filed on 2/11/2026, in which claim(s) 1-20 is/are presented for further examination.
Claim(s) 1, 9, 13 and 18 has/have been amended.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 2/11/2026 has been entered.
Response to Amendment
Applicant’s amendment(s) to claim(s) 1, 13 and 18 has/have been accepted. The rejection(s) of the claim(s), under 35 U.S.C. 101, as being directed to an abstract idea without significantly more, has/have been withdrawn. Consequently, the rejection(s) of claim(s) 2-12, 14-17, 19 and 20, which depend(s) from claim(s) 1, 13 and 18, has/have also been withdrawn.
Applicant’s amendment(s) to claim(s) 1, 9, 13 and 18 has/have been accepted.
The examiner thanks applicant’s representative for pointing out where s/he believes there is support for the amendment(s).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20, filed on 2/11/2026, have been fully considered but they are not persuasive.
Applicant’s arguments with respect to the rejection(s) of claim(s) 1-20, under 35 U.S.C. 103, see the middle of page 13 to page 14 of applicant’s remarks, filed on 2/11/2026, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-5, 7, 13-15 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qadrud-Din et al., US 2024/0289561 A1 (hereinafter “Qadrud”) in view of Liu et al., US 11,755,626 B1 (hereinafter “Liu”) in further view of Luzhnica et al., US 11,516,158 B1 (hereinafter “Luz”) in further view of Radhakrishnan et al., US 12,111,858 B1 (hereinafter “Rad”).
Claims 1, 13 and 18
Qadrud discloses a method comprising:
receiving a text interaction record at a database system, the text interaction record including interaction text from one or more messages between a client machine and a service provider (Qadrud, Fig. 2, see database system 214 is part of text generation interface system 210 and see also client machines 202 and 204 linked to text generation interface system 210 [i.e., under BRI, “service provider”]; and Qadrud, Fig. 8, see 802 “Receive user input”, 804 “Chat input message”, 806, “Determine chat prompt”, 808 “Chat prompt”, 810 “Chat message”, 812 “Chat prompt message”, 814 “Generate raw chat response”, 816 “Chat response message”, 818 “Parse chat response message”, 820 “Parsed chat response”, 822 “Chat output message” and 824 “Present user output”);
determining, by one or more processors associated with the database system (Qadrud, [0113], see processor), an input database record creation prompt that includes natural language instructions to generate database record field text based on the text interaction record, the input database record creation prompt including some or all of the interaction text (Qadrud, Fig. 1, see 102 “Determine relevance scores for portions of text included in a document based on a comparison with a natural language criterion, 104 “Select a subset of the text portions based on the relevance scores” and 106 “Transmit to a remote text generation modeling system a criteria evaluation prompt [i.e., under BRI, “input database record creation prompt”] including an instruction to evaluate some or all of the subset of the text portions against the natural language criterion”; Qadrud, [0026], see “A text generation interface system may take as input one or more arbitrary documents [i.e., chat transcript], process them via optical text recognition, segment them into portions, and process the segmented text via various tasks based on need. ... , an input document is divided into chunks via a chunking technique. Then, chunks are inserted into prompt templates for processing by a large language model such as the GPT-3 or GPT-4 available from OpenAI. …”; Qadrud, [0031], see “… techniques and mechanisms described herein provide for the division of text into chunks, and the incorporation of those chunks into prompts that can be provided to a large language model. …”; and Qadrud, ]0050], see “… a skill may be associated with one or more prompts. For instance, the skill 234 is associated with the prompt templates 236 and 238. A prompt template may include information such as instructions that may be provided to the text generation modeling system 270 [i.e., under BRI, “natural language instructions”]);
transmitting the input database record creation prompt to a large language model for completion (Qadrud, [0031], see “… techniques and mechanisms described herein provide for the division of text into chunks, and the incorporation of those chunks into prompts that can be provided to a large language model. …”, where the prompts have to be transmitted to the large language model to start processing);
receiving a completed database record creation prompt from the large language model, the completed database record creation prompt including a text element created by the large language model based on the input database record creation prompt (Qadrud, Fig. 9, see 908 “Summarize prompt”, 21 “Summarize prompt messages”, 914 “Generate raw summaries”, 916 “Summarize response messages”, 918 “Parse summary response messages”, 920 “Parsed summary response”, 922 “Summary message” and 924 “Present summary output”; and Qadrud, [0080] and [0081], see “The one or more responses are parsed at 416 to produce a parsed response. ... For example, in some systems a large language model may be configured to complete a prompt. Hence, a response message received from the large language model may include the instructions and/or the input text. … , parsing the one or more responses may involve combining text from different responses. For instance, a document may be divided into a number of portions, each of which is summarized by the large language model. The resulting summaries may then be combined to produce an overall summary of the document.”);
generating, by the one or more processors (Qadrud, [0113], see processor), a database record in the database system, the database record including a database field storing the text element (Qadrud, [0056], see “… the query cache 222 may be instructed to return a predetermined result to a query that has already been sent to the text generation modeling system 270 rather than sending the same query again. …” disclosing that past searches and their results are saved, which means a record of them was generated in the system to retrieve; and Qadrud, [0217], see presenting the summary by storing the summary in a file);
determining, by the one or more processors (Qadrud, [0113], see processor), a text interaction topic based on the interaction text (Qadrud, Fig. 8, see the chat session [i.e., “interaction text”] depicted; and Qadrud, [0077], see a prompt may be determined by supplementing and/or modifying a prompt template based on the input text. For instance, a portion of input text may be added to a prompt template at an appropriate location. As one example, a prompt template may include a set of instructions for causing a large language model to generate a correspondence document. The prompt template may be modified to determine a prompt by adding a portion of input text that characterizes the nature of the correspondence document to be generated. The added input text may identify information such as the correspondence recipient, source, topic [i.e., “interaction topic”], and discussion points);
accessing a text embedding model (Qadrud, [0348], see a machine learning model may be implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13 . For instance, a machine learning model may be implemented as a bi-encoder in which text portions are separately encoded and then mapped to a common embedding space [i.e., the text embedding model is accessed in order to encode the text to the embedding space]);
determining, by the one or more processors and via the text embedding model (Qadrud, [0113], see processor; and Qadrud, [0348], see a machine learning model may be implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13 . For instance, a machine learning model may be implemented as a bi-encoder in which text portions are separately encoded and then mapped to a common embedding space), a text embedding representing the interaction text in a vector space (Qadrud, [0338], see the classification model may include a text embedding model that positions text in a vector space; and Qadrud, [0348] & [0349], see a machine learning model may be implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13 . For instance, a machine learning model may be implemented as a bi-encoder in which text portions are separately encoded and then mapped to a common embedding space. Then, at 1406, the relevance score may depend on the distance between the criterion and the text portion in the embedding space),
determining a database query based on the text interaction topic and the text embedding (Qadrud, [0351], see one or more text embedding models may be created using a training data set. The text embedding models may then be used to produce relevance scores for a number of different queries and text portions [i.e., “text interaction topic”]. The relevance scores may then be used to create a loss function for hyperparameter tuning of the number of layers of neurons and number of neurons per layer in a cross-encoder model. …);
executing, by the one or more processors (Qadrud, [0113], see processor) the database query (Qadrud, Fig. 14, step 1420 “Determine an answer to the query based on the included text portions”)
updating the text embedding model using at least the one or more reference interaction records (Qadrud, [0338], see a trained classification model is determined at 1316 based on the training data [i.e., discloses training/updating the model]).
Qadrud does not appear to explicitly disclose determining a text embedding in a multi-dimensional vector space;
the determining of the text embedding including using a conversation embedding process to map a conversation to a fixed-length vector, the conversation embedding process including:
separately embedding utterances of the conversation into utterance embeddings using an embedder model,
generating attention scores for the utterances using a feed forward network, and
determining conversational context embeddings as a weighted average of the utterance embeddings, the weighted average determined by the attention scores;
determining a database query implementing a search vector including an index;
executing the database query to identify one or more reference interaction records having an index similar to the index of the database query, the one or more reference interaction records including a reference vector representing reference interaction text in the multi-dimensional vector space.
Liu discloses determining a text embedding in a multi-dimensional vector space (Liu, Col. 39, line 35-Col. 40, line 6, see the word embedding stage is comprised of operations of mapping each token (word) of a tokenized document [i.e., “text”] to a multi-dimensional vector [i.e., “text embedding in a multi-dimensional vector space”] resulting in a vectorized document. … the word embedding stage may also include operations of mapping each token (word) of a tokenized topic to a multi-dimensional vector resulting in one or more vectorized topics);
determining a database query implementing a search vector including an index (Liu, Col. 39, line 35-Col. 40, line 6, see the multi-dimensional vectors are stored in a data store that is a component of or otherwise accessible to the text classification system and the mapping operation may include searching the stored multi-dimensional vectors for the multi-dimensional vector [i.e., “database query implementing a search vector”] that corresponds to a particular token and retrieving the same; and Liu, Col. 31, lines 23-33, see an event retrieved from an index in response to a query [i.e., “database query including an index”] can be considered a row with a column for each field value);
executing the database query to identify one or more reference interaction records having an index similar to the index of the database query, the one or more reference interaction records including a reference vector representing reference interaction text in the multi-dimensional vector space (Liu, Col. 29, lines 15-22, see, at block 410, the query system 114 combines the partial results and/or events to produce a final result for the query; Liu, Col. 39, line 35-Col. 40, line 6, see the multi-dimensional vectors are stored in a data store that is a component of or otherwise accessible to the text classification system and the mapping operation may include searching the stored multi-dimensional vectors for the multi-dimensional vector [i.e., “reference vector representing reference interaction text in the multi-dimensional vector space”] that corresponds to a particular token and retrieving the same; and Liu, Col. 31, lines 23-33, see an event retrieved from an index in response to a query [i.e., “database query”] can be considered a row with a column for each field value; and Liu, Fig. 4A, see steps 402 “Receive query”, 404 “Process query” and 410 “Combine portions/finalize results”).
Qadrud and Liu are analogous art because they are from the same field of endeavor such as language modeling.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Qadrud and Liu before him/her, to modify the interaction text of Qadrud to include the multi-dimensional embedding of Liu because it would reduce data dimensionality.
The suggestion/motivation for doing so would have been to be able to train large language models.
Therefore, it would have been obvious to combine Liu with Qadrud to obtain the invention as specified in the instant claim(s).
The combination of Qadrud and Liu does not appear to explicitly disclose determining of the text embedding including using a conversation embedding process to map a conversation to a fixed-length vector, the conversation embedding process including:
separately embedding utterances of the conversation into utterance embeddings using an embedder model,
generating attention scores for the utterances using a feed forward network, and
determining conversational context embeddings as a weighted average of the utterance embeddings, the weighted average determined by the attention scores.
Luz discloses determining of the text embedding including using a conversation embedding process to map a conversation to a fixed-length vector (Luz, Col. 5, line 56-Col. 7, line 3, see “… US 2018/0329884 describes a neural contextual conversation learning system/methods comprising receiving an inquiry string [i.e., “conversation”] using a recurrent neural network (RNN) encoder-decoder architecture [i.e., “conversation embedding process”] adapted to improve a relevancy of the generated response string by adapting the generated response based on an identified probabilistic latent conversation domain, the apparatus comprising: a first RNN configured to receive the inquiry string as a sequence of vectors x and to encode [i.e., “map”] a sequence of symbols into a fixed length vector representation, …”).
Qadrud, Liu and Luz are analogous art because they are from the same field of endeavor such as language modeling.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Qadrud, Liu and Luz before him/her, to modify the multi-dimensional embedding of interaction text of the combination of Qadrud and Liu to include the conversation embedding of Luz because it would allow the understanding and processing of the semantic meaning and contextual relationships within human conversations.
The suggestion/motivation for doing so would have been to generate effective communications efficiently, see Luz, Col. 23, line 66-Col. 24, line 62.
Therefore, it would have been obvious to combine Luz with the combination of Qadrud and Liu to obtain the invention as specified in the instant claim(s).
The combination of Qadrud, Liu and Luz does not appear to explicitly disclose the conversation embedding process including:
separately embedding utterances of the conversation into utterance embeddings using an embedder model,
generating attention scores for the utterances using a feed forward network, and
determining conversational context embeddings as a weighted average of the utterance embeddings, the weighted average determined by the attention scores.
Rad discloses the conversation embedding process including:
separately embedding utterances of the conversation into utterance embeddings using an embedder model,
generating attention scores for the utterances using a feed forward network, and
determining conversational context embeddings as a weighted average of the utterance embeddings, the weighted average determined by the attention scores (Rad, Col. 8, lines 33-49, see the network may be initially fine-tuned, for instance to maximize retrieval scores for retrieving past conversations or knowledge articles related to example conversations in the training set. Then, when a new conversation context is received with different utterances in turn for a human agent and an individual at a remote computing device, a new conversation embedding may be obtained as follows. First, utterances may be separately embedded using a sentence embedder model (e.g., F.sub.u). Second, utterance attention scores may be determined for the utterances using a feed forward network (e.g., F.sub.a). Third, conversational context embeddings may be determined as the weighted (by attention) average of the individual utterance embeddings).
Qadrud, Liu, Luz and Rad are analogous art because they are from the same field of endeavor such as language modeling.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Qadrud, Liu, Luz and Rad before him/her, to modify the multi-dimensional conversation embedding of interaction text of the combination of Qadrud, Liu and Luz to include the conversation embedding of Rad because it would allow the understanding and processing of the semantic meaning and contextual relationships within human conversations.
The suggestion/motivation for doing so would have been to have higher-quality interactions with substantially less manual text generation than when using conventional techniques, see Rad, Col. 2, lines 48-60.
Therefore, it would have been obvious to combine Rad with the combination of Qadrud, Liu and Luz to obtain the invention as specified in the instant claim(s).
Claim(s) 13 and 18 recite(s) similar limitations to claim 1 and is/are rejected under the same rationale.
With respect to claim 13, Qadrud discloses a non-transitory computer readable media (Qadrud, [0113], see memory).
With respect to claim 18, Qadrud discloses a database system comprising:
a communication interface (Qadrud, [0113], see interface);
a processor (Qadrud, [0113], see processor);
a large language model interface (Qadrud, [0042], see model interface); and
a database query engine (Qadrud, [0026], see database searches).
Claims 2, 3, 14 and 19
With respect to claims 2, 3, 14 and 19 (i.e., claims 14 and 19 are claims 2 and 3 combined), the combination of Qadrud, Liu, Luz and Rad discloses wherein the input database record creation prompt includes a plurality of identifiers corresponding with a plurality of text fields to include in the database record (Qadrud, [0348], see “…, a machine learning model may be implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13 . For instance, a machine learning model may be implemented as a bi-encoder in which text portions are separately encoded and then mapped to a common embedding space. Then, at 1406, the relevance score may depend on the distance between the criterion and the text portion in the embedding space.”, which discloses similar text is mapped to common term), and wherein the input database record creation prompt includes a plurality of sets of natural language instructions to generate text corresponding with the plurality of text fields (Qadrud, ]0050], see “… a skill may be associated with one or more prompts. For instance, the skill 234 is associated with the prompt templates 236 and 238. A prompt template may include information such as instructions that may be provided to the text generation modeling system 270 [i.e., under BRI, “natural language instructions”]).
Claims 4, 15 and 20
With respect to claims 4, 15 and 20, the combination of Qadrud, Liu, Luz and Rad discloses wherein determining the input database record creation prompt comprises combining a base prompt with the interaction text, the base prompt including a fillable portion that is replaced with the interaction text (Qadrud, [0050], see “… A prompt template may also include one or more fillable portions that may be filled based on information determined by the orchestrator 230. For instance, a prompt template may be filled based on information received from a client machine, information returned by a search query, or another information source. …”).
Claim 5
With respect to claim 5, the combination of Qadrud, Liu, Luz and Rad discloses wherein the one or more messages are text-based messages transmitted in a live chat session (Qadrud, [0115], see “User input is received at 802. In some embodiments, the user input may be received via a chat interface such as iMessage, Google Chat, or SMS. …”).
Claim 7
With respect to claim 7, the combination of Qadrud, Liu, Luz and Rad discloses further comprising:
determining the search vector based on the interaction text via a processor at the database system, the text embedding representing the interaction text in a multi-dimensional vector space (Qadrud, [0338], see the classification model may include a text embedding model that positions text in a vector space).
Claim(s) 6, 8-12, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qadrud in view of Liu in further view of Luz in further view of Rad in further view of Alexander et al., US 2021/0149949 A1 (hereinafter “Alexander”).
Claim 6
Claim 6 incorporates all of the limitations above.
With respect to claim 6, the combination of Qadrud, Liu, Luz and Rad discloses wherein wherein the interaction text is determined by converting the one or more messages to text (Qadrud, [0026], see “A text generation interface system may take as input one or more arbitrary documents [i.e., chat transcript], process them via optical text recognition, segment them into portions, and process the segmented text via various tasks based on need. ... ”).
The combination of Qadrud, Liu and Luz does not appear to explicitly disclose wherein the one or more messages are voice-based messages.
Alexander discloses wherein the one or more messages are voice-based messages (Alexander, [0020], see “… , the corpus of digital exchanges includes a plurality of exchanges. Each of the exchanges can be represented by utterances [i.e., under BRI, “voice”] transmitted between two or more entities. …”).
Qadrud, Liu, Luz, Rad and Alexander are analogous art because they are from the same field of endeavor such as language modeling.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Qadrud, Liu, Luz, Rad and Alexander before him/her, to modify the interaction text multi-dimensional embedding of the combination of Qadrud, Liu, Luz and Rad to include the voice messages of Alexander because this would allow the processing of voice messages.
The suggestion/motivation for doing so would have been the ability to process more kinds of inputs, specifically voice input.
Therefore, it would have been obvious to combine Alexander with the combination of Qadrud, Liu, Luz and Rad to obtain the invention as specified in the instant claim(s).
Claims 8 and 16
Claims 8 and 16 incorporate all of the limitations above.
With respect to claims 8 and 16, the combination of Qadrud, Liu, Luz and Rad discloses further comprising:
the one or more reference interaction records including the reference interaction text (Qadrud, Fig. 8, see 802 “Receive user input” and 804 “Chat input message”), and the input database record creation prompt including some or all of the reference interaction text (Qadrud, [0031], see “… techniques and mechanisms described herein provide for the division of text into chunks, and the incorporation of those chunks into prompts that can be provided to a large language model. …”).
With respect to claim 16, the combination of Qadrud, Liu, Luz and Rad discloses further comprising:
determining the search vector based on the interaction text via a processor at the database system, the text embedding representing the interaction text in the multi-dimensional vector space (Qadrud, [0338], see the classification model may include a text embedding model that positions text in a vector space).
The combination of Qadrud, Liu, Luz and Rad does not appear to explicitly disclose retrieving the one or more reference interaction records from the database system based on the search vector.
Alexander discloses retrieving the one or more reference interaction records from the database system based on the search vector (Alexander, [0059] and [0060], see vector representation of utterances and finding the closet cluster pairs).
Qadrud, Liu, Luz, Rad and Alexander are analogous art because they are from the same field of endeavor such as language modeling.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, having the teachings of Qadrud, Liu, Luz, Rad and Alexander before him/her, to modify the interaction text multi-dimensional embedding of the combination of Qadrud, Liu and Luz to include the vector searching of Alexander because this would allow efficient searching.
The suggestion/motivation for doing so would have been the ability to quickly find matches.
Therefore, it would have been obvious to combine Alexander with the combination of Qadrud, Liu, Luz and Rad to obtain the invention as specified in the instant claim(s).
Claim 9
With respect to claim 9, the combination of Qadrud, Liu, Luz, Rad and Alexander discloses wherein the text embedding model is trained using a plurality of training data interactions including the one or more reference interaction records (Qadrud, [0348], see machine learning model implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13; Qadrud, Fig. 13; and Qadrud, [0351], see text embedding models may be created using a training data set).
Claim 10
Claim 10 incorporate all of the limitations above.
The combination of Qadrud, Liu, Luz and Rad does not appear to explicitly disclose determining a text interaction topic based on the interaction text, wherein the search vector also includes the text interaction topic.
Alexander discloses determining a text interaction topic based on the interaction text (Alexander, [0026], see “…, a corpus of digital exchanges is processed to extract topics from the exchanges. Each topic can be represented by a set of utterances”), wherein the search vector also includes the text interaction topic (Alexander, [0059] and [0060], see vector representation of utterances and finding the closet cluster pairs).
See claims 8 and 16 for the motivation to combine.
Claim 11
With respect to claim 11, the combination of Qadrud, Liu, Luz, Rad and Alexander discloses wherein the text interaction topic is determined by applying the large language model to some or all of the interaction text (Qadrud, [0031], see “… techniques and mechanisms described herein provide for the division of text into chunks, and the incorporation of those chunks into prompts that can be provided to a large language model. …”).
Claim 12
With respect to claim 12, the combination of Qadrud, Liu, Luz, Rad and Alexander discloses wherein the text interaction topic is determined by applying a topic model to some or all of the interaction text (Alexander, [0028], see extracting topics from the exchanges).
Claim 17
With respect to claim 17, the combination of Qadrud, Liu, Luz, Rad and Alexander discloses further comprising:
determining a text interaction topic based on the interaction text (Alexander, [0026], see “…, a corpus of digital exchanges is processed to extract topics from the exchanges. Each topic can be represented by a set of utterances”), wherein the search vector also includes the text interaction topic (Alexander, [0059] and [0060], see vector representation of utterances and finding the closet cluster pairs), wherein the text interaction topic is determined by applying a topic model to some or all of the interaction text (Alexander, [0028], see extracting topics from the exchanges), the topic model being trained using a plurality of training data interaction records including the one or more reference interaction records (Qadrud, [0348], see machine learning model implemented as a pre-trained text embedding model trained as discussed with respect to FIG. 13; Qadrud, Fig. 13; and Qadrud, [0351], see text embedding models may be created using a training data set).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
– Miller, 2023/0244721 for automated metadata asset creation using machine learning models;
– Baxter, 2025/0355914 for optimized embedding searching; and
– Shi et al., 2023/0244874 for sentiment scoring for remote communication sessions.
Point of Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUBERT G CHEUNG whose telephone number is (571) 270-1396. The examiner can normally be reached M-R 8:00A-5:00P EST; alt. F 8:00A-4:00P EST.
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HUBERT G. CHEUNG
Assistant Examiner
Art Unit 2161
Examiner: Hubert Cheung
/Hubert Cheung/Assistant Examiner, Art Unit 2161Date: April 10, 2026
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
Prosecution Insights