CTFR 18/985,230 CTFR 81378 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. Claims 1, 3-10, and 12-23 pending for examination. Claim(s) 1, 3, 5-10,12, 14-20 have been amended. Claim(s) 2 and 11 have been cancelled. Claim(s) 21-23 have been newly added. This action is Final. Response to Arguments Applicant’s arguments filed 3/2/2026 with respect to 35 U.S.C. 112(b) rejection have been fully considered and the rejection has been withdrawn as the claim(s) have been amended. 07-37 AIA Applicant's arguments filed 3/2/2026 with respect to 35 U.S.C. 101 rejection have been fully considered but they are not persuasive. Applicant’s Remarks: At pages 3-8, the Examiner asserts that claim 1 allegedly encompasses organizing human activity and mental processes with significantly more. Applicant respectfully disagrees and submits that claim 1 is directed to statutory subject matter under § 101. Notwithstanding, to advance prosecution of this application, Applicant has amended claim 1 to recite (with emphasis added): [...] receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result. Applicant respectfully submits that amended claim 1 is patent-eligible under Step 2A, Prong 2 of the Alice Test for the reasons given in the Deputy Commissioner for Patent's recent Memorandum titled, Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, issued August 5, 2025 ("the Memo"). Regarding Step 2A, Prong 2, the Memo states (at page 4, with emphasis added): [...] Additionally, the Memo states that Examiners should evaluate the specification to determine if the disclosure provides sufficient details that one of ordinary skill in the art would recognize the claimed invention as improving the functioning of a computer. (Id.) In this case, Applicant's specification expressly describes how the claimed invention provides a technological improvement. For example, paragraphs 0022-0024 of the specification disclose: [...] As detailed above, the specification discloses embodiments that improve computational efficiency, enable faster retrieval, improve storage efficiency, and reduce network communication between systems. The above advantages are reflected in the features of claim 1. For example, claim 1 recites "generating a prompt for a machine learning model trained to output recommended subjects for electronic conversations, wherein the prompt incorporates the content information and the context information." By generating a recommended subject based on both the content of the conversation and customer-specific context, the claimed invention produces an accurate subject that reflects the true substance of the electronic conversation. Claim 1 also recites "updating the electronic conversation by replacing the current subject with the recommended subject" and "logging the updated electronic conversation in a database that associates the one or more electronic messages with the recommended subject." By storing conversations in association with accurate subject metadata, subsequent retrieval operations can rely on indexed subject fields rather than full-text searches across message bodies, the claimed invention reduces computer processing and increases computational efficiency. Additionally, it improves storage efficiency by allowing for stable and non-fragmented indexing structures. Moreover, because relevant conversations can be retrieved using a query against the logged subject, the claimed invention reduces network communication by minimizing repeated or iterative requests between systems that access the electronic messaging data. Consequently, the claimed invention recites a particular way of improving the functioning of electronic messaging systems. Accordingly, claim 1 is patent-eligible at least under Step 2A, Prong 2 of the Alice Test. For all the foregoing reasons, claim 1 is not directed to a judicial exception under Step 2A of the Alice Test and is, therefore, patent-eligible under § 101. Independent claims 10 and 19, although having different scopes than claim 1, recite similar features to those recited in claim 1. Hence, claims 10 and 19 also satisfy the test for subject-matter eligibility. Claims 2 and 11 are canceled. Claims 3-9, 12-18, and 20 each depend from a respective one of independent claims 1, 10, and 19. Thus, by virtue of their dependence, claims 3-9, 2-18, and 20 are patent-eligible as well. Withdrawal of the § 101 rejection of claims 1-20 is, therefore, respectfully requested. Examiner’s Response : The examiner respectfully disagrees. The examiner respectfully notes that emphasized features of “ receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result” are noted to be limitations that recite an abstract idea and fall under “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite managing personal behavior or relationships or interactions between people” and/or “Mental Processes” i.e., Step 2A-Prong 1. The examiner respectfully notes that an “electronic conversation” is an element in the steps that is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f) , i.e., Step 2A-Prong 2. Further, the examiner respectfully notes that the argued improvement is found to be an improvement to the abstract idea itself. The improvement with respect to computational efficiency, enable faster retrieval, improve storage efficiency, and reduce network communication between systems is an ancillary effect of using a generic computer component to merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f) . Therefore, the examiner finds these arguments not persuasive . 07-37 AIA Applicant's arguments filed 3/2/2026 with respect to 35 U.S.C. 103 rejection have been fully considered but they are not persuasive. Applicant Argues: Thus, the Examiner asserts that Ayloo's intent or key terms equate the claimed "prompt" and Ayloo's machine learning model to the claimed "machine learning model." However, "to rely on equivalence as a rationale supporting an obviousness rejection, the equivalency must be recognized in the prior art, and cannot be based on applicant's disclosure or the mere fact that the components at issue are functional or mechanical equivalents." (MPEP § 2144.06 (citing In re Ruff, 256 F.2d 590 (CCPA 1958); Smith V. Hayashi, 209 USPQ 754 (Bd. of Pat. Inter. 1980)). In this case, Ayloo extracts the intent and unique terms from the content of a single email. (Id. at I 0025-0027.) The system applies a vector representing the intent to a classifier to determine templates for a subject line. (Id. at III 0028-0034). Additionally, the system scores vectors representing different combinations of the unique terms using a machine learning model. The system then suggests a subject line using a template including the highest-scoring unique terms. (Id. at Я 0026 and 0043.) Thus, the vectors input to the machine learning model represent unique terms in a single message. However, as acknowledged by the rejection, the vectors lack content information of electronic messages related to the electronic conversation and context information of customer-specific interactions and data, as recited by claim 1. As such, Ayloo's vectors are not equivalent to the claimed "prompt incorporat[ing] the content information and the context information." Additionally, Ayloo's machine learning model outputs scores for the vectors. Consequently, it is not equivalent to the claimed "model trained to output recommended subjects for electronic conversations. Furthermore, even if Ayloo's vectors and machine learning model were functionally equivalent to the claimed "prompt" and "machine learning model" (which they are not), it is improper to rely on equivalency because no such equivalence is recognized in the prior art. Moreover, because Ayloo fails to disclose or suggest the claimed "prompt" and "machine learning model," it necessarily follows that Ayloo also fails to disclose, "applying the machine learning model to the prompt, to obtain a recommended subject." Accordingly, Ayloo provides no basis for the § 103 rejection. Examiner’s Response: The examiner respectfully disagrees. Ayloo does in fact disclose a “prompt” and a “machine learning model” that would “apply[ing] the machine learning model to the prompt, to obtain a recommended subject. This teaching is affirmed in Ayloo, see FIG. 5 – Present formulated subject to the user and [0036] - An intent of email content may include but is not limited to an action, a request for information, a request for time, a statement, a commitment, a specific or general request for information, etc. and [0043] - Based on the one or more key terms in the key term vector 518 and based on the intent 510 , a subject line may be formulated .... As depicted in FIG. 5, the portions within the area 522 may be performed by one or more machine learning approaches discussed above; that is, 522 may be a machine learning model trained to suggest one or more subject lines, where such model may be trained for one or more users and/or one or more organizations. In some cases, the model is consistently retrained based on the acceptance and/or denial of a suggested subject line . As depicted FIG. 5, clearly shows input (i.e., prompt) of vector/intent into a model are used to obtain a recommended subject line. The examiner respectfully notes, the features as argued, “ content information of electronic messages related to the electronic conversation and context information of customer-specific interactions and data” are taught by Janakiraman. Thus, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Therefore, the examiner finds this argument not persuasive. Applicant Argues: Janakiraman cannot overcome Ayloo's deficiencies. Janakiraman discloses a conversation advisor that analyzes a message to determine whether the message's tone and salutation are appropriate for a recipient. If so, the system suggests replacement terms. The Examiner asserts that the conversation advisor corresponds to the claimed "machine learning model." However, Janakiraman lacks any disclosure of a machine learning model, model training, model parameters, or inference. Instead, Janakiraman discloses the conversation advisor is "functional logic" (Id. ⁋ 0027 "program logic" (Id. At ⁋ 0030). Notably, Janakiraman mentions that the conversation advisor performs semantic analysis (Id. at 0038). However, semantic analysis does not necessarily involve inferring an output using learned parameters, as in training a ML model. For example, rule-based semantic analysis (such as in a spell checker) does not constitute a machine learning model. Moreover, as noted above, Janakiraman lacks any disclosure of suggesting a machine learning model. Thus, Janakiraman does not disclose "a machine learning model trained to output recommended subjects for electronic conversations," as recited in claim 1. Moreover, even if Janakiraman conversation advisor was functionally equivalent (which it is not), it would still be improper to rely on such equivalency because no such equivalency is recognized in the prior art. (MPEP § 2144.06.) For the above reasons, both Ayloo and Janakiraman fail to disclose or suggest the above-identified features of claim 1. Accordingly, no combination of Ayloo and Janakiraman teaches or suggests the claimed invention. Consequently, the applied art cannot support a § 103 rejection of claim 1 for at least this reason. Examiner’s Response: The examiner respectfully disagrees. Janakiraman does in fact teach “machine learning model” as noted in the rejection below, see ⁋ 0023 - The conversation advisor may also include a learning component that learns from the actions taken by the author in response to the advice ( emphasis added ). Thus shows a learning component that is trained based on action/response. Thus, the conversation advisor is found to be machine learning model that is trained. Further, Janakiraman teaches from the previous augment above “ content information of electronic messages related to the electronic conversation and context information of customer-specific interactions and data.” Thus, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Therefore, the examiner finds this argument not persuasive. Applicant Argues: Additionally, Ayloo and Janakiraman cannot support a § 103 rejection of claim 1 because they cannot be properly combined to arrive at the claimed invention. According to MPEP 2143.01(VI), if the proposed modification or combination of the prior art would change the principle of operation of the prior art invention being modified, then the teachings of the references are not sufficient to render the claims prima facie obvious. In this case, the Examiner's proposed combination of Ayloo and Janakiraman is improper because it would change the principle of operation of Ayloo. Ayloo generates subject lines based on analysis of message content based on intent determination and templates. Differently, Janakiraman does not generate subject lines. Instead, Janakiraman evaluates the appropriateness of message wording using semantic analysis. Incorporating Janakiraman's teachings into Ayloo would replace Ayloo's intent determination and templates with Janakiraman's semantic logic. This modification alters the basic principle under which Ayloo operates. Therefore, the proposed combination cannot support a prima facie case of obviousness. Furthermore, the proposed combination of Ayloo and Janakiraman is improper because it would render the Ayloo unsatisfactory for its intended purpose. (MPEP §2143.01.) In particular, Ayloo's intent determination and template-selection mechanisms would be rendered inoperative if subject generation were instead driven by recipient-specific historical context, disclosed by Janakiraman. Thus, there is no suggestion or motivation to make the proposed modification. Examiner’s Response: In response to applicant's argument that Ayloo and Janakiraman cannot support a § 103 rejection of claim 1 because they cannot be properly combined to arrive at the claimed invention, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. Applicant Argues: For all the foregoing reasons, Ayloo and Janakiraman cannot support a § 103 rejection of amended claim 1. Carmel, which was applied against canceled claim 2, does not overcome the above-identified deficiencies of Ayloo and Janakiraman with regard to amended claim 1, and the Examiner does not rely on Carmel for such teaching or suggestion. (See Office Action, pp. 17-18.) Accordingly, amended claim 1 is patentable over Ayloo, Janakiraman, and Carmel. Amended independent claims 10 and 19, although having different scopes than claim 1, recite features similar to claim 1. Accordingly, claims 10 and 19 distinguish over Ayloo, Janakiraman, and Carmel for reasons similar to those set forth above regarding claim 1. Claims 4, 6-8, 13, and 15- 17 each depend from a respective one of independent claims 1 and 10. Therefore, by virtue of their respective dependencies, as well as by virtue of reciting additional features that are neither taught nor suggested by the cited references, claims 4, 6-8, 13, and 15-17 are distinguishable over Ayloo, Janakiraman, and Carmel. Additionally, claims 4, 6-8, 13, and 15-17 recite other distinguishing subject matter. Accordingly, Applicant respectfully requests that the § 103 rejection of claims 1, 4, 6-8, 10, 13, 15-17, and 19 be withdrawn. Claims 2 and 11 are canceled. Claims 3, 5, 9, 12, 14, 18, and 20 each depend from a respective one of independent claims 1, 10, and 19. Therefore, by virtue of their respective dependencies, as well as by virtue of reciting additional features that are neither taught nor suggested by the cited references, claims 3, 5, 9, 12, 14, 18, and 20 are distinguishable over Ayloo, Janakiraman, and Carmel. Haynes and Laban do not overcome the deficiencies of Ayloo, Janakiraman, and Carmel with regard to claims 3, 5, 9, 12, 14, 18, and 20, and the Examiner does not rely on Haynes and Laban for any such teaching or suggestion. Additionally, claims 3, 5, 9, 12, 14, 18, and 20 recite other distinguishing subject matter. Accordingly, Applicant respectfully requests that the § 103 rejections of claims 3, 5, 9, 12, 14, 18, and 20 be withdrawn. Examiner’s Repones: The examiner respectfully disagrees for the reasons set forth above . 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. Claim(s) 1-23 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Step 1: claim(s) 1-23 are directed to a manufacture, process and/or machine. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03 . Prong 1, Step 2A: claim 1, and similar claim(s) 10 and 19, taken as representative, recites at least the following limitations that recite an abstract idea: One or more non-transitory computer readable media comprising instructions that, when executed by one or more hardware processors, cause performance of operations comprising: receiving a[n] electronic message comprising at least part of an electronic conversation with a particular customer, the electronic message comprising a first subject that describes the electronic message; obtaining content information of one or more electronic messages related to the electronic conversation; obtaining context information associated with the particular customer for interpreting the electronic conversation, wherein the context information comprises customer-specific interactions and data; generating a prompt for a machine learning model trained to output recommended subjects for electronic conversations, wherein the prompt incorporates the content information and the context information ; applying the machine learning model to the prompt, to obtain a recommended subject; updating the electronic conversation by replacing the first subject with the recommended subject; and logging the updated electronic conversation in a database that associates the one or more electronic messages with the recommended subject. receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result. The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II) , in that they recite managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of these limitations for claim 1, and for similar claim(s) 10 and 19 includes receiving a message comprising at least part of a conversation with a particular customer, the message comprising a current subject that describes the message; obtaining content information of one or more messages related to the electronic conversation; obtaining context information associated with the particular customer for interpreting the conversation, wherein the context information comprises customer-specific interactions and data; output recommended subjects for conversations... to obtain a recommended subject; updating the conversation by replacing the current subject with the recommended subject; logging the updated conversation and associating the one or more messages with the recommended subject; receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result, thus, claim 1, and similar claim(s) 10 and 19 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite managing personal behavior or relationships or interactions between people. The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III) , in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and for similar claim(s) 10 and 19, i.e., media, receiving electronic messages, prompting with specific data to ML model trained to output results, logging in a database, and device/processor; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and similar claim(s) 10 and 19, includes receiving a message comprising at least part of a conversation with a particular customer, the message comprising a current subject that describes the message; obtaining content information of one or more messages related to the electronic conversation; obtaining context information associated with the particular customer for interpreting the conversation, wherein the context information comprises customer-specific interactions and data; output recommended subjects for conversations... to obtain a recommended subject; updating the conversation by replacing the current subject with the recommended subject; logging the updated conversation and associating the one or more messages with the recommended subject; receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result, thus, which, encompass steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 10 and 19, recite i.e., media, receiving electronic messages, prompting with specific data to ML model trained to output results, logging in a database, and device/processor. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, ⁋⁋ [0027]-[0030] and ⁋ [0038]) . These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 10 and 19 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d). Since claim 1, and similar claim(s) 10 and 19 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 10 and 19 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d). Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 10 and 19, i.e., media, receiving electronic messages, prompting with specific data to ML model trained to output results, logging in a database, and device/processor; thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 10 and 19 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05 . Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 10 and 19 is ineligible. Regarding Claims 3-9, 12-18, and 20-23, claims 3-9, 12-18, and 20-23 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions) i.e., further features related to evolving subjects. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component (i.e., claim 4-5 and 13-17 – recite GUI and claim 9 and 18 – updating a ML model). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible. 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, 3-4, 6-8, 10, 12-13, 15-17, and 19-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ayloo (US 2021/0133287 A1) in view of Janakiraman (US 2016/0182410 A1) and Carmel et al. (US 2009/0055481 A1) . Regarding Claim 1; Ayloo discloses one or more non-transitory computer readable media comprising instructions that, when executed by one or more hardware processors (FIG. 10), cause performance of operations comprising: receiving an electronic message comprising at least part of an electronic conversation with a particular customer ([0040] - In some examples, the subject line formulator 452 may preserve a conversation identifier associated with the email content. For example, in an instant where a user replies to an existing email or email thread, the subject of the email thread may be modified or replaced by the subject line formulator 452), the electronic message comprising a current subject that describes the electronic message ([0024] - In accordance with examples of the present disclosure, an intelligent email subject line suggestion and reformulation system 101 may generate a subject in place of or otherwise to be included as part of a subject provided by a user in the subject line 110 and [0040]); [...] generating a prompt for a machine learning model trained to output recommended subjects for electronic conversations, wherein the prompt incorporates [first] information and [second] information (FIG. 5 – Present formulated subject to the user and [0036] - An intent of email content may include but is not limited to an action, a request for information, a request for time, a statement, a commitment, a specific or general request for information, etc. and [0043] - Based on the one or more key terms in the key term vector 518 and based on the intent 510 , a subject line may be formulated .... As depicted in FIG. 5, the portions within the area 522 may be performed by one or more machine learning approaches discussed above; that is, 522 may be a machine learning model trained to suggest one or more subject lines, where such model may be trained for one or more users and/or one or more organizations. In some cases, the model is consistently retrained based on the acceptance and/or denial of a suggested subject line ); applying the machine learning model to the prompt, to obtain a recommended subject (FIG. 5 – Present formulated subject to the user and [0036] and [0043] - Based on the one or more key terms in the key term vector 518 and based on the intent 510 , a subject line may be formulated .... As depicted in FIG. 5, the portions within the area 522 may be performed by one or more machine learning approaches discussed above ; that is, 522 may be a machine learning model trained to suggest one or more subject lines, where such model may be trained for one or more users and/or one or more organizations. In some cases, the model is consistently retrained based on the acceptance and/or denial of a suggested subject line); updating the electronic conversation by replacing the first subject with the recommended subject ([0043] - In some examples, the formulated subject line may be presented to a user prior to the formulated subject line being provided to or otherwise presented at the subject line 110 of the email composition window 100. For example, a user may be able to select the formulated subject line or deny the use of the formulated subject line; a selected formulated subject line may be presented in the subject line 110 whereas denying the formulated subject line may cause a subject line, if present, to remain in the subject line 110); and logging the updated electronic conversation in a database that associates the one or more electronic messages with the recommended subject ([0040] - In some examples, the subject line formulator 452 may preserve a conversation identifier associated with the email content. For example, in an instant where a user replies to an existing email or email thread, the subject of the email thread may be modified or replaced by the subject line formulator 452; however, in order to preserve the email thread, or conversation, where threads, or conversations, may be grouped by a common subject line, the intelligent email subject line suggestion and reformulation system 412 may preserve a conversation identifier, in metadata for example, and the conversation identifier may be included as part of the email message .) Ayloo fails to explicitly disclose: obtaining content information of one or more electronic messages related to the electronic conversation; obtaining context information associated with the particular customer for interpreting the electronic conversation, wherein the context information comprises customer-specific interactions and data; generating a prompt for a machine learning model trained to output recommended ..., wherein the prompt incorporates the content information and the context information; [...] receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result. However, in an analogous art, Janakiraman teaches one or more non-transitory computer readable media comprising instructions that, when executed by one or more hardware processors (FIG. 2), cause performance of operations comprising: [receiving a first electronic message...] ([0035]-[0036] - In block 10-2 of FIG. 4, the conversation advisor 10 is invoked and accesses the electronic message 30 in the memory 16 (e.g., by performing a file-read operation).) obtaining content information of one or more electronic messages related to the electronic conversation ([0038] - In block 10-8 of FIG. 4, the conversation advisor 10 performs semantic analysis of the electronic message 30 to extract words and phrases representing recipient fitness information that is indicative of whether the electronic message is appropriate for the intended recipient(s)); obtaining context information associated with the particular customer for interpreting the electronic conversation, wherein the context information comprises customer-specific interactions and data ([0037] - In block 10-6 of FIG. 4, the conversation advisor 10 gathers and analyzes historical data stored in the persistent storage device 20 relating to a conversation history that links the author of the electronic message to each intended recipient); generating [by] a machine learning model trained to output recommended [ highlighting inconsistent word or phrase], wherein the [machine learning model trained] incorporates the content information and the context information ([0023] - The conversation advisor may also include a learning component that learns from the actions taken by the author in response to the advice and [0043] - In block 10-10 of FIG. 4, the conversation advisor 10 generates a fitness result for the electronic message 30 by comparing the recipient fitness information to the historical data determined in block 10-6. The fitness result may take various forms, including but not limited to an identification of each word or phrase representing the recipient fitness information that is inconsistent with the historical data. The fitness result may also include a suggested alternative word or phrase that is consistent with the historical data and [0044] - For the convenience of the author, the fitness result may be output for display within the electronic message itself, for example, by highlighting each inconsistent word or phrase, and suggesting an alternative word or phrase. The fitness result output would thus include advice to the author of the electronic message concerning how to correct instances where the recipient fitness information is inconsistent with the intended recipient. These suggestions will constitute a warning with specific guidelines that need to be followed in the electronic message for a particular recipient or group of recipients); [applying the machine learning model ..., to obtain a recommended [highlighting inconsistent word or phrase]] (FIG. 4) . Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Janakiraman to the prompt for a machine learning model trained to output recommended subjects for electronic conversations, wherein the prompt incorporates [first] information and [second] information of Ayloo to include obtaining content information of one or more electronic messages related to the electronic conversation; obtaining context information associated with the particular customer for interpreting the electronic conversation, wherein the context information comprises customer-specific interactions and data ; generating [by] a machine learning model trained to output recommended [ highlighting inconsistent word or phrase], wherein the [machine learning model trained] incorporates the content information and the context information One would have been motivated to combine the teachings of Janakiraman to Ayloo to do so as it provides / allows an electronic message authoring tool for helping users avoid making message authoring mistakes (Janakiraman, [0005]) However, in an analogous art, Carmel teaches receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject ([0021] - A selected subject 132 can replace an original subject 122, as shown in interface element 140. In one embodiment, an original subject heading 122 can be retained and remain accessible by a user. Thus, the subject heading 140 for an email message can actually be implemented as a set of stacked subject lines, where the stack includes original user entered subjects, as well as one or more replacement subjects generated by engine 112 . A user can use a stack ordering tool 142 to choose whether original headings are displayed exclusively, whether final headings of conveyed email messages are displayed exclusively, whether one or more recommended headings are displayed (i.e., such as according to an associated relevancy score), or whether an entire stack of subject lines are to be displayed in a user established priority order. Use of stacked subject lines can be used when searching an email history for a desired message . Use of stacked subject lines ensures that users are not penalized when remembering an original email subject line instead of remembering a replacement subject heading generated by engine 112) As construed from this citation searching using a stacked subject line could include use of the original or replacement, thus, returning a desired message from the email history ; responsive to the search query ([0021] - ...searching...): identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject ([0021] - Use of stacked subject lines can be used when searching an email history for a desired message); and surfacing the updated electronic conversation as a search result ([0021] - Use of stacked subject lines can be used when searching an email history for a desired message ). Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Carmel to the operations of Ayloo in view of Janakiraman to include receiving a search query comprising one or more terms, the one or more terms being included in the recommended subject and not being included in the first subject; responsive to the search query: identifying the updated electronic conversation based on a match between the one or more terms and the recommended subject; and surfacing the updated electronic conversation as a search result. One would have been motivated to combine the teachings of Carmel to Ayloo in view of Janakiraman to do so as it provides / ensures that users are not penalized when remembering an original email subject line instead of remembering a replacement subject heading when searching (Carmel, [0021]) Regarding Claim 3; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo disclose [concepts of] a plurality of electronic conversations ([0040] - In some examples, the subject line formulator 452 may preserve a conversation identifier associated with the email content. For example, in an instant where a user replies to an existing email or email thread, the subject of the email thread may be modified or replaced by the subject line formulator 452), Carmel further teaches wherein the operations further comprise: receiving a request to filter a ... electronic [conversation] based on one or more filtering criteria, wherein the one or more filtering criteria comprise at least one criterion associated with the recommended subject but not associated with the first subject ([0021] - A selected subject 132 can replace an original subject 122, as shown in interface element 140. In one embodiment, an original subject heading 122 can be retained and remain accessible by a user. Thus, the subject heading 140 for an email message can actually be implemented as a set of stacked subject lines, where the stack includes original user entered subjects, as well as one or more replacement subjects generated by engine 112 . A user can use a stack ordering tool 142 to choose whether original headings are displayed exclusively, whether final headings of conveyed email messages are displayed exclusively, whether one or more recommended headings are displayed (i.e., such as according to an associated relevancy score), or whether an entire stack of subject lines are to be displayed in a user established priority order. Use of stacked subject lines can be used when searching an email history for a desired message . Use of stacked subject lines ensures that users are not penalized when remembering an original email subject line instead of remembering a replacement subject heading generated by engine 112) As construed from this citation searching using a stacked subject line could include use of the original or replacement, thus, returning a desired message from the email history ; and responsive to the request: generating a filtered list of electronic [conversation] comprising the updated electronic conversation ([0021] - Use of stacked subject lines can be used when searching an email history for a desired message ). Similar rationale and motivation is noted the combination of Carmel to Ayloo in view of Janakiraman and Carmel, as per claim 1, above. Regarding Claim 4; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo further discloses wherein the operations further comprise: presenting the recommended subject to a user via a graphic user interface (GUI) ([0043] - In some examples, the formulated subject line may be presented to a user prior to the formulated subject line being provided to or otherwise presented at the subject line 110 of the email composition window 100 and [0065]); and receiving approval of the recommended subject from the user via the GUI prior to updating the electronic conversation ([0043] - In some examples, the formulated subject line may be presented to a user prior to the formulated subject line being provided to or otherwise presented at the subject line 110 of the email composition window 100.... In some cases, the model is consistently retrained based on the acceptance and/or denial of a suggested subject line and [0065]). Regarding Claim 6; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo further discloses wherein the operations further comprise: obtaining one or more rules associated with the particular customer that specify one or more customer-specific requirements for generating the recommended subject ([0031] - For example, a template ( Noted to be a rule ) may be defined and/or customized to a user, organization, or otherwise, and may include one or more slots that are to be filled based on the template and the information pertaining to an identified intent); wherein the prompt further incorporates the one or more customer-specific requirements ([0031] - As depicted in FIG. 3A, one or more templates 304 may be matched to an identified request; for example, a first template 304 which may include the plurality of slots 308 associated with a request, topic, and deadline 312. In some examples, the intelligent email subject line suggestion and reformulation system 101 may identify information 316 to fill in or otherwise be placed into the one or more slots 308/312. Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112). Regarding Claim 7; Ayloo in view of Janakiraman and Carmel disclose the media of claim 6. Ayloo further discloses wherein the one or more rules cause the machine learning model to include one or more tags, from a plurality of tags associated with the particular customer, in the recommended subject (FIG. 3A-3D and [0031] - As depicted in FIG. 3A, one or more templates 304 may be matched to an identified request; for example, a first template 304 which may include the plurality of slots 308 associated with a request, topic, and deadline 312 ( As noted tags). In some examples, the intelligent email subject line suggestion and reformulation system 101 may identify information 316 to fill in or otherwise be placed into the one or more slots 308/312. Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112 and [0043]). Regarding Claim 8; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo further discloses wherein the prompt causes the machine learning model to determine (a) a first tag of a plurality of tags categorizing the electronic message (FIG. 3A-3D and [0031] - As depicted in FIG. 3A, one or more templates 304 may be matched to an identified request; for example, a first template 304 which may include the plurality of slots 308 associated with a request, topic, and deadline 312 ( As noted tags). In some examples, the intelligent email subject line suggestion and reformulation system 101 may identify information 316 to fill in or otherwise be placed into the one or more slots 308/312. Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112) , and (b) a semantic description of the electronic message (FIG. 3A-3D, specifically, FIG. 3A depicts “sematic descriptions” Action Required: and Prepare Budge Report and DUE by EOW and [0031] - Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112 and [0043]). Regarding Claim 8; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo further discloses wherein the prompt causes the machine learning model to determine (a) a first tag of a plurality of tags categorizing the electronic message (FIG. 3A-3D and [0031] - As depicted in FIG. 3A, one or more templates 304 may be matched to an identified request; for example, a first template 304 which may include the plurality of slots 308 associated with a request, topic, and deadline 312 ( As noted tags). In some examples, the intelligent email subject line suggestion and reformulation system 101 may identify information 316 to fill in or otherwise be placed into the one or more slots 308/312. Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112) , and (b) a semantic description of the electronic message (FIG. 3A-3D, specifically, FIG. 3A depicts “sematic descriptions” Action Required: and Prepare Budge Report and DUE by EOW and [0031] - Accordingly, the intelligent email subject line suggestion and reformulation system 101 may generate a subject 320 corresponding to at least on intent identified in the email content 112 and [0043]). Regarding Claim 21; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Carmel further teaches wherein surfacing the updated electronic conversation comprises: retrieving one or more previous electronic conversations based on a match between the one or more terms being included in subjects of the one or more previous electronic conversations ([0021] - A selected subject 132 can replace an original subject 122, as shown in interface element 140. In one embodiment, an original subject heading 122 can be retained and remain accessible by a user. Thus, the subject heading 140 for an email message can actually be implemented as a set of stacked subject lines, where the stack includes original user entered subjects, as well as one or more replacement subjects generated by engine 112 . A user can use a stack ordering tool 142 to choose whether original headings are displayed exclusively, whether final headings of conveyed email messages are displayed exclusively, whether one or more recommended headings are displayed (i.e., such as according to an associated relevancy score), or whether an entire stack of subject lines are to be displayed in a user established priority order. Use of stacked subject lines can be used when searching an email history for a desired message . Use of stacked subject lines ensures that users are not penalized when remembering an original email subject line instead of remembering a replacement subject heading generated by engine 112). As construed from this citation searching using a stacked subject line could include use of the original or replacement, thus, returning a desired message from the email history matching a stacked subject line . Similar rationale and motivation is noted for the combination of Carmel to Ayloo in view of Janakiraman and Carmel, as per Claim 1, above. Regarding Claim(s) 10, 12-13, and 15-17 and 22; claim(s) 10, 13, and 15-17 and 22 is/are directed to a/an method associated with the media claimed in claim(s) 1, 4, and 6-8 and 21. Claim(s) 10, 13, and 15-17 and 22is/are similar in scope to claim(s) 1, 4, and 6-8 and 21, and is/are therefore rejected under similar rationale. Regarding Claim(s) 19-20 and 23; claim(s) 19-20 and 23 is/are directed to a/an system associated with the media claimed in claim(s) 1 and 3 and 21. Claim(s) 19-20 and 23 is/are similar in scope to claim(s) 1 and 3 and 21, and is/are therefore rejected under similar rationale . 07-21-aia AIA Claim (s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ayloo (US 2021/0133287 A1) in view of Janakiraman (US 2016/0182410 A1) and Carmel et al. (US 2009/0055481 A1) and further in view of Haynes et al. (US 2012/0084645 A1) . Regarding Claim 5; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo in view of Janakiraman and Carmel fails to explicitly disclose wherein the operations further comprise: presenting the recommended subject to a user via a GUI; and receiving an alternate subject from the user via the GUI prior to updating the electronic conversation; wherein updating the electronic conversation comprises: replacing the first subject using the alternate subject as the recommended subject. However, in an analogous art, Haynes teaches wherein the operations further comprise: presenting the recommended subject to a user via a GUI (FIG. 2 – Replacement Subject and [0027]-[0028] - In interface 230, a user can define a template 232 to be applied to emails of the type shown by message 210. For example, the template 232 can apply to messages from a subscription source of technews1@subscription.com that are directed to a specific email recipient, such as John@mymail.com. The interface 230 can permit the user to establish expressions 234 defining a replacement subject) ; and receiving an alternate subject from the user via the GUI prior to updating the electronic conversation ([0029] - When specifying variables in the expression 234, a user can optionally specify a desired format, which can be different from an original format. For example, a date associated with $D variable can be specified as having a mm/dd format. Any expression 234 defining technique can be used for the invention, which is not limited to the illustrated expression defining technique); wherein updating the electronic conversation comprises: replacing the first subject using the alternate subject as the recommended subject (FIG. 2 – Apply). Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Haynes to the operations of Ayloo in view of Janakiraman and Carmel to include wherein the operations further comprise: presenting the recommended subject to a user via a GUI; and receiving an alternate subject from the user via the GUI prior to updating the electronic conversation; wherein updating the electronic conversation comprises: replacing the first subject using the alternate subject as the recommended subject. One would have been motivated to combine the teachings of Carmel to Ayloo in view of Janakiraman and Carmel to do so as it provides / allows customizing email subjects for ... generated email messages (Haynes, [0003]). Regarding Claim(s) 14; claim(s) 14 is/are directed to a/an method associated with the media claimed in claim(s) 5, Claim(s) 14 is/are similar in scope to claim(s) 5, and is/are therefore rejected under similar rationale . 07-21-aia AIA Claim (s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ayloo (US 2021/0133287 A1) in view of Janakiraman (US 2016/0182410 A1) and Carmel et al. (US 2009/0055481 A1) and further in view of Laban et al. (US 2024/0095464 A1) . Regarding Claim 9; Ayloo in view of Janakiraman and Carmel disclose the media of claim 1. Ayloo further teaches [concepts of] the recommended subject and the subject ([0040] - In some examples, the subject line formulator 452 may preserve a conversation identifier associated with the email content. For example, in an instant where a user replies to an existing email or email thread, the subject of the email thread may be modified or replaced by the subject line formulator 452), Ayloo in view of Janakiraman and Carmel fails to explicitly disclose wherein the operations further comprise: determining a difference between tokens included in the recommended subject and tokens included in the subject and updating the machine learning model based on a [sic] difference between the first subject and the recommended subject However, in an analogous art, Laban teaches wherein the operations further comprise: determining a difference between tokens included in the [word] and tokens included in the [ground truth] ([0045] - For example, a cross-entropy loss is calculated as differences between the predicted token (e.g., word token) distribution and the ground-truth , and thus is a metric that evaluates how far away a neural network model generates a predicted output value from its target output value (also referred to as the “ground-truth” value)); and updating the machine learning model based on a [sic] difference between the [ground truth] and the [word] ([0045] - Given the computed cross-entropy loss , the negative gradient of the loss function is computed with respect to each weight of each layer individually. Such negative gradient is computed one layer at a time, iteratively backward from the last layer to the input layer of the neural network. Parameters of the neural network are updated backwardly from the last layer to the input layer (backpropagating) based on the computed negative gradient to minimize the loss . The backpropagation from the last layer to the input layer may be conducted for a number of training samples in a number of training epochs. In this way, parameters of the neural network may be updated in a direction to result in a lesser or minimized loss, indicating the neural network has been trained to generate a predicted output value closer to its target output value. ) Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Laban to the operations of Ayloo in view of Janakiraman and Carmel to include wherein the operations further comprise: determining a difference between tokens included in the [word] and tokens included in the [ground truth] and updating the machine learning model based on a [sic] difference between the [ground truth] and the [word] One would have been motivated to combine the teachings of Laban to Ayloo in view of Janakiraman and Carmel to do so as it provides / allows an improved a reading and comprehension assistance tool (Laban, [0002] and [0004]). Regarding Claim(s) 18; claim(s) 18 is/are directed to a/an method associated with the media claimed in claim(s) 9, Claim(s) 18 is/are similar in scope to claim(s) 9, and is/are therefore rejected under similar rationale. Conclusion 07-39 AIA THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT). 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, JESSICA LEMIEUX can be reached at (571)270-3445. 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. /ASFAND M SHEIKH/Primary Examiner, Art Unit 3626 Application/Control Number: 18/985,230 Page 2 Art Unit: 3626 Application/Control Number: 18/985,230 Page 3 Art Unit: 3626 Application/Control Number: 18/985,230 Page 4 Art Unit: 3626 Application/Control Number: 18/985,230 Page 5 Art Unit: 3626 Application/Control Number: 18/985,230 Page 6 Art Unit: 3626 Application/Control Number: 18/985,230 Page 7 Art Unit: 3626 Application/Control Number: 18/985,230 Page 8 Art Unit: 3626 Application/Control Number: 18/985,230 Page 9 Art Unit: 3626 Application/Control Number: 18/985,230 Page 10 Art Unit: 3626 Application/Control Number: 18/985,230 Page 11 Art Unit: 3626 Application/Control Number: 18/985,230 Page 12 Art Unit: 3626 Application/Control Number: 18/985,230 Page 13 Art Unit: 3626 Application/Control Number: 18/985,230 Page 14 Art Unit: 3626 Application/Control Number: 18/985,230 Page 15 Art Unit: 3626 Application/Control Number: 18/985,230 Page 16 Art Unit: 3626 Application/Control Number: 18/985,230 Page 17 Art Unit: 3626 Application/Control Number: 18/985,230 Page 19 Art Unit: 3626 Application/Control Number: 18/985,230 Page 20 Art Unit: 3626 Application/Control Number: 18/985,230 Page 21 Art Unit: 3626 Application/Control Number: 18/985,230 Page 22 Art Unit: 3626 Application/Control Number: 18/985,230 Page 23 Art Unit: 3626 Application/Control Number: 18/985,230 Page 24 Art Unit: 3626 Application/Control Number: 18/985,230 Page 25 Art Unit: 3626 Application/Control Number: 18/985,230 Page 26 Art Unit: 3626 Application/Control Number: 18/985,230 Page 27 Art Unit: 3626 Application/Control Number: 18/985,230 Page 28 Art Unit: 3626 Application/Control Number: 18/985,230 Page 29 Art Unit: 3626 Application/Control Number: 18/985,230 Page 30 Art Unit: 3626 Application/Control Number: 18/985,230 Page 31 Art Unit: 3626 Application/Control Number: 18/985,230 Page 32 Art Unit: 3626 Application/Control Number: 18/985,230 Page 33 Art Unit: 3626 Application/Control Number: 18/985,230 Page 34 Art Unit: 3626
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