Detailed Action
Claims 1-2, 4-12, 14-20 are pending in this application. Claims 3,13 were cancelled. This is a response to the Amendments/Remarks filed on 2/19/26. This is a Final Rejection.
Claim Objections
Claims 5-7, objected to because of the following informalities:
As per claims 5-7, recites “ a flagged electronic communication”, should be “the flagged electronic communication” since claims 1 recites “flagging…the electronic communication for review”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4,14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 4,14 recites the term “inappropriate or undesirable information” are relative terms which renders the claim indefinite. The term “inappropriate or undesirable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Support for these term is para.36, however the spec does not define “inappropriate or undesirable” with objective criteria (e.g., policy rules, content categories, compliance standards); “inappropriate or undesirable” could vary depending on the user, organization, or context.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 14 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 14 depends on cancelled claim 13. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-12, 14-20 rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0394008 issued to Sundaram et al.(Sundaram) in view of US 2024/0354750 issued to Almasan et al.(Almasan).
As per claim 1,11, one or more non-transitory computer-readable media storing computer-executable instructions, which, when executed on a processor on a computer system, perform a method for filtering outgoing electronic communications generated at a user device prior to transmission to recipients using artificial intelligence (AI) operating in a computing environment(Fig.1, 7A; para.21,243;machine learning model interpreted as AI), the method comprising:
receiving, at a computing system comprising a processor, an electronic communication of the outgoing electronic communications to be transmitted from a first user device to a second user device(para.21; email);
initiating, by the processor, an AI filter engine for analyzing the electronic communication that is received in order to determine whether the electronic communication of a user of the first user device is consistent with an electronic record of historical communications in which the user has engaged([0021] Accordingly, the disclosure herein describes integrating a feature into the email gateway that may pull email information and send it to a cloud based system. The system may then identify whether the target recipient is an intended or unintended recipient. Both heuristics and machine learning techniques may be used to make this identification. In some examples, historical data may be analyzed to identify relationships between users, context of communications between users, and the like. In some arrangements, historical email data may be used to train a machine learning model. The analyzed historical data and/or machine learning model may detect potentially misdirected email based on types of data included in the email, whether the email contains sensitive information, email handles of the email recipients, whether a reply or reply-all selection was made, and the like…. [0037] At step 203, the misdirected email identification platform 110 may train a misdirected email model. For example, using the historical message information, the misdirected email identification platform may train a machine learning model to detect potentially misdirected email based on types of data included in the email (e.g., sensitive information, email handles of recipients, whether reply-all selections were made, and/or otherwise).);
when the processor determines that the electronic communication is consistent with the electronic record of historical communications, authorizing, by the AI filter engine, the electronic communication to be transmitted to the second user device([0052] Referring to FIG. 2C, at step 211, based on identifying that the messaging information was a context match for the message sender (based on the knowledge graph and machine learning analysis), as well as satisfied the data loss prevention information/criteria, the misdirected email identification platform 110 may send one or more commands directing the enterprise network gateway system 120 to route the first message to the target recipient (e.g., the recipient user device 160). At step 212, based on or in response to the one or more commands directing the enterprise network gateway system 120 to route the first message to the recipient user device 160, the enterprise network gateway system 120 may route the first message to the recipient user device 160. At step 213, the recipient user device 160 may receive and display the first message routed at step 212.); and
when the processor determines that the electronic communication is inconsistent with the electronic record of historical communications, flagging, by the AI filter engine, the electronic communication for review(Fig.3-6,[0008],[0048], [0053] Returning to step 211, if the misdirected email identification platform 110 determined that the messaging information did not satisfy the data loss prevention information/criteria, the misdirected email identification platform 110 may proceed to step 214. At step 214, the misdirected email identification platform 110 may send a data loss prevention notification, indicating that data loss prevention criteria was not satisfied, to the initiating user device 130. In some instances, the misdirected email identification platform 110 may also send one or more commands directing the initiating user device 130 to display the data loss prevention notification).
wherein the analyzing of the electronic communication to determine consistency of the electronic communication with the electronic record comprises analyzing a subject line, message, or text of the electronic communication for incorrect or missing information, or analyzing the electronic communication for an incorrect or missing attachment([0009],[0020], [0040], [0044] At step 209, the misdirected email identification platform 110 may input the identified nearest neighbor information and the messaging information into the misdirected email identification model to identify whether or not the context of the first message is an exact match with the context of other, previously sent, messages between the message sender and the message recipient. In some instances, this may be referred to as a first level match. In some instances, this may cause the misdirected email model to compare the messaging information to historical messaging information between the message sender and the identified nearest neighbors to identify whether or not the context of the first message matches the context of other, previously sent, messages between the message sender and the message recipient. …... In some instances, the misdirected email identification model may have specific match thresholds for each of the topics, named entities, keywords, and/or other information. In other instances, the thresholds may be general context thresholds, corresponding to a number of matches between any of the categories (e.g., topics, named entities, keywords, and/or other information). In some instances, the misdirected email identification model may identify an exact match if at least one topic, named entity, and keyword are identified in the first message that matches the historical messages. In some instances, the misdirected email identification platform 110 may also analyze the message sender, message recipients, dates, times, subject lines, attachments (e.g., content of the attachment, file name, attachment label, and/or other information), and/or other information of the first message. [0070] At step 226, the misdirected email identification platform 110 may identify a data loss prevention result indicating whether or not the data loss prevention information/criteria (sent at step 204) is satisfied. For example, the misdirected email identification platform 110 may analyze the messaging information using the heuristics described above at step 204, such as 1) are all other recipients are on a different domain than the target recipient, 2) are there are recipients with multiple domains listed on a CC line, 3) comparing the target recipient with an auto-populated list (e.g., populated to include similar addresses with a webmail or company domain), 4) loose DLP rules that may be used to warn users, and/or other rules. In some instances, the loose DLP rules may include: 1) emails with pre-configured keywords in a subject line or the content, 2) emails to pre-configured sensitive clients, domains, domain categories, or the like, 3) emails with confidential tags in attachments to external recipients, 4) emails with links to sensitive documents, and/or other rules. In some instances, the misdirected email identification platform 110 may store the heuristics in a data loss prevention model, and input of the messaging information into the data loss prevention model may cause the data loss prevention model to output the data loss prevention result (which may, e.g., indicate whether or not any of the heuristics rules are violated)… [0083] At step 236, the misdirected email identification platform 110 may send a notification to the initiating user device 130 indicating that an approximate friends historical match is detected. For example, the misdirected email identification platform 110 may send a notification indicating a potential spelling mistake in the recipient address, and, in some instances, a recommended correction. In some instances, the misdirected email identification platform 110 may also send one or more commands directing the initiating user device 130 to display the approximate friends historical match notification..)
Sundaram does not explicitly teach a quantum processor, in a quantum computing environment, wherein the quantum processor processes data as a plurality of qubits.
Almasan explicitly teaches a quantum processor, in a quantum computing environment, wherein the quantum processor processes data as a plurality of qubits(Fig.1, [0028],[0024] Further, the environment 100 includes a quantum computer 175 that includes one or more quantum computing processors 178, a one or more quantum memories 180, a network interface module 182, and a database 184. In the field of computer arts, a quantum computer or quantum processor utilizes quantum bits, referred to as qubits, to represent, process, and store information and/or data. While a classical electrical bit is limited to a binary state of on or off, 1 or 0, etc., a qubit can exist in superpositions of states allowing for multiple qubit states to exist, be stored, or be used for processing, at a same time. Therefore, a quantum computer may be considered to perform multiple processes in parallel, whereas a classical computer can only perform such processes sequentially in a serial manner. Using qubits, quantum processors are capable of processing data at much higher speeds and much greater quantities than classical computers. As such, quantum computers may be beneficial in performing optimizations, complex ML modeling, and training of ML models and AI.).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Sundaram’s teaching of using machine learning(AI) trained with historical data for filtering of email messages on a computer to apply the teaching of Almasan of a quantum computing environment with a quantum processor for processing qubits in order to provide the predictable result of using machine learning(AI) on quantum processor of a quantum computing environment for filtering of email messages.
One ordinary skill in the art would have been motivated to combine the teachings in order to process data at higher speeds and in greater quantities(Almasan, para.24)
As per claim 2,12, Sundaram in view of Almasan teaches the media/method of claim 1,11, wherein: the AI filter engine comprises an AI model that has been trained with the electronic record of historical communications in which the user has engaged by reviewing contents of the outgoing electronic communications(Sundaram; [0039][0084][0095] At step 248, the misdirected email identification platform 110 may feed the messaging information and any outputs from the misdirected email identification model back into the model. Additionally or alternatively, the misdirected email identification platform 110 may feed any user feedback (e.g., from the message sender) back into the misdirected email identification model. In doing so, the misdirected email identification platform 110 may establish a dynamic feedback loop that may continuously improve accuracy of the misdirected email identification model by updating based on any newly received or otherwise current information and/or model outputs. Additionally or alternatively, the misdirected email identification platform 110 may update the user graph based on the messaging information (e.g., add the message recipient and/or increase a trustworthiness of an existing recipient). In doing so, the misdirected email identification platform 110 may improve data loss prevention techniques performed by the misdirected email identification platform 110 over time([0104] ... At step 751, the computing platform may update the misdirected email identification model based on any information of the first message, outputs of the misdirected email identification model, and/or user feedback); and the step of initiating, by the quantum processor, an AI filter engine for analyzing the electronic communication comprising analyzing, by the quantum processor, the electronic communication that is received to determine whether the electronic communication is consistent with the AI model([0052] Referring to FIG. 2C, at step 211, based on identifying that the messaging information was a context match for the message sender (based on the knowledge graph and machine learning analysis), as well as satisfied the data loss prevention information/criteria, the misdirected email identification platform 110 may send one or more commands directing the enterprise network gateway system 120 to route the first message to the target recipient (e.g., the recipient user device 160). At step 212, based on or in response to the one or more commands directing the enterprise network gateway system 120 to route the first message to the recipient user device 160, the enterprise network gateway system 120 may route the first message to the recipient user device 160. At step 213, the recipient user device 160 may receive and display the first message routed at step 212; [0053] Returning to step 211, if the misdirected email identification platform 110 determined that the messaging information did not satisfy the data loss prevention information/criteria, the misdirected email identification platform 110 may proceed to step 214. At step 214, the misdirected email identification platform 110 may send a data loss prevention notification, indicating that data loss prevention criteria was not satisfied, to the initiating user device 130. In some instances, the misdirected email identification platform 110 may also send one or more commands directing the initiating user device 130 to display the data loss prevention notification)
As per claim 4,14, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/method of claim 1,11, wherein the incorrect information includes one or more of incorrect contact information, spelling or grammatical mistakes, or inappropriate or undesirable information as defined at the quantum computing system to be inappropriate or undesirable(Sundaram, Figs. 3-6, [0009][0020][0083] At step 236, the misdirected email identification platform 110 may send a notification to the initiating user device 130 indicating that an approximate friends historical match is detected. For example, the misdirected email identification platform 110 may send a notification indicating a potential spelling mistake in the recipient address, and, in some instances, a recommended correction. In some instances, the misdirected email identification platform 110 may also send one or more commands directing the initiating user device 130 to display the approximate friends historical match notification).
As per claims 5,15, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/system of claim 1,11, further comprising: blocking, by the quantum processor, a flagged electronic communication from being transmitted to the second user device(Sundaram, Fig.8, step 825).
As per claims 6,16, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/system of claim 1,11,, further comprising: returning a flagged electronic communication to the first user device and not transmitting the flagged electronic communication to the second user device(Sundaram, Fig.3-6,8, step 825).
As per claims 7,17, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/system of claim 1,11, further comprising: suggesting, by the AI filter engine to the first user device, changes to a flagged electronic communication based on the electronic record to make the flagged electronic communication consistent with the electronic record to enable the flagged electronic communication to be transmitted to the second user device(Sundaram, Fig.6, [0084]…. In some instances, the initiating user device 130 may display a graphical user interface similar to graphical user interface 600, which indicates that although no approximate context matches have been identified in the message senders network, an approximate historical recipient has been identified (which may, e.g., be due to a spelling mistake in the recipient address). In some instances, the initiating user device 130 may display a difference between the recipient address and an alternative, suggested recipient address. In some instances, the approximate friends historical match notification may also include an option to engage in email security compliance training..)
As per claims 8,18, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/system of claim 1,11,, further comprising: when the quantum processor determines that the electronic communication is consistent with the electronic record of historical communications, transmitting the electronic communication to the second user device(Sundaram, [0052] Referring to FIG. 2C, at step 211, based on identifying that the messaging information was a context match for the message sender (based on the knowledge graph and machine learning analysis), as well as satisfied the data loss prevention information/criteria, the misdirected email identification platform 110 may send one or more commands directing the enterprise network gateway system 120 to route the first message to the target recipient (e.g., the recipient user device 160)..; and updating the electronic record of historical communications upon transmission of the electronic communication(Sundaram, [0104] ... At step 751, the computing platform may update the misdirected email identification model based on any information of the first message, outputs of the misdirected email identification model, and/or user feedback).
As per claims 9,19, Sundaram in view of Almasan teaches the one or more non-transitory computer-readable media/system of claim 1,11, wherein the electronic record of historical electronic communications includes one or more of emails, text messages, or instant messages(Sundarmam, Abstract).
As per claims 10,20, Sundaram in view of Almasan teaches one or more non-transitory computer-readable the media/system of claim 1,11, wherein the quantum processor analyzes the electronic communication in real time(Sundarmam, Abstract).
Response to Arguments
The applicant amended claims 2-10, 15-17 to overcome the claim objection therefore that objection is withdrawn. The applicant did not address claims 5-7, therefore that objection still stands.
Applicant's arguments filed 2/19/26 have been fully considered but they are not persuasive. The applicant argues in substance,
a) The amendment overcomes the 112(b) rejection and that the specific criteria as to how or why the information is inappropriate or undesirable need not be specified.
In reply to a); The examiner agrees that the how and why the information is inappropriate or undesirable does not need to be specified, however the “what” does need to be specified. In other words, what information is considered to be “inappropriate or undesirable”? The applicants amendment merely includes who(quantum computing system) is determining inappropriate or undesirable information.
The specification does not provide any information to what is considered to be inappropriate or undesirable information. In other words, information to one person can be inappropriate or undesirable however that same information would not be inappropriate or undesirable to another person. For example, information concerning football can be inappropriate or undesirable to one person but not another person. Therefore given the applicant’s specification one of ordinary skill in the art would not be reasonably apprised of the scope of what information is considered to be “inappropriate or undesirable”.
b) the prior art does not teach “wherein the analyzing of the electronic communication by the quantum processor to determine consistency of the electronic communication with the electronic record comprises analyzing a subject line, message, or text of the electronic communication for incorrect or missing information, or analyzing the electronic communication for an incorrect or missing attachment” because Sundaram only discusses checking an email for a wrong recipient”.
In reply to b); The examiner agrees that Sundaram teaches checking an email for a wrong recipient as admitted by applicant, by checking an email for a wrong recipient teaches analyzing of the email for “text of the electronic communication for incorrect or missing information”, therefore partially teaches the amended limitation. In further, Sundaram, [0009],[0020], [0040], [0044],[0070],[0083], also teaches using historical messages and rules for analyzing the message sender, message recipients, dates, times, subject lines, attachments (e.g., content of the attachment, file name, attachment label, and/or other information), and/or other information of the first message therefore teach “wherein the analyzing of the electronic communication to determine consistency of the electronic communication with the electronic record comprises analyzing a subject line, message, or text of the electronic communication for incorrect or missing information, or analyzing the electronic communication for an incorrect or missing attachment”.
Sundaram does not teach the quantum processor which is taught by Almasan, Fig.1, [0028],[0024].
Therefore the combination of Sundaram in view of Almasan teaches
wherein the analyzing of the electronic communication by the quantum processor to determine consistency of the electronic communication with the electronic record comprises analyzing a subject line, message, or text of the electronic communication for incorrect or missing information, or analyzing the electronic communication for an incorrect or missing attachment” because Sundaram only discusses checking an email for a wrong recipient
c) there is no motivation to combine the teachings of Sundaram and Almasan because Sundaram relates to misdirected email data loss prevention while Almasan relates to a totally different topic of secure transaction processing using machine learning and quantum computing.
In reply to c); The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference. The test is what the combined teachings of those references would have suggested to those of ordinary skill in the art. In re Keller, 642 F.2d 413, 425 (CCPA 1981). Applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, Almasan, para.24, provides the motivation to combine and use quantum computer/processor to process data at higher speeds and in greater quantities than classical computers. Almasan, para.24 goes further and provides more motivation to use quantum computers to benefit in performing optimizations, complex ML modeling, and training of ML models and AI. Therefore Almasan provides a motivation to use quantum computers/processor and it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Sundaram’s teaching of using machine learning(AI) trained with historical data for filtering of email messages on a computer to apply the teaching of Almasan of a quantum computing environment with a quantum processor for processing qubits in order to provide the predictable result of using machine learning(AI) on quantum processor of a quantum computing environment for filtering of email messages. In other words, using the quantum computer/processor as taught by Almasan to perform the methods of Sundaram and/or substitute the quantum computer/processors for Sundaram’s classical computer/servers.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
US 2021/0034814 issued to Aggarwal et al., teaches amethod may include receiving an electronic message from a sender. The method may further include parsing the electronic message into a set of sections, the set of sections including structured sections and an unstructured section. The method may further include detecting etiquette errors in the structured sections of the electronic message, wherein the etiquette errors include at least one of a missing word, a redundant word, an incorrect usage of a word, a style error, an emotional punctuation error, or a punctuation error. The method may further include generating an etiquette score based on the etiquette errors.
US 2020/0059447 issued to Bahar teaches a method and system for verifying that an electronic communication is sent to the intended recipient prior to the communication actually being sent comprises: detecting a recipient name in a body of the communication and determining whether the detected recipient name is a match or potential mismatch with a destination indication or name associated with the destination indication
US 2020/0151620 issued to Chao et al., teaches detecting the transmission of messages, analyzing said messages, calculating a message risk score and transmitting a warning notification, one or more computer processors detect transmission of a message from a user to a selected recipient. The one or more computer processors extract message information from the detected message. The one or more computer processors retrieve one or more historical conversations between the user and the selected recipient of the detected message. The one or more computer processors determine a risk score corresponding to sending the detected message to the selected recipient based on applying the extracted message information and the retrieved historical conversations to a cognitive model.
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.
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/BACKHEAN TIV/
Primary Examiner, Art Unit 2459