DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/2/2025 has been entered.
Response to Amendment and Arguments
Claim Objection
Applicant didn’t address the claim objection, therefor the objection stands.
35 U.S.C. 101 Rejections
Applicant’s amendment and argument toward rejection is persuasive, as it was agreed upon the interview that the amendment would overcome the 101 rejection, because the model as recited in the amended claim is no longer considered a generic computer component.
35 U.S.C. 103 Rejections
Applicant’s arguments are moot in view of the new or modified grounds of rejection that
were necessitated by the amendments to the Claims. Applicant’s arguments are directed to material that is added by the most recent amendments to the independent Claims. Response, p. 2.
Claim Objections
Claim 17 objected to because of the following informalities: Line 3, “a general language” should read “a general language model”.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Forester in view of Zhao (US 20190333020), and further in view of Enders (US 20150074095)
Regarding claim 1, Foerster discloses: 1. A method comprising: generating, by a computing device (see fig. 1, message system 160), an input based on a plurality of messages, the input including content of the plurality of the messages and a prompt for an outcome, the plurality of messages including a received message originated from a first client device (see fig. 1, computing device 110A/B) and another message yet to be sent from a second client device(see fig. 1, computing device 110A/B) to the first client device, ([0002] “The disclosed subject matter relates to techniques for detecting missing information during electronic message exchanges between users, such as via email or instant messaging. Rather than determining whether the user has failed to attach a document, a message processing system evaluates draft messages for deficiencies in textual content of the message before the draft message is sent. [0003] The message processing system prompts the user as to what seed subjects appear to be unaddressed by the draft reply message prior to sending the reply message. In such situations, the received message may be used to determine what information may be expected by the recipient of the draft reply message (e.g., often the sender of the received message), and the draft reply message may be used to determine whether the user has fulfilled those expectations.”)
and the outcome indicative of a level of responsiveness of the another message to the received message; ([0016] “Before the drafting user sends draft message 104 to its intended recipients, message processing system 100 analyzes draft message 104 for various types of deficiencies. For example, message processing system 100 may identify aspects of message 102 to which the drafting user did not respond in draft message 104, or message processing system 100 may identify aspects of draft message 104 that are incomplete, nebulous, or otherwise is likely to instigate a follow-up request from the recipient.” [0076] “The comparison score generated by the comparison model represents a likelihood as to whether the draft component addresses the received component.”)
determining, the outcome responsive to the prompt based on the content of the plurality of messages included in the input by a valid response model that is previously trained to distinguish between irrelevant, incomplete, and complete message responses ([0016] Before the drafting user sends draft message 104 to its intended recipients, message processing system 100 analyzes draft message 104 for various types of deficiencies. For example, message processing system 100 may identify aspects of message 102 to which the drafting user did not respond in draft message 104, or message processing system 100 may identify aspects of draft message 104 that are incomplete, nebulous, or otherwise is likely to instigate a follow-up request from the recipient. [0032] Message processing module 120 may use a machine learning model trained with historical messages to identify deficient components of draft messages 104. In some examples, the machine learning model may be trained with received messages, draft messages, reply messages, and/or subsequent replies to determine when a particular draft reply does or does not address a seed subject, or when a draft message contains an ambiguity or missing component of information that is likely to lead to a subsequent email to clarify the ambiguity.) Also see [0016, 0035-0038] and fig. 1 and 5.
and identify a recommended or standardized format to use to respond to the plurality of messages; ([0033] Message processing module 120 may pre-populate the reply message with a template reply sentence, enabling the user to complete the template with information details, or may otherwise suggest an edit to satisfy the identified deficiency. For example, based on Patricia's unanswered question text “what time do you leave for the airport,” message processing module 120 may add a template reply sentence of “I leave for the airport at ______” into the draft reply message.)
detecting, via a (see fig, 5 flow chart, (550), identify, deficient components in the subject draft reply message.) Also see [0025] using web application. Also see [0060-0062] analyzing content of a draft message.
and causing to display (see fig. 5 flow chart, (560) outputting for display), based on the detecting and in a user interface of the second client device, the outcome before transmission of the another message, the outcome to enable modification the another message to adjust responsiveness of the another message relative to the received message. ([0077] “After analysis and detection of deficiencies in the draft reply message, message processing module 120 may provide indication of each identified deficiency. Some examples may include highlighting or otherwise identifying received content components not addressed in the draft reply message. For example, message processing module 120 may cause sentence 310B in received message text 146 to be highlighted. Some examples may include automatically amending reply message text 144 by adding a template reply that can be edited by the drafting user, or by prompting the user with multiple likely replies. As such, the drafting user may review the identified deficiencies and may choose to either correct those deficiencies or continue sending the draft reply message unaltered.”)
Forester discloses use of web application, but does not explicitly disclose using a browser extension.
However, Zhao discloses: browser extension ([0028] A user interacts with the messaging system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a messaging system specific component. For example, the component may be a stand-alone application, one or more application plug-ins, and/or a browser extension.)
Forester and Zhao are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Forester to combine the teaching of Zhao, because extension could offer options for secure browsing and/or customization and personalization (Zhao, [0028]).
Forester/Zhao does not explicitly disclose relevancy or irrelevance aspect of the message/response.
Enders discloses: determining, the outcome responsive to the prompt based on the content of the plurality of messages included in the input by a valid response model that is previously trained to distinguish between irrelevant, incomplete, and complete message responses ([0039] Such a computer system can analyze user responses to these questions using one or more model answers and can identify, based on a variety of metrics like relevance and/or completeness, gaps in the user's knowledge.) Also see [0132, 0134, 0138]
Forester/Zhao/Enders are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Forester/Zhao to combine the teaching of Enders, because the technique described can be use to analyze questions and answers to identify semantic elements (word meanings) and can used those semantic elements to determine completeness, relevance, conciseness, and/or timeliness of a question and/or an answer (Enders, [0004]).
Regarding claim 2, Foerster/Zhao/Enders discloses: 2. The method of claim 1,
Forester further discloses: wherein the content indicates one or more questions included in the received message. ([0003] “Such seed subjects may include questions posed to the user, information requested from the user, subjects addressed by the received message, etc. Responsive to identification of the seed subjects, the message processing system analyzes the draft reply message to detect any seed subjects from the received message that are not addressed in the draft reply message.”)
Regarding claim 3, Foerster/Zhao/Enders discloses: 3. The method of claim 2,
Forester further discloses: wherein the content indicates responses to the one or more questions. ([0003] “The message processing system prompts the user as to what seed subjects appear to be unaddressed by the draft reply message prior to sending the reply message. In such situations, the received message may be used to determine what information may be expected by the recipient of the draft reply message (e.g., often the sender of the received message), and the draft reply message may be used to determine whether the user has fulfilled those expectations.”)
Regarding claim 4, Foerster/Zhao/Enders discloses: 4. The method of claim 2,
Forester further discloses: wherein the outcome indicates whether or not text of the another message is relevant to the one or more questions. ([0003] “The message processing system prompts the user as to what seed subjects appear to be unaddressed by the draft reply message prior to sending the reply message. In such situations, the received message may be used to determine what information may be expected by the recipient of the draft reply message (e.g., often the sender of the received message), and the draft reply message may be used to determine whether the user has fulfilled those expectations.”)
Regarding claim 5, Foerster/Zhao/Enders discloses: 5. The method of claim 2,
Forester further discloses: wherein causing to display the outcome comprises causing the computing device to: ([0012] “FIG. 3 is a conceptual diagram illustrating an example screen shot of a graphical user interface of a computing device configured to analyze a draft reply message for deficiencies in reply content, in accordance with one or more aspects of the present disclosure.”) Also see flow chart on fig. 5, (560) displaying deficient components.
and highlight, within the outcome, a portion of text of the received message not addressed by text of the another message. ([0033] “In some examples, message processing module 120 may highlight a portion of the received message that appears to be unaddressed by the draft reply.”)
Regarding claim 6, Foerster/Zhao/Enders discloses: 6. The method of claim 1,
Forester further discloses: wherein determining the outcome comprises determining the outcome using one or more natural language processing (NLP) models, ([0074] “Message processing module 120 may perform natural language processing on sentences 310A, 310B to identify a first received message component 312A, the quarterly sales report, and a second received message component 312B, the question regarding a flight and, more specifically, a departure time of that flight. Message processing module 120 also performs natural language processing on sentence 320A to identify what subjects are addressed by the draft reply message.”)
and wherein the one or more NLP models include: a general language model. ([0035] “For detecting deficient reply message content, a “comparison” model of message system 160, in some examples, is trained to automatically identify message components of a received message that are unaddressed by message components of an associated draft reply message. The comparison model's training comes from observations of past user behavior with regard to the messaging service provided by message system 160 and accessed by computing device 110. For instance, the comparison model may be a neural network, a long-short-term memory model, or other machine-learned model that is configured to determine from several signals associated with a message, whether replies to that message addressed the received message's components. The comparison model receives, as input, a received content element from an electronic message (e.g., a sentence or clause from text of the received message) and a reply content element (e.g., a sentence or clause from the draft reply message). The model produces, as output, an indication of a likelihood as to whether the reply content element addresses the received content element. To evaluate a given draft reply, message processing module 120 may parse the received message and the draft reply message into individual sentences or clauses and, for each component of the received message, may use the model against each of the reply message components of the draft reply message to determine whether that component of the received message has been addressed by at least one of the reply message components.”) Also see [0036] informational component model, and [0046] machine learning models, and [0067]. Also see [0037] which discloses a valid response model.
Regarding claim 7, Foerster/Zhao/Enders discloses: 7. The method of claim 1,
Forester further discloses: wherein the plurality of messages comprise one or more of: email messages, text messages, or chatroom messages. ([0002] “The disclosed subject matter relates to techniques for detecting missing information during electronic message exchanges between users, such as via email or instant messaging. [0024] Examples of electronic messages include: instant messages, chat messages, electronic mail (e-mail) messages, social media communications, voicemail messages, video messages, or any other type of person-to-person communication that is accessed via a computing device.”)
Regarding claim 8, Foerster/Zhao/Enders discloses: 8. The method of claim 1,
Forester further discloses: wherein causing to display the outcome comprises autocompleting the prompt. ([0005] “By automatically detecting deficiencies in draft messages, the message processing system may cause a user to focus his or her attention on the deficiencies; thereby reducing the number of messages needed to be exchanged by users.”) Also see [0003-0004, 0035-0036, 0044, 0077] prompting user or adding template reply.
Regarding claim 9, Foerster/Zhao/Enders discloses: 9. The method of claim 1,
Forester further discloses: wherein the content is sent by the second client device in response to receiving, at the second client device, a user input indicating that the another message should be sent. ([0024] “Messaging client 116 and messaging service module 162 communicate via network 130 to provide a messaging service to computing devices 110. Examples of a messaging service include: e-mail service, text messaging service, short message service, simple service messaging, multimedia message service, social media messaging service, voice message service, video message service, or any other service that facilitates the exchange of human-readable electronic messages.”) Also see fig.1, computing device 110A and 110B and [0025-0030].
Regarding claim 10, Foerster/Zhao/Enders discloses: 10. The method of claim 1,
Forester further discloses: wherein the content is sent by the second client device in real time as the another message is composed. ([0031] “For example, after the drafting user consents to message system 160 analyzing his or her electronic messages for potential content deficiencies, message processing module 120 may analyze message 102 to determine one or more seed subjects contained within the message, and may analyze draft message 104 (e.g., at the time the drafting user initiates sending the e-mail) to determine whether draft message 104 addresses each of the seed subjects identified from message 102.”) Also see fig 1, 110A.
Regarding claim 11, Foerster/Zhao/Enders discloses: 11. The method of claim 1,
Forester further discloses: further comprising: receiving, along with the content, additional content indicating previous messages included on a messaging string along with the received message and the another message, wherein generating the input further comprises generating, based on the additional content, the input. ([0117] “determining, based on the plurality of question answer pairs from a plurality of prior messages and the one or more deficient components, one or more responses predicted to be received after sending the subject draft message and a respective probability for each of the one or more responses predicted to be received, wherein the respective probability indicates a likelihood that the associated responses is predicted to be received; and identifying, by the at least one processor and based on the respective probabilities, at least one of the one or more responses predicted to be received as the one or more candidate phrases.”)
Regarding claim 12, Foerster discloses: A computing system comprising: a processor; memory storing computer executable instructions that, when executed by the processor, cause the computing system to: ([0007] “In another example, the disclosure is directed to a computing device that includes a storage device that stores one or more modules, and at least one processor that executes the one or more modules.” [0053] “In some examples, storage component 248 is a temporary memory, meaning that a primary purpose of storage component 248 is not long-term storage. Storage components 248 on computing device 210 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.” [0054] “Storage components 248 may store program instructions”)
As of the rest the claim elements, they recite the elements from the method of claim 1, thus, the rationale applied in rejection of claim 1 is also applicable to claim 12.
Regarding claim 20, Foerster discloses: One or more non-transitory computer-readable media storing instructions that, when executed by a computing system comprising at least one processor, a communication interface, and memory, cause the computing system to: ([0007] “In another example, the disclosure is directed to a computing device that includes a storage device that stores one or more modules, and at least one processor that executes the one or more modules.” [0053] “In some examples, storage component 248 is a temporary memory, meaning that a primary purpose of storage component 248 is not long-term storage. Storage components 248 on computing device 210 may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.” [0054] “Storage components 248 in some examples include one or more non-transitory computer-readable storage mediums. … Storage components 248 may store program instructions” [0048] “One or more communication units 242 of computing device 210 may communicate with external devices via one or more wired and/or wireless networks by transmitting and/or receiving network signals on the one or more networks.”)
As of the rest the claim elements, they recite the elements from the method of claim 1, thus, the rationale applied in rejection of claim 1 is also applicable to claim 20.
Claims 13-19 are computing system claims with limitations similar to the limitations of Claims 2-8 respectively and are rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Goyal (US 20190347319) discloses: “[0064] The machine learning algorithm used by the system may also be trained separately for a variety of industries using documents that are found in each industry. For example, the system may train a machine learning algorithm on insurance documents. When a new document is uploaded to the system, the system may determine what industry it belongs to and use a machine learning model that has been specifically trained for the relevant industry.” See [0064, 0079, 0089] for additional details.
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/PHILIP H LAM/Examiner, Art Unit 2656