DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed December 19th, 2025, in which claims 1-4, 6-11, 13-17, 19 and 20 have been amended, claims 5, 12 and 18 have been cancelled and claims 21-23 have been added. The amendments have been entered, and claims 1-4, 6-11, 13-17 and 19-23 are currently pending in the case. Claims 1, 8 and 15 are independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6-11, 13-17 and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Claim 1 is directed to A method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“(i) correct one or more corruptions or deviations contained in at least one of the chat messages” this encompasses the mental correction of observed messages.
“(ii) prioritize the chat messages” this encompasses the mental prioritization of observed messages.
“prioritize the chat messages and to identify one or more recommended actions associated with one or more of the chat messages” this encompasses the mental prioritization of observed messages, and mental identification of recommend actions associated with the observed messages.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “obtaining…chat messages being sent to at least one user”, “the graphical user interface includes one or more controls associated with the one or more recommended actions” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “using at least one processing device”, “applying, using the at least one processing device, at least one machine learning model”, “initiating, using the at least one processing device, display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “using at least one processing device”, “applying, using the at least one processing device, at least one machine learning model”, “initiating, using the at least one processing device, display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further “obtaining…chat messages being sent to at least one user” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Further, “the graphical user interface includes one or more controls associated with the one or more recommended actions” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Further, the additional element is directed to well‐understood, routine, and conventional activity, as stated in Wikipedia ("Graphical user interface", Wikipedia, 23 December 2021) “The actions in a GUI are usually performed through direct manipulation of the graphical elements.” Therefore, the claim is ineligible.
Regarding claim 2, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“correct the one or more corruptions or deviations contained in the at least one chat message” This encompasses the mental correction of observed messages.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “a second machine learning model trained to” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 3, the rejection of claim 2 is incorporated and further:
Step 2A Prong 1: a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the second machine learning model is trained using supervised learning; and the first machine learning model is trained using reinforcement learning” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 4, the rejection of claim 2 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“prioritize the chat messages and reduce a quantity of the chat messages provided to the at least one user based on at least one objective or intent associated with the at least one user” This encompasses the mental process of prioritizing observed messages based on an observed objective or intent of a user.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the first machine learning model is trained to autonomously” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 6, the rejection of claim 2 is incorporated and further:
Step 2A Prong 1: a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the second machine learning model comprises multiple pipelines, a first of the pipelines configured to process chat messages containing unstructured language contents, a second of the pipelines configured to process chat messages containing structured contents” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 7, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“prioritizes one or more of the chat messages associated with the geographic area above one or more chat messages associated with other geographic areas” this encompasses the mental prioritization of observed messages associated with a particular geographic area.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the at least one machine learning model prioritizes”, “the graphical user interface comprises a map showing a geographic area associated with one or more operations being monitored or controlled by the at least one user” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 8:
Step 1: Claim 1 is directed to An apparatus, therefore it falls under the statuary category of a machine.
Step 2A Prong 1: The claim recites, in part:
“(i) correct one or more corruptions or deviations contained in at least one of the chat messages” this encompasses the mental correction of observed messages.
“(ii) prioritize the chat messages” this encompasses the mental prioritization of observed messages.
“prioritize the chat messages and to identify one or more recommended actions associated with one or more of the chat messages” this encompasses the mental prioritization of observed messages, and mental identification of recommend actions associated with the observed messages.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “obtaining…chat messages being sent to at least one user”, “the graphical user interface includes one or more controls associated with the one or more recommended actions” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “at least one processing device configured to”, “apply at least one machine learning model to”, “and initiate display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “at least one processing device configured to”, “apply at least one machine learning model to”, “and initiate display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “obtain chat messages being sent to at least one user” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Further, “the graphical user interface includes one or more controls associated with the one or more recommended actions” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Further, the additional element is directed to well‐understood, routine, and conventional activity, as stated in Wikipedia ("Graphical user interface", Wikipedia, 23 December 2021) “The actions in a GUI are usually performed through direct manipulation of the graphical elements.” Therefore, the claim is ineligible.
Regarding claims 9-11 and 13-14:
The rejection of claim 8 is further incorporated, the rejection of claims 2-4 and 6-7 are applicable to claims 9-11 and 13-14, respectively.
Regarding claim 15:
Step 1: Claim 1 is directed to A non-transitory computer readable medium, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“(i) correct one or more corruptions or deviations contained in at least one of the chat messages” this encompasses the mental correction of observed messages.
“(ii) prioritize the chat messages” this encompasses the mental prioritization of observed messages.
“prioritize the chat messages and to identify one or more recommended actions associated with one or more of the chat messages” this encompasses the mental prioritization of observed messages, and mental identification of recommend actions associated with the observed messages.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “obtaining…chat messages being sent to at least one user”, “the graphical user interface includes one or more controls associated with the one or more recommended actions” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “A non-transitory computer readable medium containing instructions that when executed cause at least one processor to”, “apply at least one machine learning model to”, “and initiate display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to”, the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements “A non-transitory computer readable medium containing instructions that when executed cause at least one processor to”, “apply at least one machine learning model to”, “and initiate display of the prioritized chat messages to the at least one user in a graphical user interface”, “a first machine learning model trained to” taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “obtain chat messages being sent to at least one user” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Further, “the graphical user interface includes one or more controls associated with the one or more recommended actions” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Further, the additional element is directed to well‐understood, routine, and conventional activity, as stated in Wikipedia ("Graphical user interface", Wikipedia, 23 December 2021) “The actions in a GUI are usually performed through direct manipulation of the graphical elements.” Therefore, the claim is ineligible.
Regarding claims 16-17 and 19-20:
The rejection of claim 8 is further incorporated, the rejection of claims 2, 4 and 6-7 are applicable to claim 16-17 and 19-20, respectively.
Regarding claim 21, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the graphical user interface includes a section identifying one or more metrics associated with operations being performed by or associated with the user” a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 22, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the at least one machine learning model supports confidentiality of information” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 23, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“processes the features to determine how corruptions or deviations should be corrected” this encompasses the mental processing of observed features to determine corrections.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the at least one machine learning model extracts features of training data” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
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 and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Vukich et al. (US 20210344635 A1) hereinafter Vukich in view of Yunus et al. ("A Context Free Spell Correction Method using Supervised Machine Learning Algorithms", 27 June 2020) hereinafter Yunus.
Regarding claim 1:
Vukich teaches A method comprising: obtaining, using at least one processing device, chat messages being sent to at least one user (Vukich, ¶106 “In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.”);
applying, using the at least one processing device, at least one machine learning model to … (ii) prioritize the chat messages (Vukich, ¶4 “utilizing, by the at least one processor, a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”); and
initiating, using the at least one processing device, display of the prioritized chat messages to the at least one user in a graphical user interface (Vukich, ¶4 “and causing to display, by the at least one processor, the plurality of electronic messages according to the current prioritized ordering on a screen of at least one computing device associated with the recipient.”)
wherein the at least one machine learning model comprises a first machine learning model trained to prioritize the chat messages (Vukich, ¶4 “a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”) and to identify one or more recommended actions associated with one or more of the chat messages (Vukich, ¶51 “In some embodiments, the task database 206 may utilize the message data 217 to determine subject attributes of the message data 217, or linked message data identified in the message data 217, or a combination thereof, and identify related tasks, such as, e.g., open work tasks and priority flags thereof in the task database 206.”); and
wherein the graphical user interface includes one or more controls associated with the one or more recommended actions (Vukich, ¶66 “In some embodiments, a user at the user computing device 260 may provide user interactions 252 in the messaging GUI relative to the prioritized list of messages 251. For example, a user may select a message for response, select a message for deletion, select a message to ignore, relocate a message in the order of the list, modify a designation of priority of a message, or other interactions and combinations thereof for one or more of the messages in the prioritized list of messages 251”).
Vukich does not teach “(i) correct one or more corruptions or deviations contained in at least one of the chat messages”
However, Yunus teaches (i) correct one or more corruptions or deviations contained in at least one of the chat messages (Yunus, col 1, Abstract “This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context”)
Kuvich and Yunus are analogous art because both references concern using machine learning for parsing and processing text-based strings. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kuvich’s message priority system to incorporate the spell correction taught by Yunus. The motivation for doing so would have been to incorporate a modern day necessity as taught by Yunus, col 1, abstract “Spell correction is a modern day necessity for a system that lets a user extract the proper result while searching different things.”.
Regarding claim 2:
Vukich in view of Yunus teaches The method of Claim 1, wherein the at least one machine learning model comprises a second machine learning model trained to correct the one or more corruptions or deviations contained in the at least one chat message (Yunus, col 1, Abstract “This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context”).
It would have been obvious to combine the teachings of Kuvich and Yunus for the reasons set forth in connection with claim 1 above.
Regarding claim 3:
Vukich in view of Yunus teaches The method of Claim 2, wherein:
the second machine learning model is trained using supervised learning (Yunus, col 1, Abstract “This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context”); and
the first machine learning model is trained using reinforcement learning (Vukich, ¶68 “Thus, the first interaction 252 may reinforce the priority model 225, e.g., the weights or parameters used by the priority 225 to generate the priority parameter 241, while the second may retrain the weights or parameters.”).
It would have been obvious to combine the teachings of Kuvich and Yunus for the reasons set forth in connection with claim 1 above.
Regarding claim 4:
Vukich in view of Yunus teaches The method of Claim 2, wherein the first machine learning model is trained to autonomously prioritize the chat messages and reduce a quantity of the chat messages provided to the at least one user (Vukich, ¶73 “In some embodiments, the prioritized ordering can form a list of only electronic messages having a priority above a particular threshold, such as, e.g., of the 5 or 10 greatest priority electronic messages, or having a priority parameter greater than, e.g., 0.5, 0.6, 0.7, 0.8, or 0.9, where the messages below the threshold may be hidden or displayed in a separate list.” Here, messages with a higher priority are prioritized, while some are hidden based on priority to reduce the quantity) based on at least one objective or intent associated with the at least one user (Vukich, ¶59 “For example, the parse engine 221 may identify, e.g., due dates associated with the work task and a date of receipt of the electronic message in the work task data 236 and message data 217, respectively.” here, the due dates and message data can be considered an objective or intent associated with a user).
Regarding claim 6:
Vukich in view of Yunus teaches The method of Claim 2, wherein second machine learning model comprises multiple pipelines, a first of the pipelines configured to process chat messages containing unstructured language contents, a second of the pipelines configured to process chat messages containing structured contents (Yunus, page 3, col 2, ¶3 “This is the most crucial part of this paper as the success of this proposed method heavily depends on the structure of input data rather than the algorithms. We have introduced three types of input as wrong words against a correct word that are to be fed into algorithms to predict an unknown wrong word. We have introduced three special terms here: Word Based Tokenization (WBT), Character Based Tokenization (CBT) and Advance Character Based Tokenization (ACBT). ” Here, the word based tokenization can be considered structured data, and the character based tokenization can be considered unstructured data.).
Regarding claim 7:
Vukich in view of Yunus teaches The method of Claim 1, wherein:
the graphical user interface comprises a map showing a geographic area associated with one or more operations being monitored or controlled by the at least one user (Vukich, ¶16 “In some embodiments, the location database 103 may include a representation of a location of each user in communication with the collaboration system 100 via a respective user computing device 160. The location may include, e.g., a latitude and longitude, a street address, a building identification, a room identification within a building, a floor within a building, among others and combinations thereof.” Here, the representation of the location can be considered the map showing the area and the user in communication with collaboration system can be considered an operation controller or monitored by the user); and
the at least one machine learning model prioritizes one or more of the chat messages associated with the geographic area above one or more of the chat messages associated with other geographic areas (Vukich, ¶36-37 “Each data item in the collaboration databases may also include metadata associated with information such as, e.g., origin of the data, destination, format, time and date, geographic location information, source identifier (ID), among other information.
In some embodiments, the emailing model 220 may leverage the data in the collaboration databases, including associated metadata, to predict message priority, such as most time-sensitive communications or communications likely to require the most attention based on scheduling information related to senders and recipients of the communication, related calendar events and times thereof, previous related messages, open work tasks and urgency thereof, among other data.” Here, messages can be prioritized based on data in the collaboration database which includes location).
Regarding claim 8:
Vukich teaches An apparatus comprising:
at least one processing device configured to: obtain chat messages being sent to at least one user (Vukich, ¶106 “In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.”); apply at least one machine learning model to … (ii) prioritize the chat messages (Vukich, ¶4 “utilizing, by the at least one processor, a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”); and
initiate display of the prioritized chat messages to the at least one user in a graphical user interface. (Vukich, ¶4 “and causing to display, by the at least one processor, the plurality of electronic messages according to the current prioritized ordering on a screen of at least one computing device associated with the recipient.”).
wherein the at least one machine learning model comprises a first machine learning model trained to prioritize the chat messages (Vukich, ¶4 “a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”) and to identify one or more recommended actions associated with one or more of the chat messages (Vukich, ¶51 “In some embodiments, the task database 206 may utilize the message data 217 to determine subject attributes of the message data 217, or linked message data identified in the message data 217, or a combination thereof, and identify related tasks, such as, e.g., open work tasks and priority flags thereof in the task database 206.”); and
wherein the graphical user interface includes one or more controls associated with the one or more recommended actions (Vukich, ¶66 “In some embodiments, a user at the user computing device 260 may provide user interactions 252 in the messaging GUI relative to the prioritized list of messages 251. For example, a user may select a message for response, select a message for deletion, select a message to ignore, relocate a message in the order of the list, modify a designation of priority of a message, or other interactions and combinations thereof for one or more of the messages in the prioritized list of messages 251”).
Vukich does not teach “(i) correct one or more corruptions or deviations contained in at least one of the chat messages”
However, Yunus teaches (i) correct one or more corruptions or deviations contained in at least one of the chat messages (Yunus, col 1, Abstract “This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context”)
Kuvich and Yunus are analogous art because both references concern using machine learning for parsing and processing text-based strings. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kuvich’s message priority system to incorporate the spell correction taught by Yunus. The motivation for doing so would have been to incorporate a modern day necessity as taught by Yunus, col 1, abstract “Spell correction is a modern day necessity for a system that lets a user extract the proper result while searching different things.”.
Regarding claims 9-14:
Claims 9-14 are rejected under the same rationale as claims 2-7 respectively.
Regarding claim 15:
Vukich teaches A non-transitory computer readable medium containing instructions that when executed cause at least one processor to: obtain chat messages being sent to at least one user (Vukich, ¶106 “In some embodiments, exemplary inventive computer-based systems of the present disclosure may be configured to be utilized in various applications which may include, but not limited to, gaming, mobile-device games, video chats, video conferences, live video streaming, video streaming or augmented reality applications, mobile-device messenger applications, and others similarly suitable computer-device applications.”);
apply at least one machine learning model to … (ii) prioritize the chat messages (Vukich, ¶4 “utilizing, by the at least one processor, a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”); and
initiate display of the prioritized chat messages to the at least one user in a graphical user interface. (Vukich, ¶4 “and causing to display, by the at least one processor, the plurality of electronic messages according to the current prioritized ordering on a screen of at least one computing device associated with the recipient.”).
wherein the at least one machine learning model comprises a first machine learning model trained to prioritize the chat messages (Vukich, ¶4 “a message prioritization machine learning model to predict a current prioritized ordering of the plurality of electronic messages based at least in part on a plurality of parameters associated with each of the plurality of electronic messages”) and to identify one or more recommended actions associated with one or more of the chat messages (Vukich, ¶51 “In some embodiments, the task database 206 may utilize the message data 217 to determine subject attributes of the message data 217, or linked message data identified in the message data 217, or a combination thereof, and identify related tasks, such as, e.g., open work tasks and priority flags thereof in the task database 206.”); and
wherein the graphical user interface includes one or more controls associated with the one or more recommended actions (Vukich, ¶66 “In some embodiments, a user at the user computing device 260 may provide user interactions 252 in the messaging GUI relative to the prioritized list of messages 251. For example, a user may select a message for response, select a message for deletion, select a message to ignore, relocate a message in the order of the list, modify a designation of priority of a message, or other interactions and combinations thereof for one or more of the messages in the prioritized list of messages 251”).
Vukich does not teach “(i) correct one or more corruptions or deviations contained in at least one of the chat messages”
However, Yunus teaches (i) correct one or more corruptions or deviations contained in at least one of the chat messages (Yunus, col 1, Abstract “This paper traverses a spell correction method using supervised machine learning algorithms in which the wrong word does not rely on any context”)
Kuvich and Yunus are analogous art because both references concern using machine learning for parsing and processing text-based strings. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Kuvich’s message priority system to incorporate the spell correction taught by Yunus. The motivation for doing so would have been to incorporate a modern day necessity as taught by Yunus, col 1, abstract “Spell correction is a modern day necessity for a system that lets a user extract the proper result while searching different things.”.
Regarding claims 16-20:
Claims 16-20 are rejected under the same rationale as claims 2 and 4-7 respectively.
Regarding claim 22:
Vukich in view of Yunus teaches The method of Claim 1, wherein the at least one machine learning model supports confidentiality of information (Vukich, ¶110 “In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, or the exemplary inventive computer-based components of the present disclosure may be configured to securely store or transmit data by utilizing one or more of encryption techniques (e.g., private/public key pair, Triple Data Encryption Standard (3DES), block cipher algorithms (e.g., IDEA, RC2, RCS, CAST and Skipjack), cryptographic hash algorithms (e.g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH), WHIRLPOOL, RNGs).”).
Regarding claim 23:
Vukich in view of Yunus teaches The method of Claim 1, wherein the at least one machine learning model extracts features of training data and processes the features to determine how corruptions or deviations should be corrected (Yunus, page 3, col 1, section 4, ¶1 “hen the words need to be encoded as integers or floating point values for use as input to a machine learning algorithm, called feature extraction (or vectorization).”).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Vukich in view of Yunus in further view of Fellows (US 20220360597 A1) hereinafter Fellows.
Regarding claim 21:
Vukich in view of Yunus teaches The method of Claim 1,
Vukich in view of Yunus does not teach “wherein the graphical user interface includes a section identifying one or more metrics associated with operations being performed by or associated with the user”
However, Fellows teaches wherein the graphical user interface includes a section identifying one or more metrics associated with operations being performed by or associated with the user (Fellows, ¶129 “The graphical user interface allows a viewer to visually contextualize the metrics, alerts, and/or events occurring in the network in light of the activities occurring in the end-point computing-devices on the common display screen.”).
Vukich in view of Yunus and Fellows are analogous art because both references concern methods for analyzing messages and activity with machine learning. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Vukic/Yunus’s messaging system to incorporate the metrics taught by Fellows. The motivation for doing so would have been to allow a user to visualize the metrics of the system as stated in Fellows, ¶129 “The graphical user interface allows a viewer to visually contextualize the metrics, alerts, and/or events occurring in the network in light of the activities occurring in the end-point computing-devices on the common display screen.”.
Response to Arguments
Applicant's arguments filed December 19th, 2025 have been fully considered but they are not persuasive.
Applicant’s amendments have overcome the claim objections and the prior art rejections of the previous office action.
Applicant’s arguments regarding the 35 U.S.C. 112(b) rejections of the previous office action have been fully considered, and are persuasive. The rejections have been withdrawn due to claim amendments.
Regarding the 35 U.S.C. 101 rejections, applicant’s arguments have been considered, but they are not persuasive.
Applicant first argues “Humans cannot mentally perform the claims since, for example, humans cannot mentally apply machine learning model to correct corruptions or deviations contained in chat messages and prioritize the chat messages accordingly or display chat messages in a graphical user interface.” However, the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). See MPEP § 2106.04(a)(2)/III. Correction and prioritization of observed messages can be performed in the human mind, or by a human using a pen and paper. Humans cannot mentally apply a machine learning algorithm, however, the application of the machine learning model was identified as an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Further, the display of messages was identified as an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Therefore, the claims are rejected under 35 U.S.C. § 101.
Applicant next argues “[t]he Applicant has also amended the claims to recite (i) the machine learning model is trained to prioritize chat messages and identify recommended actions associated with the chat messages and (ii) the graphical user interface that includes one or more controls associated with the one or more recommended actions. Again, this has nothing to do with organizing human activity and cannot be performed mentally.” The MPEP states “If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application”. See MPEP § 2106.04(a). Here, the prioritization of the messages has been identified as a mental process in Step 2A Prong One, and the use of a graphical user interface has been identified as adding insignificant extra-solution activity to the judicial exception in Step 2A Prong Two. Therefore, the claims are rejected under 35 U.S.C. § 101.
Applicant next argues “the Applicant's claims are directed to solving a specific technical problem.” And further that “While reducing and prioritizing chat messages one-by-one could theoretically be performed, it cannot be effectively performed in a timely manner in, for instance, a command and control center for first-responders engaging in numerous chat sessions with supervisors and other personnel in the field or other locations, such as during a natural disaster or other incident.” However the MPEP states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” See MPEP § 2106.5(f). While the claimed system may be faster or more accurate than a human performing the mental process, as claimed the claims are directed to a mental process and the additional elements amount to adding the words “apply it” (or an equivalent) with the judicial exception, or merely use a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Applicant’s arguments with respect to the 103 rejections of the previous Office Action have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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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/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122