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 . Claims 1-6 and 8-20 have been reviewed and are under consideration by this office action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/05/2025 has been entered.
Notice to Applicant
The following is a Non-Final Office action. Applicant amended claims and previously cancelled claim 7. Claims 1-6 and 8-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the claims do not recite an abstract idea and points to limitations such as a trained ML model, an AI generative model, and finetuning an ML model.
Examiner respectfully disagrees. The claims are directed towards the abstract idea of accessing project metadata, accessing communication data, determining a recommendation, and providing a user with the recommendation all of which are concepts capable of being performed in the human mind (i.e. via pen and paper). The additional element of finetuning is separated from the abstract idea portions and addressed in Steps 2A-Prong 2 and 2B.
Applicant contends that claims recite elements that integrate the abstract idea into a practical application and further cites to the specification.
Examiner respectfully disagrees. The additional elements are each addressed in Steps 2A-P2 and 2B and are determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
The 101 Rejection is updated and maintained below.
Response to Arguments - 35 USC § 102/103
Applicant contends the amended claims are not taught by the currently cited prior art.
Examiner finds the arguments are moot in view of the new line of 103 Rejections seen below.
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 1-6 and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1-6 and 8-20 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 11, and 18 recite a series of steps for accessing project metadata, accessing communication data, determining a recommendation, and providing a user with the recommendation:
Regarding Claim(s) 1, (additional elements bolded) A method comprising: accessing, by a communication analytics platform, project metadata associated with a project;
accessing, by the communication analytics platform, communication data associated with the project;
executing, by the communication analytics platform, a trained machine learning model to obtain a recommendation of one or more communication channels for next-step communication for the project based on the project metadata and the communication data;
wherein the trained ML model was trained using historical communication data, historic project metadata, and historical selected channels associated with historical projects;
executing, by the communication analytics platform, a generative artificial intelligence (AI) model to generate content for the next-step communication via a corresponding recommended communication channel based on the project metadata and the communication data; and
providing, by the communication analytics platform, the recommendation of one or more communication channels and the generated content for the next step communication to a user associated with the project; and
finetuning, by the communication analytics platform, the trained ML model based on a selection of the one or more communication channels by the user associated with the project for the next-step communication.
Regarding Claim(s) 11 and 18, A system comprising: a communications interface; a non-transitory computer-readable medium; and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non- transitory computer-readable medium to/A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:
access project metadata associated with a project;
access communication data associated with the project;
execute a trained machine learning model to obtain a recommendation of one or more communication channels for next-step communication for the project based on the project metadata and the communication data;
wherein the trained ML model was trained using historical communication data, historic project metadata, and historical selected channels associated with historical projects;
execute a generative artificial intelligence (AI) model to generate content for the next-step communication based on the project metadata and the communication data using a generative artificial intelligence (AI) model; and
provide the recommendation of one or more communication channels and the generated content for the next-step communication to a user associated with the project; and
finetune the trained ML model based on a selection of the one or more communication channels by the user associated with the project for the next-step communication.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards accessing project metadata, accessing communication data, determining a recommendation, and providing a user with the recommendation all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards recommending and facilitating next step-actions (See Specification, [13]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an a communication analytics platform; executing, by the communication analytics platform, a trained machine learning model to obtain; the trained ML model was trained; executing, by the communication analytics platform, a generative artificial intelligence (AI) model to generate content; finetuning, by the communication analytics platform, the trained ML model; and A system comprising: a communications interface; a non-transitory computer-readable medium; and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non- transitory computer-readable medium to/A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to; . The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 3, 5, 6, 12, 14, 15, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Regarding Claim(s) 2, the claim further recite the additional element(s) of a third-party platform, wherein the communication analytics platform is integrated with the third-party platform and receiving a permission to access the project metadata on the third-party platform from an authorized user associated with the project via a client device. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B. Examiner further notes that receiving a permission… via a client device is an activity that has been recognized by the courts as well-understood, routine, and conventional activity (See MPEP 2106.05(d)(i) OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
Regarding Claim(s) 4 and 13, the claim further recite the additional element(s) of communication data comprises recordings or summaries for video meetings, recordings or summaries for phone calls, recordings or summaries for in-person meetings, emails, or chat messages. This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 8 and 16, the claim further recite the additional element(s) of a trained ML model the comprises a classification model (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B. Examiner further notes that model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs.
Regarding Claim(s) 9, the claim further recite the additional element(s) of the generative AI model is trained. (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 10, 17, and 20, the claim further recite the additional element(s) of a finetuning the generative Al model based on the edited content. (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B. Examiner further notes that model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs.
Regarding Claim(s) 19, the claim further recite the additional element(s) of wherein the trained ML model is retrained using user selections of communication channels. (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B. Examiner further notes that model retraining enables the model in production to make the most accurate predictions with the most up-to-date data. Model retraining does not change the parameters and variables used in the model. It adapts the model to the current data so that the existing parameters give healthier and up-to-date outputs.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-5, 8, 9, 11-14, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arngren et al. (US 20180189407 A1) in view of Chakraborty (US 20240179538 A1) (herein after referred to as “Chakra”).
Regarding Claim(s) 1, Arngren teaches: A method comprising: accessing, by a communication analytics platform, project metadata associated with a project; accessing, by the communication analytics platform, communication data associated with the project; (Arngren, [22]; a method for recommending a communication type to a user for a communication session. The method is performed by a connection manager server and comprises the steps of: causing reception of one or more contacts from the user; causing identification of a current communication context for the user and the one or more contacts, respectively; causing identification of a recommended communication type for the user and the one or more contacts, based on historical communication data; and causing return of the identified recommended communication type to the user and Arngren, [27]; cause store of historical communication data, for a communication session between a user and one or more contacts, into a database, wherein the historical communication data comprises communication data, a session identifier, a communication type, a communication context, and a user identifier for each contact having participated in the communication session.
executing, by the communication analytics platform, a trained machine learning model to obtain a recommendation of one or more communication channels for next-step communication for the project based on the project metadata and the communication data; (Arngren, [23]; The recommended communication type may be identified with a decision model. The method may further comprise a step of causing building the decision tree with historical communication data by machine learning from historical communication sessions of the user and Arngren, [42]; According to an eights aspect, it is presented a connection manager server configured to recommend a communication type to a user for a communication session. The connection manager server comprises: a processor; and a computer program product storing instructions that, when executed by the processor, causes the connection manger server to: cause reception of one or more contacts from the user; cause identification of a current communication context for the user and the one or more contacts, respectively; cause identification of a recommended communication type for the user and the one or more contacts, based on historical communication data)
wherein the trained ML model was trained using historical communication data, historic project metadata, and historical selected channels associated with historical projects; (Arngren, [42]; cause reception of one or more contacts from the user; cause identification of a current communication context for the user and the one or more contacts, respectively; cause identification of a recommended communication type for the user and the one or more contacts, based on historical communication data and Arngren, [84]; An example of training, or building, of a recommendation database is described with reference to FIG. 5. CDRs and additional data, such as communication context, device type and content type, are preprocessed into one record per UE communication session. The CDRs often comprises a lot of user related data, but further data such as geographical location and sensor data from the UE 10 may also be added into the record. A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type). Examiner interprets the sessions as projects.
finetuning, by the communication analytics platform, the trained ML model based on a selection of the one or more communication channels by the user associated with the project for the next-step communication. (Arngren, [78]; The connection manager may be used to recommend or enable a recommendation of the most appropriate communication type for a user considering historic and client data. The communication type is the way two or more users can communicate with each other and Arngren, [84]; A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type. Further, a number of decision labels are derived by grouping similar context labels, such as the decision label “location?” to similar context labels such as “home” or “work”. Further labels may be time of day and application. A geographic position of home and work may be derived by analyzing the geographical coordinates during office hours vs evenings and weekends. When the training set is labeled, supervised learning is performed.).
While Arngren teaches a communication analytics platform, AI models, and determining a communication and content type, Arngren does not appear to explicitly teach generated content. However, Arngren in view of the analogous art of Chakra (i.e. communications analysis) does teach: executing, by the communication analytics platform using generative artificial intelligence (AI) model1, content for the next-step communication via a corresponding recommended communication channel based on the project metadata and the communication data; and (Chakra, [51]; , the communication mode suggestion may include a content modification suggestion for the intended message content. The communication history may indicate examples of the user providing a negative and/or adversarial response, and the machine learning model may find common conditions and common content/word choice of messages when such responses were generated. The communication mode suggestion may suggest to avoid certain catch phrases and/or formatting that may trigger a negative response. The communication mode history may also provide a recommendation about a length of message and may indicate that messages of a shorter length have an increased chance of receiving a quick and helpful reply. The content modification suggestion may be generated as a machine-recognized way for the first party to facilitate a quicker and/or more favorable response from the desired second party.). Examiner notes that the system of Chakra discloses using a machine learning model (i.e. AI generative model) to generate common word choices avoiding certain words and further suggestions generated by the model).
providing, by the communication analytics platform, the recommendation of one or more communication channels and the generated content for the next-step communication to a user associated with the project. (Chakra, [30]; in a hypothetical case where computer 101 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way. EUD 103 can display, or otherwise present, the recommendation to an end user and Chakra, [57]; a machine learning model with the historical communication data (as further discussed herein), the communication mode management program 116 which may use and/or access a trained machine learning model may define a user fingerprint to understand availability and tendency of a user to respond at a specific point in time based on the schedule of the user. Recommendations for best communicating with this worker may be generated based on the user fingerprint. The recommendations may include a type of message, a message platform, and/or message content modifications).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren including a communication analytics platform, AI models, and determining a communication and content type with the teachings of Chakra including generated content for a next step communication in order to generate content most likely to receive a response (Chakra, [57]; a machine learning model with the historical communication data (as further discussed herein), the communication mode management program 116 which may use and/or access a trained machine learning model may define a user fingerprint to understand availability and tendency of a user to respond at a specific point in time based on the schedule of the user. Recommendations for best communicating with this worker may be generated based on the user fingerprint. The recommendations may include a type of message, a message platform, and/or message content modifications).
Regarding Claim(s) 3 and 12, Arngren/Chakra teaches: The method of claim 1, wherein the project metadata comprises project name, project stage, project size, last activity time, number of communications, close date, parties in the project, contact information for different parties. (Arngren, [17]; The historical data may comprise one or more of the following: a session identifier, a user identifier for each contact having been part of the communication session, a group identifier, a communication type, a communication context, a user equipment type, and communication data and Arngren, [21]; The communication context may comprise one or more of the following: user equipment, time of day, location, and accelerometer data and Arngren, [27]; y the processor, causes the connection manger server to: cause store of historical communication data, for a communication session between a user and one or more contacts, into a database, wherein the historical communication data comprises communication data, a session identifier, a communication type, a communication context, and a user identifier for each contact having participated in the communication session and Arngren, [97]; The time_window is a parameter that can vary t-x<time_window<t+x, wherein t is the start time for the communication session and Arngren, [112]; The importance factor may be calculated as: the number of contacts having participated in the communication session divided with the total number of contacts. The thread factor may be calculated as: the number of times each content item appear for the communication session, divided with the number of sessions).
Regarding Claim(s) 4 and 13, Arngren/Chakra teaches: The method of claim 1, wherein the communication data comprises recordings or summaries for video meetings, recordings or summaries for phone calls, recordings or summaries for in-person meetings, emails, or chat messages. (Arngren, [30]; connection manger server to: cause determination of an importance factor for each of one or more content items for one or more content types of historical communication data for the communication session, wherein the historical communication data comprises a communication context; cause determination of a thread factor for each of the one or more content items for the one or more content types of the historical communication data for the communication session and Arngren, [78]; The communication type is the way two or more users can communicate with each other. At a general level, the communication type may simply be a content type such as text, speech, audio, picture or video. At a more detailed level, the communication type may be a specific application such as email, SMS or a certain software application including text communication, or a certain software application for video communication, e.g. Skype, Lync and WebEx). Examiner notes that Arngren teaches the content of the communications (i.e. email, text, etc.), but Chakra further teaches use of email (Chakra, [42]).
Regarding Claim(s) 5 and 14, Arngren/Chakra teaches: The method of claim 3, wherein the communication data further comprises communication metadata comprising dates, times, durations, and parties associated with a communication occurrence. (Arngren, [37] The historical data may comprise one or more of the following: a session identifier, a user identifier for each contact having been part of the communication session, a group identifier, a communication type, a communication context, a user equipment type, and communication data and Arngren, [83]; a classification of a prioritized communication type can be derived through a decision tree. At each level in the tree several options are possible, i.e. for location, day of week, common cluster of called and calling, device type, and preferred communication type of called. E.g. depending on location home/office, combined with day of week weekend/work day, combined with common cluster golf club/company, combined with device type smart phone/computer different options for a prioritized communication is proposed and Arngren, [101]; a web interface is sent to the connection manager server. Also, search query context parameters are submitted, such as device used, time of day, location and accelerometer data).
Regarding Claim(s) 8 and 16, Arngren/Chakra teaches: The method of claim 7, wherein the trained ML model comprises a classification model; (Arngren, [79]; At start of a communication, as a second step, the model is used to classify which communication type that is most appropriate, as exemplified in FIG. 2).
Regarding Claim(s) 9, Arngren/Chakra teaches: The method of claim 1, wherein the generative AI model is trained using historical communication data and historical communication data associated with past projects. (Arngren, [83-84]; A decision model in the form of a decision tree may be generated manually by experts via for example Prolog or automatically generated from training data. The training data may be generated from for example Calling Data Records (CDRs) or deep packet inspections… An example of training, or building, of a recommendation database is described with reference to FIG. 5. CDRs and additional data, such as communication context, device type and content type, are preprocessed into one record per UE communication session. The CDRs often comprises a lot of user related data, but further data such as geographical location and sensor data from the UE 10 may also be added into the record. A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type. Further, a number of decision labels are derived by grouping similar context labels, such as the decision label “location?” to similar context labels such as “home” or “work”. Further labels may be time of day and application).
Regarding Claim(s) 11 and 18, Arngren/Chakra teaches: A system comprising: a communications interface; a non-transitory computer-readable medium; and one or more processors communicatively coupled to the communications interface and the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non- transitory computer-readable medium to: access project metadata associated with a project; (Arngren, [19]; A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium and Arngren, [22]; a method for recommending a communication type to a user for a communication session. The method is performed by a connection manager server and comprises the steps of: causing reception of one or more contacts from the user; causing identification of a current communication context for the user and the one or more contacts, respectively; causing identification of a recommended communication type for the user and the one or more contacts, based on historical communication data; and causing return of the identified recommended communication type to the user and Arngren, [27]; cause store of historical communication data, for a communication session between a user and one or more contacts, into a database, wherein the historical communication data comprises communication data, a session identifier, a communication type, a communication context, and a user identifier for each contact having participated in the communication session).
execute a trained machine learning model to obtain a recommendation of one or more communication channels for next-step communication for the project based on the project metadata and the communication data; (Arngren, [23]; The recommended communication type may be identified with a decision model. The method may further comprise a step of causing building the decision tree with historical communication data by machine learning from historical communication sessions of the user and Arngren, [42]; According to an eights aspect, it is presented a connection manager server configured to recommend a communication type to a user for a communication session. The connection manager server comprises: a processor; and a computer program product storing instructions that, when executed by the processor, causes the connection manger server to: cause reception of one or more contacts from the user; cause identification of a current communication context for the user and the one or more contacts, respectively; cause identification of a recommended communication type for the user and the one or more contacts, based on historical communication data
wherein the trained ML model was trained using historical communication data, historic project metadata, and historical selected channels associated with historical projects; (Arngren, [42]; cause reception of one or more contacts from the user; cause identification of a current communication context for the user and the one or more contacts, respectively; cause identification of a recommended communication type for the user and the one or more contacts, based on historical communication data and Arngren, [84]; An example of training, or building, of a recommendation database is described with reference to FIG. 5. CDRs and additional data, such as communication context, device type and content type, are preprocessed into one record per UE communication session. The CDRs often comprises a lot of user related data, but further data such as geographical location and sensor data from the UE 10 may also be added into the record. A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type). Examiner interprets the sessions as projects).
finetune the trained ML model based on a selection of the one or more communication channels by the user associated with the project for the next-step communication. (Arngren, [78]; The connection manager may be used to recommend or enable a recommendation of the most appropriate communication type for a user considering historic and client data. The communication type is the way two or more users can communicate with each other and Arngren, [84]; A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type. Further, a number of decision labels are derived by grouping similar context labels, such as the decision label “location?” to similar context labels such as “home” or “work”. Further labels may be time of day and application. A geographic position of home and work may be derived by analyzing the geographical coordinates during office hours vs evenings and weekends. When the training set is labeled, supervised learning is performed).
While Arngren teaches a communication analytics platform, AI models, and determining a communication and content type, Arngren does not appear to explicitly teach generated content. However, Arngren in view of the analogous art of Chakra (i.e. communications analysis) does teach: execute a generative artificial intelligence (AI) model to generate content for the next-step communication via a corresponding recommended communication channel based on the project metadata and the communication data using a generative artificial intelligence (AI) model; (Chakra, [51]; , the communication mode suggestion may include a content modification suggestion for the intended message content. The communication history may indicate examples of the user providing a negative and/or adversarial response, and the machine learning model may find common conditions and common content/word choice of messages when such responses were generated. The communication mode suggestion may suggest to avoid certain catch phrases and/or formatting that may trigger a negative response. The communication mode history may also provide a recommendation about a length of message and may indicate that messages of a shorter length have an increased chance of receiving a quick and helpful reply. The content modification suggestion may be generated as a machine-recognized way for the first party to facilitate a quicker and/or more favorable response from the desired second party). Examiner notes that the system of Chakra discloses using a machine learning model (i.e. AI generative model) to generate common word choices avoiding certain words and further suggestions generated by the model).
provide the recommendation of one or more communication channels and the generated content for the next-step communication to a user associated with the project. (Chakra, [30]; in a hypothetical case where computer 101 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way. EUD 103 can display, or otherwise present, the recommendation to an end user and Chakra, [57]; a machine learning model with the historical communication data (as further discussed herein), the communication mode management program 116 which may use and/or access a trained machine learning model may define a user fingerprint to understand availability and tendency of a user to respond at a specific point in time based on the schedule of the user. Recommendations for best communicating with this worker may be generated based on the user fingerprint. The recommendations may include a type of message, a message platform, and/or message content modifications).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren including a communication analytics platform, AI models, and determining a communication and content type with the teachings of Chakra including generated content for a next step communication in order to generate content most likely to receive a response (Chakra, [57]; a machine learning model with the historical communication data (as further discussed herein), the communication mode management program 116 which may use and/or access a trained machine learning model may define a user fingerprint to understand availability and tendency of a user to respond at a specific point in time based on the schedule of the user. Recommendations for best communicating with this worker may be generated based on the user fingerprint. The recommendations may include a type of message, a message platform, and/or message content modifications).
Further regarding Claim(s) 18, Arngren/Chakra teaches: A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to: (Arngren, [19]; A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arngren et al. (US 20180189407 A1) in view of Chakraborty (US 20240179538 A1) (herein after referred to as “Chakra”), and Dintenfass et al. (US 20180322204 A1).
Regarding Claim(s) 2, Arngren/Chakra teaches: The method of claim 1, wherein the project is managed via a third-party platform, wherein the communication analytics platform is integrated with the third-party platform, (Arngren, [02]; The alternative ways to communicate between users is growing and today the sender has the initiative and decides how to contact the recipients. Examples of communication applications are voice, Short Message Service (SMS), Multimedia Message Service (MMS), WhatsUp, SnapChat, Facebook, email, Lync, LinkedIn, Kik, Skype, and IM+).
While Arngren/Chakra teaches accessing project metadata on a third party platform, Arngren does not appear to teach receiving permission. However, Arngren/Chakra in view of the analogous art of Dintenfass (i.e. data management) does teach: wherein the method further comprises receiving a permission to access the project metadata on the third-party platform from an authorized user associated with the project via a client device. (Dintenfass, [01, 43]; The present invention embraces a system for efficient user management of resources for a third party… Embodiments of the present invention also provide a system for configuring and executing a secure communication network for authorizing access to safeguarded resources, the system comprising a memory device; and one or more processing devices operatively coupled to the memory device, wherein the one or more processing devices are configured to execute computer-readable program code to receive a request from a first user to grant a second user access to an account associated with the first user; in response to receiving the request to grant the second user the access to the account associated with the first user, configure a secure dedicated communication channel between a computing device of the first user and a computing device of the second user; transmit, via the secure dedicated communication channel, to the computing device of the second user, the request to grant the second user the access to the account associated with the first user; receive, from the computing device of the second user and Dintenfass, [103]; “User” as used herein may refer to an individual or entity that is authorized and authenticated to utilize a system for managing resources as described herein. In some embodiments, the user may be an individual entrusted to manage resources for a third party. In some embodiments, the user may be associated with or related to the third party such that the user is an administrator, manager, operator, or the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren/Chakra including accessing project metadata on a third party platform with the teachings of Dintenfass including providing permission to access third party data in order to have a specific user that is able to control who is able to access specific data. (Dintenfass, [103]; “User” as used herein may refer to an individual or entity that is authorized and authenticated to utilize a system for managing resources as described herein. In some embodiments, the user may be an individual entrusted to manage resources for a third party. In some embodiments, the user may be associated with or related to the third party such that the user is an administrator, manager, operator, or the like).
Claim(s) 6, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arngren et al. (US 20180189407 A1) in view of Chakraborty (US 20240179538 A1) (herein after referred to as “Chakra”), and Shevchenko et al. (US 11727205 B1).
Regarding Claim(s) 6 and 15, While Arngren/Chakra teaches the use of classification models and recommended channels of communication, Arngren/Chakra does not appear to teach a ranked list of channels. However Arngren/Chakra in view of the analogous art of Shevchenko (i.e. communication preferences) does teach: The method of claim 1, further comprising: generating a ranked list of recommended communication channels; (Shevchenko, [col. 58-59, lines 65-10]; The AIA may identify other communication context, such as a communication channel, platform, or medium (e.g., web, mobile, specific communication clients and collaborative editors, chat, text/voice, and the like), a communication type, form, genre (e.g., email thread, short/instant message, shared document, social media post/comment, voice message, and the like), prior communications between the user 404 and user 412, location and time of day/week for the user(s), and the like. The AIA may identify that the channel, time or the form of the communication are not optimal given the receiver and the use case/goals and suggest alternatives, such as based on the preferences explicitly provided by the user 412 or derived from receiver's previous communication patterns observed and learned by the AIA and Shevchenko, [col. 64, lines 11-26]; representations of the previous content and context are fed into a natural language generation model that is trained on large-scale communication data that may be mixed with users' prior communication and/or effective communications of other people similar to the user (e.g., with respect to industry group, and the like), and/or effective examples of communication in the same or similar scenario (e.g., with respect to communication type, use case, and the like) such as giving a higher weight to the mixed-in data. The outputs of the model are ranked based on the receiver's communication profile and other contextual information. Output candidates can be ranked by the probability of them triggering desired reaction or outcome, or based on their similarity to the user's communication style).
providing the ranked list of recommended communication channels to the user; and receiving a selection of a recommended communication channel for the next- step communication from the ranked list of recommended communication channels. (Shevchenko, [col. 64, lines 11-26]; representations of the previous content and context are fed into a natural language generation model that is trained on large-scale communication data that may be mixed with users' prior communication and/or effective communications of other people similar to the user (e.g., with respect to industry group, and the like), and/or effective examples of communication in the same or similar scenario (e.g., with respect to communication type, use case, and the like) such as giving a higher weight to the mixed-in data. The outputs of the model are ranked based on the receiver's communication profile and other contextual information. Output candidates can be ranked by the probability of them triggering desired reaction or outcome, or based on their similarity to the user's communication style. The highest-ranking outputs are suggested for the user to confirm/select. The user may confirm/select a completion to use as-is or modify).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren/Chakra including the use of classification models and recommended channels of communication with the teachings of Shevchenko including ranked lists of channels and selections in order to correlate user preferences to predict outcomes (Shevchenko, [col. 32, lines 45-55]; The electronic reaction data may be used to generate at least one of an updated first communication profile or an updated second communication profile. At least one of the updated first communication profile or the updated second communication profile may be used to predict a most likely reaction outcome in a second electronic communication. The electronic reaction data may be used to train a machine learning model that is configured to at least one of generate a communication content or modify a communication content).
Regarding Claim(s) 19, Arngren/Chakra teaches: The non-transitory computer-readable medium of claim 18, further comprising processor-executable instructions configured to cause one or more processors to: wherein the classification model comprises a trained machine-learning (ML) model.; (Arngren, [79]; At start of a communication, as a second step, the model is used to classify which communication type that is most appropriate, as exemplified in FIG. 2).
wherein the trained ML model is retrained using user selections of communication channels for next-step communications. (Arngren, [42]; cause reception of one or more contacts from the user; cause identification of a current communication context for the user and the one or more contacts, respectively; cause identification of a recommended communication type for the user and the one or more contacts, based on historical communication data and Arngren, [84]; An example of training, or building, of a recommendation database is described with reference to FIG. 5. CDRs and additional data, such as communication context, device type and content type, are preprocessed into one record per UE communication session. The CDRs often comprises a lot of user related data, but further data such as geographical location and sensor data from the UE 10 may also be added into the record. A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type). Examiner interprets the sessions as projects).
While Arngren/Chakra teaches the use of classification models and recommended channels of communication, Arngren/Chakra does not appear to teach a ranked list. However Arngren/Chakra in view of the analogous art of Shevchenko (i.e. communication preferences) does teach: generate a ranked list of recommended communication channels using a classification model, (Shevchenko, [col. 58-59, lines 65-10]; The AIA may identify other communication context, such as a communication channel, platform, or medium (e.g., web, mobile, specific communication clients and collaborative editors, chat, text/voice, and the like), a communication type, form, genre (e.g., email thread, short/instant message, shared document, social media post/comment, voice message, and the like), prior communications between the user 404 and user 412, location and time of day/week for the user(s), and the like. The AIA may identify that the channel, time or the form of the communication are not optimal given the receiver and the use case/goals and suggest alternatives, such as based on the preferences explicitly provided by the user 412 or derived from receiver's previous communication patterns observed and learned by the AIA and Shevchenko, [col. 64, lines 11-26]; representations of the previous content and context are fed into a natural language generation model that is trained on large-scale communication data that may be mixed with users' prior communication and/or effective communications of other people similar to the user (e.g., with respect to industry group, and the like), and/or effective examples of communication in the same or similar scenario (e.g., with respect to communication type, use case, and the like) such as giving a higher weight to the mixed-in data. The outputs of the model are ranked based on the receiver's communication profile and other contextual information. Output candidates can be ranked by the probability of them triggering desired reaction or outcome, or based on their similarity to the user's communication style).
provide the ranked list of recommended communication channels to the user; and receive a selection of a recommended communication channel for the next- step communication from the ranked list of recommended communication channels, (Shevchenko, [col. 64, lines 11-26]; representations of the previous content and context are fed into a natural language generation model that is trained on large-scale communication data that may be mixed with users' prior communication and/or effective communications of other people similar to the user (e.g., with respect to industry group, and the like), and/or effective examples of communication in the same or similar scenario (e.g., with respect to communication type, use case, and the like) such as giving a higher weight to the mixed-in data. The outputs of the model are ranked based on the receiver's communication profile and other contextual information. Output candidates can be ranked by the probability of them triggering desired reaction or outcome, or based on their similarity to the user's communication style. The highest-ranking outputs are suggested for the user to confirm/select. The user may confirm/select a completion to use as-is or modify).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren/Chakra including the use of classification models and recommended channels of communication with the teachings of Shevchenko including ranked lists of channels and selections in order to correlate user preferences to predict outcomes (Shevchenko, [col. 32, lines 45-55]; The electronic reaction data may be used to generate at least one of an updated first communication profile or an updated second communication profile. At least one of the updated first communication profile or the updated second communication profile may be used to predict a most likely reaction outcome in a second electronic communication. The electronic reaction data may be used to train a machine learning model that is configured to at least one of generate a communication content or modify a communication content).
Claim(s) 10, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arngren et al. (US 20180189407 A1) in view of Chakraborty (US 20240179538 A1) (herein after referred to as “Chakra”), and Rogynskyy et al. (US 20190361910 A1).
Regarding Claim(s) 10, 17, and 20, Arngren/Chakra teaches: The method of claim 1, further comprising: wherein the generative AI model is trained using historical communication data and historical communication data associated with past projects; (Arngren, [83-84]; A decision model in the form of a decision tree may be generated manually by experts via for example Prolog or automatically generated from training data. The training data may be generated from for example Calling Data Records (CDRs) or deep packet inspections… An example of training, or building, of a recommendation database is described with reference to FIG. 5. CDRs and additional data, such as communication context, device type and content type, are preprocessed into one record per UE communication session. The CDRs often comprises a lot of user related data, but further data such as geographical location and sensor data from the UE 10 may also be added into the record. A training set is made up by a plurality of records, one for each UE communication session. Next, the training set is labeled in the following way. Each communication session is tagged with communication context, device type and content type. Further, a number of decision labels are derived by grouping similar context labels, such as the decision label “location?” to similar context labels such as “home” or “work”. Further labels may be time of day and application).
While Arngren/Chakra teaches training a generative AI model and creating content, neither appear to explicitly teach receiving edits to finetune the model. However, Arngren/Chakra on view of the analogous art of Rogynskyy (i.e. communications analysis) does teach: receiving a user edit of the content from a user associated with the project to create edited content, and finetuning the generative Al model based on the edited content. (Rogynskyy, [221]; once an electronic activity is matched with a record object, a user can accept or reject the linking. Additionally, the user can change or remap the linking between the electronic activity and the record object. An indication of the acceptance, rejection, or remapping can be used to update the machine learning model or reorder the matching strategies as discussed in relation to FIGS. 11 and 12. The updated model can be used in the future linking of electronic activity to nodes and the nodes to record objects by the record object identification engine 315. To train the machine learning models, the system can scan one or more systems of record that include manually matched electronic activity and record objects. The previous manually matched data can be used as a training set for the machine learning models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Arngren/Chakra including training a generative AI model and creating content with the teachings of Rogynskyy including receiving edits and finetuning model with the edits in order to ensure accurate values (Rogynskyy, [251]; electronic activities the node graph generation system 200 can be configured to better utilize the electronic activities to more accurately identify nodes and record objects to which the electronic activity should be linked).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/Examiner, Art Unit 3624
1 Generative artificial intelligence - Generative artificial intelligence is a subset of AI that utilizes machine learning models to create new, original content, such as images, text, or music, based on patterns and structures learned from existing data. <https://teaching.cornell.edu/generative-artificial-intelligence>