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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed May 24, 2023, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
Acknowledgment is made of the information disclosure statements filed March 24, 2026, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
Step 1: The claim does not fall within one of the four statutory categories of invention (process, machine, manufacture, or composition of matter), or,
Step 2: The claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
MPEP 2106.04(a)(2)(I) states: "The mathematical concepts grouping is defined as
mathematical relationships, mathematical formulas or equations, and mathematical
calculations."
MPEP 2106.04(a)(2)(III) states: "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental
processes include observations, evaluations, judgements, and opinions.
Further, the MPEP states: "The courts do not distinguish between mental processes that
are performed entirely in the human mind and mental processes that require a human to use a
physical aid (e.g. pen and paper or a slide run) to perform the claim limitation.
Using the two-step inquiry, it is clear that Claims 1-20 are each directed to non-statutory
subject matter as shown below:
Please note the following:
The following groups of claims are expressed in different statutory categories:
Claims 1-8 are directed to a method for operating a customized chatbot using artificial intelligence to understand customer queries, generate recommended courses of action, automate marketing campaigns, and facilitate payment processing.
Claims 9-14 are directed to a non-transitory computer-readable tangible media storing machine-executable instructions which, when executed by a processor of a computing device in a cloud network, cause the processor to carry out a process/operation.
Claims 15-20 are directed to a system/apparatus comprising of a display device, communication circuitry, data storage memory, and processing circuitry configured to carry out a process for operating a customized chatbot using artificial intelligence to understand customer queries, generate recommended courses of action, automate marketing campaigns, and facilitate payment processing.
With respect to Claims 1, 9, and 15, which are independent claims with identical claim limitations:
Step 1: Claim 1 is directed to a method, also known as a process, which is one of the four statutory categories of patentable subject matter. Claim 9 is directed to a non-transitory computer-readable tangible media on which machine-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 15 is directed to a system and or apparatus for providing a chatbot response for a recommendation in relation to fulfilling a payment/marketing intent, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter. Claim 15 is directed to a non-transitory machine-readable medium on which machine-executable instructions are stored, corresponding to an article of manufacture, which is one of the four statutory categories of patentable subject matter.
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“identifying a meaning of the message….”; Identifying a meaning of a message is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“generating, ----- , a response recommending an action to fulfill one in the plurality of intents, wherein: ------ uses the information retrieved from the knowledge base to derive the recommended action, and the recommended action is more relevant to the identified meaning of the message than other actions to fulfill any intent in the plurality of intents; Generating a recommendation response to fulfill a plurality of intents that is more relevant to the identified meaning of the message than other actions to fulfill an intent is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“and automatically performing the recommended action.”; Automatically performing a recommended action is an abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into a practical application:
“….using at least one natural language processing algorithm;”; Utilizing a natural language processing algorithm only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“receiving a message via one of a plurality of channels, each channel being communicatively coupled to one or more applications executing in one or more computing devices separate from the…;”; Receiving a message through communication channels coupled with applications that will execute on computing devices is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“retrieving information from a knowledge base coupled to the server, wherein: the information is relevant to the identified meaning of the message, the information further comprises attributes of a plurality of intents, and the plurality of intents comprises at least (i) payment processing and (ii) automated marketing actions;”; Retrieving information from a knowledge base linked/coupled to a server is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g). Examiner’s Note: Underlined portion recites, “coupled to the computing device in the cloud network” as per claim 9. Underlined portion recites, “stored in the memory” as per claim 15. Same U.S.C. 101 rationale applies.
“----- using at least one machine learning model,-----, wherein: the at least one machine learning model uses the information retrieved from the knowledge base to derive the recommended action,-----;”; Utilizing a machine learning model to generate and derive a response recommending an action to fulfill one of the intents only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“displaying the response in a user interface;”; Displaying a response in a user interface only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Receiving a message through communication channels linked to applications that will execute on computing devices and retrieving information from a knowledge base coupled to a server/computing device/memory constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). The usage of communication channels, applications, cloud networks, and computing devices is generally linked to a particular technological environment or field of use (IT/Comm Tech./Cloud) - see MPEP 2106.05(h). Utilizing a machine learning model to generate and derive a response recommending an action to fulfill one of the intents and using a natural language processing algorithm to identify a meaning of a message amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Utilizing a user interface to display a response(s) amount to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of a user interface to display a response is generally linked to a particular technological environment or field of use (GUI/Design) - see MPEP 2106.05(h).
Therefore, Claims 1,9, and 15 are directed to non-statutory subject matter and rejected.
With respect to Claim 2, which is dependent on Claim 1 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“replying to the message; appointment booking; form filling; --------; and troubleshooting.”; The intent to reply to a message, book an appointment, fill out a form, and troubleshoot are abstract ideas of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“data storage;”; The presence of data storage is considered insignificant extra-solution activity (memory storage) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Data storage constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner. - See MPEP 2106.05(d)(II).
Therefore, Claim 2 is directed to non-statutory subject matter and rejected.
With respect to Claim 3, which is dependent on Claim 1 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“the message comprises a transaction between a customer and a business, and the knowledge base comprises past data from other transactions conducted by the business.”; A message depicting a transaction between a customer and a business is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception.
Therefore, Claim 3 is directed to non-statutory subject matter and rejected.
With respect to Claims 4, 10, and 16 which is dependent on Claims 1, 9, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“and processing the payment using the data.”; Processing payment whilst utilizing data is an abstract idea of a commercial or legal interaction (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). -See MPEP § 2106.04(a)(2)(II)(B).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“recommending, using one or more machine learning models, one in a plurality of payment types for processing the payment, wherein the one or more machine learning models uses the knowledge base to derive the recommended one in the plurality of payment types;”; Utilizing a machine learning model to recommend and derive a payment type only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“displaying a payment form in the user interface, the payment form being relevant to the recommended one in the plurality of payment types;”; Displaying a payment form in a user interface only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“receiving data through the payment form;”; Receiving data through a payment form is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a machine learning model to recommend and derive/mine a payment type amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Utilizing a user interface to display a payment form amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of a user interface to display a payment form is generally linked to a particular technological environment or field of use (GUI/Design) - see MPEP 2106.05(h). Receiving data through a payment form constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II).
Therefore, Claims 4, 10, and 16 are directed to non-statutory subject matter and rejected.
With respect to Claims 5, 11, and 17 which are dependent on Claims 1, 9, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating a schedule for the recommended plurality of marketing actions based on the customer data;”; Generating a schedule for the recommended plurality of marketing actions is an abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
“and automatically performing the recommended plurality of marketing actions according to the generated schedule.”; Automatically performing the plurality of marketing actions according to the generated schedule is an abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“recommending, using one or more machine learning models, a plurality of marketing actions, wherein: the plurality of marketing actions comprises at least one of an email campaign and a social media post campaign, the knowledge base includes customer preferences for the plurality of marketing actions, and the one or more machine learning models uses the knowledge base to derive the recommended plurality of marketing actions;”; Utilizing a machine learning model to recommend and derive/mine a plurality of marketing actions only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“retrieving customer data from the knowledge base;”; Retrieving customer data from a knowledge base is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a machine learning model to recommend, derive, and mine a plurality of marketing actions amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Retrieving information from a knowledge base constitutes as storing and retrieving information in memory which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner. - See MPEP 2106.05(d)(II).
Therefore, Claims 5, 11, and 17 are directed to non-statutory subject matter and rejected.
With respect to Claims 6, 12, and 18 which are dependent on Claims 1, 9, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“looking up at least one calendar, the calendar comprising a plurality of available dates and times for an appointment;”; Looking up potential available dates for an appointment on a calendar is an abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). - See MPEP § 2106.04(a)(2)(II)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“recommending, using one or more machine learning models, a date and time in the plurality of available dates and times for the appointment, wherein the one or more machine learning models uses the knowledge base to derive the recommended date and time;”; Utilizing a machine learning model to recommend and derive/mine a possible date and time for the appointment only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“and sending a calendar link for the appointment with the recommended date and time.”; Sending a calendar link for an appointment is considered insignificant extra-solution activity (mere data outputting) - see MPEP 2106.05(g).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a machine learning model to recommend and derive/mine a possible date and time for the appointment amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h). Sending a calendar link for an appointment constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner." - See MPEP 2106.05(d)(II).
Therefore, Claims 6, 12, and 18 are directed to non-statutory subject matter and rejected.
With respect to Claims 7, 13, and 19 which are dependent on Claims 1, 9, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating queries based on fields to be filled in the form;”; Generating queries based on fields to be filled in a form is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“auto-populating the fields in the form with the responses;”; Auto-populating fields in a form with responses is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“displaying the queries in the user interface;”; Displaying a query in a user interface only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“receiving responses to the queries from the user interface;”; Receiving responses to the queries is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
“adding the responses to the knowledge base;”; Adding/transmitting responses to the knowledge base is considered insignificant extra-solution activity (post-solution activity + memory storage) - see MPEP 2106.05(g).Examiner’s Note: This limitation is exclusive to Claim 7.
“and sending the filled form via the one of the plurality of channels.”; Sending the filled form through a plurality of communication channels is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
“storing the responses to the queries in a data store;”; Storing the responses to the knowledge base is considered insignificant extra-solution activity (post-solution activity + memory storage) - see MPEP 2106.05(g).Examiner’s Note: This limitation is exclusive to Claims 13 and 19.
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a user interface to display a query amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of a user interface to display a query is generally linked to a particular technological environment or field of use (GUI/Design) - see MPEP 2106.05(h). Receiving responses to queries and sending the filled form through a plurality of communication channels constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner." - See MPEP 2106.05(d)(II). Storing as well as adding/transmitting responses to the knowledge base/data store after initially receiving the responses from the user interface constitutes as storing and retrieving information in memory as well as a post-solution activity which has been recognized as well‐understood, routine, and conventional when claimed in a generic manner." - See MPEP 2106.05(d)(II).
Therefore, Claims 7, 13, and 19 are directed to non-statutory subject matter and rejected.
With respect to Claims 8, 14, and 20 which are dependent on Claims 1, 9, and 15 respectively:
Step 2A, Prong 1: A judicial exception is recited in the claims as they recite mental processes, which are abstract ideas:
“generating a series of questions related to the one or more errors;”; Generating a series of questions related to error(s) is an abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion) -See MPEP § 2106.04(a)(2)(III).
“generating an instruction based on the recommended action;”; Generating an instruction based on recommended actions is an abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2)(II)(C).
Step 2A, Prong 2: The claims do not recite additional elements that integrate the judicial exception into practical application:
“displaying the series of questions in the user interface;”; Displaying the series of questions in a user interface only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“receiving answers to the series of questions from the user interface;”; Receiving answers to the series of questions is considered insignificant extra-solution activity (mere data gathering) - see MPEP 2106.05(g).
“recommending, using one or more machine learning models, an action to repair the one or more errors, wherein the one or more machine learning models use the knowledge base to derive the recommended action to repair the one or more errors;”; Utilizing a machine learning model to recommend and derive/mine an action to repair the error(s) only amounts to "apply it" and the mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
“and displaying the instruction in the user interface.”; Displaying the instructions in a user interface only amounts to "apply it" and the mere instructions to apply the abstract idea using a generic computer component - see MPEP 2106.05(f)(1) in addition to generally links the use of the abstract idea to a particular technological environment or field of use - See MPEP § 2106.05(h).
Step 2B: The claims do not recite additional elements that amount to significantly more than the judicial exception. Utilizing a user interface to display a series of questions and instructions amounts to "apply it" (or an equivalent) and mere instructions to implement an abstract idea on a computer using a generic computer component or merely uses a computer in its ordinary capacity as a tool to perform an existing process. -See MPEP 2106.05(f)(2). The usage of a user interface to display a series of questions and instructions is generally linked to a particular technological environment or field of use (GUI/Design) - see MPEP 2106.05(h). Receiving answers to the series of questions constitutes as receiving or transmitting data over a network, e.g., using the internet to gather data which has been recognized as well‐understood, routine, and conventional when they are claimed in a generic manner." - See MPEP 2106.05(d)(II). Utilizing a machine learning model to recommend and derive/mine an action to repair the error(s) amounts to "apply it" and mere instructions to implement an abstract idea on a computer. The claim fails to recite details of how a solution or outcome to a problem is accomplished because it is unclear how the "AI system" or "machine learning" is used nor does the specification make it clear how these actions are performed - see MPEP 2106.05(f)(1)). The usage of a machine learning model is generally linked to a particular technological environment or field of use (AI/ML) - see MPEP 2106.05(h).
Therefore, Claims 8, 14, and 20 are directed to non-statutory subject matter and rejected.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 9, and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, hereinafter "Schnitt"). Schnitt was filed on March 11th, 2022 and this date is before the effective filing date of the instant application, i.e., May 24th, 2023. Therefore, Schnitt constitutes prior art under 35 U.S.C. 102(a)(2).
With respect to Claims 1, 9 and 15:
Schnitt teaches:
"receiving a message via one of a plurality of channels, each channel being communicatively coupled to one or more applications executing in one or more computing devices separate from the server;” (Paragraph [0143] details receiving (extracting) a message (documents) through multiple channels (crawling system) with individual channels (crawlers), “In embodiments, the information extraction system 204 may receive documents obtained from the crawlers 220 via the crawling system 202. The information extraction system 204 may extract text and any other relevant data that represents information about a company, an individual, an event, or the like.” Paragraph [0164] further discloses manipulation of received message data within the same structural embodiment, “The machine learning system 212 may receive the messages, the intended objectives of the messages, and outcome data indicating whether the message was successful (e.g., generated a lead, elicited a response, was read by the recipient, and the like). As the directed content system 200 generates and sends messages and obtains outcome data relating to those messages, the machine learning system 212 may reinforce a generative model based on the text of the messages and the outcome data.” Paragraph [0089] discloses that these system components can be coupled to applications, “FIG. 2 provides a detailed functional block diagram of certain components and elements of a content development platform, including elements for extracting key phrases from a primary online content object, a content cluster data store 132 that stores clusters of topics, and a content development and management application 150 having a user interface that allows users to develop content.”) Examiner’s Note: Replace underlined portion of claim with “the computing device in the cloud network” in regards to claim 9 and “the apparatus” in regards to claim 15.
"identifying a meaning of the message using at least one natural language processing algorithm;” (Paragraph [0143] describes identifying a meaning (intent) of the message (document), “Of particular interest to users of the directed content platform 200 disclosed herein, such as marketers and salespeople, are documents that contain information about events that indicate the direction or intent of a company and/or direction or intent of an individual.” Paragraph [0144] discloses determining/analyzing this message meaning with the use of natural language processing, “The information extraction system 204 may utilize natural language processing and/or machine learned classification models (e.g., neural networks) to identify entities, events, and relationships from one or more parsed documents.”)
"retrieving information from a knowledge base coupled to the server, wherein: the information is relevant to the identified meaning of the message, the information further comprises attributes of a plurality of intents, and the plurality of intents comprises at least (i) payment processing and (ii) automated marketing actions;” (Paragraph [0138] details retrieving message related (document) information from a knowledge base, “…for pulling information at scale from one or more information sources. In embodiments, the crawling system 202 may obtain information from external information sources 230 accessible via a communication network 280 (e.g., the Internet), a private network, the proprietary database 208 (such as a content management system, a customer relationship management database, a sales database, a marketing database, a service management database, or the like), or other suitable information sources.” Paragraph [0139] discloses that this information is relevant to the message, “In embodiments, the information extraction system 204 may pull relevant information from each of a variety of data sources…” Paragraph [0084] defines the specific plurality of intents of the system(s)’ including payment processing (electronic commerce) and automated marketing actions (marketing strategy and communications), “a platform is provided having a variety of methods, systems, components, services, interfaces, processes, components, data structures, and other elements (collectively referred to as the "content development platform 100 ” except where context indicates otherwise), which enable automated development, deployment, and management of content, typically for an enterprise, that is adapted to support a variety of enterprise functions, including marketing strategy and communications, website development, search engine optimization, sales force management, electronic commerce, social networking, and others.” Paragraph [0087] further reinforces that the automated marketing intent is considered when recommending a fulfillment action, “a "core topic” 106 or main subject for a promotional or marketing effort, related to one or more topics, phrases, or the like extracted based on the methods and systems described herein from a primary online content object 102…”) Examiner’s Note: Platform 100 is linked to the Customer Relationship Management (CRM) platform in the Multi-Service Business Platform 510 [0104] and [0123], therefore payment processing like automated marketing actions, is amongst the plurality of "intents" that this system is capable of performing because the same Multi-Service Business Platform 510 encompasses a payment system 524.
“generating, using at least one machine learning model, a response recommending an action to fulfill one in the plurality of intents,” (Paragraph [0087] teaches generating a recommendation response using machine learning, “A recommendation or suggestion tool, to be described further below, can recommend or suggest sub-topics, or conversely, it can dissuade or suggest avoidance of sub-topics based on automated logic, which can be enabled by a machine learned process.” Paragraph [0089] further discloses generating a recommendation response action to fulfill an intent (generating suggested topics for content to write), “…a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise.”)
“wherein: the at least one machine learning model uses the information retrieved from the knowledge base to derive the recommended action, and the recommended action is more relevant to the identified meaning of the message than other actions to fulfill any intent in the plurality of intents;” (Paragraph [0088] discloses deriving the recommended action from a knowledge base with the utilization of machine learning, “Associations derived from this processing and analysis are stored and further used in subsequent machine learning based analyses of other sites. Data derived from the analysis and storage of the above pages, content and extracted analytics may be organized in an electronic data store, which is preferably a large aggregated database and which may be organized, for example, using MYSQLTM or a similar format.” Paragraph [0090] reinforces that the derived recommended action is relevant to the identified meaning of the message, “The suggestions may in some aspects provide a content context model for guiding promoters (e.g., marketers) towards a best choice of topical content to prepare and put up on their websites, including suitable and relevant recommendations for work products such as articles and blog posts and social media materials that would promote the promoters' main topics or subjects of interest or sell the products and services of the marketers using the system and method.”)
“displaying the response in a user interface;” (Paragraph [0089] discloses displaying/presenting the recommendation response (suggested topic) in a user interface, “The models 118, which may access a corpus of content extracted by crawling a relevant set of pages on the Internet, are applied to the key phrases 112 to establish the clusters, which arrange topics around a core topic based on semantic similarity. From the content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150.”)
“and automatically performing the recommended action.” (Paragraph [0089] teaches performing the recommended action (suggested topic), “In particular, the platform 100 enables the driving of viewers who are interested in the topics that differentiate the enterprise to the online presence content, such as the main web pages, of the enterprise. Performance of the topics may be tracked, such as in a reporting and analytics system 180, such that performance-based suggestions may be provided by the suggestion generator 134, such as by suggesting more suggested topics 138 that are similar to ones that have driven increases in traffic to the primary online content object 102.”)
Therefore, Claims 1, 9, and 15 are rejected.
With respect to Claim 3:
Schnitt teaches:
"the message comprises a transaction between a customer and a business, and the knowledge base comprises past data from other transactions conducted by the business.” (Paragraph [0104] discloses that the properties of the message (documents/data) represent a transaction between a customer and a business as well as prior data on past transactions, “In embodiments, the customer relationship management system 158 may include one or more customer data records 164, such as reflecting data on groups of customers or individual customers, including demographic data, geographic data, psychographic data, data relating to one or more transactions, data indicating topics of interest to the customers, data relating to conversations between agents of the enterprise and the customers, data indicating past purchases, interest in particular products, brands, or categories, and other customer relationship data.” Paragraph [0104] further discloses other means of messages can be interpreted with the use of this data from a knowledge base, “In embodiments, a conversational agent 182 may be provided within or integrated with the platform 100, such as for automating one or more conversations between the enterprise and a customer. The conversational agent 182 may take suggested topics from the suggestion generator 134 to facilitate initiation of conversations with customers around topics that differentiate the enterprise,”)
Therefore, Claim 3 is rejected.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 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 non-obviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, filed on March 11, 2022, hereinafter "Schnitt"), in view of Hutchinson, (U.S Patent Application Publication No. US 20210383489 A1, filed on August 16, 2019), further in view of Winnick, (U.S Patent Application Publication No. US 20150032669 A1, filed on June 29, 2014, hereinafter “Winnick”).
With respect to Claim 2:
Schnitt teaches:
“replying to the message;” (Paragraph [0199] teaches replying to messages, “…the system 200 may be used to generate a smart reply, such as for an automated agent or bot that supports a chat function, such as a chat function that serves as an agent for sales, marketing or service. For example, if representatives typically send out a link or reference in response to a given type of question from a customer within a chat, the system 200 can learn to surface the link or reference to a service person during a chat, to facilitate more rapidly getting relevant information to the customer. Thus, the system 200 may learn to select from a corpus a relevant document, video, link or the like that has been used in the past to resolve a given question or issue.”)
“form filling;” (Paragraph [0332] teaches form filling, “For example, the customization system 520 may include a form filling service 622 for receiving the custom object information for the custom object. For example, the form filling service 622 may provide a form (e.g., via a GUI) that may include prompts (e.g., spaces in a form)…”)
“data storage; and” (Paragraph [0105] teaches the presence of a data storage, “…the methods and systems may include an automated crawler 104 that crawls the primary online content object 102 and storing a set of results from the crawling in a data storage facility 108. In embodiments, the data storage facility is a cloud-based storage facility, such as a simple storage facility (e.g., an S3™ bucket provided by Amazon™), and/or on a web service platform (e.g., the Amazon Web Services™ (AWS) platform). In embodiments, the data storage facility is a distributed data storage facility.”)
Schnitt does not appear to explicitly disclose:
“appointment booking;”
However, Hutchinson teaches:
“appointment booking;” (Paragraph [0260] teaches appointment booking, “A command pertains to one or more person or electronic ways to interact with, refer to, search for a person or product, book, buy, request, retrieve, and response may be displayed. Another example may be command that pertain to booking an appointment or nightly reservation.”)
The combination of Schnitt and Hutchinson does not appear to explicitly disclose:
“troubleshooting.”
However, Winnick teaches:
“troubleshooting.” (Paragraph [0052] teaches troubleshooting, “The system would next guide the user through a troubleshooting dialog to generate data on the problem and, hopefully, resolve the issue. This troubleshooting process is detailed in the example FIGS. 4-6 which disclose a process the system may lead the user through.”)
Schnitt, Hutchinson, and Winnick are analogous art and in the same field of invention because all three references pertain to automated customer service and technical support systems that leverage machine learning, data analytics, and structured workflows to enhance service efficiency whilst reducing operation friction. While Schnitt teaches developing an improved complex-business management system that caters to the insightful preferences and concerns of the consumer base, Hutchinson teaches enhancing scheduling, booking, pricing, and payment administration-interfaces to resolve time-coordination, planning, and organizational issues. Similarly, Winnick teaches streamlining troubleshooting to reduce costs associated with consumer support efforts. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Schnitt (synchronizing customizability with CRM systems for marketing and sales activities) with the teachings of Hutchinson (user-friendly scheduling and booking engines for generating personalized dynamic insights) and the teachings of Winnick (accurate and knowledge equipped advanced troubleshooting services) in order to reduce the need for human support agents by allowing customers to resolve their own technical or payment issues, while simultaneously gathering data to improve future performances and centralize inventory/availability oversight. One of ordinary skill in the art would be motivated to do so because by integrating Hutchinson and Winnick’s frameworks into the methods of Schnitt one would be able to recognize that a system as such, "allows for reduced customer service staffing. Further, those issues that persist past the automated system can be targeted to representatives most apt to resolve them. Thus, the efficiency is realized via faster and more efficient resolution time by the human operators., {0043 of Winnick}.” It’s worth noting, “The benefits of the engine include increased revenue, marketing incentives, brand recognition, improvements for the social good, and strategic development of branding, {0159 of Hutchinson}.”
Therefore, Claim 2 is rejected.
Claim(s) 4, 5, 10, 11, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, filed on March 11, 2022, hereinafter "Schnitt"), in view of Angeli et. Al, (U.S Patent Application Publication No. US 20220210106 A1, filed on October 22, 2021, hereinafter "Angeli").
With respect to Claims 4, 10, and 16:
Schnitt does not appear to explicitly disclose the extent of Claims 4, 10, and 16.
However, Angeli teaches:
"recommending, using one or more machine learning models, one in a plurality of payment types for processing the payment, wherein the one or more machine learning models uses the knowledge base to derive the recommended one in the plurality of payment types;” (Paragraph [0029] discloses recommending payment processing with the assistance of machine learning, “That is, in some examples, communications sent and/or received can be used as training data to train a model that can be used for determining context of future incoming communications…a response can be associated with a recommendation for responding to the incoming communication…In some examples, the machine learning and/or artificial intelligence can be used to facilitate a payment transaction.” Paragraph [0137] further discloses deriving the recommended payment type from a knowledge base of a plurality of payment types to facilitate payment processing, “Furthermore, in at least one example, the service provider 612 can provide catalog management services to enable the merchant 616 (A) to maintain a catalog, which can be a database storing data associated with items that the merchant 616 (A) has available for acquisition (i.e., catalog management services). In at least one example, the catalog may include a plurality of data items and a data item of the plurality of data items may represent an item that the merchant 6121 (A) has available for acquisition. The service provider 612 can offer recommendations related to pricing of the items, placement of items on the catalog, and multi-party fulfilment of the inventory.”)
“displaying a payment form in the user interface, the payment form being relevant to the recommended one in the plurality of payment types; (Paragraph [0068] discloses a user interface being utilized for specific payment processing, “In some examples, the user interface 200 can be presented via a point-of-sale application (e.g., associated with a payment processing service offered by the service provider) and/or another application associated with a service provided by the service provider.” Paragraph [0149] discloses a payment form being communicated/presented/displayed in a user interface (payment application), “In at least one example, the service provider 612 can communicate with instances of a payment application (or other access point) installed on devices 606 configured for operation by users 614.” Paragraph [0151] further discloses a payment form being presented, “The online form may include one or more fields to receive user interaction and engagement. Examples include name and other identification of the user, shipping address of the user, etc. Some of these fields may be configured to receive payment information, such as a payment proxy, in lieu of other kinds of payment mechanisms, such as credit cards, debit cards, prepaid cards, gift cards, virtual wallets, etc.”)
“receiving data through the payment form;” (Paragraph [0151] discloses receiving data through a payment form, “Some of these fields may be configured to receive payment information, such as a payment proxy, in lieu of other kinds of payment mechanisms, such as credit cards, debit cards, prepaid cards, gift cards, virtual wallets, etc.”)
“and processing the payment using the data.” (Paragraph [0134] teaches processing payment data, “The service provider 612 can offer payment processing services for processing payments on behalf of the merchants 616, as described above. For example, the service provider 612 can provision payment processing software, payment processing hardware and/or payment processing services to merchants 616, as described above, to enable the merchants 616 to receive payments from the customers 620 when conducting POS transactions with the customers 620.”)
Schnitt and Angeli are analogous art and in the same field of invention because both references pertain to automating complex, multi-intent customer interactions across channels and applications to provide personalized responses and actions. While Schnitt teaches developing a multi-faceted business system that facilitates marketing and sales activities, Angeli teaches enabling advanced support and service interactions to improve consumer management and customer relationships. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Schnitt (synchronizing customizability with CRM systems for marketing and sales activities) with the teachings of Angeli (contextual consolidation of communication and recommendation opportunities) in order to reduce human-generated errors, increase data-driven targeting of marketing campaigns, and enhance personalized engagements by accentuating customer experiences. One of ordinary skill in the art would be motivated to do so because by integrating Angeli's framework into the methods of Schnitt one would be able to, "manage and/or respond to communications in a single location without needing to access multiple different communication channels and/or user interfaces, {0026 of Angeli}” and, “provide improvements to virtual assistants (e.g., bots) such that virtual assistants can understand requests/queries and respond to said requests/queries with more accuracy, {0029 of Angeli}.”
Therefore, Claims 4, 10, and 16 are rejected.
With respect to Claims 5, 11, and 17:
Schnitt teaches:
“retrieving customer data from the knowledge base;” (Paragraph [0201] discloses retrieving customer data (account information) from a system with a knowledge graph, “In embodiments, the system 200 may be used to support communications by service professionals. For example, chat functions are increasingly used to provide services, such as to help customers with standard activities, like resetting passwords, retrieving account information, and the like. In embodiments, the system 200 may serve a relevant resource, such as from a knowledge graph, which may be customized for the recipient with content that is relevant to the customer's history (such as from a CRM system) or that relates to events of the customer's organization (such as extracted by the information extraction system).” Paragraph [0218] further discloses systems with similar functionalities and properties deriving their data from a knowledge base, “…and a knowledge base 1624. The platform 1600 may include additional or alternative components without departing from the scope of the disclosure. Furthermore, the platform 1600 may include, be integrated with or into, or communicate with the capabilities of a CRM system and/or other enterprise systems, such as the content development platform 100 and/or directed content system 200 described in the disclosure.”)
“generating a schedule for the recommended plurality of marketing actions based on the customer data;” (Paragraph [0388] discloses that scheduling of marketing actions is made possible through the customization features of the business platform 510 that performs initial intent-based generation of recommendations, “The multi-service business platform 510 may include custom objects that may be configured to support a custom application architecture of a user that may connect with the CRM system 502/CMS 508 of the multi-service business platform 510…business may include custom objects such as schedule objects, class objects, “my calendar” (gigantic web application) that may be built on top of the CMS 508 and the CRM system 502 such that users may build and present CMS-driven apps integrated with the multi-service business platform 510.”)
Schnitt does not appear to explicitly disclose:
"recommending, using one or more machine learning models, a plurality of marketing actions, wherein:”
“the plurality of marketing actions comprises at least one of an email campaign and a social media post campaign,”
“the knowledge base includes customer preferences for the plurality of marketing actions,”
“and the one or more machine learning models uses the knowledge base to derive the recommended plurality of marketing actions;”
“and automatically performing the recommended plurality of marketing actions according to the generated schedule.”
However, Angeli teaches:
"recommending, using one or more machine learning models, a plurality of marketing actions, wherein:” (Paragraph [0052] teaches that communication-based recommendations can be generated, “the communication management component 116 can utilize the context data to generate recommendations and/or perform operations with respect to messaging.” Paragraph [0053] discloses the presences of a plurality of marketing actions requiring a plurality of channels for transmission, “In at least one example, certain types of communications (e.g., marketing communications may not be sent via one communication channel but may be sent via another communication channel. In such an example, the context data associated with a communication-indicating a type of communication can be used to determine how to route the communication. In some examples, the communication management component 116 can utilize merchant and/or customer preference(s) to determine which communication channel(s) and/or platform(s) to send communications.” Paragraph [0054] teaches machine learning being utilized to recommend a marketing action (communication), “In at least one example, the context determination component 118 can utilize a machine-trained model (e.g., a classifier, a neural network, etc.) to determine which communication channel(s) and/or platform(s) to route communications. In some examples, such a model can be trained based at least in part on previous communications associated with users of the service provider.”)
“the plurality of marketing actions comprises at least one of an email campaign and a social media post campaign,” (Paragraph [0054] teaches that a marketing action (communication) can include an email and social media campaign, “The machine-trained model can output one or more classes, wherein each class represents a different communication channel or platform. In some examples, such classes can be representative of different identifiers of an individual user, wherein each identifier corresponds to a different communication channel (e.g., email address/email, phone number/text message, identifier/social media platform, etc.). Classes can be ranked or otherwise arranged such to identify the recommended communication channel and/or platform for routing a communication.”)
“the knowledge base includes customer preferences for the plurality of marketing actions,” (Paragraph [0060] teaches a knowledge base (data store) that contains customer preferences (user/merchant profiles), “In at least one example, the data store(s) 122 can store user profiles 124, which can include merchant profiles, customer profiles, and so on.” Paragraph [0061] further discloses the presence of customer preferences in the knowledge base, “Merchant profiles can store, or otherwise be associated with, data associated with merchants. For instance, a merchant profile can store, or otherwise be associated with, information about a merchant (e.g., name of the merchant geographic location of the merchant, operating hours of the merchant, employee information, merchant preferences (e.g., learned or merchant-specified),”)
“and the one or more machine learning models uses the knowledge base to derive the recommended plurality of marketing actions;” (Paragraph [0054] discloses metadata (generated knowledge base) being invoked to facilitate machine learning to populate a recommendation (communication), “Such communications can be associated with metadata indicating content of such communications, date and/or time of such communications, communication channels and/or platforms associated with such communications, users associated with such communications, and/or the like. Such data can be used to train a model, for example using machine learning mechanisms.”)
“and automatically performing the recommended plurality of marketing actions according to the generated schedule.” (Paragraph [0055] teaches automatically performing the marketing actions (communications), “recommendations related to communication channels and/or platforms for routing communications can be surfaced via the consolidated communication user interface 130. In some examples, the communication management component 116 can utilize such a recommendation to perform an operation without input from the merchant 104 (e.g., automatically).” Paragraph [0056] further teaches that “performing” can be influenced and directly correlated with a schedule based on other relevant identifiers, “In at least one example, the communication management component 116 can aggregate communications and/or other contextual data (e.g., appointments, receipts, feedback received, orders, fulfillment actions, payments, etc.) based at least in part on such communications and/or other contextual data being associated with a same token, or other identifier.”)
Therefore, Claims 5, 11, and 17 are rejected.
Claim(s) 6, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, filed on March 11, 2022, hereinafter "Schnitt"), in view of Hutchinson, (U.S Patent Application Publication No. US 20210383489 A1, filed on August 16, 2019).
With respect to Claims 6, 12, and 18:
Schnitt does not appear to explicitly disclose the extent of Claims 6, 12, and 18.
However, Hutchinson teaches:
“wherein: the recommended action is booking an appointment, and performing the recommended action comprises:” (Paragraph [0219] teaches performing the recommended action which is booking an appointment, “FIG . 5. is an electronic action interface, 500, comprising actions, 501, advertisement, 502, command, 503, display, 504, and features, 505. Actions are presented that allow a person or electronic method to select the next or previous record for a feature. An advertisement is presented as type or visual. A command allows a person or electronic to perform an action such as to book an appointment time.”)
“looking up at least one calendar,” (Paragraph [0263] discloses looking up at least one calendar, “Calendar interfaces may appear within a calendar program, interact with a calendar program, and combinations thereof. Actions may access one or more calendars, retrieve one or more calendars, search one or more calendars for data , synch one or more calendar data, and cause revisions to one or more calendars, and combinations thereof.”)
“the calendar comprising a plurality of available dates and times for an appointment;” (Paragraph [0057] teaches that a component of the calendar system comprises of a potential date/time availability feature, “Availability is a time or resource may be searched, booked, and/or scheduled.” Paragraph [0287] further discloses checking the calendar(s) for open availabilities, “and check calendars for one or more of and not limited to scheduling conflicts, limitations of time, travel time between locations, travel options, costs of travel options, availability of travel, and combinations thereof.”)
“recommending, using one or more machine learning models, a date and time in the plurality of available dates and times for the appointment,” (paragraph [0069] teaches utilizing machine learning to perform the functionalities required to book/schedule an appointment i.e. checking for date/time availabilities, “For example, a scheduling engine may comprise statistical machine learning according to affects that are ascertained through processing text input and customer feedback. The scheduling, booking, payment, affect, and handler engines utilize the statistical associations between activities, affects, and ratings in order to generate booking features, action interfaces, displays, and unique prices according to a person's affect history, interests, and profile.”)
“wherein the one or more machine learning models uses the knowledge base to derive the recommended date and time;” (Paragraph [0096] discloses that a knowledge base (relation database) is utilized to derive recommendations (scheduling actions/commands), “Relations may be utilized as inputs for an engine to at least in part determine an output of one or more actions, commands, displays, electronics, features, interfaces, prices, schedules, and values. Specific types of relations among particulars may be organized in a memory as a database, library, or storage data pertaining to one or more of and not limited to statistical interpretation, forecasting, extrapolation, prediction, interpolation, inferences, and machine learning about data.”)
“and sending a calendar link for the appointment with the recommended date and time.” (Paragraph [0264] teaches displaying a calendar link for a scheduled event, “The interface in this embodiment pertains to an interface from a electronic calendar display. Interfaces may also include displays that link to visuals that comprise hypertext, structured query language script, programming script, or JavaScript method. Visuals may comprise one or more of and is not limited to data and commands pertaining to actions, records, calendars, schedules, reservations, schedulable, bookable, scheduled, booked, payable, paid, advertised data, portions thereof, and combinations thereof.” Paragraph [0277] teaches providing a calendar link for the appointment, “Commands may display a link to activity specifics such as a record or event URL in this case a film icon for a movie), a calendar, links to a schedule of participants…”)
Therefore, Claims 6, 12, and 18 are rejected.
Claim(s) 7, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, filed on March 11, 2022, hereinafter "Schnitt"), in view of Tremblay et. Al, (U.S Patent Application Publication No. US 20220343250 A1, filed on April 21, 2022).
With respect to Claims 7, 13, and 19:
Schnitt does not appear to explicitly disclose the extent of Claims 7, 13, and 19.
However, Tremblay teaches:
“generating queries based on fields to be filled in the form;” (Paragraph 180 discloses generating queries (message template) based on fields to be filled in the form (objective/intent of the message), “As is discussed below, the message data 262 may be received from a user via a client device 260 and/or may be generated for the user by the systems described herein. In embodiments, message data may include a message template that includes content that is to be included in the body of the message… In some embodiments, the system may automatically infer or generate message templates from historical data provided by the user and/or other users of the system 200… In some embodiments, the system 200 may further rely on the objective of a message to generate the template.” Paragraph [0581] also mentions an instance of generating queries, “In example embodiments, the custom action definition may include: an action name (e.g., label given to the action in the workflows application) and action inputs (e.g., fields that may be filled out by a user to control the action's behavior and/or the selected values that may be included in the request that may be sent to the action web address such as “actionUrl”).”)
“displaying the queries in the user interface;” (Paragraph [0194] teaches sending filled out message templates of personalized messages and displaying/presenting the queries (personalized messages) in a user interface (dashboard), “personalized messages 218 sent by the system 200…The results from the analytical engine 290 may be presented to the user. The presentation of the results can be achieved using a variety of methods including, but not limited to, a web-based dashboard, reports or summary emails.” Paragraph [0230] mentions a display and user interface that handles messages/chat-bot related functions, “In embodiments, the client configuration system 1602 presents a graphical user interface (GUI) to a client user via a client device 1640. In embodiments, the GUI may include one or more drop down menus that allows users to select different service features (e.g., chat bots, automated follow up messages, FAQ pages, communication integration, and the like).”)
“receiving responses to the queries from the user interface;” (Paragraph [0194] mentions receiving responses to the queries, “the feedback data 272 corresponding to personalized messages sent by the system 200 and potential responses to the personalized messages 218 may be received by the system and analyzed by an analytical engine 290…” Paragraph [0245] mentions another instance of receiving responses to queries, “The request may be received in a number of different manners. For example, a request may be received from a contact request (e.g., a contact fills out a form from the client's website or a website hosted by the platform 1600 on behalf of the client), a chat bot (e.g., when a contact raises a specific issue in a chat with the chat bot),” Paragraph [0343] further mentions receiving responses to queries via a user interface, “For example, the customization system 520 may include a form filling service 622 for receiving the custom object information for the custom object. For example, the form filling service 622 may provide a form (e.g., via a GUI) that may include prompts (e.g., spaces in a form) for the user to submit or input custom object information…”)
“adding the responses to the knowledge base;” (Paragraph [0309] mentions updating the message templates with content that would be saved/stored in a knowledge base, “The multi-client service platform 1600 may present a graphical user interface that allows a user to upload or enter the message template. In response to receiving the communication template, the multi-client service platform 1600 may store the message template and may associate the template with the workflow item that uses the template.” Paragraph [0315] further mentions the presence of a knowledge base within this platform 1600, “In embodiments, the platform 1600 may include any software libraries and modules needed to support the service features defined by the client in the client-specific service system data structure. The client-specific service system data structure may further include references to the proprietary database(s) 1620, the knowledge graph 1622, and/or knowledge base 1624…”) Examiner’s Note: This specific limitation is exclusive to Claim 7.
“storing the responses to the queries in a data store;” (Paragraph [0186] discloses storing the query responses (contents of a personalized message template) in a data store, “Upon generating a personalized message, the content generation system 216 may then output the personalized message. Outputting a personalized message may include transmitting the personalized message to an account of the intended recipient (e.g., an email account, a phone number, a social media account, and the like), storing the personalized message in a data store,”) Examiner’s Note: This specific limitation is exclusive to Claim(s) 13 and 19.
“auto-populating the fields in the form with the responses;” (Paragraph [0208] discloses auto-populating, “In embodiments, the system 200 may be used in situations where a user, such as a sales, marketing, or service person, has a message template, and the system is used to fill in an introductory sentence with data that comes from a node 252 of the knowledge graph 210 that matches one or more attributes of the targeted recipient. This may include taking structured data from the knowledge graph 210 about organizations and populating a sentence with appropriate noun phrases and verb phrases (of appropriate tense). Paragraph [0186] further mentions auto-populating fields in the form, “ In embodiments, the content generation system 216 may obtain the proper contact information of an intended recipient and may populate a “to” field of the message with the proper contact information.”)
“and sending the filled form via the one of the plurality of channels.” (Paragraph [0186] discloses sending/delivering a filled-out message template, via a plurality of channels, “A respective personalized message 218 may be delivered to a recipient indicated in the recipient list using a variety of messaging systems including, but not limited to, email, short message service, instant messaging, telephone, facsimile, social media direct messages, chat clients, and the like.”)
Schnitt and Tremblay are analogous art and in the same field of invention because both references pertain to a consolidated, custom-actionable customer relationship management platform designed to bridge the gap between marketing, sales, and services. While Schnitt teaches a need for real-time dynamic scalability across multiple interfaces, Tremblay teaches centralized, data-driven platforms designed to connect disparate systems and reduce manual labor. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Schnitt (synchronizing customizability with CRM systems for marketing and sales activities) with the teachings of Tremblay (a core architecture designed to link financial transactions with automated business logic) in order to provide event-driven architecture, secure data handling, API integration, and transactional integrity to automate business processes. One of ordinary skill in the art would be motivated to do so because by integrating Tremblay's framework into the methods of Schnitt one would be able to note, "some advantages may include customization for users with respect to their business industry or field, specific customization towards each user's business itself such that one user in a business industry (e.g., car industry) may have different custom object needs with respect to another user in the same business industry, increased speed of development of various new types of objects by users and by developers of the multi-service business platform, etc., {0329 of Tremblay}.”
Therefore, Claims 7, 13, and 19 are rejected.
Claim(s) 8, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schnitt et. Al, (U.S Patent Application Publication No. US 20220292465 A1, filed on March 11, 2022, hereinafter "Schnitt"), in view of Podgorny et. Al, (U.S Patent No. US 11,093,951 B1, filed on September 25, 2017, hereinafter “Podgorny”), further in view of Winnick, (U.S Patent Application Publication No. US 20150032669 A1, filed on June 29, 2014, hereinafter “Winnick”).
With respect to Claims 8, 14, and 20:
Schnitt does not appear to explicitly disclose the extent of Claims 8, 14, and 20.
However, Podgorny teaches:
“displaying the series of questions in the user interface;” (Paragraph [0146] discloses displaying questions (queries) in a user interface, “user interface 400 includes a feedback interface 406 that displays user experience elements 408 that are associated with one or more topics that a user can select to verify, add, or remove topics that are associated with the user query 402 , the response 404 , or the combination of the user query 402 and the response 404.”)
“receiving answers to the series of questions from the user interface;” (Paragraph [0100] discloses receiving answers and providing feedback to the questions (queries) from the user interface, “verify, add, or remove topics that are associated with the user query 402 , the response 404 , or the combination of the user query 402 and the response 404 . Consequently, in various embodiments, feedback interface 406 and user experience elements 408 are used to provide structured and/or unstructured feedback from one or more content generating users to self-help relationship model update sub-system 126.”)
“recommending, using one or more machine learning models, an action to repair the one or more errors, wherein the one or more machine learning models use the knowledge base to derive the recommended action to repair the one or more errors;” (Paragraph [0018] teaches utilizing machine learning/artificial intelligence to generate an action to repair errors (self-help content) whilst collaborating with a knowledge base (data management system) in order to do so, “In one embodiment, Artificial Intelligence (AI) and/or machine learning processes is/are used to identify self-help content that is responsive to a user query by analyzing and searching the two or more data management systems' customer self-help systems using one or more supervised, and/or unsupervised, and/or semi supervised, machine learning methods on the training set data to generate an initial self-help relationship model predicting the relationship between customer self-help system content of the two or more data management systems' customer self-help systems.”)
“generating an instruction based on the recommended action;” (Paragraph [0095] discloses providing a generated instruction based on the recommended action (self-help content), “In accordance with one embodiment, operation 142 proceeds to operation 144, where, according to one embodiment, self-help content search sub-system 124 provides to the user the self-help content that is responsive to the user query from one or more customer self-help systems that have self-help content that is relevant to the user query.” Paragraph [0096] further discloses instances of instructions being generated and fed into the operations, “In accordance with one embodiment, operation 144 proceeds to operation 146 , and to FIG. 1B, where, according to one embodiment, self-help relationship model update sub-system 126 provides the self-help content that is responsive to the user query to one or more content generating users. In various embodiments, the content generating users, also referred to herein as “trusted users,” or “content generating/trusted users,” are users who have been vetted by the data management systems provider as knowledgeable or whose feedback is otherwise likely to be trustworthy.”)
“and displaying the instruction in the user interface.” (Paragraph [0121] teaches displaying/providing the instructions (self-help content data) in the user interface, “In accordance with one embodiment, self-help content search sub-system 124 includes a user interface 216 , through which self-help content search sub-system 124 receives user query data 218 representing a user query, and by which self-help content search sub-system 124 provides self-help content data 220 representing self-help content, to a user in response to receipt of user query data 218 .”)
The combination of Schnitt and Podgorny does not appear to explicitly disclose:
“generating a series of questions related to the one or more errors;” (Paragraph [0144] mentions generating questions, “user interface 400 includes a user query 402 that is an example of a statement or question that might be submitted by a user to a customer self-help system to retrieve a response from the customer self-help system.”)
However, Winnick teaches:
“generating a series of questions related to the one or more errors;” (Paragraph [0038] mentions generating questions specifically related to resolving errors that require troubleshooting, “In addition the data banks 210 may include user account information, troubleshooting logs, equipment model information, and diagnostic information. All of this data may be employed by a system diagnostics engine 204 to generate a series of questions that will guide the user to a resolution regarding the problem.”)
Schnitt, Podgorny, and Winnick are analogous art and in the same field of invention because all three references pertain to automated customer service and technical support systems that leverage machine learning, data analytics, and structured workflows to enhance service efficiency. While Schnitt teaches a need for improving the functioning of computer systems, information networks, and data stores in relation to feedback based payment processing, Podgorny teaches refining a clientele self-help system to integrate artificial intelligence in order to produce relevant and personalized assistance. Similarly, Winnick teaches streamlining troubleshooting to reduce costs associated with consumer support. It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to implement the base reference of Schnitt (synchronizing customizability with CRM systems for marketing and sales activities) with the teachings of Podgorny (generating relevant self-help content for business-management systems) and the teachings of Winnick (accurate and knowledge equipped advanced troubleshooting services) in order to reduce the need for human support agents by allowing customers to resolve their own technical or payment issues, while simultaneously gathering data to improve future performances. One of ordinary skill in the art would be motivated to do so because by integrating Podgorny and Winnick’s frameworks into the methods of Schnitt one would be able to recognize that a system as such, "allows for reduced customer service staffing. Further, those issues that persist past the automated system can be targeted to representatives most apt to resolve them. Thus, the efficiency is realized via faster and more efficient resolution time by the human operators., {0043 of Winnick}.” It also, “helps build and maintain trust and loyalty in the parent data management systems. This, in turn, results in repeat customers, efficient delivery of services, and reduced abandonment of use of the parent data management system; thereby making more efficient use of both human and non-human resources., {0030 of Podgorny}”
Therefore, Claims 8, 14, and 20 are rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOR F CHEEMA whose telephone number is (571)272-9642. The examiner can normally be reached Monday-Friday 7:30am-5:00pm alternative Fridays off.
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/N.F.C./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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