Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections – 35 USC §112
2. The following is a quotation of 35 U.S.C. §112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. §112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claims 1-17 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation, “identify, using a selected one of the plurality of intent identification models, an intent of an individual…” There is insufficient antecedent basis for this limitation in the claim. Specifically, claim 1 does not properly introduce the term “the plurality of intent identification models” before it is referred to in this limitation. For the purpose of examination, this limitation has been interpreted as stating, “identify, using a selected one of a plurality of intent identification models, an intent of an individual…”
Since claims 15 and 17 have the substantially same issue as claim 1, claims 15 and 17 are rejected for the grounds and rationale used to reject claim 1. Since claims 2-14 and 16 include the respective limitations of claims 1, 15, or 17, these claims are rejected for the grounds and rationale used to reject claims 1, 15, and 17. Appropriate correction or clarification of these claims is required. No new matter may be added.
Double Patenting
4. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
5. Claims 1-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-16, 18, and 19 of U.S. Patent No. 12340346. Although the claims at issue are not identical, they are not patentably distinct from each other. A mapping between the limitations of these claims is provided below.
Instant Application
Issued Patent
1. A computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
identify, using a selected one of the plurality of intent identification models, an intent of an individual;
select, based on the intent of the individual, one or more engagement output generation models;
generate, using the selected one or more engagement output generation models, a customer engagement output;
identify, using one or more communication channel models, a communication channel, wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output; and
send one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format;
continuously train the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual,
wherein training of the one or more intent orchestration models comprises training one or more supervised learning models to automatically assemble a labelled dataset of historical data by initially inputting a manually labelled dataset into one or more intent orchestration models comprising the plurality of intent identification models and automatically generating the labelled dataset as a function of the manually labelled dataset, such that the one or more intent orchestration models compare the labelled dataset to a real-time dataset comprising the post-historical data to identify the intent of the individual.
2. The computing platform of claim 1, wherein the computing platform is synced with a plurality of sources and receives the historical data in real time as it is received by the plurality of sources.
3. The computing platform of claim 1, wherein the historical data comprises one or more of: prior call data, prior interaction data, clickstream data, claims data, preferences, and voice transcriptions.
4. The computing platform of claim 1, wherein the one or more supervised learning models comprises one or more of: support vector machines models, linear regression models, logistic regression models, naive Bayes models, linear discriminant analysis models, decision trees models, k-nearest neighbor models, neural networks models, and similarity learning models.
5. The computing platform of claim 1, wherein the computing platform is further cause to: receive real-time data corresponding to the individual indicating that the customer engagement output should be generated.
6. The computing platform of claim 5, wherein the real-time data comprises information indicating that the individual was in an accident.
7. The computing platform of claim 1, wherein identifying the selected one of the plurality of intent identification models comprises selecting the one of the plurality of intent identification models that further comprises selecting one of: a model to predict consumer reason for contact, a model to predict importance of consumer need, a model to predict idea product offering/features, a model to predict that a consumer is purchasing a car, a model to predict whether a crash has occurred, and a model to determine a consumer cohort.
8. The computing platform of claim 1, wherein identifying the intent comprises identifying one or more of: what interactions have previously taken place with the individual, how immediate a need is to the individual, what is unique about a situation, a reason for contact, offers/features the individual is interested in, or that the individual is purchasing a car.
9. The computing platform of claim 1, wherein identifying the intent comprises one or more of: using voice transcription or clickstream data to identify a reason that the individual contacted an enterprise organization, using demographics or clickstream data to interpret whether the individual is actively browsing options or identify frequently asked questions, using demographics, clickstream data, life events, or social event to determine product offerings, using geospatial triggers, timestamps, or life events to interpret location data, or using telematics data, timestamps, or geospatial triggers to interpret driving data and determine whether a crash occurred.
10. The computing platform of claim 1, wherein selecting the one or more engagement output generation models comprises selecting a model to determine a best method of resolution, a model to determine whether an automated solution or human interaction is appropriate, a model to determine a change in consumer cover needs, a model to determine a type of loss/severity of loss, or a model to determine a best method of contact.
11. The computing platform of claim 1, wherein the customer engagement output comprises one or more of: a quote, an answer, an amount owed, pricing options, a scheduled inspection, or a claim.
12. The computing platform of claim 1, wherein generating the customer engagement output comprises one or more of: identifying a path of resolution based on a reason for customer contact, adding consumer and relevant information to an agent queue based on a determination that the individual is actively browsing options or frequently asked question lists, preparing product recommendations based on clickstream or customer cohort information, preparing a workflow for adding a new car to a policy based on a determination that the individual is visiting dealerships, or preparing a workflow related to filing a claim based on a determination that a crash has occurred.
13. The computing platform of claim 1, wherein identifying the communication channel comprises selecting a communication format most likely to provoke consumer engagement with the customer engagement output.
14. The computing platform of claim 1, wherein the communication channel comprises one of: a chatbot, an email, a text, a toggle option, a push notification, a third party application programming interface, a social media post, an automated process, a manual process, or a user interface, and wherein the customer engagement output is formatted based on the communication channel.
15. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory:
identifying, using a selected one of the plurality of intent identification models, an intent of an individual;
selecting, based on the intent of the individual, one or more engagement output generation models;
generating, using the selected one or more engagement output generation models, a customer engagement output;
identifying, using one or more communication channel models, a communication channel, wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output;
sending one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format; and
continuously training the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual;
wherein training the one or more intent orchestration models further comprises training one or more supervised learning models to automatically assemble a labelled dataset of historical data by initially inputting a manually labelled dataset into one or more intent orchestration models comprising the plurality of intent identification models and automatically generating the labelled dataset as a function of the manually labelled dataset.
16. The method of claim 15, wherein the computing platform is synced with a plurality of sources and receives the historical data in real time as it is received by the plurality of sources.
17. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
identify, using a selected one of the plurality of intent identification models, an intent of an individual;
select, based on the intent of the individual, one or more engagement output generation models;
generate, using the selected one or more engagement output generation models, a customer engagement output;
identify, using one or more communication channel models, a communication channel, and wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output;
send one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format; and
continuously train the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual;
wherein training of the one or more intent orchestration models comprises training one or more supervised learning models to automatically assemble a labelled dataset of historical data by initially inputting a manually labelled dataset into one or more intent orchestration models comprising the plurality of intent identification models and automatically generating the labelled dataset as a function of the manually labelled dataset.
(claim 1) A computing platform, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
(claim 1) identify, using the selected one of the plurality of intent identification models, an intent of the individual;
(claim 1) select, based on the intent of the individual, one or more engagement output generation models;
(claim 1) generate, using the selected one or more engagement output generation models, a customer engagement output;
(claim 1) identify, using one or more communication channel models, a communication channel, wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output; and
(claim 1) send one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
(claim 1) the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
(claim 1) wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format;
(claim 1) continuously train the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual,
(claim 1) wherein training of the one or more intent orchestration models comprises training one or more supervised learning models to automatically assemble a labelled dataset of the historical data by initially inputting a manually labelled dataset into the one or more intent orchestration models and automatically generating the labelled dataset as a function of the manually labelled dataset, such that the one or more intent orchestration models compare the labelled dataset to a real-time dataset comprising the post-historical data to identify the intent of the individual.
(claim 3) wherein the computing platform is synced with the plurality of sources and receives the historical data in real time as it is received by the plurality of sources.
(claim 4) wherein the historical data comprises one or more of: prior call data, prior interaction data, clickstream data, claims data, preferences, and voice transcriptions.
(claim 5) wherein the one or more supervised learning models comprises one or more of: support vector machines models, linear regression models, logistic regression models, naïve Bayes models, linear discriminant analysis models, decision trees models, k-nearest neighbor models, neural networks models, and similarity learning models.
(claim 6) wherein identifying the data corresponding to the individual comprises receiving real-time data corresponding to the individual indicating that the customer engagement output should be generated.
(claim 7) wherein the real-time data comprises information indicating that the individual was in an accident.
(claim 8) wherein selecting the one of the plurality of intent identification models comprises selecting one of: a model to predict consumer reason for contact, a model to predict importance of consumer need, a model to predict idea product offering/features, a model to predict that a consumer is purchasing a car, a model to predict whether a crash has occurred, and a model to determine a consumer cohort.
(claim 9) wherein identifying the intent comprises identifying one or more of: what interactions have previously taken place with the individual, how immediate a need is to the individual, what is unique about a situation, a reason for contact, offers/features the individual is interested in, or that the individual is purchasing a car.
(claim 10) wherein identifying the intent comprises one or more of: using voice transcription or clickstream data to identify a reason that the individual contacted an enterprise organization, using demographics or clickstream data to interpret whether the individual is actively browsing options or identify frequently asked questions, using demographics, clickstream data, life events, or social event to determine product offerings, using geospatial triggers, timestamps, or life events to interpret location data, or using telematics data, timestamps, or geospatial triggers to interpret driving data and determine whether a crash occurred.
(claim 11) wherein selecting, the one or more engagement output generation models comprises selecting a model to determine a best method of resolution, a model to determine whether an automated solution or human interaction is appropriate, a model to determine a change in consumer cover needs, a model to determine a type of loss/severity of loss, or a model to determine a best method of contact.
(claim 12) wherein the customer engagement output comprises one or more of: a quote, an answer, an amount owed, pricing options, a scheduled inspection, or a claim.
(claim 13) wherein generating the customer engagement output comprises one or more of: identifying a path of resolution based on a reason for customer contact, adding consumer and relevant information to an agent queue based on a determination that the individual is actively browsing options or frequently asked question lists, preparing product recommendations based on clickstream or customer cohort information, preparing a workflow for adding a new car to a policy based on a determination that the individual is visiting dealerships, or preparing a workflow related to filing a claim based on a determination that a crash has occurred.
(claim 14) wherein identifying the communication channel comprises selecting a communication format most likely to provoke consumer engagement with the customer engagement output.
(claim 15) wherein the communication channel comprises one of: a chatbot, an email, a text, a toggle option, a push notification, a third party application programming interface, a social media post, an automated process, a manual process, or a user interface, and wherein the customer engagement output is formatted based on the communication channel.
(claim 16) A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory:
(claim 16) identifying, using the selected one of the plurality of intent identification models, an intent of the individual;
(claim 16) selecting, based on the intent of the individual, one or more engagement output generation models;
(claim 16) generating, using the selected one or more engagement output generation models, a customer engagement output;
(claim 16) identifying, using one or more communication channel models, a communication channel, wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output;
(claim 16) sending one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
(claim 16) the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
(claim 16) wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format; and
(claim 16) continuously training the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual;
(claim 16) wherein training the one or more intent orchestration models further comprises training one or more supervised learning models to automatically assemble a labelled dataset of the historical data by initially inputting a manually labelled dataset into the one or more intent orchestration models and automatically generating the labelled dataset as a function of the manually labelled dataset.
(claim 18) wherein the computing platform is synced with the plurality of sources and receives the historical data in real time as it is received by the plurality of sources.
(claim 19) One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
(claim 19) identify, using the selected one of the plurality of intent identification models, an intent of the individual;
(claim 19) select, based on the intent of the individual, one or more engagement output generation models;
(claim 19) generate, using the selected one or more engagement output generation models, a customer engagement output;
(claim 19) identify, using one or more communication channel models, a communication channel, and wherein the one or more communication channel models identify the communication channel by analyzing the intent of the individual to determine the communication channel that will provoke the individual to engage with the customer engagement output;
(claim 19) send one or more commands directing an enterprise user device to format the customer engagement output based on the communication channel to generate a communication channel format for the customer engagement output and display the customer engagement output on a graphical user interface associated with the communication channel,
(claim 19) the customer engagement output generated using the selected one or more engagement output generation models selected based on the intent of the individual identified using the selected one of the plurality of intent identification models,
(claim 19) wherein sending the one or more commands directing the enterprise user device to display the customer engagement output causes the enterprise user device to display the customer engagement output using the communication channel and on the graphical user interface associated with the communication channel in the communication channel format; and
(claim 19) continuously train the one or more intent orchestration models based on post-historical data comprising the identified intent and real-time data corresponding to the individual;
(claim 19) wherein training of the one or more intent orchestration models comprises training one or more supervised learning models to automatically assemble a labelled dataset of the historical data by initially inputting a manually labelled dataset into the one or more intent orchestration models and automatically generating the labelled dataset as a function of the manually labelled dataset.
Therefore, because claims 1, 3-16, 18, and 19 of the issued patent teach each limitation of claims 1-17 of the instant application, claims 1-17 of the instant application are anticipated by claims 1, 3-16, 18, and 19 of the issued patent.
Citation of Pertinent Prior Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Vijayaraghavan (U.S. Pre-Grant Publication No. 20130268468): Vijayaraghavan discloses systems and methods that are most closely related to the systems and methods of the instant application. Vijayaraghavan discloses systems that enable identification of customer characteristics and behavior, and predicts the customer's intent. However, Vijayaraghavan does not explicitly teach several of the specific claimed processes and features of the independent claims. A suitable combination of prior art references could not be identified to reasonably cure the deficiencies of Vijayaraghavan.
Sanghvi (U.S. Pre-Grant Publication No. 20210303317): Sanghvi discloses computing platforms that apply cognitive automation to generating user interfaces.
Kannan (U.S. Pre-Grant Publication No. 20130282430): Kannan discloses systems for improving customer experiences during online commerce by providing unique experiences to customers as a result of anticipating customer needs, simplifying customer engagement based on predicted customer intent, and updating system knowledge about customers with information gathered from new customer interactions.
Gao (U.S. Patent No. 10380609): Gao discloses an automated predictive analytics system for generating sales leads with lead engagement recommendations. The system determines similarities between fitness, engagement, and intent characteristics of a plurality of target clients and fitness, engagement, and intent characteristics of an entity's existing clients.
Teo (U.S. Patent No. 11715111): Teo discloses a machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with a business application. The model may be trained using data regarding prior interactions between a business application and a plurality of users.
Zhang (U.S. Pre-Grant Publication No. 20210374353): Zhang discloses methods for utilizing machine learning models such as artificial neural networks for efficient prediction of intents of natural language expressions, for example, in a chatbot conversation.
Conclusion
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/John H. Holly/Primary Examiner, Art Unit 3696
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