Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application claims priority to U.S. Provisional Application No. 63/313,512 filed on 2/24/2022.
Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, U.S. Provisional application No. 63/313,512 (hereinafter “the ‘512 provisional application”) fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Independent claims 1, 14 and 15 each recite, inter alia, “encoding, by a data processing apparatus, program instructions on an artificially-generated propagated signal for transmission to a suitable receiver apparatus for an operation of the computer-implemented method comprising: generating, by the data processing apparatus, a training dataset that includes multiple training samples, each training sample mapping at least one extracted feature of a particular communication to a metric indicative of progress of a sales process, wherein the training dataset further includes vectors corresponding to each step of the sales process; and training, by the data processing apparatus, a machine learning model using the training dataset to generate a trained model that accepts as input, features extracted from the run-time communication associated with the sales process, wherein training the machine learning model comprises: performing vector comparisons to identify clusters of steps of the sales process, and identifying commonalities in outcomes of the identified clusters of steps; outputting during the run-time, by data processing apparatus via the trained machine learning model, a suggested follow-up communication to improve progress of the sales process.”
The as-filed specification of the ‘512 provisional application fails to provide adequate support or enablement for at least these elements of claims 1 and 14-15. That is, the as-filed specification of the ‘512 provisional application fails to provide adequate support or enablement for at least the above-noted generating and training steps/operations.
For example, the ‘512 provisional is silent regarding any “generating a training dataset” and then “training a machine learning model using the training dataset” and “outputting” as recited in claims 1 and 14-15.
Thus, the as-filed specification of the ‘512 provisional application fails to provide adequate support or enablement for at least the above-noted elements of claims 1 and 14-15. Based on their respective dependencies from independent claim, the specification of the ‘512 provisional application also fails to provide adequate support or enablement for dependent claims 2-13 and 16-20.
Therefore, the effective filing date for claims 1-20 of the instant application is the filing date of the instant application, 2/17/2023. Examiner will consider if the ‘512 provisional application supports each of the other claims if a rejection would need to rely upon an intervening reference between the actual filing date of the instant application, 2/17/2023, and the 2/24/2022 filing of the ‘512 provisional application.
Claim Rejection - 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 an abstract idea without significantly more.
Step 1: Claims 1-13 are method type claims, corresponding to a process. Claim 14 is directed to a system corresponding to a machine. Claims 15-20 are directed to a non-transitory computer readable medium storing instructions, corresponding to an article of manufacture. Therefore, claims 1-20 are directed to either a process, a machine, or an article of manufacture.
With respect to claim 1:
2A Prong 1:
extracting, from the plurality of communications, one or more features for each communication in the plurality of communications in run-time based on a pre-generated taxonomy (mental process of judgment – a user can manually track and extract one or more features from the plurality of communications in the mind - nothing in the claim prohibits this process from being performed mentally or with pen and paper);
generating a training dataset that includes multiple training samples, each training sample mapping at least one extracted feature of a particular communication to a metric indicative of progress of a sales process, wherein the training dataset further includes vectors corresponding to each step of a sales process (mental process of judgment – a user can generate a training dataset with multiple samples that map a feature of a common to a metric indicating progress of a sale in the mind or with pen and paper);
accepts as input, features extracted from the run-time communication associated with the sales process and outputs a suggested follow-up communication to improve progress of the sales process (mental process of opinion or judgement – a user can manually track and outputs a suggested follow-up communication to improve progress of the sales process in the mind).
performing vector comparisons to identify clusters of steps of the sales process, and identifying commonalities in outcomes of the identified clusters of steps; (mental process of judgment – a user can perform comparison to identify clusters and commonalities in outcome from data in the mind or with pen and paper)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
encoding, by a data processing apparatus, program instructions on an artificially-generated propagated signal for transmission to a suitable receiver apparatus for an operation of the computer-implemented method comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
obtaining a plurality of communications via disparate communication channels of a challenge engine included in the intelligent assistance each communication in the plurality of communications representing one of: a phone conversation transcript, an email, or a chat transcript via disparate communication channels between a business development representative (BDR) and a potential customer (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g));
training a machine learning model using the training dataset (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Obtaining, using an intelligent assistance of the data processing apparatus; and extracting, generating, training by the data processing apparatus and outputting during the run-time by the data processing apparatus via the trained machine learning model Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements at a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
encoding, by a data processing apparatus, program instructions on an artificially-generated propagated signal for transmission to a suitable receiver apparatus for an operation of the computer-implemented method comprising: (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
obtaining a plurality of communications via disparate communication channels of a challenge engine included in the intelligent assistance each communication in the plurality of communications representing one of: a phone conversation transcript, an email, or a chat transcript via disparate communication channels between a business development representative (BDR) and a potential customer (This step is directed to obtaining a plurality of communications, which is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP 2106.05(d)(II)(i));
training a machine learning model using the training dataset (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f)).
Obtaining, using an intelligent assistance of the data processing apparatus; and extracting, generating, training by the data processing apparatus and outputting during the run-time by the data processing apparatus via the trained machine learning model Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
With respect to claim 2:
2A Prong 1:
The claim recites: wherein extracting the one or more features includes detecting one or more keywords conforming to the taxonomy (mental process of judgment – a user can manually track and extract one or more features detecting one or more including detecting one or more keywords conforming to the taxonomy in the mind or with pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 3:
2A Prong 1:
The claim recites: wherein generating the training dataset further comprises computing, based on each communication, a score or a label indicating a quality of the communication (mental process of judgment or evaluation– a user can compute, based on each observed communication, a score or a label indicating a quality of the communication. in the mind or with pen and paper);
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 4:
2A Prong 1:
the claim recites the additional elements of “wherein training the machine learning model further comprises classifying a particular communication as beneficial or detrimental to the progress of the sales process” (mental process of judgment or evaluation– a user can classify a particular communication in the mind or with pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 5:
2A Prong 1:
The claim recites: extracting, from the run-time communication, at least one feature conforming to the taxonomy (mental process of judgment – a user can manually extract at least one feature conforming to the taxonomy, this can be done mentally or by pen and paper);
2A Prong 2: This judicial exception is not integrated into a practical application.
receiving, the run-time communication associated with the sales process (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g));
providing the extracted at least one feature to the trained model (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g));
obtaining, from the trained model, the suggested follow-up communication to improve the progress of the sales process. This step is directed to obtaining a plurality of communications, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g));
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
providing the extracted at least one feature to the trained model and receiving, the run-time communication associated with the sales process (providing the extracted feature data to the model and receiving, the run-time communication associated with the sales process is the well-understood, routine, conventional activity of receiving or transmitting data over a network – see MPEP 2106.05(d)).
obtaining, from the trained model, the suggested follow-up communication to improve the progress of the sales process. (This step is the well-understood, routine, conventional activity of receiving or transmitting data over a network – see MPEP 2106.05(d));
With respect to claim 6:
2A Prong 1: Claim 6 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the suggested follow-up communication is displayed on a user-interface presented to a BDR participating in a sales pitch with a potential customer (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and necessary data outputting - see MPEP 2106.05(g));
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the suggested follow-up communication is displayed on a user-interface presented to a BDR participating in a sales pitch with a potential customer (This step is the well-understood, routine, conventional activity of presenting offer and statistics – see MPEP 2106.05(d)(II) (citing OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93));
With respect to claim 7:
2A Prong 1:
wherein training the machine learning model further comprises contextualizing the training samples based on data from one or more sources (mental process of evaluation/judgment or opinion – a user can manually track and contextualize the training samples based on observed data from one or more sources, this can be done mentally or by pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 8:
2A Prong 1: Claim 8 is directed to a method as depending from claim 7, thus the analysis for patent eligibilities of claim 7 and of base claim 1 are incorporated herein.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the one or more sources comprise a semantic graph database storing information internal to an organization associated with the BDR (This step is directed to receiving information, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions... Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “wherein the one or more sources comprise a semantic graph database storing information internal to an organization associated with the BDR” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, and storing information in memory (i.e., the generically-recited graph database), as discussed in MPEP § 2106.05(d).
With respect to claim 9:
2A Prong 1: Claim 9 is directed to a method as depending from claim 8, thus the analysis for patent eligibilities of intervening claims 7-8 and of base claim 1 are incorporated herein.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the semantic graph database stores information obtained from servers external with respect to the organization associated with the BDR (This step is directed to receiving and storing information, which is understood to be insignificant extra-solution activity and data gathering - see MPEP 2106.05(g)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions... Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of “the semantic graph database stores information obtained from servers external with respect to the organization associated with the BDR” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, and storing information in memory (i.e., the generically-recited graph database), as discussed in MPEP § 2106.05(d).
With respect to claim 10:
2A Prong 1:
wherein mapping the at least one extracted feature comprises generating a vector indicative of the progress of the sales process. (mental process of evaluation/judgment/opinion – a user can manually track and map at least one extracted feature and generate/create a vector indicative of the progress of the sales process, this can be done mentally or by pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 11:
2A Prong 1:
wherein generating the vector comprises encoding the at least one extracted feature as structured data in the vector (mental process of evaluation – a user can manually encode the one or more features based on the observed features and the pre-generated taxonomy, this can be done mentally or by pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 12:
2A Prong 1:
wherein extracting the one or more features includes detecting one or more keywords conforming to the taxonomy (mental process of judgment – a user can manually extract the one or more features and detect keywords conforming to on the taxonomy, this can be done mentally or by pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 13:
2A Prong 1:
wherein extracting the one or more features includes detecting one or more keywords conforming to the taxonomy (mental process of judgment – a user can manually track and extract one or more features based on a pre-generated taxonomy comprises identifying a decision maker associated with the potential customer, this can be done mentally or by pen and paper).
2A Prong 2: The claim does not recite any additional elements.
2B: The claim does not recite any additional elements.
With respect to claim 14:
Claim 14 is a system claim having similar limitations as of method claim 1, therefore claim 14 is rejected under the same rational as of claim 1. The additional limitations of claim 14 are addressed below.
Additional elements:
A system, comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
With respect to claim 15:
Claim 15 is a program product claim having similar limitations as of method claim 1, therefore claim 15 is rejected under the same rational as of claim 1. The additional limitations of claim 15 are addressed below.
Additional elements:
A non-transitory computer readable medium storing instruction that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Claims 16-20 has similar limitations as of rejected method claims 6-10, therefore they are rejected under the same rational as of claims 6-10 as disclosed above.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1, extracting features from run-time communication which is utilized to generate training dataset and used in training the model. However claim also recites the on page 4 of claim 1 that the output of follow-up communication is also generated using the trained model. It is unclear what constitutes training a model with a training dataset and then outputting suggestion/prediction using same dataset after training during run-time. It is unclear how a model can be trained and utilized after training for inference for the same dataset at run-time. For purpose examination the claims are interpreted as training the model using a dataset and the output/prediction is performed with a different dataset after training is complete.
Claim 1 also recites in lines 19-25 that training a machine learning model comprises “performing vector comparison…. and identifying commonalities in outcome….” without any further details. It is unclear how a machine learning model can be training by performing those two steps without providing any output or validation of the output. It is unclear how a training step can constitutes of only performing certain steps that do not provide any result or inference. Moreover the claim model during training do not even disclose to produce any result or output or suggestion and therefore it is unclear how a model once trained as recited in lines 27-29 produces output of suggested follow-up communication.
Independent claims 14 and 15 has similar deficiency as claim 1 above, therefore they are rejected under the same rational.
Claims dependent on claims 1, 14 and 15 also fail to cure the deficiency of the independent claims and are therefore rejected under the same rational.
Claim Rejection - 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pat et al (US 2024/0202096 A1, hereinafter "Pat1") in view of Kewalramani et al. (US 2022/0383125 A1, hereinafter "Kewalramani").
Regarding claim 1
With respect to claim 1, Pat discloses the invention as claimed including a computer-implemented method comprising:
encoding, by a data processing apparatus, program instructions on an artificially-generated propagated signal for transmission to a suitable receiver apparatus for an operation of the computer-implemented method comprising (par. 0012-0015; 0020, the computing device stores the program instruction for execution of the function alone or in combination of remote computers storing program instructions for execution which implies the program instructions are encoded for communication between different devices of the system) :
obtaining, using an intelligent assistance of the data processing apparatus (par 0019-0020), a plurality of communications via disparate communication channels of a challenge engine included in the intelligent assistance (par. 0025), each communication in the plurality of communications representing one of: a phone conversation transcript, an email, or a chat transcript via the disparate communication channel between a business development representative (BDR) and a potential customer (see, e.g., par. 0023; 0038, “The analytics module 250 also may have access to … interaction content (e.g., audio and transcripts of the interactions and … interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department” [i.e., access/obtain communications/ interactions representing audio/phone and chat transcripts between an agent/BDR and customer]);
extracting, by the processing apparatus, from the plurality of communications, one or more features for each communication in the plurality of communications in run-time (see, e.g., ¶¶ 23, 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.” [i.e., extract/access features from each communication]);
generating, by the data processing apparatus, a training dataset that includes multiple training samples, each training sample mapping at least one extracted feature of a particular communication to a metric indicative of progress of a sales process, wherein the training dataset further includes vectors corresponding to each step of a sales process (see, e.g., ¶ 3, “generating, via a training data process, training data samples from respective journey data samples including vectors, each of the journey data samples including a customer journey”, 47, lines 31-32 and lines 47-48, “each training data sample includes a sequence of vector embeddings representing a sequence of customer journey events [i.e., including customer-agent communications] and a journey outcome … machine learning model may identify common features in the training dataset.”, and 52, “to identify common patterns in customer journeys, it is vital to focus on only the important events (milestone events) that lead to achieving an outcome and have predictive value. Such milestone events are events upon which accurate predictions can be made about a customer's next actions, wants, or needs.” [i.e., generate training data including training samples mapping a feature of a communication to a milestone/metric indicating progress of a customer journey/sales process]); and
training, by the data processing apparatus, a machine learning model using the training dataset to generate a trained model that accepts as input, features extracted from the run-time communication associated with the sales process, wherein training the machine learning model comprises:
performing vector comparisons to identify clusters of steps of the sales process (par. 0057; par. 0060), and
identifying commonalities in outcomes of the identified clusters of steps (par. 0057; par. 0060 clustering based on similarity);
outputting during the run-time, by data processing apparatus via the trained machine learning model, a suggested follow-up communication to improve progress of the sales process.(see, e.g., ¶ 47, “The sequence to sequence model may be trained on training data samples”, 49, “outcomes may be modeled to determine a "next best action" for a business to take in relation to a customer that is either to produce a desired result, such as make a sale” [i.e., suggested follow-up communication to improve progress of a sale], and 64, “This additional information can then be used for additional predictive insights, including "next best action" recommendations.” [i.e., training a model to output a next best action/suggested follow-up to improve a sales process/customer journey]).
Pat does not explicitly teach: extracting … one or more features for each communication in the plurality of communications based on a pre-generated taxonomy.
However, Kewalramani teaches:
extracting … one or more features for each communication in the plurality of communications based on a pre-generated taxonomy (see, e.g., ¶ 55-56, “common schema may define a common intermediate representation. For … MAP, CRM … extract all relevant and usable information from the tables of each of these different data sources into a common intermediate representation of each activity … This enables automation of the taxonomy”, “models, which are each associated with a different taxonomy for a different attribute of the activities represented by the activity records, are applied to the activity records.” [i.e., extracting from the activity records/communications features based on a pre-generated taxonomy]).
Pat and Kewalramani are analogous art because they are both are from the same field of endeavor and are both related to using machine learning to make recommendations to business representatives regarding customers (see, e.g., Pat, Abstract and ¶ 3 and Kewalramani, Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosed system of Pat to incorporate the teachings of Kewalramani to provide a “common schema [that] may define a common intermediate representation” in order to “extract all relevant and usable information from the tables of each of these different data sources into a common intermediate representation of each activity”, which “enables automation of the taxonomy” (see, e.g., Kewalramani, ¶ 55). One of ordinary skill in the art would have been motived to combine the system of Pat with the common representation, schema and taxonomy of Kewalramani because “Advantageously, the table(s) of the common intermediate representation have a fixed structure according to the common schema, so that features can be extracted from the same columns during each iteration of subprocess”, as suggested by Kewalramani. (see, e.g., Kewalramani, ¶ 55).
Regarding claim 2
Pat further teaches:
The computer-implemented method of claim 1 wherein extracting the one or more features includes detecting one or more keywords (see, e.g., ¶¶ 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.”, 37, “In accordance with functionality described herein, such features may include prompts for … audio or video conferencing, call analysis, keyword spotting, etc.” [i.e., extract/access features from each communication includes detecting keywords]).
Pat does not explicitly teach: The computer-implemented method of claim 1 wherein extracting the one or more features includes detecting one or more keywords conforming to the taxonomy.
However, Kewalramani teaches:
The computer-implemented method of claim 1 wherein extracting the one or more features includes detecting one or more keywords conforming to the taxonomy (see, e.g., ¶¶ 7-8, “store the predicted action class, the predicted channel class, and the predicted type class in association with the activity record as a taxonomized activity record”, “Extracting the action features, extracting the channel features, and extracting the type features may each comprise: deriving one or more keywords from the activity record; and converting the one or more keywords into a vector” [i.e., extracting features includes detecting keywords from a taxonomized activity record - conforming to the taxonomy]).
The motivation to combine Pat and Kewalramani is the same as discussed above with respect to claim 1.
Regarding claim 3
Pat further teaches:
The computer-implemented method of claim 1 wherein generating the training dataset further comprises computing, based on each communication, a score or a label indicating a quality of the communication (see, e.g., Pat, ¶ 64, “As a final step, after the model is trained, the attention scores may be calculated for certain of the events appearing in selected customer journeys. Such customer journeys may be selected as those cases where the trained model is successful at predicting the outcome.”).
Regarding claim 4
Pat further teaches:
The computer-implemented of claim 1 wherein training the machine learning model further comprises classifying a particular communication as beneficial or detrimental to the progress of the sales process (see, e.g., ¶ 47, “The sequence to sequence model may be trained on training data samples”, 49, “outcomes may be modeled to determine a "next best action" for a business to take in relation to a customer that is either to produce a desired result, such as make a sale” [i.e., suggested follow-up communication to improve progress of a sale], and 64, “This additional information can then be used for additional predictive insights, including "next best action" recommendations.” [i.e., training a model to output a next best action/suggested follow-up to improve a sales process/customer journey]).
Regarding claim 5
Pat further teaches:
The computer-implemented method of claim 1, further comprising: receiving, the run-time communication associated with the sales process (see, e.g., ¶ 22, “Further, the terms “interaction” and “communication” are used interchangeably, and generally refer to any real-time [i.e., run-time communications] and non-real-time interaction that uses any communication channel including, without limitation, telephone calls (PSTN or VoIP calls), emails, voicemails, video, chat, screen-sharing, text messages, social media messages, WebRTC calls, etc.” and 38, “The analytics module 250 also may have access to … interaction content (e.g., audio and transcripts of the interactions and … interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department” [i.e., receiving run-time communications/interactions representing audio/phone and chat transcripts between an agent/BDR and customer associated with the sales process]);
extracting, from the run-time communication, at least one feature (see, e.g., ¶¶ 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.” [i.e., extract/access features from each communication]);
providing the extracted at least one feature to the trained model (see, e.g., ¶ 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.” [i.e., extract/access features from each communication for developing and training algorithms and models], 37, “In accordance with functionality described herein, such features may include prompts for … audio or video conferencing, call analysis, keyword spotting, etc.” [i.e., extract/access features from each communication]);
and obtaining, from the trained model, the suggested follow-up communication to improve the progress of the sales process (see, e.g., ¶ 47, “The sequence to sequence model may be trained on training data samples”, 49, “outcomes may be modeled to determine a "next best action" for a business to take in relation to a customer that is either to produce a desired result, such as make a sale” [i.e., suggested follow-up communication to improve progress of a sale], and 64, “This additional information can then be used for additional predictive insights, including "next best action" recommendations.” [i.e., suggested follow-up communication to improve progress of a sale]).
Pat does not explicitly teach: extracting, from the run-time communication, at least one feature conforming to the taxonomy.
However, Kewalramani teaches:
extracting, from the run-time communication, at least one feature conforming to the taxonomy (see, e.g., ¶¶ 55-56, “common schema may define a common intermediate representation. For … MAP, CRM … extract all relevant and usable information from the tables of each of these different data sources into a common intermediate representation of each activity … This enables automation of the taxonomy”, “models, which are each associated with a different taxonomy for a different attribute of the activities represented by the activity records, are applied to the activity records.” [i.e., extracting from the activity records/communications features conforming to the taxonomy]);
The motivation to combine Pat and Kewalramani is the same as discussed above with respect to claim 1.
Regarding claim 6
Pat further teaches:
The computer-implemented method of claim 1, wherein the suggested follow-up communication is displayed on a user-interface presented to a BDR participating in a sales pitch with a potential customer (see, e.g., ¶ 47, “The sequence to sequence model may be trained on training data samples”, 48, “enhanced models can then be used to provide more accurate visualizations as well as provide effective next best action recommendations”,49, “outcomes may be modeled to determine a "next best action" for a business to take in relation to a customer that is either to produce a desired result, such as make a sale” [i.e., suggested follow-up communication to improve progress of a sale], 64, “This additional information can then be used for additional predictive insights, including "next best action" recommendations.” [i.e., training a model to output a next best action/suggested follow-up to improve a sales process/customer journey] and 74, “In exemplary embodiments, the method may further include the step outputting one or more recommendations regarding actions to take with a future customer interacting with the website of the business … Such recommendations may be performed in real time in response to live interactions and/or executed automatically.”).
Regarding claim 7
Pat further teaches The computer-implemented method of claim 1, wherein training the machine learning model further comprises contextualizing the training samples based on data from one or more sources (see, e.g., ¶ 3, “generating, via a training data process, training data samples from respective journey data samples, each of the journey data samples including a customer journey”, 47, lines 31-32 and lines 47-48, “Such models are designed to remember or “store” information from previous inputs, which allows them to make use of context and dependencies between time steps [i.e., contextualizing]… each training data sample includes a sequence of vector embeddings representing a sequence of customer journey events [i.e., including customer-agent communications] and a journey outcome … machine learning model may identify common features in the training dataset.”, and 52, “to identify common patterns in customer journeys, it is vital to focus on only the important events (milestone events) that lead to achieving an outcome and have predictive value. Such milestone events are events upon which accurate predictions can be made about a customer's next actions, wants, or needs.” [i.e., generate training data including training samples mapping a feature of a communication to a milestone/metric indicating progress of a customer journey/sales process]).
Regarding claim 10
Pat further teaches:
The method of claim 1, wherein mapping the at least one extracted feature comprises generating a vector indicative of the progress of the sales process (see, e.g., Abstract, “The training data process includes generating a vector embedding for each of the events included within the journey data samples that captures the value for each of the event attributes” [i.e., generating a vector mapping], ¶ 3, “generating, via a training data process, training data samples from respective journey data samples, each of the journey data samples including a customer journey”, 47, lines 31-32 and lines 47-48, “each training data sample includes a sequence of vector embeddings representing a sequence of customer journey events [i.e., including customer-agent communications] and a journey outcome … machine learning model may identify common features in the training dataset.”, and 52, “to identify common patterns in customer journeys, it is vital to focus on only the important events (milestone events) that lead to achieving an outcome and have predictive value. Such milestone events are events upon which accurate predictions can be made about a customer's next actions, wants, or needs.” [i.e., generate training data including training samples mapping a feature of a communication to a milestone/metric indicating progress of a customer journey/sales process]).
Regarding claim 11
Pat further teaches:
The method of claim 10, wherein generating the vector comprises encoding the at least one extracted feature as structured data in the vector (see, e.g., ¶ 3, “generating, via a training data process, training data samples from respective journey data samples, each of the journey data samples including a customer journey”, 47, lines 31-32 and lines 47-48, “each training data sample includes a sequence of vector embeddings representing a sequence of customer journey events [i.e., including customer-agent communications] and a journey outcome … machine learning model may identify common features in the training dataset.”, and 52, “to identify common patterns in customer journeys, it is vital to focus on only the important events (milestone events) that lead to achieving an outcome and have predictive value. Such milestone events are events upon which accurate predictions can be made about a customer's next actions, wants, or needs.” [i.e., generate training data including training samples mapping a feature of a communication to a milestone/metric indicating progress of a customer journey/sales process]).
Regarding claim 12
Pat further teaches:
The computer-implemented method of claim 1, wherein extracting the one or more features … comprises extracting at least one of the following information: an industry associated with the sales process, a product, a budget of the potential customer, a job title of the potential customer, a need of the potential customer, and a timeframe (see, e.g., ¶¶ 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.” [i.e., extract/access features from each communication]).
Pat does not explicitly teach: The computer-implemented method of claim 1, wherein extracting the one or more features based on the pre-generated taxonomy comprises extracting at least one of the following information: an industry associated with the sales process, a product, a budget of the potential customer, a job title of the potential customer, a need of the potential customer, and a timeframe.
However, Kewalramani teaches:
The computer-implemented method of claim 1, wherein extracting the one or more features based on the pre-generated taxonomy comprises extracting at least one of the following information: an industry associated with the sales process, a product, a budget of the potential customer, a job title of the potential customer, a need of the potential customer, and a timeframe (see, e.g., Kewalramani, ¶ 51, “A MAP will generally provide a dashboard that enables marketing personnel to plan, coordinate, manage, and measure online and offline marketing campaigns, and to manage leads (i.e., potential customers) generated by the marketing campaigns, with the goal of converting those leads into actual customers (i.e., purchasers of a product offered by the organization)”).
The motivation to combine Pat and Kewalramani is the same as discussed above with respect to claim 1.
Regarding claim 13
Pat further teaches:
The computer-implemented method of claim 1, wherein extracting the one or more… features comprises identifying a decision maker associated with the potential customers (see, e.g., ¶ 38-39, “access to … data related to interactions and interaction content (e.g., audio and transcripts of the interactions and events detected therein), interaction metadata (e.g., customer identifier, agent identifier, medium of interaction, length of interaction, interaction start and end time, department, tagged categories) … analytic module 250 may retrieve such data from the storage device 220 for developing and training algorithms and models.”, “layers of processing are used to extract progressively higher level features from data.” [i.e., extract/access features from each communication]).
Pat does not explicitly teach: The computer-implemented method of claim 1, wherein extracting the one or more features based on a pre-generated taxonomy comprises identifying a decision maker associated with the potential customer.
However, Kewalramani teaches:
The computer-implemented method of claim 1, wherein extracting the one or more features based on a pre-generated taxonomy comprises identifying a decision maker associated with the potential customer (see, e.g., Kewalramani, ¶ 52, “A MAP generally provide a dashboard that enables marketing personnel [i.e., BDR / decision maker] to plan, coordinate, manage, and measure online and offline marketing campaigns, and to manage leads (i.e., potential customers) generated by the marketing campaigns, with the goal of converting those leads into actual customers (i.e., purchasers of a product offered by the organization)”).
The motivation to combine Pat and Kewalramani is the same as discussed above with respect to claim 1.
Regarding claim 14
Claim 14 recites substantially the same limitations as claims 1, except this claim is directed to a “system”. Therefore, this claim is rejected under the same rationale as addressed above.
Pat further discloses: A system, comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations (see, e.g., Pat, ¶ 22, “components, modules, and/or servers … may include one or more processors executing computer program instructions and interacting with other system components for performing the various functionalities described herein. ”, ¶ 27, “the storage device 220 may be configured to include databases and/or store data related to any of the types of information described herein, with those databases and/or data being accessible to the other modules or servers of the contact center 200 in ways that facilitate the functionality described herein.”).
Regarding claim 15
Claim 15 recites substantially the same limitations as claim 1, except this claim is directed to a “non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations”. Therefore, this claim is rejected under the same ground and reasoning as claim 1, discussed above.
Pat further discloses: A non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations (see, e.g., Pat, ¶ 12, “The present invention may be computer implemented using different forms of data processing equipment, for example, digital microprocessors and associated memory, executing appropriate software programs.”, 14, “As shown in the illustrated example, the computing device 100 may include a central processing unit (CPU) or processor 105 and a main memory 110 ... The computing device 100 further may include additional elements, such as a memory port 140, a bridge 145, I/O ports, one or more additional input/output devices 135D, 135E, 135F, and a cache memory 150 in communication with the processor 105.”, 22, “components, modules, and/or servers … may include one or more processors executing computer program instructions and interacting with other system components for performing the various functionalities described herein.”).
Claims 16-17, 20 are program product claims having similar limitations as of method claims 6-7 and 10 as rejected above, therefore they are rejected under the same rational.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Pat in view of Kewalramani and further in view of non-patent literature McHugh (“Taxonomies, Ontologies, Semantic Models & Knowledge Graphs”, 2022; hereinafter "McHugh").
Regarding claim 8
Pat further teaches wherein the one or more sources comprise a ... database storing information internal to an organization associated with the BDR (see, e.g., ¶ 13, “various servers and computer devices thereof may be located on local computing devices 100 (i.e., on-site or at the same physical location as contact center agents), remote computing devices 100 (i.e., off-site or in a cloud computing environment, for example, in a remote data center connected to the contact center via a network), or some combination thereof.”, 27, “it should be understood that, unless otherwise specified, the storage device 220 may be configured to include databases and/or store data related to any of the types of information described herein, with those databases and/or data being accessible to the other modules or servers of the contact center 200 in ways that facilitate the functionality described herein.”).
Although Pat in view of Kewalramani substantially teaches the claimed invention, Pat in view of Kewalramani does not explicitly teach but McHugh teaches:
The method of claim 7, wherein the one or more sources comprise a semantic graph database storing information (see, e.g., McHugh, page 4, “Knowledge graphs are models that instantiate the taxonomy and ontology via a semantic model using the actual data and associated relationships [i.e., a semantic graph] … These relationships contain data and metadata about the relationship between nodes, which is very different from the inferred relationships between columns of data in a relational database.” [i.e., the sources include a semantic graph database storing information]).
Pat, Kewalramani and McHugh are analogous art because they are each from the same field of endeavor and are each related to using machine learning to make recommendations to business representatives regarding customers (see, e.g., Pat, Abstract and ¶ 3, Kewalramani, Abstract, and McHugh, pages 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pat in view of Kewalramani to incorporate the teachings of McHugh to use McHugh’s database and semantic graph (see, e.g., McHugh, pages 4-5). One of ordinary skill in the art would have been motived to combine Pat in view of Kewalramani with the database and semantic graph of McHugh because “we can achieve progressive improvements to the improvement of the data model without creating and injecting new code … incremental improvements to the knowledge graph are critical to implementing Artificial Intelligence (AI) because this mimics how the human brain can reassess a concept or situation based on new data and derive a course correction”, as suggested by McHugh (see, e.g., McHugh, pages 4-5).
Regarding claim 9
Pat further teaches wherein the … database stores information obtained from servers external with respect to the organization associated with the BDR (see, e.g., ¶ 13, “various servers and computer devices thereof may be located on local computing devices 100 (i.e., on-site or at the same physical location as contact center agents), remote computing devices 100 (i.e., off-site or in a cloud computing environment, for example, in a remote data center connected to the contact center via a network), or some combination thereof.”, 27, “it should be understood that, unless otherwise specified, the storage device 220 may be configured to include databases and/or store data related to any of the types of information described herein, with those databases and/or data being accessible to the other modules or servers of the contact center 200 in ways that facilitate the functionality described herein.”).
Although Pat in view of Kewalramani substantially teaches the claimed invention, Pat in view of Kewalramani does not explicitly teach but McHugh teaches:
the semantic graph database (see, e.g., McHugh, page 4, “Knowledge graphs are models that instantiate the taxonomy and ontology via a semantic model using the actual data and associated relationships … These relationships contain data and metadata about the relationship between nodes, which is very different from the inferred relationships between columns of data in a relational database.”).
The motivation to combine Pat, Kewalramani and McHugh is the same as discussed above with respect to claim 8.
Claims 18-19 are program product claim having similar limitations as of method claim 8-9 above, therefore they are rejected under the same rational as of claims 8-9 above.
Response to Arguments
Applicant's arguments filed 3/30/2026 has been fully considered but they are not persuasive.
Argument:
a) In remarks page 11, applicant argues provisional application page 11 discloses support for the claimed limitations as the figure discloses extracting feature from communication from different channel and classifying extracted samples that depicts mapping the extracted samples and training the machine learning model.
b) Regarding 101 in pages 11-15 applicant argues the claim invention is not an abstract idea rather a technical solution to a technical problem of training a machine learning model for providing efficient suggestion. The invention helps in reducing resource usage and improve sales efficiency which is achieved by performing a series of technical steps as claimed including training a machine learning model by performing vector analysis to identify cluster and finding commonalities in outcome of cluster. Therefore applicant submits the output of follow-up communication during run-time by the trained machine learning model involves computational model and real world data which cannot be performed mentally.
c) Regarding 101 in pages 15-18 discloses the claimed invention as amended clearly improves technology or technical field as the subject matter addresses technical challenge of analysis conversation during sales process to suggest follow-up communication performing claimed limitations as recited. Applicant further argues similar to DDR holding application invention integrates the claimed invention into practical application as the program instructions are encoded in electronic signal for transmission using disparate communication channels included in the virtual assistance construes the claim into practical application.
d) Regarding 101 in pages 18-20 applicant argues that the claimed limitation of training a model with feature extraction as claimed similar to DDR holding overrides the routine and conventional events and provides likelihood of successful deal between a customer and sales representative is significant more than mere allegation of judicial exception.
e) Regarding 103 rejection applicant argues the cited reference Pat fails to disclose the amended claim limitation "wherein training the machine learning model comprises: performing vector comparisons to identify clusters of steps of the sales process, and identifying commonalities in outcome of the identified clusters of steps; and outputting during the run-time, by the trained machine learning model, a suggested follow-up communication to improve progress of the sales process.”. Applicant argues that Pat fails to disclose the amended claim limitation as the reference do not disclose suggested follow-up communication during run-time as disclosed also Pat never trains the machine learning model to generate recommendation in real-time to move the sales proves to a successful resolution
Response to argument:
Regarding argument (a) examiner respectfully disagrees with the applicant. The cited page 11 of the provisional application shows basic elements of feature extraction, learning process and classification but fail to disclose the claimed limitations in details as presented in the claims as indicated in the previously issues office action and above. The cited page 11 or any other section of the provisional application fails to disclose the details as highlighted below in the provisional application “encoding, by a data processing apparatus, program instructions on an artificially-generated propagated signal for transmission to a suitable receiver apparatus for an operation of the computer-implemented method comprising:….generating, by the data processing apparatus, a training dataset that includes multiple training samples, each training sample mapping at least one extracted feature of a particular communication to a metric indicative of progress of a sales process, wherein the training dataset further includes vectors corresponding to each step of the sales process; and training, by the data processing apparatus, a machine learning model using the training dataset to generate a trained model that accepts as input, features extracted from the run-time communication associated with the sales process, wherein training the machine learning model comprises: performing vector comparisons to identify clusters of steps of the sales process, and identifying commonalities in outcomes of the identified clusters of steps; outputting during the run-time, by data processing apparatus via the trained machine learning model, a suggested follow-up communication to improve progress of the sales process.”. The remarks fails to clearly address examiners raised issue in the previous office action and instead of showing clear support of the previously claimed elements introduced new limitations in the claim which further fails to show support in the provisional application. The presented argument discloses some elements(i.e. extraction of feature, training and classification) of the claimed feature but fails to disclose the details as presented in the claim.
Applicant is requested to clearly show support for each highlighted limitation below in the further response to show clear support for the claimed limitation if applicant believes the application should be entitled to the priority claim of the provisional application.
Regarding argument (b) examiner respectfully disagrees with the applicant. The claimed invention in generated related to providing follow-up suggestion to a customer service agent during a sales process to improve sales based on previous experience or analyzed data. The claim inventive concept as recited in the claim is an abstract idea as disclosed in the issued office action. The usage of machine learning model as recited in the claims is nothing more than a mere implementation of the model as a tool to perform the claimed abstract idea. The claimed training step as recites disclose limitations that are nothing more than groups information or available data together to identify cluster which discloses commonality between available information and can be performed mentally with the aid of pen and paper. The claim does not disclose any specific technical feature of training the model other than clustering information which itself appears to be a mental process as recited in the claims.
Regarding argument (c) examiner respectfully disagrees with the applicant. The claimed problem or solution as argued is not a technical problem and the claimed solution as recited in the claim except utilization of model as a generic tool is not a technical solution either. The claimed problem and solution is essentially providing suggested follow-up communication to a sales representative during a sales process is similar to a manager providing suggestion to a sales agent at a car dealership during an ongoing sales process based on their experience. The claimed invention is nothing more than a mental process and also could be treated as organization of human activity. Additionally applicant’s claimed invention has no similarity to the claimed invention of DDR holding and therefore the argument is not applicable to the claims as presented. Moreover program instructions are encoding in electronic signal for transmission using disparate communication channels included in the virtual assistance is nothing more than utilizing a computer and generic application as a tool to perform the claimed invention.
Regarding argument (d) examiner respectfully disagrees with the applicant. Applicant’s claimed invention has no similarity to the claimed invention of DDR holding and therefore the argument is not applicable to the claims as presented. Additionally the claim as recited do not disclose any claim limitation that is significant more than the abstract idea as the claim recited is essentially providing suggested follow-up communication to a sales representative during a sales process is similar to a manager providing suggestion to a sales agent at a car dealership during an ongoing sales process based on their experience. The claimed invention is nothing more than a mental process and also could be treated as organization of human activity.
Regarding argument (e) examiner respectfully disagrees with the applicant. Pat discloses training a machine learning model for performing run-time recommendation-based customer interaction data available from different channels including time-series of information of customer journey. The trained model is utilized in the business environment during interaction with a customer in an ongoing process to recognize continuation with a customer for understating of customers journey and provide recommendation in a dynamic manner for the next best action that can make a sale. (see, e.g., ¶ 47, “The sequence to sequence model may be trained on training data samples”, 49, “outcomes may be modeled to determine a "next best action" for a business to take in relation to a customer that is either to produce a desired result, such as make a sale” [i.e., suggested follow-up communication to improve progress of a sale], and 64, “This additional information can then be used for additional predictive insights, including "next best action" recommendations.” [i.e., suggested follow-up communication to improve progress of a sale]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH AL KAWSAR whose telephone number is (571)270-3169. The examiner can normally be reached M-F 7:30am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Wiley can be reached at (571) 272-4150. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDULLAH AL KAWSAR/ Supervisory Patent Examiner, Art Unit 2127
Prosecution Insights