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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 2/20/2025, Applicant responded on 5/20/2025. Amended claims 1, 10, 12, 18. Cancelled claim 11.
Claims 1-6, 8-10, 12-14, and 16-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 10, 12, 18 are sufficient to overcome part of the 112 rejections set forth in the previous action. However, Applicant did not address all of the 112 rejections set forth in the previous action, see below.
Applicant's amendments to claims 1, 10, 12, 18 are not sufficient to overcome the 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 10, 12, 18 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Applicant respectfully submits that Applicant's previous remarks and arguments have been either ignored or not properly considered. For example, Applicant made numerous arguments regarding the impracticality of performing many, if not all of the alleged "mental steps" in the human mind. The Examples provided and analyzed by Applicant include several illustrative claims that show the types of "processes" that the USPTO has found to be impractical to perform in the human mind, and therefore are not considered directed to abstract ideas The Office Action Page 7 of 10 provides no evidence or analysis showing how these steps are, in fact, practical for the human mind to perform. Instead, the Office Action summarily states all of the steps "can include a human using their mind and pen and paper". Once again, the Office Action relies on an alleged "broadest reasonable interpretation" that is overly broad and most importantly, not reasonable…the Office Action maintains that the steps of the recited claims are mathematical concepts. Applicant previously argued, and cited numerous Examples provided by the USPTO, that the claims do not recite mathematical relationships, formulas, or calculations. This argument has not been refuted or otherwise even mentioned by the Office Action. As the PTO guidance states, "While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims." See Example 38; see also Example 41, where an actual mathematical formula is claimed, yet the USPTO found the claims are integrated into a practical application because "the combination of additional elements use the mathematical formulas and calculations in a specific manner that sufficiently limits the use of the mathematical concepts to the practical application…” The Examiner respectfully disagrees.
Examiner respectfully notes, Examiner did not ignore or improperly consider Applicant’s remarks. Examiner presented proper responses as required by MPEP. See below.
Firstly, when analyzing under Step 2A Prong1, the claims are not only directed to “mental processes” and “mathematical concepts”, but also “certain methods of organizing human activities”. Applicant’s “overly board” recitations of “machine learning model” and of highly generic addition elements do not align with any of the USPTO Examples that were found to be eligible. The claims are not being unreasonably interpreted when the Applicant “broadly” recites “machine learning model”, a reasonable interpretation includes mathematical prediction modelling being performed by pen and paper. See MPEP 2106.04(a)(2), 2106.04(d).
Secondly, “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because “[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).”. “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” See MPEP 2106.04(a)(2). Here, “training…model to predict…”, “comparing predictions of customer actions…”, “adjust weights….”, “target values are weighted to favor a minority class of the target values…”, “generating a feature vector….”, “inputting the feature vector…”, “…based on the predicted propensity of the customer contacting the organization…”, are mathematical concepts and steps performed by pen and paper to predict and classify human customer behaviors.
Thirdly, Step 2A Prong1 and Step 2A Prong2 inquiries do not require the Office or the Examiner to present “evidence”, but rather whether abstract ideas are recited in Step 2A Prong1 and if additional elements are recited in Step 2A Prong2. “When performing the analysis at Step 2A Prong One, it is sufficient for the examiner to provide a reasoned rationale that identifies the judicial exception recited in the claim and explains why it is considered a judicial exception (e.g., that the claim limitation(s) falls within one of the abstract idea groupings). Therefore, there is no requirement for the examiner to rely on evidence, such as publications or an affidavit or declaration under 37 CFR 1.104(d)(2), to find that a claim recites a judicial exception. Cf. Affinity Labs of Tex., LLC v. Amazon.com Inc., 838 F.3d 1266, 1271-72, 120 USPQ2d 1210, 1214-15 (Fed. Cir. 2016) (affirming district court decision that identified an abstract idea in the claims without relying on evidence); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-64, 115 USPQ2d 1090, 1092-94 (Fed. Cir. 2015) (same); Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1347, 113 USPQ2d 1354, 1357-58 (Fed. Cir. 2014) (same).” “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” See MPEP 2106.07(a). Examiner has provided the limitations that fall into their respective abstract idea groupings in Step 2A Prong 1 below. The additional elements beyond the abstract ideas are identified in Step 2A Prong2 below.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s remarks are moot in light of new grounds of rejection necessitated by Applicant’s amendments.
Claim Rejections - 35 USC § 112(b)
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.
Claim 1-6, 8-10 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 1 recites “…to predict a propensity of the customer contacting the organization using a traditional contact action…”, “…the predicted propensity of the customer contacting the organization using a traditional contact action…”. It is unclear if these elements refer to the same traditional contact action.
Claims 2-6, 8-10 depend on claim 1, and do not cure the aforementioned deficiencies of claim 1, and thus, claims 2-6, 8-10, is rejected for the reasons set forth above regarding claim 1, as a result.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8-10, 12-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (similarly, 12) recites, “A method comprising:
training, at a …, a … model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … model, wherein training the … model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the … model, wherein the target values are weighted to favor a minority class of the target values;
receiving from …, clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website;
extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information;
generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier;
inputting the feature vector to the … model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and
automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer based on the predicted propensity of the customer contacting the organization using a traditional contact action.”
Claim 18 recites, “A method comprising:
training, at a …, a … classification model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … classification model, wherein training the … classification model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the …classification model, wherein the target values are weighted to favor a minority class of the target values;
receiving from …, clickstream information of a customer visiting a support website of an organization, the clickstream information is indicative of an active web session of the customer on the support website;
extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information;
generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier;
inputting the feature vector to the … classification model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and
responsive to a prediction of the customer having the propensity to contact the organization using the traditional contact action, automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …training, at a …, a … model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … model, wherein training the … model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the … model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer based on the predicted propensity of the customer contacting the organization using a traditional contact action.… training, at a …, a … classification model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … classification model, wherein training the …classification model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the …classification model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a support website of an organization, the clickstream information is indicative of an active web session of the customer on the support website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … classification model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and responsive to a prediction of the customer having the propensity to contact the organization using the traditional contact action, automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to, … training, at a …, a … model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … model, wherein training the … model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the … model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer based on the predicted propensity of the customer contacting the organization using a traditional contact action.… training, at a …, a … classification model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … classification model, wherein training the … classification model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the …classification model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a support website of an organization, the clickstream information is indicative of an active web session of the customer on the support website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … classification model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and responsive to a prediction of the customer having the propensity to contact the organization using the traditional contact action, automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer……; therefore, the claims are directed to a mental process.
Further, the limitations regarding, … training, at a …, a … model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … model, wherein training the … model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the … model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer based on the predicted propensity of the customer contacting the organization using a traditional contact action.… training, at a …, a … classification model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … classification model, wherein training the … classification model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the …classification model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a support website of an organization, the clickstream information is indicative of an active web session of the customer on the support website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … classification model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and responsive to a prediction of the customer having the propensity to contact the organization using the traditional contact action, automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer……, under the broadest reasonable interpretation, is human making observation of human customer visiting and navigating a website visit and to predict if human agents need to offer assistance to the human customer to make a sale, which is commercial interactions and managing personal behavior or relationships or interactions between people, therefore, the claims are directed to organizing human activities.
Further, the limitations regarding, … training, at a …, a … model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … model, wherein training the … model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the … model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a website of an organization, the clickstream information is indicative of an active web session of the customer on the website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer based on the predicted propensity of the customer contacting the organization using a traditional contact action.… training, at a …, a … classification model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust weights in the … classification model, wherein training the … classification model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust weights in the …classification model, wherein the target values are weighted to favor a minority class of the target values; receiving from …, clickstream information of a customer visiting a support website of an organization, the clickstream information is indicative of an active web session of the customer on the support website; extracting, by the …, a customer identifier and a purchased product identifier from the clickstream information; generating a feature vector representing the clickstream information and including the customer identifier and the purchased product identifier; inputting the feature vector to the … classification model to predict a propensity of the customer contacting the organization using a traditional contact action during the active web session on the website; and responsive to a prediction of the customer having the propensity to contact the organization using the traditional contact action, automatically establishing a … assistance session …, to provide proactive customer support relating to the purchased product identifier to the customer……, are mathematical concepts.
Accordingly, the claims are directed to a mental process, organizing human activities, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 12, 18: computing device, a machine learning (ML), a client device, by the computing device, virtual assistance session on the client device, by the computing device, A system comprising: one or more non-transitory machine-readable mediums configured to store instructions; and one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to carry out
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “receiving, by a …, clickstream information…”, “extracting…”, ”generating…”, “inputting...” “providing…”, “prompting…”, “….establishing…” these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “receiving, by a …, clickstream information…”, “extracting…”, ”generating…”, “inputting...”, data output – “providing…”, “prompting…”, “….establishing…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0065] Model training and validation phase 208 can include training and validating the ML classification model (e.g., the gradient boosting classifier) using the modeling dataset. For example, various classification algorithms such as a gradient boosting algorithm, logistic regression, decision tree, random forest, and/or other suitable classification algorithm, may be trained and tested. In one embodiment, the modeling dataset can be separated into two (2) groups: one for training the ML classification model and the other for validating (or “evaluating”) the ML classification model.
[0074] Fig. 4 is a flow diagram of an example process 400 for providing proactive support to customers visiting a support website, in accordance with an embodiment of the present disclosure. Process 400 may be implemented or performed by any suitable hardware, or combination of hardware and software, including without limitation the components of network environment 100 shown and described with respect to Figs. 1A and 1B, the computing device shown and described with respect to Fig. 5, or a combination thereof. For example, in some embodiments, the operations, functions, or actions illustrated in process 400 may be performed, for example, in whole or in part by data collection module 112, WPI module 116, including engines 120 and 122, and virtual assistant interface module 118, or any combination of these including other components of proactive customer support service 110 described with respect to Figs. 1A and 1B.
[0082] Fig. 5 is a block diagram illustrating selective components of an example computing device 500 in which various aspects of the disclosure may be implemented, in accordance with an embodiment of the present disclosure. As shown, computing device 500 includes one or more processors 502, a volatile memory 504 (e.g., random access memory (RAM)), a non-volatile memory 506, a user interface (UI) 508, one or more communications interfaces 510, and a communications bus 512.
[0083] Non-volatile memory 506 may include: one or more hard disk drives (HDDs) or other magnetic or optical storage media; one or more solid state drives (SSDs), such as a flash drive or other solid-state storage media; one or more hybrid magnetic and solid-state drives; and/or one or more virtual storage volumes, such as a cloud storage, or a combination of such physical storage volumes and virtual storage volumes or arrays thereof.
[0084] User interface 508 may include a graphical user interface (GUI) 514 (e.g., a touchscreen, a display, etc.) and one or more input/output (I/O) devices 516 (e.g., a mouse, a keyboard, a microphone, one or more speakers, one or more cameras, one or more biometric scanners, one or more environmental sensors, and one or more accelerometers, etc.).
[0085] Non-volatile memory 506 stores an operating system 518, one or more applications 520, and data 522 such that, for example, computer instructions of operating system 518 and/or applications 520 are executed by processor(s) 502 out of volatile memory 504. In one example, computer instructions of operating system 518 and/or applications 520 are executed by processor(s) 502 out of volatile memory 504 to perform all or part of the processes described herein (e.g., processes illustrated and described in reference to Figs. 1A through 4). In some embodiments, volatile memory 504 may include one or more types of RAM and/or a cache memory that may offer a faster response time than a main memory. Data may be entered using an input device of GUI 514 or received from I/O device(s) 516. Various elements of computing device 500 may communicate via communications bus 512.
[0086] The illustrated computing device 500 is shown merely as an illustrative client device or server and may be implemented by any computing or processing environment with any type of machine or set of machines that may have suitable hardware and/or software capable of operating as described herein.
[0087] Processor(s) 502 may be implemented by one or more programmable processors to execute one or more executable instructions, such as a computer program, to perform the functions of the system. As used herein, the term “processor” describes circuitry that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the circuitry or soft coded by way of instructions held in a memory device and executed by the circuitry. A processor may perform the function, operation, or sequence of operations using digital values and/or using analog signals.
[0088] In some embodiments, the processor can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors (DSPs), graphics processing units (GPUs), microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory.
[0089] Processor 502 may be analog, digital or mixed signal. In some embodiments, processor 502 may be one or more physical processors, or one or more virtual (e.g., remotely located or cloud computing environment) processors. A processor including multiple processor cores and/or multiple processors may provide functionality for parallel, simultaneous execution of instructions or for parallel, simultaneous execution of one instruction on more than one piece of data.
[0090] Communications interfaces 510 may include one or more interfaces to enable computing device 600 to access a computer network such as a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or the Internet through a variety of wired and/or wireless connections, including cellular connections.
[0091] In described embodiments, computing device 500 may execute an application on behalf of a user of a client device. For example, computing device 500 may execute one or more virtual machines managed by a hypervisor. Each virtual machine may provide an execution session within which applications execute on behalf of a user or a client device, such as a hosted desktop session. Computing device 500 may also execute a terminal services session to provide a hosted desktop environment. Computing device 500 may provide access to a remote computing environment including one or more applications, one or more desktop applications, and one or more desktop sessions in which one or more applications may execute.
[0092] In the foregoing detailed description, various features of embodiments are grouped together for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited. Rather, inventive aspects may lie in less than all features of each disclosed embodiment.
[0093] As will be further appreciated in light of this disclosure, with respect to the processes and methods disclosed herein, the functions performed in the processes and methods may be implemented in differing order. Additionally or alternatively, two or more operations may be performed at the same time or otherwise in an overlapping contemporaneous fashion. Furthermore, the outlined actions and operations are only provided as examples, and some of the actions and operations may be optional, combined into fewer actions and operations, or expanded into additional actions and operations without detracting from the essence of the disclosed embodiments.
[0094] Elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above. Other embodiments not specifically described herein are also within the scope of the following claims.
[0095] Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the claimed subject matter. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”
[0096] As used in this application, the words “exemplary” and “illustrative” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “exemplary” and “illustrative” is intended to present concepts in a concrete fashion.
[0097] In the description of the various embodiments, reference is made to the accompanying drawings identified above and which form a part hereof, and in which is shown by way of illustration various embodiments in which aspects of the concepts described herein may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made without departing from the scope of the concepts described herein. It should thus be understood that various aspects of the concepts described herein may be implemented in embodiments other than those specifically described herein. It should also be appreciated that the concepts described herein are capable of being practiced or being carried out in ways which are different than those specifically described herein.
[0098] Terms used in the present disclosure and in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including, but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes, but is not limited to,” etc.).
[0099] Additionally, if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.
[00100] In addition, even if a specific number of an introduced claim recitation is explicitly recited, such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two widgets," without other modifiers, means at least two widgets, or two or more widgets). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” is used, in general such a construction is intended to include A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together, etc.
[00101] All examples and conditional language recited in the present disclosure are intended for pedagogical examples to aid the reader in understanding the present disclosure, and are to be construed as being without limitation to such specifically recited examples and conditions. Although illustrative embodiments of the present disclosure have been described in detail, various changes, substitutions, and alterations could be made hereto without departing from the scope of the present disclosure. Accordingly, it is intended that the scope of the present disclosure be limited not by this detailed description, but rather by the claims appended hereto.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-6, 8-10, 12-14, and 16-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-6, 8-9, 12-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20140222503A1 to Vijayaraghavan et al., (hereinafter referred to as “Vijayaraghavan”) in view of US Patent Publication to US20220108334A1 to Chauhan et al., (hereinafter referred to as “Chauhan”) in view of US Patent Publication to US20130268468A1 to Vijayaraghavan et al., (hereinafter referred to as “Vijayaraghavan2”)
As per Claim 1, Vijayaraghavan teaches: (Currently Amended) A method comprising:
training, at a computing device, a machine learning (ML) model to predict whether a customer will perform a particular action using a modeling dataset comprising a plurality of training samples, each training sample generated from a corpus of historical web journey data of customer web sessions on the website, each training sample of the plurality of training samples to adjust … in the machine learning model, wherein training the ML model includes inputting different portions of the training dataset and comparing predictions of customer actions with target values of the training samples to adjust … in the ML model…; (in at least [0052] FIG. 3 is a flow diagram showing model development and deployment flow according to the invention. In FIG. 3, a data preparation phase (170) takes past clickstream data, converts the data to a category map, and then performs page categorization [0053] A training and test data phase (172) partitions the data into training and test data, e.g. 70% of the data is used as training data and 30% of the data is used as test data. This stage determines intent type based upon business needs, e.g. purchase, non-purchase, or purchase with assistance such as chat, self-serve purchase, browser, etc. Based upon the determined intent, a response variable, i.e. class label, is defined. [0054] A model training phase (174) uses the training data to train the model at each click [0055] A model evaluation phase (176) uses the test data, at each click, to compute precision and recall measures. [0056] A dynamic decision rule determination phase (178) determines the threshold at each click, by trial and error, such that a specified measure of predictive accuracy is achieved. [0057] A model deployment phase (179) deploys the model on the Web server. When a new user starts a website visit, the model is evaluated at each point of the journey until the user abandons the website. Based on the dynamic decision rule, the user is classified into a probable intent class and appropriate action is taken, such as offering chat to the user if the dynamic decision rules indicates so. [0066] FIG. 4 is a diagram of a modified Naïve Bayes model according to the invention. To understand this model, consider the online user's session visit, i.e. click stream data. Once the raw data is pre-processed, the transformed data as obtained in steps (170) and (171) is obtained. [0067] The set of (k+m) variables, referred to herein as predictors, is measured for an online user who is browsing an online commerce site. The set of (k+m) predictors may be denoted by X. Further, assume Xi: i=1, 2 . . . k to be the set of k predictors which are available at the outset of the visit. The predictors may include variables, such as session start time, Internet protocol (IP) used, operating system (OS) used, browser used, and so on. X indicates the fixed length set of variables. [0068] consider Ut=(u1; u2; u3 . . . ut) to be a sequence of uniform resource locators (URLs) that were viewed by a user during the navigational journey up to the click (t=1, 2 . . . ). Due to its dynamic nature, Ut is referred as a variabl