DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 2-10, 12-16, and 18-23 are pending of which claims 2, 12 and 18 are in independent form.
Claims 2-10, 12-16, and 18-23 rejected on the ground of nonstatutory double patenting.
Claims 2-10, 12-16, and 18-23 are rejected under 35 U.S.C. 101.
Claims 2-10, 12-16, and 18-23 are rejected under 35 U.S.C. 103.
Response to Arguments
Applicant’s arguments with respect to claim(s) 2-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding 35 USC 101 (Abstract Idea):
Applicants’ arguments have been fully considered but are not persuasive. Although, the Applicant argues that the claimed modification of the touchpoint sequence constitutes a specific improvement to computer functionality analogous to Enfish, McRO, and Data Engine, the claims do not recite a particular improvement to the operation or functionality of the server, client device, browser, mobile application, or other computer technology. Rather, the claims use these computer components to obtain user-interaction data, evaluate whether a sequence of touchpoints is incomplete or nonrepresentative based on identified patterns, and modify the information presented to the user by adding a touchpoint. The claimed software environment therefore implements the underlaying information collection, evaluation. And modification rather than improving the functionality of the computer or software technology itself. Accordingly, the additional limitation/amendments do not integrate the recited abstract idea into a practical application under Step 2A, Prong Two. The case laws applicant relied on in not persuasive because those cases involved claims reflecting specific improvements to computer functionality or technological processes. The 35 USC 101 reflection is therefore maintained.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 11810004 B2. Although the claims at issue are not identical, they are not patentably distinct from each other.
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 2-10, 12-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) using a feedback loop to optimize user experience.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter.
Independent claims 2 is directed to a method, which is a process.
Independent claims 12 is directed to a system, including a memory and a processors.
Independent claim 18 directed to non-transitory, tangible computer-readable device, which is directed to one of the four statutory subject matters.
Independent All other claims depend on claims 2, 12, and 18. As such, claims 2-21 are directed to a statutory category.
Regarding claims 2, 12 and 18:
With respect to step 2A, prong one (Judicial Exception), the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes and/or certain methods of organizing human activity.
The claim recites the following limitations directed to an abstract idea:
Obtaining interaction data comprising touchpoints of user interaction,
Adjusting user experience by modifying a sequence of touchpoints
Identifying patterns based on user interaction across users,
Determining sequence of touchpoints from a pattern indicating an incomplete user experience,
Modifying the sequences of touch points by adding a touchpoint.
The claim is directed to analyzing user interactions data to identify patterns and modifying a user experience based on that analysis.
The claims fall within:
Mental Process (evaluating user behavior, recognizing patterns, determining whether a sequence is incomplete, deciding how to adjust the experience).
These operations correspond to: pattern recognition performed on generic off the shelf technology.
There are no steps performed that provides a technical improvement to the computing system itself.
Thus, the claims recite an abstract idea (mental process/mathematical concepts/information processing).
With respect to step 2A, Prong Two (Particular Application), the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
The claims recite the use of:
A server,
A client device,
Interaction data,
Touchpoints.
The server merely performs the abstract data processing. The client device is simply a source of data. The interaction data is generic data. The touchpoints are conceptual representation of user interactions. The pattern recognition system performs the abstract steps and does not impose and meaningful technological constraints. The operations are generic mental process/mathematical/information analysis steps used to implement the abstract idea.
The claims do not:
Improve computer functionality
Improve data processing techniques
Provide a new technical architecture
Provide a new way to collect or store data
Solve a technical problem.
There are no improvements to computer functionality or any specific technical solution to a computer centric problem. Instead, the computer and semiconductor environment are used as tools to execute abstract mathematical encoding, data analysis, and decision making, with the result merely being applied in a generic manner.
There is no recitation of, a new data structure that changes computer operation, improved network functioning, an unconventional indexing technique, a specific hardware solution.
Instead, the claims recite conventional and generic computer functions performed in a routine manner, which does not amount to a practical application.
With respect to Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recited components are merely generic computer/database elements performing their routine, well-understood, and conventional functions. See Alive, MPEP 2016.05(d).
The steps mentioned in the independent claims are merely generic generic server, generic client device, routine data collection and processing. Courts have consistently helped such high-level information management operations are conventional.
Considering claims as a whole, the ordered combination of elements also reflects nothing more than the typical workflow of distributed systems, and therefore DOES NOT add “significantly more” than the abstract idea.
Such generic, high‐level, and nominal involvement of a computer or computer‐based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent‐eligible, as noted at pg.74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. Further, See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359‐60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093‐94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to 'implement[ing] the abstract idea of intermediated settlement on a generic computer', it cannot save O/P's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257‐1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claimpatent‐eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) ("the interactive interface limitation is a generic computer element".).
The additional elements are broadly applied to the abstract idea at a high level of generality ("similar to how the recitation of the computer in the claims in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer,") as explained in MPEP § 2106.05(f)) and they operate in a well‐understood, routine, and conventional manner.
MPEP § 2106.0S(d)(II) sets forth the following:
The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
• Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec ... ; TLI Communications LLC v. AV Auto. LLC ... ; OIP Techs., Inc., v. Amazon.com, Inc ... ; buySAFE, Inc. v. Google, Inc ... ;
• Performing repetitive calculations, Flook ... ; Bancorp Services v. Sun Life ... ;
• Electronic recordkeeping, Alice Corp ... ; Ultramercial ... ;
• Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc ... ;
• Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank ... ; and
• A web browser's back and forward button functionality, Internet Patent
• Corp. v. Active Network, Inc. ...
. . . Courts have held computer-implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
The dependent claims have been fully considered as well, however, similar to the findings for claims above, these claims are similarly directed to the “Mental Processes” grouping of abstract ideas set forth in the 2019 PEG, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
Regarding claims 3, 13, and 19 (Types of Interaction Data),
The claim recites:
Specific types of interactions: website interaction, mobile application interaction, POS/payment/financial interaction,…etc.
This merely identifies different categories of input. There are no changes to: how data is collected, how patterns are determined, or how the system operates technically. These fall under: Mental Process.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 4, 14, and 20 (Descriptive Attributes of Touchpoints/Interaction Metadata),
The claim recites:
Additional attributes of interactions: time, duration, types if interaction, wait time, user satisfaction, corrective action ,…etc.
This merely adds descriptive information about interactions. There are no: improved timing systems, improved data structure, improved communication protocol. These fall under: Mental Process.
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 5, 6, 7, 15, 16, and 21 (Pattern Identification, Filtering, and Thresholding),
The claim recites:
Identifying patterns across multiple users and touchpoints,
Applying parameters to filter patterns,
Using threshold such as:
Minimum occurrence
Time-based filtering
Coverage threshold across users.
This merely applies rule based and threshold-based evaluation. There are no changes to: technicality of how the patterns is computed, any algorithmic improvement, system performance and architecture. These fall under: Mental Process (evaluating and selecting pattern), Mathematical Algorithm (threshold, filtering).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claim 8 (Iterative Parameter Adjustment),
The claim recites:
Iteratively modifying filtering parameters until threshold condition is satisfied.
This merely repeats the same evaluation process in a loop. There are no changes to: How parameters are applied, how convergence is achieved, and technical mechanism. These fall under: Mental Process (trial-and-error refinement), Mathematical Algorithm (iterative adjustment).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 9, 10 (Validation and Scoring of Patterns),
The claim recites:
Validating whether the touchpoints are represented in patterns,
Determining accuracy using a performance score.
This merely adds evaluation metrics. There are no changes to: How validation is performed technically, how scores are computed, any improvement to model accuracy mechanism. These fall under: Mental Process (checking completeness and accuracy), Mathematical Algorithm (scoring, evaluation).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Regarding claims 22, and 23 (Modification of User Experience (Feedback Loop)),
The claim recites:
Adjusting the user experience based on the feedback loop.
This merely modified an interaction based on ffedback. There are no changes to: how the system implements feedback, underlying computer functionality. These fall under: Mental Process (deciding how to adjust the interactions).
This does not change the nature of the abstract idea. It does not add a technical improvement to an abstract idea, such as improving computer functionality, data structure, or processing architecture.
There is no practical application, and no inventive step, the claims are still considered abstract.
Claim Rejections - 35 USC § 103
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.
Claim(s) 2-7 and 9, 10, 12-16, 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over Yerradoddi; Chengal et al. (US 20200320534 A1) [Yerradoddi] in view of Yan; Zhenyu et al. (US 20190278378 A1) [Yan].
Regarding claims 2, 12 and 18, Yerradoddi discloses, a method, comprising: obtaining, by a server from a client device, interaction data comprising one or more touchpoints concerning one or more user interactions of a user (event analysis manager receives information related to the transaction and user device ¶ [0045]-[0046], monitoring user interactions of the user ¶ [0054], obtaining interaction data ¶ [0056], user tracking module obtains interaction data from user device and interface ¶ [0040]); and
when the sequence of touchpoints form a pattern that indicates the user experience is incomplete or not representative of a full user experience (detecting events such as disputes or chargeback based on user behavior ¶ [0013], visiting certain pages indicates higher likelihood of an issue ¶ [0017], interaction pattern over time used to determine outcomes ¶ [0020], identifying multiple behavioral pattern across multiple systems ¶ [0057]-[0060]) with respect to a plurality of patterns based on a plurality of user interactions of a plurality of users with a plurality of touchpoints (generating list of computing systems ¶ [0057], monitoring interaction across multiple systems and over time ¶ [0020], tracking interactions across multiple users and interfaces ¶ [0040])
However, Yerradoddi does not explicitly facilitate adjusting, by the server, a user experience provided by a software application executed by the client device, the software application comprising a mobile application or a browser configured to provide a website, by modifying including a sequence of touchpoints presented via the software application based on the interaction data …., wherein modifying the sequence of touchpoints comprises adding a touchpoint to the sequence of touchpoints.
Yan discloses, adjusting, by the server, a user experience provided by a software application executed by the client device (To illustrate, in one or more embodiments, the content management system 102, via the server device 101 and the third-part server device 114, execute a digital content campaign and provide digital content through multiple media channels to the client devices 112a-b. During the digital content campaign, the content management system 102 monitors user interactions at the user client devices 112a-b to determine touchpoints and corresponding conversions …. Moreover, the deep learning attribution system 104 can utilize the server device 101 to generate conversion predictions for potential touchpoints for individual users (e.g., additional digital content to be sent through one or more media channels) using the trained touchpoint attribution attention neural network and select a media channel for a particular user based on the conversion predictions ¶ [0062]-[0063]. Also see ¶ [0072]-[0075], [0162]-[0167]), the software application comprising a mobile application or a browser configured to provide a website (For instance, a touchpoint is created when a user interacts with the entity via an electronic message, a web browser, or an Internet-enabled application. Examples of digital content that are associated with touchpoints include digital advertisements, free software trials, and website visits. Further, example digital media channels include email, social media, organic search, paid search, and, in-app notifications ¶ [0035]; also see ¶ [0038], [0051], [0053], [0069]. … can provide digital content to a client device (e.g., the client devices 112a) through the selected media channel ¶ [0063], [0225]-[0227]), by modifying including a sequence of touchpoints presented via the software application based on the interaction data (a target touchpoint sequence does not result in a conversion. … identify a touchpoint that, if added to the second target touchpoint sequence 214 (e.g., the fourth touchpoint 212d) would have the highest probability of resulting in a conversion… to identify additional touchpoints to add to the second target touchpoint sequence 214 to improve the likelihood of conversion. For example, upon adding a second email touchpoint, the second target touchpoint sequence 214 has a conversion probability of 40%. Further adding an in-app notification touchpoint further increases the conversion probability to 60% ¶ [0072]-[0074]. Also see ¶ [0160]-[0165]),
wherein modifying the sequence of touchpoints comprises adding a touchpoint to the sequence of touchpoints (obtains a target touchpoint sequence 512 corresponding to a target user (e.g., touchpoint interactions by the target user) and potential touchpoints 510. In one or more embodiments, the deep learning attribution system 104 adds a potential touchpoint to the target touchpoint sequence 512 to create a first modified target touchpoint sequence ¶ [0161]; modified target touchpoint sequence by adding a different touchpoint type to the end of the sequence ¶ [0163]; add additional touchpoint types to the modified target touchpoint sequences until the conversion probability threshold is satisfied ¶ [0165]; adding a potential touchpoint to the target touchpoint sequence, the potential touchpoint corresponding to a first media channel. In additional embodiments, the series of acts 900 includes the acts of utilizing the trained touchpoint attribution attention neural network to generate a second conversion probability based on adding a second potential touchpoint to the target touchpoint sequence, the second potential touchpoint corresponding to a second media channel ¶ [0227]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Yan’s system would have allowed Yerradoddi to facilitate adjusting, by the server, a user experience provided by a software application executed by the client device, the software application comprising a mobile application or a browser configured to provide a website, by modifying including a sequence of touchpoints presented via the software application based on the interaction data …., wherein modifying the sequence of touchpoints comprises adding a touchpoint to the sequence of touchpoints. The motivation to combine is apparent in the Yerradoddi’s reference, because there is a need to improve non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify significant touchpoints and/or measure performance of touchpoints in digital content campaigns.
Regarding claims 3, 13 and 19, the combination of Yerradoddi and Yan discloses, wherein the interaction data includes at least one of a website interaction, a mobile application interaction, a transaction card terminal interaction, a payment interaction, a withdrawal interaction, a deposit interaction, a returned payment interaction, a bill payment interaction, a customer service interaction, a virtual assistant interaction, a point of sale interaction, a financial product interaction, a financial product application interaction, and a financial account interaction (Yerradoddi: interaction with merchant website …purchase transaction with a merchant…payment transaction processing ¶ [0012], [0046], webpages visited, links selected … chat sessions with merchant interface ¶ [0056], mobile applications included as monitored systems ¶ [0057], [0054], transaction conducted via merchant system …payment transaction handling ¶ [0045]).
Regarding claims 4, 14 and 20, the combination of Yerradoddi and Yan discloses, wherein a touchpoint of the one or more touchpoints includes information regarding a time that a user interaction occurred, information regarding a specific type of the user interaction, information concerning how the user interaction was initiated, information concerning why the user interaction was initiated, information concerning what was conveyed during the user interaction, information concerning how long the user interaction lasted, information concerning a wait time associated with the user interaction, information concerning whether the user interaction is associated with an existing user interaction issue, or information concerning whether a corrective action was taken to address the user's dissatisfaction with the user interaction (Yerradoddi: monitoring interactions over a period of time ¶ [0020], tracking interactions during a duration ¶ [0054], identifies types of interaction …user clicking links, initiating sessions, navigating pages…chat sessions and page interactions imply conveying information content…duration of webpage access ¶ [0056], visiting FAQ/dispute page indicates intent…behavior indicating transaction issues ¶ [0017], detecting events such as disputes or chargeback based on user behavior ¶ [0013], performing remedial action ¶ [0046], ], continuing to monitor interactions after remedial action ¶ [0070]).
Regarding claims 5, 15 and 21, the combination of Yerradoddi and Yan discloses, identifying, by the server, the plurality of patterns based on the plurality of user interactions of the plurality of users with the plurality of touchpoints (Yerradoddi: analyzing user interactions across multiple computing systems ¶ [0057], monitoring interactions over a period of time ¶ [0020], determining likelihood based on monitored interactions ¶ [0040]);
applying a parameter to filter the plurality of patterns (Yerradoddi: determining likelihood using thresholds ¶ [0046], applying decision threshold to determine actions ¶ [0053]); and
setting the plurality of patterns as the filtered plurality of patterns when an evaluation criterion is satisfied (Yerradoddi: comparing likelihood against threshold ¶ [0046], performing actions when the threshold condition is satisfied ¶ [0053]).
Regarding claims 6 and 16, the combination of Yerradoddi and Yan discloses, wherein the parameter comprises a minimum number of occurrences of an identified pattern or a period of time for filtering the plurality of patterns (Yerradoddi: monitoring interactions over a period of time ¶ [0020], event occurs when likelihood reaches predetermined threshold ¶ [0013], evaluating patterns based on accumulated interaction data ¶ [0046], tracking interactions during a duration ¶ [0054]. Examiner specifies that, the likelihood is derived from aggregated interaction data, which necessarily reflects repeated occurrence of interaction behavior (patterns)).
Regarding claim 9, the combination of Yerradoddi and Yan discloses, wherein the evaluation criterion comprises a validation check of the user experience (Yan: As shown in FIG. 4A, the deep learning attribution system 104 classifies the touchpoint sequence representation 422. For instance, the deep learning attribution system 104 feeds the touchpoint sequence representation 422 to the classification layer 424, which predicts whether the input training touchpoint sequence results in a conversion based on the touchpoint sequence representation 422 (e.g., based on the weighted combination of all touchpoint input states) ¶ [0118], also see ¶ [0122]-[0123], [0127]) to determine whether each touchpoint of the user experience is represented in a pattern of the user (Yan: By employing an LSTM neural network as the RNN/LSTM layer 410, the deep learning attribution system 104 can obtain another layer of touchpoint representation using the dense vectors 408 as input. For instance, for each touchpoint in a training touchpoint sequence, the RNN/LSTM layer 410 incorporates the specific sequence of preceding touchpoints in the training touchpoint sequence. Additionally, the RNN/LSTM layer 410 enables the deep learning attribution system 104 to encode contextual information from the previous touchpoints (e.g., historical touchpoint data) into each touchpoint in the training touchpoint sequence. Indeed, the RNN/LSTM layer 410 models sequential inputs by integrating the time series sequence of previous touchpoints into each touchpoint ¶ [0102]; also see ¶ [0108]-[0113]).
Regarding claim 10, the combination of Yerradoddi and Yan discloses, wherein the evaluation criterion comprises a performance score to determine an accuracy of the plurality of patterns (Yerradoddi: determining likelihood values ¶ [0046], using prediction outputs to drive decisions ¶ [0053]).
Regarding claims 11 and 17, (Canceled).
Regarding claims 22 and 23, the combination of Yerradoddi and Yan discloses, further comprising applying a feedback loop based on an evaluation of the filtered plurality of patterns to modify the parameter or adjust the user experience (Yan: As shown, the deep learning attribution system 104 feeds the training touchpoint paths 434 into the touchpoint attribution attention neural network 400a as part of training ¶ [0095], [0118]; As shown in FIG. 4A, the touchpoint attribution attention neural network 400a includes a loss layer 428 that provides feedback 430 to train various layers of the touchpoint attribution attention neural network 400a ¶ [0122], [0125]-[0126]; also see ¶ [0147], [0148], [0155]).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yerradoddi in view of Yan in view of Joseph; Sony et al. (US 10521856 B1) [Joseph]
Regarding claim 7, the combination of Yerradoddi and Yan discloses, wherein the evaluation criterion comprises a pattern coverage threshold value to indicate whether [a minimum number of users from among the plurality of users] are represented in the filtered plurality of patterns (Yerradoddi: determining likelihood using threshold values ¶ [0046], analyzing interactions across multiple users ¶ [0057]).
However, neither Yerradoddi nor Yan explicitly facilitate a minimum number of users from among the plurality of users.
Joseph discloses a minimum number of users from among the plurality of users (In general, when disambiguating the categorization of a financial transaction, the spending-pattern score of a user u in a user-segment cohort to a predefined category c may be calculated as Spend-Pattern Score(u,c)=TF(c in cohort categories(u)).Math.IDF(c), where cohort categories(u) includes the categories in the user-segment cohort, N is the number of users in the user-segment cohort, and N(c) is the number of users in the user-segment cohort that have used predefined category c at least once [col. 6, ll. 30-53]; also see [col. 10, ll. 10-33], [col. 15, ll. 7-19], [col. 16, ll. 4-20]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Joseph’s system would have allowed Yerradoddi and Yan to facilitate a minimum number of users from among the plurality of users. The motivation to combine is apparent in the Yerradoddi and Yan’s reference, because there is a need to improve effective and useful of categorizing financial transactions.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yerradoddi in view of Yan in view of Singla; Anurag et al. (US 20140165140 A1) [Singla]
Regarding claim 8, the combination of Yerradoddi and Yan discloses, in response to determining that the minimum number of users [fails to satisfy the pattern] coverage threshold value, [iteratively modifying] the parameter until the minimum number of users satisfies the pattern coverage threshold value (Yerradoddi: determining whether likelihood exceeds a threshold ¶ [0046], likelihood evaluated over time and compared to a threshold ¶ [0060], identifying events based on evaluated likelihood ¶ [0013]).
However, neither Yerradoddi nor Yan explicitly facilitates fails to satisfy the pattern, iteratively modifying.
Singla discloses, fails to satisfy the pattern (threshold range used for determining validity of matching ¶ [0069], determining whether sequences match baseline or fail ¶ [0080]-[0084], deviations from baseline indicate anomaly ¶ [0066]),
iteratively modifying (baseline periodically updated to reflect changes ¶ [0062], new sequences fed back to baseline generation ¶ [0092], threshold range applied and adjusted for evaluation ¶ [0069], rule outcomes depend on updated matching continues ¶ [0080]-[0084]).
It would have been obvious to one ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Singla’s system would have allowed Yerradoddi and Yan to facilitate fails to satisfy the pattern, iteratively modifying. The motivation to combine is apparent in the Yerradoddi and Yan’s reference, because there is a need to improve secure access for confidential and protected information.
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 MOHAMMAD S ROSTAMI whose telephone number is (571)270-1980. The examiner can normally be reached Mon-Fri From 9 a.m. to 5 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571)270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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9/22/2026
/MOHAMMAD S ROSTAMI/Primary Examiner, Art Unit 2154
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