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
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the documents filed on 12/29/2025. Applicant rejected prior art of Gershey (US_12015812) in view of McClure (US_20250148188) presented in the Office Action dated by 12/02/2025. No amendments to claims have been made; the resubmitted claims 1 – 20 dated by 12/29/2025 are identical to previously posted claim set by 05/20/2024. Reconsideration of the claims set is requested.
Response to Arguments
Applicant's arguments in Arguments/Remarks filed on 12/29/2025 (hereafter Remarks) referring to the Office Action by 12/02/2025 (hereafter OA) have been fully considered but they are not persuasive.
Referring to MPEP 2143.01, applicant stated on p.11 of the Remarks that a combination of Gershey – McClure is improper arguing that combining McClure to Gershey will “change the principle of operation” of Gershey.
Examiner respectfully disagrees. Applicant on p. 13 of Remark considered only one alternative of the Gershey method related to monitoring in real-time the match-state data ignoring another important alternative of the disclosed method. However, Gershey in col. 1, ll. 43-47 in the summary of the invention discloses: “The instant disclosure includes a method, a system, and a computer program for self-correcting match states, including a machine learning prediction system and a data processing mechanism that can operate in an error-prone or noisy multimedia content delivery environment” (emphasis added). Details of data processing and error corrections are disclosed by Gershey in col. 17, ll. 31-44 and depicted in Fig. 5. Examiner respectfully submits that “data processing mechanism” disclosed by Gershey is an important alternative of the method. The motivation in OA suggest to combine operations of content analysis engine using the predefined lexicon, i.e., database, of McClure with the data processing of Gershey to correct data anomaly, i.e., errors, that improves applicability of the data processing mechanism of the disclosed method alternative by Gershey. According to MPEP 2143.01.I, “The disclosure of desirable alternatives does not necessarily negate a suggestion for modifying the prior art to arrive at the claimed invention… the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…" (emphasis added).
On pp. 12 of the Remarks, applicant further argues that prior art of Gershey in OA does not disclose claim limitation of a” memory operable to store: an interaction validation pathway comprising one or more software applications configured to process interaction data associated with an interaction request “. As noted above, this statement is based on a partial analysis of the concept of Gershey. The limitation “interaction data” is very broad covering any data circulating and interacting via respective software within a computing system, e.g., textual, linguistic, numerical, mathematical, statistical, entertainment, audio, video content, etc. Gershey, discloses in col. 7, ll.11-15 an interaction control of multimedia content for validation. Computing network of Gershey controls for anomalies the interconnections withing the network monitoring data packets paths as disclosed in col. 18, ll. 32-36 and depicted in Fig. 2, i.e., processing an interaction data for interaction validation pathway.
On p. 13 applicant further stated that “Nothing in McClure discloses any stored corrective operation "configured to correct a content error associated with the interaction data."
Examiner respectfully submits, that limitations “content errors” and “interaction data” are used in claims at very high level of generality. As noted above, under “interaction data” one can understand any information circulating within a network. The limitation “content error” could be interpreted within BRI, e.g., as a disclosure in an incorrect format that is considered in particular in banking sector as an error causing issues for the bank, see McClure [0068]: “the disclosure may be in the incorrect format. Such an error may cause issues for the bank (e.g., fines or other legal issues in the event regulators identify the incorrect disclosure)”.
On p. 16 of the Remarks applicant further stated that McClure does not disclose any element that is "configured to correct a content error associated with the interaction data." The Office Action identifies no passage in McClure that applies a stored correction to fix a data error.
Examiner respectfully disagrees. A very general term “content correction” could be interpreted as a correction of any issue of an information processing and identified by the validation system of McClure. For example, McClure, in para. [0068] discloses: “users of the disclosure validation system 102 are made more aware of any flaws of the digital content, and are thus enabled to more readily correct any identified issues.”.
On p. 16 of the Remarks applicant further stated The Office Action does not explain how McClure's lexicons/templates could be understood as a "plurality of pre-determined content corrections ... configured to correct a content error associated with the interaction data."
Examiner respectfully submits that very broad term “content” and “content corrections” may represent any content of information, e.g., a lexicon comprising predefined terms or any other specific data. The content analysis engine of McClure performs identification of any anomalies/error of the content and correct it. For example, McClure, in para. [0066] discloses: (the content analysis engine 208 may determine that a required disclosure applies to the digital content in response to identifying at least one term of the predefined lexicon.). Corrections of identified issues, i.e., errors, are performed by the validation system 102 of McClure as disclosed in para. [0070]: (users of the disclosure validation system 102 are made more aware of any flaws of the digital content, and are thus enabled to more readily correct any identified issues) and depicted in Fig. 1.
In response to applicant further comments on pp. 17, 18, examiner respectfully submits that the comments are frequently focused on different wordings in claims and prior art. For example, the claim language is using limitation “simulated environment” and/or “simulated interaction”, but Gershey discloses machine learning predictive applications.
On p. 18 applicant stated that Gershey “does not "generate modified interaction data" and re-process it "using the one or more software applications in the interaction validation pathway." The claimed post-simulation reinsertion into the multi-application pathway is absent from the cited art”.
Examiner respectfully disagrees. As noted above, the limitation “interaction data” is very broad and could be interpreted as any data or information circulating in a computing system. Gershey, in col. 4, ll.31-37 discloses an intelligent technology, INT, platform configured to analyze and correct “surrounding data in multimedia content”. The detected anomalies in the content are corrected using ML resources, i.e., specialized software units within the ML platform, i.e., the software package.
Examiner respectfully submits that Gershey discloses predictions, i.e., simulations, of anomalies, system states, match states, specified events, etc., that are the results of machine learning predictive operations, as disclosed in col. 2, ll.3-5 and depicted in Fig. 3. Gershey discloses in details implementation of a machine learning platform. It should be obvious for a person of ordinary skills in the art that the machine learning, ML, is a tool essentially based on simulations, by definition: ML enhances virtual reality per simulation of both environment and interactions thus enabling more immersive and intelligent tracking of events (see e.g., Z-H. Zhou, S. Liu, Machine Learning, University Press, 2016).
In summary, based on the above the prior art of Gershey and McClure represent a proper combination rendering the claims prima facie obviousness according to MPEP 2143.01. The claims are written very broad. The limitations like “interaction”, “interaction data”, “content error”, “content correction” are used in claims at very high level of generality. On the other hand, disclosure by applicant in para. [0024 – 0029] and [0033 – 0048] of SPECS presents variety of details pointing on specific operations in a banking sector that are out of scope of the instant claim set disclosure. The arguments used in the Remarks do not overcome the prior art. Accordingly rejection under 103 maintained.
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.
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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable Gershey et al. (US 12015812) (hereafter Gershey) and in view of McClure et al. (US 20250148188) (hereafter McClure)
As per claim 1 Gershey discloses: A system comprising: a memory operable to store: an interaction validation pathway comprising one or more software applications configured to process interaction data associated with an interaction request; (Gershey, discloses in col. 7, ll.11-15 an interaction control of multimedia content for validation; Gershey in col. 4, ll. 17-21 discloses a system comprising computer resources, i.e., software, as clarified in col.19, ll.24-28, and hardware “for validation, documentation or interaction control”, col.7, ll.14-15, for content processing (as depicted inFig.1), a memory (col.8, ll.38-40), the system with database to process requested operations, col.5, ll.45-47);
[a plurality of pre-determined content corrections, wherein each pre-determined content correction in the plurality of pre-determined content corrections is configured to correct a content error associated with the interaction data;]
and a machine learning model; and a processor operably coupled to the memory, (Gershey, in col.11, ll.5-10 discloses error content processing by the error correction system 13 checking error corrections stored in database using database table 40 as depicted in Fig. 2); the processor configured to execute the machine learning model, (Gershey in col.2, ll.56-58 discloses usage of machine learning platform in the system) the processor further configured to: receive the interaction request from a user device, (Gershey in col.5, ll.45-48, ll.54-56 discloses retrieve data request for information content processing), wherein the interaction request comprises the interaction data; process the interaction data using the one or more software applications in the interaction validation pathway; receive, from the one or more software applications, a first content error associated with processing the interaction data in the interaction validation pathway (Gershey, in col.11, ll.33-37 discloses the error correction system 13 for the data contents analysis and verification in view of previously error detection);
[and determine whether one or more of the plurality of pre-determined content corrections from the memory are configured to correct the first content error,
wherein if the one or more of the pluralities of pre-determined content corrections are not configured to correct the first content error,
the processor is further configured to: generate a first content correction using the machine learning model, wherein the machine learning model is trained based at least in part upon the plurality of pre-determined content corrections stored in the memory;]
generate a simulated environment for processing the interaction data with a simulated interaction validation pathway (Examiner note: Computing network of Gershey controls for anomalies the interconnections withing the network monitoring data packets paths as disclosed in col. 18, ll. 32-36 and depicted in Fig. 2, i.e., processing an interaction data for interaction validation pathway) (Gershey, in col. 4, ll.31-37 discloses an intelligent technology, INT, platform configured to analyze and correct “surrounding data in multimedia content”); apply the first content correction to the first content error in the simulated environment; and determine whether the first content correction corrects the first content error in the simulated environment, wherein if the first content correction is configured to correct the first content error in the simulated environment, (Examiner note: limitation “simulation” is met in Gershey by the operation “prediction”, i.e., prediction or simulation of anomalies, system states, match states, specified events, that are results of machine learning predictive operations, simulations, as disclosed in col. 2, ll.3-5 and depicted in Fig. 3) (Gershey discloses in col. 2, ll.34-46 variety of predictions, i.e., simulated environment, including how to apply detected corrections);
the processor is further configured to: generate modified interaction data by applying the first content correction to the first content error in the interaction data; and process the modified interaction data using the one or more software applications in the interaction validation pathway (Examiner note: ) (Gershey discloses in col. 4, ll.12-20 different computer resources, i.e., software applications, handling respective contents that could be corrected using disclosed intelligent platform provided by servers 10, 20 and communication device 30 as disclosed in col.7 ll.8-15 and depicted in Fig. 1).
Gershey, fails to explicitly disclose a predetermined list of content corrections for further comparative analysis and processing in the system.
However, McClure discloses: plurality of pre-determined content corrections, wherein each pre-determined content correction in the plurality of pre-determined content corrections is configured to correct a content error associated with the interaction data; (McClure, in para. [0068] discloses: “the disclosure may be in the incorrect format. Such an error may cause issues for the bank (e.g., fines or other legal issues in the event regulators identify the incorrect disclosure)”; McClure discloses in para. [27] a predefined lexicon comprising variety of corrections including elements, phrases, templates etc., that is used by the validation system 102 to correct detected issues in contents [0070]);
and determine whether one or more of the plurality of pre-determined content corrections from the memory are configured to correct the first content error, (McClure in para. [0064] discloses content analysis engine 208 using the predefined lexicon to determine required content corrections)
wherein if the one or more of the pluralities of pre-determined content corrections are not configured to correct the first content error, (McClure in para. [0069] discloses conditions of applying the corrections to a content based on the predefined threshold);
the processor is further configured to: generate a first content correction using the machine learning model, wherein the machine learning model is trained based at least in part upon the plurality of pre-determined content corrections stored in the memory; (McClure in para. [0068-0069] discloses content analysis engine 208 utilizing machine learning model trained on usage the predefined corrections per validation system 102 [0070], as depicted in Figs. 1,2)
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention to modify Gershey, in view of teaching of McClure because they both disclose content corrections due to errors in interaction by an information exchange.
The motivation to combine would be to modify Gershey for teaching of McClure for usage of predefined lexicon to perform content corrections.
As per claim 2 Gershey as modified discloses: The system of claim 1, wherein after determining that the first content correction is configured to correct the first content error in the simulated environment (Gershey discloses in col. 2, ll.34-46 variety of predictions, i.e., simulated environment, including how to apply detected corrections), the processor is further configured to: store the first content correction in the memory with the plurality of pre-determined content corrections. (McClure discloses in para. [27] a predefined lexicon comprising variety of corrections and the validation system 102 performing detection and respective content corrections stored in database 106).
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention to modify Gershey, in view of teaching of McClure because they both disclose content corrections due to errors in interaction by an information exchange.
The motivation to combine would be to modify Gershey for teaching of McClure for usage of predefined lexicon to perform content corrections.
As per claim 3 Gershey as modified discloses: The system of claim 1, wherein determining whether the one or more of the plurality of pre-determined content corrections from the memory are configured to correct the first content error (McClure discloses in para. [27] a predefined lexicon comprising variety of corrections including elements, phrases, templates etc., that is used by the validation system 102 to correct detected issues in contents [0070]) further comprises using the processor to: apply the one or more of the plurality of pre-determined content corrections to the first content error in the simulated environment, wherein if the one or more of the plurality of pre-determined content corrections is configured to correct the first content error in the simulated environment, the processor is configured to: generate the modified interaction data by applying the one or more of the plurality of pre-determined content corrections to the first content error; (McClure in para. [0069] discloses conditions of applying the corrections to a content based on the predefined threshold)
and process the modified interaction data using the one or more software applications in the interaction validation pathway; wherein if the one or more of the plurality of pre-determined content corrections is not configured to correct the first content error in the simulated environment, the processor is configured to generate the first content correction using the machine learning model. (McClure in para. [0068-0069] discloses content analysis engine 208 utilizing machine learning model trained on usage the predefined corrections per validation system 102 [0070], as depicted in Figs. 1,2).
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention to modify Gershey, in view of teaching of McClure because they both disclose content corrections due to errors in interaction by an information exchange. The motivation to combine would be to modify Gershey for teaching of McClure for usage of machine learning model trained on usage of predefined lexicon to perform content corrections.
As per claim 4 Gershey as modified discloses: The system of claim 1, wherein if the first content correction generated by the machine learning model is not configured to correct the first content error in the simulated environment (McClure in para. [0069] discloses conditions of applying the corrections to a content based on the predefined thresholds), the processor is further configured to: generate a second content correction using the machine learning model (McClure in para. [0069] discloses content analysis engine 208 utilizing the machine learning model to detect issues for corrections, and in para. [0070] the validation system 102 is disclosed enabled to correct identified issues);
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention to modify Gershey, in view of teaching of McClure because they both disclose content corrections due to errors in interaction by an information exchange and conditions to process based on predefined thresholds. The motivation to combine would be to modify Gershey for teaching of McClure for usage of predefined lexicon to perform content corrections.
Gershey as modified further discloses: apply the second content correction to the first content error in the simulated environment; (Gershey discloses in col. 2, ll.34-46 variety of predictions, i.e., simulated environment, including how to apply detected content corrections);
and determine whether the second content correction is configured to correct the first content error in the simulated environment, wherein if the second content correction is configured to correct the first content error in the simulated environment, (Gershey, in col.11, ll.33-37 discloses the error correction system 13 for the data contents analysis and verification in view of previously error detection);
the processor is further configured to: generate the modified interaction data by applying the second content correction to the first content error in the interaction data; and process the modified interaction data using the one or more software applications in the interaction validation pathway. (Gershey, in col.19, ll.24-28 discloses computer resources including different software and applications “for validation, documentation or interaction control”, col.7, ll.14-15).
As per claim 5 Gershey as modified discloses: The system of claim 4, wherein after determining that the second content correction is configured to correct the first content error in the simulated environment, the processor is further configured to: store the second content correction in the memory with the plurality of pre-determined content corrections (McClure in para. [0069] discloses conditions of applying the corrections to a content based on the predefined thresholds).
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention to modify Gershey, in view of teaching of McClure because they both disclose content corrections due to errors in interaction by an information exchange and conditions to process based on predefined thresholds. The motivation to combine would be to modify Gershey for teaching of McClure for usage of predefined lexicon to perform content corrections.
As per claim 6 Gershey as modified discloses: The system of claim 1, wherein a first portion of the one or more software applications in the interaction validation pathway are configured to process the interaction data in series and a second portion of the one or more software applications are configured to process the interaction data in parallel (Examiner note; sequential and parallel data processing are standard operation of the programmable software disclosed in specialized literature, e.g., W. Stallings. Cryptography and Network Security. Principles and Practice. (5th edition, copyright 2011)) (Gershey, in col.19, ll.24-28 discloses variety of computer resources including different software and applications “for validation, documentation or interaction control”, col.7, ll.14-15)
As per claim 7 Gershey as modified discloses: The system of claim 1, wherein the interaction validation pathway comprises: a first software application configured to receive the interaction data, wherein the first software application is configured to branch the interaction data into a first interaction data set and a second interaction data set; a second software application configured to receive the first interaction data set from the first software application (Gershey, in col. 14, ll.38-49 discloses an error correction unit 170 that includes two different subunits, i.e., branches, an anomaly predictor 170A and Match State Collector and Error corrector, MSCAE, 170B as depicted in Fig. 3); and a third software application configured to receive the second interaction data set (Gershey, in col.19, ll.24-28 discloses variety of computer resources including different software and applications).
As per claim 8 Gershey as modified discloses: The system of claim 7, wherein the first content error is associated with branching the interaction data into the first interaction data set and the second interaction data set; wherein the first content correction in the simulated environment is configured to allow the first software application to branch the first interaction data into the first interaction data set and the second interaction data set (Gershey in col. 14, ll.38-49 and in Fig. 3 discloses an error correction unit 170 that comprises two separate units 170A and 170B processing data separately, i.e., first and second software applications).
As per claim 9, claim 9 encompasses same or similar scope as claim 1. Therefore, claim 9 is rejected based on the same reasons set forth above in rejecting claim 1.
As per claims 10 – 16, claims 10 – 16 encompass same or similar scope as claims 2 – 8, respectively. Therefore, claims 10 – 16 are rejected based on the same reasons set forth above in rejecting claim 2 – 8.
As per claim 17, claim 17 encompasses same or similar scope as claim 1. Therefore, claim 17 is rejected based on the same reasons set forth above in rejecting claim 1.
As per claims 18 – 20, claims 18 – 20 encompass same or similar scope as claims 2 – 4, respectively. Therefore, claims 18 – 20 are rejected based on the same reasons set forth above in rejecting claim 2 – 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Lamm US_20230297785.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 VLADIMIR IVANOVICH GAVRILENKO whose telephone number is (313) 446-6530. The examiner can normally be reached on Monday-Friday 7:30-4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Vladimir I. Gavrilenko/Examiner, Art Unit 2431
/SHIN-HON (ERIC) CHEN/Primary Examiner, Art Unit 2431
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