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 21-40 are pending in this instant application per preliminary amendment filed on 11/20/2025 by Applicant, wherein original Claims 1-20 filed on 11/20/2025 were cancelled, and replaced with new Claims 21-40. Claims 21 and 31 are two independent claims reciting computer-implemented system and computer-implemented method claims, with Claims 22-30 and 32-40 being respective dependent claims.
One/1 IDS has been filed by the Applicant so far on 11-20-2025 that has been considered and entered.
This Office Action is a non-final rejection on merits in response to the preliminary amendment claims filed by the Applicant on 20 NOVEMBER 2025 for its original application of the same date that is titled: “Enhanced Image Transaction Processing Solution and Architecture”.
Accordingly, pending Claims 21-40 are now being rejected herein.
Claim Objections
Claims 21 & 31/32/33 are objected to because of the following informalities:
Claim 21, lines 12-13 recites “a neural network machine learning model” that is unclear to Examiner. Because “neural network” (NN) is a subset of “machine learning” (ML), and thus claimed limitation of “a neural network machine learning model” does not make sense.
Claim 31, lines 9-10 recites “a neural network machine learning model” that is unclear to Examiner. Because “neural network” (NN) is a subset of “machine learning” (ML), and thus claimed limitation of “a neural network machine learning model” does not make sense.
Claims 32 and 33, line 1 preamble recites “computer implemented method of claim 31” that is inconsistent with preamble of independent Claim 31. Independent Claim 31 recites “computer-implemented method” with a hyphen, and dependent Claims 34-40 also recite their preamble with a hyphen as “computer-implemented method”. Examiner suggests modifying said preamble in Claims 32-33 to be read as “computer-implemented method”.
Appropriate correction is required.
Double Patenting Rejection
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 (TD) must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer (TD) 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 (eTD) 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 (eTDs), refer to ---
www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of US Patent No. 12,505,413 (of the parent application 17/658624). Although the claims at issue are not identical, they are not patentably distinct from each other because they recite similar limitations directed to:
“systems and methods, and computer readable media for image transaction processing are disclosed. The method receives an input of images of documents. The method may then analyze the input using an image processing engine to determine attributes associated with the images of the documents and identify an account linked to the attributes and a transaction associated with the account. The method may also evaluate confidence level of association links between the transaction and the account based on confidence scores of the attributes that may identify a type of the attribute. The method may use the transaction and account to split the images of documents into sets of images of documents with each set of images with confidence level of an association link between the transaction and the account associated with them being greater than a threshold value”.
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.
(NOTE: Latest ‘amendments to the claims’ filed by the Applicant on 11/20/2025 as “preliminary amendment” are shown as underlined additions, and all deletions may not be shown.)
Claims 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more, wherein Claims 21 and 31 are independent system and method claims respectively.
Exemplary Analysis
Claim 31: Ineligible.
The claim recites a series of steps. The claim is directed to a method reciting a series of steps, which is a statutory category of invention (Step 1--YES).
The claim is analyzed to determine whether it is directed to a judicial exception. The claim recites the limitations of: determining, from one or more images of documents, one or more attributes associated with the one or more images of documents; conducting multiple iterations of analyzing, the one or more images of documents to determine the one or more attributes; grouping attributes into sets of attributes to identify different types of documents using the multiple iterations of analysis; determining a document type associated with the one or more images of documents; determine a transaction, wherein contents of the transaction are determined based on the values of the one or more attributes; determine a boundary of the transaction using the determined transaction and by grouping the one or more images of documents in an order; calculate confidence scores of the one or more attributes associated with the one or more images of the documents, wherein a confidence score of an attribute identifies a type of the attribute; evaluate confidence level of association links between the transaction and the account, wherein the confidence level of association links is based on the calculated confidence scores; review the one or more sets of images if the confidence level of an association link between the transaction and the account is lower than a threshold value to determine whether a secondary review is needed; and upon determining a secondary review is needed, re-analyze the one or more sets of images. In other words, the claim describes a procedure for automated transaction processing using an image processing architecture that semantically understands contents to determine and process transactions (see para [0002] of the filed Specification, Technical Field). These limitations, as drafted, are steps of a method that, under its broadest reasonable interpretation, covers performance of the limitations via a method of organizing human activity such as fundamental economic principles or practices (at least based on “image transaction processing”), and/or commercial or legal interactions (including agreements in the form of contracts; marketing or sales activities or behaviors; business relations), and/or managing behavior or relationships or interactions between people (including following rules or instructions), but for the recitation of generic computer/s and/or computer component/s such as processor and/or image processing engine. Examiner notes that the claim describes a method for "image transaction processing" that describe a series of steps for: receiving an input and analyze the input, determine a transaction associated with an account and evaluate confidence level of association links between transaction and the account, using one or more images. These limitations fall under the “certain methods of organizing human activity” group (Step 2A1--YES).
Next, the claim is analyzed to determine if it is integrated into a practical application. The claim recites additional elements of: an image processing engine and a neural network machine learning model (aka the machine learning model). The image processing engine and the neural network machine learning model in the steps are recited at a high level of generality, i.e., as generic processors performing generic computer/s functions of processing data. These generic processors are no more than mere instructions to apply the exception using generic computer/s and/or computer component/s. Accordingly, these additional elements do not integrate the abstract idea into a practical application, because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to the abstract idea (Step 2A2--NO).
Next, the claim is analyzed to determine if there are additional elements in this claim that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer/s and/or computer component/s. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer and/or computer components over a network cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The disclosure does not provide any indication that these engines and models (processor/s and/or engine/s) are anything other than generic processors and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05 (d) (II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner as it is here. Mere instructions to apply an exception using a generic computer processor/component cannot provide an inventive concept. Thus, the claim is not patent eligible.
Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional elements do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B--NO), and the claim is not patent eligible.
The analysis above applies to all statutory categories of the invention including independent system Claim 21, which perform the steps similar to those of independent method Claim 31. Furthermore, the limitations of dependent method Claims 32-40, further narrow the independent method Claim 31 with additional steps and limitations (e.g., wherein the attribute type is at least one of: amount, date, or name; wherein the multiple iterations of analyzing further includes extracting text elements from the one or more images of documents; wherein the method further includes identifying a relationship between the extracted text elements; wherein the grouping of attributes further includes grouping attributes based on relationships learned from previous iterations of analysis; wherein the neural network is further trained using at least one of document structure, document type, or document relationships; further including determining an account associated with the one or more images of documents; wherein determining the transaction associated with the account further comprises at least one of: identifying an expenditure associated with the account, and identifying a payment associated with the account; or identifying a payment associated with one or more transactions; the order of the one or more images of documents varies based on a transaction; wherein the order of the documents is based on document type; etc.), and do not resolve the issues raised in rejection of the independent method Claim 31. Similarly, dependent system Claims 22-30 also further narrow its independent system Claim 21, which are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Therefore, said Claims 21-40 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.
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. The 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.
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.
(NOTE: Latest ‘amendments to the claims’ filed by the Applicant on 11/20/2025 as “preliminary amendment” are shown as underlined additions, and all deletions may not be shown.)
Claims 21-40 are rejected under 35 USC 103 as unpatentable over a combination of references (van Dam in view of Madden, Smith, Rubenstein, Amtrup and Li), and as described below for each claim/ limitation.
Exemplary Analysis for Rejection of Claims 21-30
Independent Claim 21 is rejected under 35 USC 103 as unpatentable over Pub.
No. US 2014/ 0279303 filed by van Dam et al. (hereinafter “van Dam”) in view of Pub. No. US 2010/ 0023438 filed by Madden, Martin P. (hereinafter “Madden”), and further in view of Pub. No. US 2015/ 0120563 filed by Smith et al. (hereinafter “Smith”), and further in view of Pub. No. US 2012/ 0095819 filed by Li, Lehmann (hereinafter “Li”), and further in view of Pub. No. US 2014/ 0365382 filed by Rubenstein et al. (hereinafter “Rubenstein”), and as described below for each claim/ limitation.
Examiner notes that all claims have been copied as recited by the Applicant to keep them readable and whole, even if the limitations within a claim that are not taught explicitly by the primary/previous reference (are noted in parentheses), but these limitations are noted explicitly as taught by a secondary/new reference whenever a secondary/new reference has been used.
Examiner notes that, for brevity in this rejection, the motivation statement has not been repeated herein every time a secondary reference has been used.
With respect to Claim 1, van Dam teaches ---
1. (New) A computer-implemented system for image transaction processing, the system comprising:
(see at least: van Dam Abstract; & para [0003] about {“FIG. 1 schematically depicts an illustrative networked architecture for capturing and processing images for financial transactions in accordance with one or more embodiments of the disclosure.”}; & para [0005] about {“FIG. 3 schematically depicts an illustrative data flow for image capture and processing for financial transactions.”}; & para [0006] about {“FIGS. 4-6 are process flow diagrams depicting illustrative methods for image capture and processing for financial transactions in accordance with one or more embodiments of the disclosure.”}; & para [0012] about {“The image may be stored in any of a variety of systems. Likewise, the association between the image and a transaction may be stored in any of a variety of systems. Such an association may be based on one or more identifiers (e.g., an identifier of the image, an identifier of the transaction), and may enable subsequent retrieval of the image when viewing the transaction, as well as potential subsequent use or processing of the image.”}; which together are the same as claimed limitations above)
van Dam teaches ---
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
(see at least: van Dam ibidem and citations listed above; & para [0030] about {“…. The processor(s) 202 may include any suitable processing unit capable of accepting digital data as input, processing the input data based on stored computer-executable instructions, generating output data, retrieving or storing data, and controlling the operation or use of various hardware resources through interfaces such as I/O interfaces 220 and network interfaces 222. The computer-executable instructions may be stored, for example, in the memory 204 and may include operating system software, application software, and so forth. The processor(s) 202 may be configured to execute the computer-executable instructions to cause various operations to be performed. …”}; & para [0031] about {“…. The memory 204 may store program instructions that are loadable and executable by the processor(s) 202, as well as data manipulated and generated by the processor(s) 202 during execution of the program instructions. Depending on the configuration and implementation of the image and transaction reconciliation computer 120, the memory 204 may be volatile memory (memory that maintains its state when supplied with power) such as random access memory (RAM) and/or non-volatile memory (memory that maintains its state even when not supplied with power) such as read-only memory (ROM), flash memory, and so forth. …”}; which together are the same as claimed limitations above)
van Dam teaches ---
determine, from one or more images of documents, one or more attributes associated with the one or more images of documents;
(see at least: van Dam ibidem and citations listed above; & para [0033] about {“The image and transaction reconciliation computer 120 may additionally include one or more input/output (I/O) interfaces 220, such as a keyboard, a keypad, a mouse or other pointing device, a pen, a voice input device, a touch input device, a display, speakers, a camera, a microphone, a printer, and so forth, for receiving user input and/or providing output to a user.”} & para [0048] for “an attribute of the transaction”; & para [0053] for “the ITRS 118 may query the one or more transaction systems 112 using any combination of attributes, identified text fields,”; & para [0058] for “the identified candidate financial transactions may be ordered by one or more attributes (e.g., date, amount, geolocation, etc.).”; & paras [0064] and [0074] for “the query may use any combination of attributes, identified text fields,”; which together are the same as claimed limitations above)
van Dam teaches ---
(conduct multiple iterations of analyzing), the one or more images of documents using (an image processing engine), to determine the one or more attributes;
(see at least: van Dam ibidem and citations listed above to include image and transaction reconciliation computer 120 for ‘one or more images of documents’; & para [0048] about {“…. In some embodiments, the geolocation may correspond to a location the image was captured. In some embodiments, the geolocation may be used to associate the image to an identified financial transaction. For example, if a transaction has an address associated with it or with an attribute of the transaction, such as a payee, then by comparing a geolocation associated with the received image to the address, the ITRS 118 may be able to identify a possible association between the transaction and the image and transmit the transaction as a candidate financial transaction for presentation to the user. …”}; & para [0053] about {“…. In some embodiments, the ITRS 118 may query the one or more transaction systems 112 using any combination of attributes, identified text fields, alternative forms of identified text fields, values derived from one or more identified text fields, or supplemental information associated with the image, such as a geolocation associated with the image. For example, if the text fields were identified from a purchase receipt, a search may be performed for any debit/payment transaction with i) a payee corresponding to the identified retailer name, ii) a date corresponding to the purchase date, and iii) an amount corresponding to the purchase amount. …”}; & para [0058] about {“At block 420, candidate financial transactions may be transmitted for presentation to a user. In some embodiments, the identified candidate financial transactions may be ordered by one or more attributes (e.g., date, amount, geolocation, etc.). The identified candidate financial transactions may be transmitted to the client application system 104 for presentation to the user. …”}; which together are the same as claimed limitations above)
van Dam teaches as disclosed above to include image capture and processing for financial transactions, but it may be argued that it may not explicitly disclose about ‘conduct multiple iterations of analyzing’. However, Madden teaches it explicitly.
(see at least: Madden Abstract and Summary of the Invention in paras [0020]-[0028]; & para [0094] about {“It will be understood that multiple iterations may be performed to further refine analysis conclusions. FIG. 1 illustrates one preferred embodiment of a system and method, embodying Steps 1-10, …”}; which together are the same as claimed limitations above to include ‘conduct multiple iterations of analyzing’)
It would have been obvious to an ordinary person of skill in the art at the time invention was made to modify the teachings of van Dam with teachings of Madden. The motivation to combine these references would be to provide relevant documents for financial transactions in one or more transaction systems of record (see para [0002] of van Dam), and to provide a system and method for analyzing and originating a contractual option arrangement for selling bank deposits at a predetermined price (see para [0001] of Madden).
van Dam teaches as disclosed above to include image capture and processing for financial transactions, but it may be argued that it may not explicitly disclose about ‘an image processing engine’. However, Smith teaches them explicitly.
(see at least: Smith Abstract and Brief Summary in paras [0002]-[0011]; & para [0033] about {“In some embodiments, the OCR process includes location fields for determining the position of data on the check image. Based on the position of the data, the system can identify the type of data in the location fields to aid in character recognition. For example, an OCR engine may determine that text identified in the upper right portion of a check image corresponds to a check number. The location fields can be defined using any number of techniques. In some embodiments, the location fields are defined using heuristics. The heuristics may be embodied in rules that are applied by the system for determining approximate location.”}; & para [0051] about {“In some embodiments, the capture application 220, the online banking application 221, and the transaction application 270 interact with the OCR engines 250 to receive or provide financial record images and data, detect and extract financial record data from financial record images, analyze financial record data, and implement business strategies, transactions, and processes. The OCR engines 250 and the client keying application 251 may be a suite of applications for conducting OCR.”}; & para [0056] about {“...... By using the check template, the system of process 100 any other system can "learn" to map the key attributes of the check for faster and more accurate processing. In some embodiments, financial records are categorized by template. …”}; which together are the same as claimed limitations above to include ‘an image processing engine’ and ‘one or more attributes’)
Examiner notes that Smith’s teaching of OCR engine/s is the same as claimed ‘an image processing engine’.
It would have been obvious to an ordinary person of skill in the art at the time invention was made to modify the teachings of van Dam with teachings of Smith. The motivation to combine these references would be to provide relevant documents for financial transactions in one or more transaction systems of record (see para [0002] of van Dam), and to provide businesses that may receive the same or similar images from a wide variety of entities, help them determine which images must be processed and retained can present even more difficulties (see para [0001] of Smith).
van Dam, Madden and Smith teach ---
(group attributes into sets of attributes) using (a neural network machine learning model) trained to identify different types of documents using the multiple iterations of analysis;
(see at least: van Dam ibidem and citations listed above to include ‘one or more attributes’; & para [0021] about {“...... Examples of transaction systems 112 may include bank core account processing systems; online banking systems; bill payment systems; person-to-person payment systems; funds transfer systems; retail payment systems.”}; & para [0024] about {“…. For example, a payment transaction may be in the payment history associated with a payment system, in a core account processing system when the transaction is posted to the financial account, and in an online banking system when posted transactions are extracted for presentation in an online banking UI.”}; & para [0057] about {“…. The ITRS 118 may optionally also identify one or more transaction systems 112 of record and/or a financial account associated with the transactions.”}; & para [0065] about {“…. For example, preference may be given to certain transaction systems 112 of record or financial accounts.”}; & para [0075] about {“{“…. For example, preference may be given to certain transaction systems 112 of record or financial accounts.”}; & para [0079] about {“FIG. 7B depicts an interface 730 displaying or presenting to a user a transaction history associated with the checking account 704. The transaction history may list multiple transactions associated with the checking account 704. Some transactions may display a receipt icon 734, while other transactions do not display 736 an icon….”}; which together are the same as claimed limitations above)
(see at least: Madden ibidem and citations listed above to include ‘conduct multiple iterations of analyzing’)
(see at least: Smith ibidem and citations listed above; & para [0032] about {“…… The OCR processes enables the system to convert handwritten or printed text and other symbols in the check image to machine encoded text such as text based files that can be edited and searched. The data in the check images can also be extracted and converted into metadata, which can then be used and incorporated into a variety of applications, documents, and processes. In some embodiments, OCR based algorithms incorporate pattern matching techniques. For example, each character in an imaged word, phrase, code, or string of alphanumeric text can be evaluated on a pixel-by-pixel basis and matched to a stored character. ….”}; & para [0063] about {“…… For example, the system may review similar transactions such as transaction with the same account numbers and payee names to determine if they were identified as being included in the ACH transaction agreement or similar agreements in the past. …”}; & para [0064] about {“…… In additional embodiments, the determination that the check image is included in the ACH transaction is based on account data, outside data, and/or customer data. For example, the system may determine that the check image is part of the ACH agreement based on the identity of the customer or the account associated with the check image. …”}; which together are the same as claimed limitations above)
van Dam, Madden and Smith teach as disclosed above, but they may not explicitly
disclose about ‘group attributes into sets of attributes’. However, Li teaches them explicitly.
(see at least: Li Abstract and Summary in paras [0004]-[0006]; & para [0022] about {“FIG. 7 depicts an exemplary set of object attributes whose selection can enable a user to narrow a plurality of objects to an object of interest, according to one embodiment.”}; & para [0093] for “{or another object meeting the set of specified object attributes ("Contingent Single Purchase");}”; & para [0097] about {“At 02000C2, Method 02000 can post to one or more Data Processing Systems a request for proposal (RFP) for any retailer offering the Object of Interest meeting the set of attributes specified in the RFP.”}; & para [0098] about {“At 02000D2, Method 02000 can either: (a) receive an offer from one or more retailers offering the Object of Interest meeting the set of attributes specified in the RFP ("Qualifying Offer"), of which one attribute can specify the value of a timestamp by which an offer must be received; or (b) not receive at least one Qualifying Offer.”}; & para [0110] about {“At 02000H1, Method 02000 can transmit the selected set of attributes and values to the IP Retailer selected, e.g., Web Server 11910.”}; & para [0112] about {“At 02000H2, Method 02000 can write the selected set of attributes and values to any data structure which can be accessed directly or indirectly by PHY point of sale (POS) 11920.”}; & para [0113] about {“At 02000I2, Method 02000 can detect one or more events related to or can be associated with any attribute and/or value in the selected set. …”}; & para [0114] about {“At 02000J2A, Method 02000 can execute one or more methods specified by an event handler associated with a detection of the event at 02000I2 and specified by, associated with, and/or related to one or more attributes and/or values in the selected set. …”}; & para [0176] about {“In a sixth embodiment illustrated by FIG. 3G, a format, Format 03000G, can include the same or different objects as the first embodiment, except the format can display the values of one or more objects with a specific configuration, i.e., a set of attributes with equal values or values within a specified range ("Equivalent Objects of Interest"). A user of Client Device 14200 can be interested in an Object of Interest or an object with one or more attributes equivalent to the Object of Interest. For example, a user of Client Device 14200 can be interested in an object which has certain values for a set of attributes and be less interested in a specific Object of Interest. In the example, the user of Client Device 14200 can be more interested in an object with the certain values for a set of attributes and a lower price and be less interested in the value of the brand associated with the object. In many examples, an object commonly referred to as a Private Label object can have the same values for a set of attributes as an object supplied by a vendor but at a lower price. When a user of Client Device 14200 specifies the values for a set of attributes, the invention can display in Format 03000G the values of one or more Equivalent Objects of Interest.”}; & para [0180] about {“Second, Method 03000G can identify a set of attributes associated with the specified Object of Interest, the class of which the Object of Interest is a member, or the specified Class of Interest. The set of attributes can comprise one or more attributes whose selection can narrow a plurality of objects in a Class of Objects to one Object of Interest or a plurality or any desired number of Equivalent Objects of Interest. For example, a User Request for a Class of Interest "Laptop Computers" can cause Method 03000G to identify a set of attributes associated with the Class of Interest, e.g., monitor size, memory or RAM, and estimated battery life.”}; & para [0181] about {“Third, Method 03000G can identify the set of values associated with each identified attribute. The set of values can comprise those values of attributes associated with any object in the Class of Interest. …”}; & para [0230] about {“At 09220, Method 09000 can apply logic to compare and/or utilize any comparator component capable of comparing the set of attributes retrieved from each Retailer object data structure. While most Retailers selling a given object probably associate the same types of attributes, each Retailer can use a different word string defining a given attribute and offer different values or set of values for any given attribute. …”}; & para [0232] about {“To determine equivalent attributes across a plurality of Retailers, Method 09000 can use any method, including, but not limited to, the following methods. In one embodiment, Method 09000 can read the set of attributes for one Product ID, e.g., "screen size" and "memory" for laptop computer Retailer A and "monitor size" and "RAM" for laptop computer Retailer B. Method 09000 can generate a table listing a plurality of equivalent terms for any given attribute, e.g., associating with the attribute measuring the size of the screen the attribute names, "screen size", "monitor size", "diagonal size", etc. Method 09000 can compare the set of attributes for each of Retailer A and Retailer B against the table, e.g., at 09240B. …”}; & para [0374] about {“…… Seventh, the value of one or more attributes of an Object of Interest purchased in a first Transaction can be significantly correlated with the value of one or more attributes of an Object of Interest purchased in a second or additional Transaction, e.g., an attribute "size" of the object "XYZ pants" purchased in a first Transaction can have the value "medium" which is probably the same value as the "size" attribute of the object "ABC pants" purchased in a second Transaction. ”}; & para [0387] about {“…… and/or (j) any correlation of one or more attributes among a plurality of Classes of Objects in the set of Transactions executed by a sample of users, e.g., PUT.sub.S. …”}; & para [0422] about {“In one embodiment, the correlation among values in attribute-value pairs for any given attribute in a set of PUT and/or PUT.sub.S can be expressed as follows:”}; & para [0453] about {“……(i) identifying the set of User Demographic attributes; (ii) generating a vocabulary of word strings associated with each attribute, e.g., if an attribute is "homeowner", candidate word strings can include "homeowner insurance" or "mortgage refinancing"; …”}; which together are the same as claimed limitations above to include ‘group attributes into set of attributes’)
It would have been obvious to an ordinary person of skill in the art at the time invention was made to modify the teachings of van Dam with teachings of Smith. The motivation to combine these references would be to provide relevant documents for financial transactions in one or more transaction systems of record (see para [0002] of van Dam), and to provide businesses that may receive the same or similar images from a wide variety of entities, help them determine which images must be processed and retained can present even more difficulties (see para [0001] of Smith), and to provide a person, who is interested in an object for purchase, help by utilizing a data processing system to find a retailer offering the object for the lowest price, any qualifying offer which can decrease the price, any qualifying reward for using a payment account of which he/she is a holder, and/or any related or competitive products (see para [0003] of Li).
van Dam, Madden and Smith teach as disclosed above, but they may not explicitly disclose about ‘a neural network machine learning model’. However, Rubinstein teaches it explicitly.
(see at least: Rubinstein Abstract and Summary in paras [0006]-[0008]; and para [0029] about {“The confidence score generator 110 may utilize the services of the machine-learning module 111 to determine the probability that a particular piece of content is inappropriate content. The machine-learning module 111 trains machine-learned models that can generate the confidence score for the reported content based on the type of report (whether for spam, pornography, racism, etc.), social data, and features of the content.”}; and para [0037] about {“In one embodiment CS is generated by the confidence score generator 110 using machine-learned algorithms that use social data to determine the probability that content is inappropriate. The machine-learned algorithms may be trained by the machine-learning module 111. The machine-learned algorithms are trained to recognize the characteristics of content that are correlated with various sorts of inappropriate content like pornography, spam, etc. For example, a photograph with certain skin tones and many unrelated users tagged in it may be recognized as pornographic spam based on these characteristics. The machine-learning module 111 may provide different machine-learned algorithms to calculate CS based on the type of inappropriate content identified by the reporting user. For example, different machine-learned algorithms may be selected to generate CS based on the content being identified as a spam photo versus a racist photo.”}; and social networking system 101; which together are the same as claimed limitations above to include ‘a neural network machine learning model’ per BRI rules)
It would have been obvious to an ordinary person of skill in the art at the time invention was made to modify the teachings of van Dam, Madden and Smith with teachings of Rubenstein. The motivation to combine these references would be to provide relevant documents for financial transactions in one or more transaction systems of record (see para [0002] of van Dam), and to provide a system and method for analyzing and originating a contractual option arrangement for selling bank deposits at a predetermined price (see para [0001] of Madden), and to provide businesses that may receive the same or similar images from a wide variety of entities, help them determine which images must be processed and retained can present even more difficulties (see para [0001] of Smith), and to fulfill need for a system that can satisfactorily resolve user reports without consuming human resources, while reducing the number of unjustified reports that are submitted to the content reviewing process (see para [0005] of Rubenstein).
van Dam, Madden, Smith, Li and Rubenstein teach ---
determine a document type associated with the one or more images of documents;
determine a transaction, wherein contents of the transaction are determined based on the values of the one or more attributes;
determine a boundary of the transaction using the determined transaction and by grouping the one or more images of documents in an order;
calculate, using the machine learning model, confidence scores of the one or more attributes associated with the one or more images of the documents, wherein a confidence score of an attribute identifies a type of the attribute;
(see at least: van Dam ibidem and citations listed above to include image and transaction reconciliation computer 120 for ‘one or more images of documents’; & para [0009] about {“…… For example, the system may determine a "best-fit" transaction based at least in part on a matching confidence level associated with each of the identified candidate financial transactions. Images may be automatically associated with the "best-fit" transaction. Transactions may be ordered according to their respective matching confidence levels. The candidate transactions may be identified and transmitted for presentation to a user.…”}; & para [0041] about {“The confidence level generation module 214 may provide functionality directed to determining &/or calculating a matching confidence level for an identified transaction candidate. The module 214 may determine &/or calculate the matching confidence level based at least in part on one or more factors, which may include the number of matching fields between an image and a transaction, how close within a tolerance range each field match is, and weighting associated with the individual field matches.”}; & para [0065] about {“… In some embodiments, the ITRS 118 may determine or calculate a matching confidence level associated with each of the identified candidate financial transactions. Determining a matching confidence level for each of the candidate financial transactions may be based at least in part on any number of a variety of factors, which may include but are not limited to the number of matched fields between the image and candidate financial transaction, the tolerance range associated with each matched text field, and different weighting associated with the matching of different fields. The ITRS 118 may then order the set of candidate financial transactions based on their respective matching confidence levels. In some embodiments the candidate financial transaction with the matching confidence level denoting the highest confidence level (which may be a numerically high or low value) in the set of identified candidate financial transactions may then be determined to be the best-fit candidate financial transaction. If there are multiple candidate financial transactions with the same matching confidence level, all the candidate financial transactions with the highest matching confidence levels may be identified as the best-fit candidates. …”}; & para [0068] about {“…. In some embodiments, a subset of the identified candidate financial transactions may be transmitted, wherein the subset includes candidate financial transactions with a matching confidence levels that meet a pre-determined threshold. …”}; & para [0075] about {“…… In some embodiments, the ITRS 118 may the ITRS 118 may determine or calculate a matching confidence level associated with each of the identified candidate financial transactions, as described in association with block 520 of FIG. 5. The ITRS 118 may order the set of candidate financial transactions based on their respective matching confidence levels. In some embodiments the candidate transaction with the matching confidence level denoting the highest confidence level (which may be a numerically high or low value) in the set of identified candidates may then be determined to be the best-fit candidate transaction. If there are multiple candidate transactions with the same matching confidence level, all the candidate transactions with the highest matching confidence levels may be identified as the best-fit candidates. …”}; which together are the same as claimed limitations above)
(see at least: Madden ibidem and citations listed above to include ‘conducting multiple iterations for analyzing’)
(see at least: Smith ibidem and citations listed above; & para [0039] about {“In further embodiments, the system of process 100 assigns a confidence level to at least a portion of the identified check data. The confidence level includes a pass/fail rating, a graded score, a percentage score, an assigned value, or any other indication that the check data is accurate, relevant, or otherwise acceptable. In this way, any data identified and extracted via the OCR processes and/or any data inputted from an operator or customer can be screened before such data is used in the business strategies and transaction described herein below. …”}; & para [0040] about {“In some embodiments, the confidence level is assigned to the check data based on the number of times the OCR processes is applied to a check image, the quality of the check image, the quality of the identified check data extracted from the check image, whether or not the check data can be verified, and the like. If the check image includes blurred text or has a low pixel count, the text produced by a first round of the OCR processes may be assigned a low confidence level. In such cases, the confidence level may be increased if the check images undergo additional rounds of the OCR processes. In other cases, the system may compare the check data to previously confirmed data to assign the confidence level. ………In cases where the confidence level is below a certain level or the check data is otherwise unsatisfactory, the system of process 100 may repeat the same of different OCR processes for at least a portion of a check image, ….”}; which together are the same as claimed limitations above)
(see at least: Li ibidem and citations listed above to include ‘group attributes into sets of attributes’)
(see at least: Rubenstein ibidem and citations listed above to include ‘a neural network machine learning model’)
Dependent Claims 22-23 are rejected under 35 USC 103 as unpatentable over van Dam in view of Madden, Smith, Li and Rubenstein as applied to the rejection of independent Claim 21 above, and further in view of Pub. No. US 2015/ 0110362 filed by Amtrup et al. (“Amtrup” hereinafter), and as described below for each claim/ limitation.
With respect to Claim 22, van Dam, Madden, Smith, Li and Rubenstein teach ---
22. (New) The computer-implemented system of claim 21, wherein the multiple iterations of analyzing further includes (extracting text elements) from the one or more
images of documents.
(see at least: van Dam ibidem and citations listed above to include image and transaction reconciliation computer 120 for ‘one or more images of documents’)
(see at least: Madden ibidem and citations listed above to include ‘conducting multiple iterations for analyzing’)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above)
van Dam, Madden, Smith, Li and Rubenstein teach as disclosed above, but they may not explicitly disclose about ‘extracting text elements’. However, Amtrup teaches it explicitly.
(see at least: Amtrup Abstract and Summary in paras [0006]-[0010]; and para [0158] about {“The image of the tender document is analyzed by performing OCR thereon. The OCR may be utilized substantially as described above to identify and/or extract characters, and particularly text characters, from the image. Even more preferably, the extracted characters include an identifier that uniquely identifies the tender document. …”}; and para [0173] about {“Based on the finite set of possible formats for the identifier data, the presently disclosed techniques may be configured to automatically normalize data obtained (e.g. via extraction) from the imaged financial document in a manner that the data obtained from the financial document matches an expected format of corresponding data, e.g. contained/depicted in textual information of the complementary document. For example, upon determining that extracted data such as a date is in a particular format (e.g. Jan. 01, 2001) other than an expected format (e.g. MM/YY), it is advantageous to convert the extracted data from the particular format to the expected format, enabling facile and accurate matching between the identifier data derived from the image and the corresponding textual information from the complementary document.”}; and para [0174] about {“......For example, in one embodiment a first iteration operates substantially as described above--extracting an identifier from an image of a document and comparing the extracted identifier to corresponding data from one or more data sources (e.g. the textual information from the complementary document, database record, the predefined business rules, etc.). …”}; which together are the same as claimed limitations above to include ‘extracting text elements’)
It would have been obvious to an ordinary person of skill in the art at the time invention was made to modify the teachings of van Dam, Madden, Smith, Li and Rubenstein with teachings of Amtrup. The motivation to combine these references would be to provide relevant documents for financial transactions in one or more transaction systems of record (see para [0002] of van Dam), and to provide a system and method for analyzing and originating a contractual option arrangement for selling bank deposits at a predetermined price (see para [0001] of Madden), and to provide businesses that may receive the same or similar images from a wide variety of entities, help them determine which images must be processed and retained can present even more difficulties (see para [0001] of Smith), and to provide a person, who is interested in an object for purchase, help by utilizing a data processing system to find a retailer offering the object for the lowest price, any qualifying offer which can decrease the price, any qualifying reward for using a payment account of which he/she is a holder, and/or any related or competitive products (see para [0003] of Li), and to fulfill need for a system that can satisfactorily resolve user reports without consuming human resources, while reducing the number of unjustified reports that are submitted to the content reviewing process (see para [0005] of Rubenstein), and to provide for the automatic extraction and recognition of the relevant information, when the layout and the forms of documents differ vastly between senders and are loosely structured, even when it is very challenging (see para [0004] of Amtrup).
With respect to Claim 23, van Dam, Madden, Smith, Li and Rubenstein teach ---
23. (New) The computer-implemented system of claim 22, wherein the processor is further configured to identifying a relationship between the extracted text elements.
(see at least: van Dam ibidem and citations listed above)
(see at least: Madden ibidem and citations listed above to include ‘conducting multiple iterations for analyzing’)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above)
(see at least: Amtrup ibidem and citations listed above to include ‘extracting text elements’)
Dependent Claims 24-30 are rejected under 35 USC 103 as unpatentable over van Dam in view of Madden, Smith, Li and Rubenstein as applied to the rejection of independent Claim 21 above, and as described below for each claim/ limitation.
With respect to Claim 24, van Dam, Madden, Smith, Li and Rubenstein teach ---
24. (New) The computer-implemented system of claim 21, wherein the grouping of
attributes further includes grouping attributes based on relationships learned from previous iterations of analysis.
(see at least: van Dam ibidem and citations listed above to include ‘one or more attributes’)
(see at least: Madden ibidem and citations listed above to include ‘conduct multiple iterations of analyzing’)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above to include ‘group attributes into sets of attributes’; and para [0356] about {“…… (a) to exploit the assignment of retailers, vendors, brands, and/or objects to classes which share similar attributes; (b) to identify relationships among a plurality of classes; and/or (c) to reduce the search space of factors and/or data, which can increase the accuracy and/or reduce the time to identify an objective. …”}; and para [0424] about {“…… The invention can enable the computation of the attribute-value pair correlation through a variety of means, including, but not limited to: (a) any other method of computing a correlation even if the relationship is not linear; and/or (b) other methods of computing a correlation among more than two random variables, …”}; which together are the same as claimed limitations above to include ‘grouping attributes based on relationships from previous iterations of analysis’)
(see at least: Rubenstein ibidem and citations listed above)
With respect to Claim 25, van Dam, Madden, Smith, Li and Rubenstein teach ---
25. (New) The computer-implemented system of claim 21, wherein the neural network is further trained using at least one of document structure, document type, or document relationships.
(see at least: van Dam ibidem and citations listed above; and paras [0010], [0028], [0050] & [0055] for “document type”; and paras [0047], [0049] & [0051] for “type of document”; which together are the same as claimed limitations above to include ‘at least one of document type’)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above to include ‘a neural network machine learning model’)
With respect to Claim 26, van Dam, Madden, Smith, Li and Rubenstein teach ---
26. (New) The computer-implemented system of claim 21, further including
determining an account associated with the one or more images of documents.
(see at least: van Dam ibidem and citations listed above to include image and transaction reconciliation computer 120 for ‘one or more images of documents’)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above; and para [0030] about {“…… In other embodiments, the check images are received from image owners, account holders, joint account holder, agents of account holders, family members of account holders, financial institution customers, payors, payees, third parties, and the like. …”}; and para [0031] about {“…… For example, the customer may upload a check image to deposit funds into an account or pay a bill via a mobile banking application using a capture device. …”}; and para [0059] about {“...... The customer includes image owners, account holders, agents of account holders, family members of account holders, financial institution customers, merchant customers, and the like. In some embodiments, the check image comprises a check issued by a payor having a third party bank account. …”}; & para [0064] about {“…… For example, the system may determine that the check image is part of the ACH agreement based on the identity of the customer or the account associated with the check image. Further still, the customer may include details regarding the check image when uploading the check image to an online account or ATM, or when otherwise depositing the check. …”}; which together are the same as claimed limitations above to include ‘determining an account associated with the one or more images of documents’)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above)
With respect to Claim 27, van Dam, Madden, Smith, Li and Rubenstein teach ---
27. (New) The computer-implemented system of claim 26, wherein determining the transaction associated with the account further comprises at least one of:
identifying an expenditure associated with the account;
identifying a payment associated with the account; or identifying a payment associated with one or more transactions.
(see at least: van Dam ibidem and citations listed above; and para [0021] about {“......Examples of transaction systems 112 may include bank core account processing systems; online banking systems; bill payment systems; person-to-person payment systems; funds transfer systems; retail payment systems.”}; & para [0024] about {“…… For example, a payment transaction may be in the payment history associated with a payment system, in a core account processing system when the transaction is posted to the financial account, …”}; which together are the same as claimed limitations above to include ‘a payment associated with the account’ or ‘a payment associated with one or more transactions’)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above; and para [0003]--[0006], [0058]--[0061], [0063] & [0071]-[0073] for “payment account”; & para [0132] about {“…… The entity can include, but is not limited to: (a) any entity enabling payment for an object and decreasing the unit price of the object and/or increasing the number of units of the object for a given price, e.g., by offering cash back for using the entity's payment method and/or payment account to purchase the object; and/or (b) any entity enabling payment for an object and decreasing the unit price of related objects and/or increasing the number of units of the related objects for a given price, e.g., by offering points for using the entity's payment method to purchase a Product A that can be redeemed for goods or services other than Product A, like travel services. The entity can include, but is not limited to: (a) any entity enabling payment for an object or related objects through linking the payment to an account not stored on the device executing the Transaction, e.g., a card offered by an issuer of credit and/or debit; and/or (b) any entity enabling payment for an object or related objects through linking the payment to an account stored on the device executing the Transaction, which can include, but is not limited to: (i) a payment method commonly referred to as a stored value card, e.g., a prepaid card or a gift card; and/or (ii) a payment method including payment account data stored on the device executing the Transaction, …”}; which together are the same as claimed limitations above to include ‘a payment associated with the account’; AND para [0132] about {“......e.g., by offering cash back for using the entity's payment method and/or payment account to purchase the object; ……… (ii) a payment method including payment account data stored on the device executing the Transaction, ……… and/or miles redeemable for travel, hotel, and/or other types of expenses. …”}; which together are the same as claimed limitations above to include ‘an expenditure associated with the account’)
(see at least: Rubenstein ibidem and citations listed above)
With respect to Claim 28, van Dam, Madden, Smith, Li and Rubenstein teach ---
28. (New) The computer-implemented system of claim 21, the order of the one or more images of documents varies based on a transaction.
(see at least: van Dam ibidem and citations listed above to include image and transaction reconciliation computer 120 for ‘one or more images of documents’; and para [0009] about {“.... For example, the system may determine a "best-fit" transaction based at least in part on a matching confidence level associated with each of the identified candidate financial transactions. Images may be automatically associated with the "best-fit" transaction. Transactions may be ordered according to their respective matching confidence levels.…”}; and para [0065] about {“…… Determining a matching confidence level for each of the candidate financial transactions may be based at least in part on any number of a variety of factors, which may include but are not limited to the number of matched fields between the image and candidate financial transaction, the tolerance range associated with each matched text field, and different weighting associated with the matching of different fields. The ITRS 118 may then order the set of candidate financial transactions based on their respective matching confidence levels. …”}; which together are the same as claimed limitations above to include ‘the order of the one or more images of documents’)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above; and para [0034] about {“…… the term quadrant is used broadly to describe the process of differentiating elements of a check image by separating portions and/or elements of the image of the check into sectors in order to define the location fields. …… In some embodiments, the system identifies each portion of the image of the check using a plurality of quadrants. In such an embodiment, the system may further analyze each quadrant using the OCR algorithms in order to determine whether each quadrant has valuable or useful information. …”}; which together are the same as claimed limitations above to include ‘the order of the one or more images of documents’)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above)
With respect to Claim 29, van Dam, Madden, Smith, Li and Rubenstein teach ---
29. (New) The computer-implemented system of claim 21, wherein the order of the documents is based on document type.
(see at least: van Dam ibidem and citations listed above to include ‘at least one of document type’ and ‘the order of the one or more images of documents’)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above to include ‘the order of the one or more images of documents’)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above)
With respect to Claim 30, van Dam, Madden, Smith, Li and Rubenstein teach ---
30. (New) The computer-implemented system of claim 21, wherein the grouping is further used to determine a status of the transaction.
(see at least: van Dam ibidem and citations listed above)
(see at least: Madden ibidem and citations listed above)
(see at least: Smith ibidem and citations listed above)
(see at least: Li ibidem and citations listed above)
(see at least: Rubenstein ibidem and citations listed above; & paras [0018] and [0024] for “status updates”; which together are the same as claimed limitations above to include ‘a status’)
With respect to Claims 31-40, the limitations of these method claims are rejected under 35 USC 103 based on the exemplary analysis above for the rejection of system Claims 21-30 as described above using cited references of van Dam in view of Madden, Smith, Li, Rubenstein and Amtrup, because the limitations of these method Claims 31-40 are commensurate in scope to limitations, and thus duplicates, of the above rejected system Claims 21-30 as described above.
Conclusion
The prior art made of record and not relied upon, listed in Form 892, that is considered pertinent to the Applicant's disclosure and review for not traversing already issued patents and/or claimed inventions by the claims of the current invention of the Applicant. Please Note that Form 892 contains more references than those cited in the rejection above under 35 USC 103, and all the references cited on said Form 892 are relevant to this application that form a part of the body of prior art.
The Examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. The Applicant should consider the entire prior art as applicable as to the limitations of the claims; and said prior art includes references with synonyms for terms used in the claims that have been interpreted under the BRI (broad reasonable interpretation) procedures of the Office. It is respectfully requested from the Applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Sanjeev Malhotra whose telephone number is (571) 272-7292. The Examiner can normally be reached during Monday-Friday between 8:30-17:00 hours on a Flexible schedule.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, the Applicant is encouraged to contact the Examiner directly.
If attempts to reach the Examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached on (571) 270-1836. The facsimile/fax phone number for the organization, where this application or proceeding is assigned, is 571-273-8300.
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Prior to initiating the first e-mail correspondence with an Examiner, Applicant is
responsible for filing a written statement with the USPTO in accordance with MPEP §502.03(II). All received e-mail messages including e-mail attachments shall be placed into this application’s record. The Examiner’s e-mail address is provided below at the end of this Office Action.
/S.M./
PSA Examiner, Art Unit 3691
sanjeev.malhotra@uspto.gov
/SANJEEV MALHOTRA/Examiner, Art Unit 3691
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