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
Claims 1-7, 9-20 have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/27/26 has been entered.
Response to Arguments
Applicant's arguments with respect to the claims have been considered but are moot in view of the new ground(s) of rejection. On 5/27/26, Applicant amended the independent claims. Applicant’s remarks address these amended features. See the new 103 with the addition of Heitmueller.
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.
Claims 1- 7, 10-13, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Nam (20180330394) in view of Heitmueller (20130211890) in view of Kalenkov (20190294921).
Claims 1, 10, 16. Examiner notes that deduction backup is interpreted as transaction information (Applicant Spec at [28]). Nam discloses a method of processing deductions for promotions comprising, at a computing system:
receiving a deduction backup including deduction charges ([17, 19]), wherein the deduction charges corresponds to previously completed transaction, and wherein the previously completed transaction is associated with a deduction previously applied during the previously completed transaction (see [19, 30] and amount paid and processing payment linked promotion).
Nam does not explicitly disclose including a set of deduction charges or to a set of previously completed transactions, and wherein the set of previously completed transactions is associated with a deduction previously applied during the set of previously completed transactions. That is, while Nam discloses deduction charges and previous transaction, Nam does not explicitly disclose a set of deduction charges or set of previous transactions. However, Nam discloses “[17]… a plurality of backup promotion data” and Nam discloses extracting backup promotion data [29] and transferring extracted ad data [126]. And, Heitmueller discloses batch processing of transactions [84] and batch and coupon processing [97, 89] and using batches table and discounts/coupons [153]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Heitmueller’s batch transferring and processing of transaction and discount information to Nam’s plurality of promotions and reconciling discount and transaction information. One would have been motivated to do this in order to better process discount and transaction information.
Nam further discloses extracting a detail of the deduction charge included in the deduction backup ([19, 20]).
Nam does not explicitly disclose dynamically training a machine learning algorithm to locate and extract details corresponding to different deduction charges included in different deduction backups, wherein the machine learning algorithm is dynamically trained using a dataset of sample deduction backups, characteristics of the sample deduction backups, and annotations corresponding to deduction charges included in the sample deduction backups;
processing the deduction backup using the machine learning algorithm to extract a set of details of the set of deduction charge included in the deduction backup, wherein the machine learning algorithm extracts the set of details by predicting locations of the set of details within the deduction backup, and wherein the locations are predicted according to a set of characteristics associated with the deduction backup.
For the preceding feature, Examiner notes Applicant Spec and annotations at [43, 44, 48, 52, 64, 77] as notes or mapping or corrections or bounding boxes or manually analyzed or feedback or template info to identify or translation objects. Note applicant Spec and template and annotate at [42, 43, 44, 64, 77]. However, Nam discloses locate and extract details corresponding to different deduction charges included in different deduction backups (see extract at [17, 19, 20] and note offline and coupon at [9]) and also automatically updating [30]. And, Kalenkov discloses scanning a document [24, 25] and that documents like checks can be scanned and expected/predicted fields for data used for scanning and then inputting the actual data [18, 22] and dynamically training a machine learning algorithm to locate and extract details corresponding to different data fields in different types of data categories that are scanned, wherein the machine learning algorithm is dynamically trained using a dataset of sample data, characteristics of the sample data types, and annotations corresponding to particular fields in the sample data (note scanning [24, 25] and different document types and expected field for different data [18, 22] and machine learning training [20] and templates [18, 27]);
processing the data using the machine learning algorithm to extract a detail of the data included in the data type, wherein the machine learning algorithm extracts the detail by predicting a location of the detail within the data, and wherein the location is predicted according to a set of characteristics associated with the data type (note scanning [24, 25] and different document types and expected field for different data [18, 22] and machine learning training [20] and templates [18, 27]; also for predict note expected and template at [18, 32, 43]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s machine learning and scanning documents to extract field data to Nam’s offline coupons processing and extracting data. One would have been motivated to do this in order to better extract the proper data (as Kalenkov states at [22] with manual work replaced by machine learning). Also, in regards to a set of deduction charges and set of transactions, Nam in view of Heitmueller renders that obvious as shown above.
Nam does not explicitly disclose updating an interface to present the set of details of the set of deduction charges and a prompt for validation of the set of details of the set of deduction charges; processinq a response to the prompt to update the dataset, wherein the dataset is updated by annotating the deduction backup according to the response, and wherein the updated dataset is used to dynamically retrain the machine learning algorithm. However, Nam discloses confirming information on discount rate processing [53] and using interfaces for processing promotions (see interface at [20, 30]). And, Kalenkov further discloses training machine learning models for scanning particular document fields and using template (see template and train at [18, 27]) and manually marking during training [26]. And, Kalenkov further discloses a training data set and training and correctly marking the data [6], training data and templates and assessing quality of results for different fields [18], and training with datasets and correctly marking [20], confirming and rebutting training hypotheses [27] and manual input during training [26]. And, Kalenkov discloses using interfaces for input [61, 62]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s using machine learning and training datasets and user input for training data for machine learning to Nam’s processing promotions and payments. One would have been motivated to do this in order to better extract the proper data (as Kalenkov states at [22] with manual work replaced by machine learning). Also, in regards to a set of deduction charges and set of transactions, Nam in view of Heitmueller renders that obvious as shown above.
Nam further discloses generating a deduction charge record corresponding to the set of deduction charges, wherein the deduction charge record includes one or more updated details of the set of deduction charges (see record and backup at [75, 92]; Also, in regards to a set of deduction charges and set of transactions, Nam in view of Heitmueller renders that obvious as shown above);
determining whether the set of deduction charges and the applied deduction are valid by comparing one or more fields of the deduction charge record to criteria for one or more active promotions (see “[20]… so as to pay the discount rate by determining whether the remaining accumulated discount rate is present at the time of receiving information on the discount rate selected by the user.” And “[30]… f) determining whether the remaining accumulated discount rate is present at the time of receiving the selection signal for the specific discount rate, by the promotion processing server 10; and g) providing the discount rate to the user when the remaining accumulated discount rate is present,” see failed at [36, 104]; see exhaust at [10] and Remaining and confirm and exhaust at [11]; see same time zone as another criteria at [27]; Also, in regards to a set of deduction charges and set of transactions, Nam in view of Heitmueller renders that obvious as shown above); and
updating the deduction charge record based on whether the set of deduction charges and the applied deduction meet the criteria of at least one of the one or more active promotions (note that the remaining amount is updated if the deduction charge is approved so that the current remaining amount is accurate, see “[30]… d) automatically updating information on a remaining accumulated discount rate over time, by the promotion processing server 10”; see [20, 30] preceding and also failed at [36, 104]; Also, in regards to a set of deduction charges and set of transactions, Nam in view of Heitmueller renders that obvious as shown above).
Nam does not explicitly disclose and wherein the one or more updated details of the set of deduction charges are generated by updating the set of details according to the response or updating the machine learning algorithm. However, Nam further discloses realtime updating ([30]). And, Kalenkov discloses updating the machine learning algorithm according to the updated deduction charge record (note machine learning and training and different hypotheses and confidence at [6], so it is interpreted that each of many different hypotheses is used and feedback provided to find the better or best hypotheses; also note new document at [26] so new document type and needs updated/preliminary processing) and also training and correct responses [6] and next steps during training [18] and training and confirming or rebutting [27]. Kalenkov also discloses detail fields in the plural (see fields [6]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s using machine learning and training datasets for machine learning to Nam’s processing promotions and payments. One would have been motivated to do this in order to better extract the proper data (as Kalenkov states at [22] with manual work replaced by machine learning).
Claim 2. Examiner notes Applicant Spec at [7, 73] in regards to this feature. Nam further discloses tables and servers (Figs. 1, 3, 4, 6) and realtime updating ([30]). Nam does not explicitly disclose the method of claim 1, wherein: the deduction backup is in a markup language and includes a tag corresponding to a field of the deduction charge record, and extracting details of the set of deduction charges includes extracting data from the deduction backup associated with the tag. However, Heitmueller discloses discounts [97, 89, 153] and XML, markup languages [58, 59] and tags and field tags [141]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Heitmueller’s tags and markup languages to Nam’s servers and tables. One would have been motivated to do this in order to better record the data to the tables and servers.
Claim 3. Nam further discloses the method of claim 1, wherein: the deduction backup is in a text-based format including text indicating the set of details of the set of deduction charges, and extracting the detail of the deduction charge includes extracting data from the deduction backup based on a location of the text in the deduction backup (Figs. 3, 6, 7 with the text information and the Remaining Amount column is interpreted to read on this, and note real-time updating at [30] and failing at [36, 104]]).
Claim 4, 15. Nam further discloses tables and servers with text data (Figs. 1, 3, 4, 6) and realtime updating ([30]). Nam does not explicitly disclose the method of claim 3 further comprising, at the computing system and before extracting details of the deduction charge, executing an optical character recognition algorithm on the deduction backup to convert the deduction backup into a text-based format. However, Kalenkov discloses documents and scanning [24m 25]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s documents and scanning field and text info to Nam’s discounts, servers and tables and text data. One would have been motivated to do this in order to better record the data to the tables and servers.
And, in further regards to claim 15, Nam further discloses extracting the detail of the deduction charge includes extracting data from the deduction backup based on a location of the text in the deduction backup (Figs. 3, 6, 7 with the text information and the Remaining Amount column is interpreted to read on this, and note real-time updating at [30] and failing at [36, 104]]).
Claims 5, 11, 17. Nam does not explicitly disclose the method of claim 1, wherein: the machine learning algorithm is further dynamically trained to identify a translation object corresponding to the deduction backup, wherein the translation object is identified according to the set of characteristics associated with the deduction backup; and the method further comprises applying the translation object to the deduction backup to predict the locations of the set of details within the deduction backup. However, Nam discloses deduction backups (see extract at [17, 19, 20] and note offline and coupon at [9]). And, Kalenkov discloses wherein: the machine learning algorithm is further dynamically trained to identify a translation object corresponding to the data type, wherein the translation object is identified according to the set of characteristics associated with the data type; and the method further comprises applying the translation object to the deduction backup to predict the location of the detail within the data type (note scanning [24, 25] and different document types and expected field for different data [18, 22] and machine learning training [20] and templates [18, 27]; also for predict and translation object note expected and template at [18, 32, 43]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s machine learning and scanning documents to extract field data to Nam’s offline coupons processing and extracting data. One would have been motivated to do this in order to better extract the proper data.
Claim 6, 12, 18. Nam further discloses tables and servers with text data (Figs. 1, 3, 4, 6) and realtime updating ([30]). Nam does not explicitly disclose the method of claim 1, wherein: the deduction backup includes an image of a document, and the locations correspond to one or more areas of the image that contain the set of details of the deduction charge. However, Kalenkov discloses documents and images and scanning and mapping with particular areas of importance (note scanning [24, 25] and different document types and expected field for different data [18, 22] and machine learning training [20] and templates [18, 27]; also for predict note expected and template at [18, 32, 43]). Kalenkov also discloses detail fields in the plural (see fields [6]). Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s documents and images to Nam’s discounts, servers and tables and text data. One would have been motivated to do this in order to better record the data to the tables and servers.
Claim 7, 13, 19. Nam does not explicitly disclose the method of claim 1, wherein: the machine learning algorithm is further dynamically trained to cluster the sample deduction backups according to one or more vectors of similarity to generate a set of clusters, wherein the machine learning algorithm uses the set of clusters to generate a corresponding set of templates; and the method further comprises identifying a template corresponding to the deduction backup, wherein the template is identified by the machine learning algorithm through identification of partial matches between the set of characteristics associated with the deduction backup and the set of clusters or updating, by the machine learning algorithm, the template according to the response to the prompt. However, Nam discloses deduction backups (see extract at [17, 19, 20] and note offline and coupon at [9]). And, Kalenkov discloses wherein: the machine learning algorithm is further dynamically trained to cluster the sample data according to one or more vectors of similarity to generate a set of clusters, wherein the machine learning algorithm uses the set of clusters to generate a corresponding set of templates; and the method further comprises identifying a template corresponding to the data, wherein the template is identified by the machine learning algorithm through identification of partial matches between the set of characteristics associated with the data and the set of clusters (see vector at [28, 44], see cluster at [21, 24], see template and train at [18, 27], also for partial matches see hypotheses for fields and confidence at [19]; also note scanning [24, 25] and different document types and expected field for different data [18, 22]) and also training and correct responses [6] and next steps during training [18] and training and confirming or rebutting [27]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Kalenkov’s machine learning and scanning documents to extract field data and training data and updating with correct or rebutted responses and templates for machine learning and data processing to Nam’s offline coupons processing and extracting data. One would have been motivated to do this in order to better extract the proper data (as Kalenkov states at [22] with manual work replaced by machine learning).
Claims 9, 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nam (20180330394) in view of Heitmueller (20130211890) in view of Kalenkov (20190294921) in view of Berliner (20120166283).
Claim 9, 14, 20. Nam further discloses the method of claim 1, further comprising, at the computing system and responsive to determining that the one or more deduction charges of the set of deduction charges is not valid ([36, 104]). Nam does not explicitly disclose initiating a dispute process, wherein initiating the dispute process comprises automatically generating a communication from a stored dispute communication template. However, Berliner discloses Promotions [69-73] and Dispute templates [205]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Berliner’s promotions and dispute templates to Nam’s promotions and failed promotions. One would have been motivated to do this in order to better handle failed promotions.
Nam does not explicitly disclose updating the machine learning algorithm according to a resolution of the dispute process. However, the preceding further discloses a resolution of the dispute process. And, Kalenkov discloses updating the machine learning algorithm according to input/feedback/results (note machine learning and training and different hypotheses and confidence at [6], so it is interpreted that each of many different hypotheses is used and feedback provided to find the better or best hypotheses; also note new document at [26] so new document type and needs updated/preliminary processing). And, the motivation for updating the machine learning algorithm is the same as the motivation for using machine learning as provided above.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
aa) Note parent CONs 18384792, 17018394 both abandonded;
ab) Aversa, Eggleston discloses batch processing of discount/coupon info;
ac) Apple, Arnold has tags and XML;
Volk and Ito also disclose relevant features.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARTHUR DURAN whose telephone number is (571)272-6718. The examiner can normally be reached on Mon-Thurs, 7-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARTHUR DURAN/Primary Examiner, Art Unit 3621 6/4/26
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