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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 14, 2026 has been entered.
Response to Arguments
This office action is in response to the amendment filed 1/14/2023. Claims 1-3, 5-9, and 11-12 are pending in this application and have been considered below. Claims 4 and 10 are canceled by the applicant.
Applicant’s arguments with respect to claims 1-3, 5-9, and 11-12 have been considered but are moot in view of new ground(s) of rejection, Van Deventer et al. (US 8,995,774 B) in view of McMahan et al. (US 11,475,350 B2), because of the amendments.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-9, and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Van Deventer et al. (US 8,995,774 B1 – hereinafter “Van Deventer”) in view of McMahan et al. (US 11,475,350 B2 – hereinafter “McMahan”)
Claims 1 and 7.
Van Deventer discloses a user terminal for distributed learning of an ID card recognition model, the user terminal (Van Deventer C4:L25-30 discloses “When the system 200 is a server-based distributed application, it may include a central component residing on a server and one or more client applications residing on client devices and communicating with the central component via a network 110 (emphasis added).”) comprising:
a receiving unit configured to receive an ID card image (Van Deventer C4:L21-25 discloses “A system 200 for automated document recognition, identification, and data extraction may provide document recognition capabilities via an online portal, special-purpose application, cloud-based application, server-based distributed application, and so forth.”; C5:L10-12 discloses “The image 145 of the ID document 120 may be taken by a camera associated with the user 140.”; See also C7:L20-30 for the various types of documents received by the camera on the smart phone);
an outputting unit configured to output an ID card recognition result using the ID card recognition (Van Deventer teaches processing the image to extract data, identifying the document type, and structuring the data; See C6:L60-45, C7:L45-60, C8:L50-67) that (C5:L10-12 discloses “The image 145 of the ID document 120 may be taken by a camera associated with the user 140.”);
wherein the ID card recognition model is released to the user terminal so that the user terminal uses the ID card recognition model (Van Deventer teaches providing the recognition application to the user/client device to perform recognition; see C4:L20-30),
Van Deventer discloses all of the subject matter as described above except for specifically teaching the portions of the claim that have been stricken through. However, McMahan in the same field of endeavor teaches model … that performs at least one convolution operation (McMahan discloses CNNs; see C7:L55-60) … a training unit configured to train the ID card recognition model using the ID card recognition result and generate update information regarding the at least one convolution operation (McMahan teaches federated learning which includes training a neural network model on a client device to update a global model; see C3:L10-20 "client computing devices can be used as nodes for performing computation on their local data in order to update a global model"; see also C4:L40-50); and a transmitting unit configured to transmit the update information to a server updating the ID card recognition model (McMahan teaches sending the model update; see C3:L20-35 "only the less sensitive model update can be transmitted"; C5:L15-25 "provide the local update to the one or more server computing devices"), wherein the training unit is further configured to generate weight change information of a layer performing the convolution operation as the update information by comparing an output value of the ID card recognition model with a user response (McMahan teaches calculating a gradient/difference based on "actual user inputs" [user response] compared to the model parameters; see C1:L15-20; see also C4:L40-60 "determining the local update can include determining a difference between the locally-trained model and the machine-learned model ... difference between the global set of parameters ... and the updated local values"; C10:L60-65 "trained by datasets including actual user inputs"; C5:L5-15 "The local update can be determined by clipping each layer j") … wherein the ID card recognition model is re-released to the user terminal and updated using the transmitted update information periodically according to a regular release cycle of the release or re-released ID card recognition model or irregularly in response to an occurrence of a set event (McMahan teaches the server updates the global model and provides it back to clients [re-release] in an iterative process driven by device availability [events] or probability [periodic/regular]; see C3:L55-C4:5 "providing ... the updated machine-learned model to one or more client computing devices"; McMahan teaches selecting devices for these updates based on probability parameters [regular/periodic cycle] or availability status such as being idle/plugged-in [set event]; see C4:L5-15 "available client computing devices can be client computing devices which are powered on, plugged into a power source, and/or in an idle mode" [set event]; C4:L20-25 "random-sized batch ... independently selected with a probability q" [regular/periodic cycle]).).
It would have been obvious to one of ordinary skill in the art to modify the ID card recognition system of Van Deventer to incorporate the distributed learning, weight update generation, and re-release cycle taught by McMahan before the effective filing date of the claimed invention. The motivation for this combination of references would have improve the accuracy of the recognition model over time by learning from actual user corrections and inputs while simultaneously preserving user privacy and reducing network bandwidth. McMahan teaches that transmitting "model updates" (weight changes) instead of raw data (which Van Deventer handles) ensures that sensitive user information remains local on the device (see McMahan C3:L20-35 "user data that is privacy sensitive can remain at the user's computing device and need not be uploaded"). This is particularly advantageous for Van Deventer's ID card system, which handles personal identification documents. Furthermore, implementing the periodic or event-driven re-release cycle of McMahan would ensure the Van Deventer system remains up-to-date with the latest recognition capabilities without continuously draining device resources.
Claims 2 and 8.
The combination of Van Deventer and McMahan discloses the user terminal of claim 7, wherein the update information is generated using the user response for the ID card recognition result provided through the user terminal (McMahan teaches the model is trained on “datasets including actual user input on users’ mobile devices” rather than proxy data; See C1:Background, C7:§2.1. Van Deventer teaches presenting structured data and recognition results to the user on the mobile device for verification or filling forms; see C6:L5-20, C8:L15-25).
Claims 3 and 9.
The combination of Van Deventer and McMahan discloses the user terminal of claim 7, wherein the ID card recognition model is updated by verifying the transmitted update information based on the layer or a version of the ID card recognition model (McMahan teaches “per-layer clipping” where the update “delta” for each specific layer j is evaluated and clipped independently to ensure the norm is bounded before updating the model; see C5:L5-15, C11:L5-15. McMahan further teaches an iterative process of updating relative to the “global set of parameters” [current version] and applied to generate an “updated machine-learning model” [new version]; see C4:L25-35 & 50-60).
Claims 5 and 11.
The combination of Van Deventer and McMahan discloses the user terminal of claim 7, wherein the ID card recognition model generates one-time update information according to an input of a captured single ID card image (Van Deventer teaches “A still image of the improved representation of the ID document may be automatically extracted from the video stream”; see C3:L40-45, C5:L35-55. It would have been obvious to generate this as a “one-time” update for that specific interaction. McMahan teaches generating a “local update” which is then discarded or summarized into the global update; see C3:L10-20).
Claims 6 and 12.
The combination of Van Deventer and McMahan discloses the user terminal of claim 8, wherein the training unit is further configured to perform a training process of training the ID card recognition model in a background environment according to an input of the ID card recognition result reflecting the user response, and wherein the transmitting unit is further configured to transmitting the update information generated by performing the training process to the server (McMahan teaches the system selects client devices that are “available,” which are defined as "powered on, plugged into a power source, and/or in an idle mode" thereby ensuring the training occurs in the background without disrupting the user; see C3:L10-20, C19:L55-65. See also C4:L35-60 "provide the local update to the one or more server computing devices … determining the local update can include determining a difference between the locally-trained model and the machine-learned model ... difference between the global set of parameters ... and the updated local values").
Conclusion
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/Ross Varndell/Primary Examiner, Art Unit 2674