CTNF 18/735,433 CTNF 94259 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-01-aia AIA 07-03-01-r-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is made non-final . Claims 1-20 are pending in the case. Claims 1, 12, and 17 are independent claims. Claim Objections 07-29-01 AIA Claim s 7 and 8 are objected to because of the following informalities: Claim 7 recites “wherein each data objects of the tabular data object” but this is a grammatical error and the underlined element should be replaced with the singular “data object”. Claim 8 recites “ therein the tabular data object” but this is a typographical error and the underlined element should be replaced with “wherein” . Appropriate correction is required. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-5, 12-16, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goodman et al. (US 2005/0257148 A1), and in view of Peterson et al. (US 2017/0357627 A1) . Regarding claim 1 , Goodman teaches a computer-implemented method, the method comprising: receiving first input data from a user, the first input data including a first field value for a first field on a user interface form provided on a user interface at a display device; in response to receiving the first input data , invoking a trained model for tabular data imputation to predict values for one or more other user interface fields of the user interface form based on the first field value for the first field (FIG. 10 and [0092-0094]: in response to receiving some input at 1030, a trained model is invoked to predict values for other UI fields of the form; For details regarding the invoked trained model for tabular data imputation, see FIG. 1 and [0046-0055] and trained intelligent autofill system 500 of FIG. 5 and [0072-0086]) ; providing one or more predicted field data values for the one or more other user interface fields on the user interface form based on an output of the trained model as recommendations for the user (FIG. 10 and [0092-0094]: one or more predicted field data values are provided based on the output of the trained model. These predicted field data values are autofilled by the trained intelligent autofill system; For details regarding the invoked trained model for tabular data imputation, see FIG. 1 and [0046-0055] and trained intelligent autofill system 500 of FIG. 5 and [0072-0086]) ; receiving second input data from the user including a second field value for a second field of the one or more other user interface fields, wherein the second input data is confirming or modifying a respective predicted field data value for the second field (FIG. 10 and [0092-0094]: at 1040, second input data is received from the user to modify a respective predicted field data value for a second field) ; in response to receiving the second field value from the user, automatically invoking the trained model to predict a third field value for a third field of the user interface form based on the first field value for the first field and the received second field value for the second field (FIG. 10 and [0092-0094]: at 1050, the trained model is invoked again to predict a third field value based on the first field value and the received second field value) ; and providing the third field value for the third field on the user interface form in addition to previously provided predicted or confirmed field data values for fields of the user interface form (FIG. 10 and [0092-0094]: As described in [0094], “After overriding, the autofill feature can be invoked again such as to fill in the remaining fields. The overwritten fields are not affected, however. In addition, the autofill feature can observe the data manually entered by the user and then autofill the rest of the fields using data that is relevant to the overwritten data.” The third field value is provided in addition to the previously provided predicted or confirmed field data values) . Although Goodman teaches receiving some input to invoke a trained model for tabular data imputation, Goodman does not explicitly teach receiving first input data from a user, the first input data including a first field value for a first field on a user interface form provided on a user interface at a display device; in response to receiving the first input data , invoking a trained model for tabular data imputation to predict values for one or more other user interface fields of the user interface form based on the first field value for the first field. Peterson teaches receiving first input data from a user, the first input data including a first field value for a first field on a user interface form provided on a user interface at a display device; in response to receiving the first input data , invoking a trained model for tabular data imputation to predict values for one or more other user interface fields of the user interface form based on the first field value for the first field (FIGS. 8U-W, [0179], and [0251-0254]: for example, a first input data is the user typing a name in a text input field “First Name”. In response to receiving the first input data, an autofill process, trained by user amendments/corrections as supported in [0030], may be invoked, resulting in a form like one seen in FIG. 8W). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Goodman by incorporating the teachings of Peterson so as to include receiving first input data from a user, the first input data including a first field value for a first field on a user interface form provided on a user interface at a display device; in response to receiving the first input data , invoking a trained model for tabular data imputation to predict values for one or more other user interface fields of the user interface form based on the first field value for the first field . Doing so would allow receiving of first input data that narrows down relevant information to prevent wasting of processing resources. For example, in Peterson, by receiving first input data for a first field value corresponding to some person’s name, the user may prompt autofill of relevant information for the desired person. In this way, the processing resources are less likely to be wasted on autofill of irrelevant information, such as that of an unintended party. Regarding claim 2 , Goodman in view of Peterson teaches the method of claim 1. Goodman in view of Peterson further teaches wherein the trained model predicts the third field value for the third field of the user interface form based on only the received first and second input data from the user without using other field data values from the provided one or more predicted field data values as recommendations for the user interface form (Goodman, FIG. 10 and [0092-0094]: the third field value may be predicted based on only some input data at 1030 and the second input data at 1040 without using other field data values) ((Peterson, for the first data object corresponding to the first field value aspect, FIGS. 8U-W, [0179], and [0251-0254])). Regarding claim 3 , Goodman in view of Peterson teaches the method of claim 1. Goodman in view of Peterson further teaches wherein, in response to receiving the first field value for the first field and the second field value for the second field from the user, updating a tabular data object stored for the user interface form by updating a first data object and a second data object to store data according to the first field value and the second field value, wherein the first data object corresponds to the first field and the second data object corresponds to the second field (Goodman, FIG. 10 and [0092-0094]: for example, the form fields form a tabular data object, including updating a second data object from a user’s name to that of their brother’s name for the NAME field) (Peterson, for the first data object corresponding to the first field value aspect, FIGS. 8U-W, [0179], and [0251-0254]) . Regarding claim 4 , Goodman in view of Peterson teaches the method of claim 1 . Goodman in view of Peterson further teaches comprising: in response to receiving fourth input data from the user including a fourth field value for a fourth field of the user interface form, the fourth field being different from the first and second fields, invoking the trained model to predict data for at least one other field of the user interface form based on the first field value, the second field value, and the fourth field value (Goodman, FIG. 10 and [0092-0094]: at 1040, fourth input data for a fourth field may be received from a user in addition to second input data for a second field) (Peterson, for the first data object corresponding to the first field value aspect, FIGS. 8U-W, [0179], and [0251-0254]) . Regarding claim 5, Goodman in view of Peterson teaches the method of claim 1 . Goodman further teaches wherein the received second input data from the user for the second field is to modify the respective predicted field data value for the second field as provided as a recommendation to the second field value (FIG. 10 and [0092-0094]: at 1040, second input data is received from the user to modify a respective predicted field data value for a second field) . Goodman does not explicitly teach wherein receiving the second input data to modify the respective predicted field data value comprises receiving a selection of a set of options for available field data values for the second field, the set of options for available field data values being configured as predefined options for the second field. Peterson teaches wherein receiving the second input data to modify the respective predicted field data value comprises receiving a selection of a set of options for available field data values for the second field, the set of options for available field data values being configured as predefined options for the second field (FIGS. 8L-N and [0241]: see the set of options 884a, 884b, and 884c. The user may select from the set of options to modify the respective predicted field data value). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Goodman in view of Peterson by incorporating the further teachings of Peterson so as to include wherein receiving the second input data to modify the respective predicted field data value comprises receiving a selection of a set of options for available field data values for the second field, the set of options for available field data values being configured as predefined options for the second field. Doing so would preclude the user from manually typing in each character the second field, saving the user time and energy. Furthermore, having a set of options displayed may prevent the user from forgetting a relevant option that otherwise may not be displayed to remind the user. Regarding claims 12-16 , the claims recite a non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations (Goodman, FIG. 11 and [0097-0105]) , comprising those corresponding to the method of claims 1-5, respectively, and are therefore rejected on the same premises. Regarding claims 17-20 , the claims recite a computer-implemented system, comprising: one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers (Goodman, FIG. 11 and [0097-0105]) , perform one or more operations, comprising those corresponding to the method of claims 1-4, respectively, and are therefore rejected on the same premises . 07-21-aia AIA Claim 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goodman et al. (US 2005/0257148 A1), in view of Peterson et al. (US 2017/0357627 A1), and in view of Furbish et al. (US 2023/0097572 A1) . Regarding claim 7 , Goodman in view of Peterson teaches the method of claim 1. Goodman in view of Peterson does not explicitly teach wherein the user interface form is associated with a tabular data object stored at a respective storage associated with the user interface form, wherein each data objects of the tabular data object corresponds to a respective user interface field of the user interface form. Furbish teaches wherein the user interface form is associated with a tabular data object stored at a respective storage associated with the user interface form, wherein each data objects of the tabular data object corresponds to a respective user interface field of the user interface form (FIG. 1 and [0037-0041]: UI form associated with tabular data object stored at a respective storage/repository 140 wherein each data object corresponds to a respective UI field of the UI form). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Goodman in view of Peterson by incorporating the teachings of Furbish so as to include wherein the user interface form is associated with a tabular data object stored at a respective storage associated with the user interface form, wherein each data objects of the tabular data object corresponds to a respective user interface field of the user interface form. Doing so would allow organized storage for later reference of the tabular data object. In this way, data objects of the tabular data object may be more efficiently retrieved and implemented by the user . Allowable Subject Matter 07-43 Claims 6 and 8-11 are objected to as being dependent upon a rejected base claim and/or for other issues, but would be allowable if corrected and/or rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure, including: US 2024/0303418 A1: intelligent form filling process that allows user to edit prepopulated values US 12353825 B2: machine learned bundle classifier model traned using labeled webpages for applying updated autofill template US 8839090 B2: automatically completing form using a master cookie file Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNY NGUYEN whose telephone number is (571)272-4980. The examiner can normally be reached M-Th 7AM to 5PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KIEU D VU can be reached on (571)272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KENNY NGUYEN/Primary Examiner, Art Unit 2171 Application/Control Number: 18/735,433 Page 2 Art Unit: 2171 Application/Control Number: 18/735,433 Page 3 Art Unit: 2171 Application/Control Number: 18/735,433 Page 4 Art Unit: 2171 Application/Control Number: 18/735,433 Page 5 Art Unit: 2171 Application/Control Number: 18/735,433 Page 6 Art Unit: 2171 Application/Control Number: 18/735,433 Page 7 Art Unit: 2171 Application/Control Number: 18/735,433 Page 8 Art Unit: 2171 Application/Control Number: 18/735,433 Page 9 Art Unit: 2171 Application/Control Number: 18/735,433 Page 10 Art Unit: 2171 Application/Control Number: 18/735,433 Page 11 Art Unit: 2171 Application/Control Number: 18/735,433 Page 12 Art Unit: 2171
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