DETAILED ACTION
This action is in response to the filing of 6-23-2023. Claims 1-20 are pending and have been considered below:
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.
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 1-20 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Adimatyam et al. (“Adimatyam” 20150067531 A1) in view of Allen et al. (“Allen” 20180239500 A1) and “Deep sequential recommendation for personalized adaptive user interfaces” Soh et al. “Soh” Pages 1-6, 3-2017.
Claim 1: Adimatyam discloses a computing platform, comprising: at least one processor (Paragraph 68); a communication interface communicatively coupled to the at least one processor (Paragraphs 67-68); and a memory storing computer-readable instructions that (Paragraph 70), when executed by the at least one processor, cause the computing platform to:
receive historical user data from a plurality of data sources(Paragraph 14; historical data);
receive, from a user and via a first computing device, a user request for event processing; receive, from at least one data source of the plurality of data sources, user specific data associated with the user (Paragraph 46; collection of user data);
transmit, to the first computing device, the first recommended modification, wherein transmitting the first recommended modification causes the first computing device to display the first recommended modification of the at least one of:
the system or the user interface; receive, from the first computing device, acceptance of the first recommended modification of the at least one of: the system or the user interface (Paragraphs 58, 83-84 and 93-94; recommended interface);
generate an instruction to modify the at least one of: the system or the user interface based on the acceptance of the first recommended modification of the at least one of: the system or the user interface; transmit, to at least the first computing device, the instruction to modify the at least one of: the system or the user interface, wherein transmitting the instruction to modify the at least one of: the system or the user interface causes the first computing device to execute the instruction and modify the at least one of: the system or the user interface(Paragraphs 58, 83-84 and 93-94; instruction to modify interface);
and update, based on at least the first recommended modification of the at least one of: the system or the user interface(Paragraphs 58, 83-84 and 93-94; interface updated);, the machine learning model;
Adimatyam discloses different sources but may not explicitly disclose wherein the historical user data is captured via a plurality of computing devices;
Allen is provided because it discloses a dynamic interface functionality which collects historical data from different data sources/computing devices (Paragraphs 5, 42, 44 and 51; data sources internal/external).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the plurality of data sources for machine learning models in order to improve predictions in Adimatyam. One would have been motivated to provide the functionality because this method provides effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with providing customized, dynamic interfaces via a plurality of different systems (Allen: Paragraph 4).
Adimatyam also may not explicitly disclose train, using the historical user data, a machine learning model to generate recommended modifications of at least one of: a system or a user interface, wherein the recommended modification of the at least one of: the system or the user interface includes modifying at least one of: functionality or a display of the at least one of: the system or the user interface;
Nor disclose execute the machine learning model, wherein executing the machine learning model includes using, as inputs, the user specific data, to output a first recommended modification of the at least one of: the system or the user interface; Soh is provided because it discloses a personalized user interface functionality implemented with neural network models (abstract), the interface is customized based on user data collected and provided to the model (Figure 1 and Page 3 Deep sequential recommendation).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate machine learning models for making predictions using the user data found in Adimatyam. One would have been motivated to provide the functionality because this method delivers a preferable user experience, increases usability and real-time adaptability (Soh: Introduction).
Claim 2: Adimatyam, Allen and Soh disclose a computing platform of claim 1, wherein the plurality of data sources includes internal data sources and external data sources (Adimatyam: Paragraphs 14 and 16; historical data from interface and set-top box and Allen: Paragraphs 5, 42, 44 and 51; data sources internal/external).
Claim 3: Adimatyam, Allen and Soh disclose a computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: receive, from the user and via a second computing device, a subsequent request for event processing; and transmit, to at least the second computing device, the instruction to modify the at least one of: the system or the user interface, wherein transmitting the instruction to modify the at least one of: the system or the user interface causes the second computing device to execute the instruction and modify the at least one of: the system or the user interface (Adimatyam: Paragraph 17; second device with custom interface, i.e. mobile device and Allen: Paragraphs 22 and 31; kiosk or mobile).
Claim 4: Adimatyam, Allen and Soh disclose a computing platform of claim 3, wherein the first computing device is a self- service kiosk of an enterprise organization and the second computing device is a mobile device of the user (Adimatyam: Paragraphs 17 and 28; multiple devices including mobile device and desktop and Allen: Paragraph 22; kiosk or mobile).
Claim 5: Adimatyam, Allen and Soh disclose a computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: receive, from the at least one data source, additional user specific data; and execute the machine learning model, wherein executing the machine learning model includes using, as inputs, the additional user specific data, to output a second recommended modification of the at least one of: the system or the user interface (Adimatyam: Paragraph 41 and Soh: Figure 1; history updated which provides additional customizations and Allen: Paragraph 64; updates).
Claim 6: Adimatyam, Allen and Soh disclose a computing platform of claim 1, wherein the first recommended modification of the at least one of: the system or the user interface includes a modification of at least one of: a font size, a volume of audio output, a number of functions available, and terminology provided to the user (Adimatyam: Paragraphs 13; resize or rename; 58 (change widgets available), Allen: Paragraphs 47 and 60; font size modification and Soh: Figure 1; elements reduced in size).
Claim 7: Adimatyam, Allen and Soh disclose a computing platform of claim 1, wherein transmitting, to at least the first computing device, the instruction to modify the at least one of: the system or the user interface further includes transmitting the instruction to a back end server, wherein transmitting the instruction to the back end server causes the back end server to execute the instruction and modify the at least one of: the system or the user interface (Adimatyam: Paragraphs 27 and 30; server and Allen: Paragraphs 99 and 102; server).
Claims 8 and 15 are similar in scope to claim 1 and therefore rejected under the same rationale.
Claim 8 method further comprising (Allen: Paragraph 67)
Claim 15 non-transitory computer readable medium (Allen: Paragraphs 95 and 105)
Claims 9 is similar in scope to claim 2 and therefore rejected under the same rationale.
Claims 10 and 16 are similar in scope to claim 3 and therefore rejected under the same rationale.
Claims 11 and 17 are similar in scope to claim 4 and therefore rejected under the same rationale.
Claims 12 and 18 are similar in scope to claim 5 and therefore rejected under the same rationale.
Claims 13 and 19 are similar in scope to claim 6 and therefore rejected under the same rationale.
Claims 14 and 20 are similar in scope to claim 7 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Tsai et al. ( 20210350391 A1) 0051 (dynamic updates)
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
1-30-2026