DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is FINAL.
This office action is in responsive to communication(s):
Amendment filed on 4/28/2026.
Application filed on 4/19/2024 with effective filing date of 4/19/2024.
The status of the claims is summarized as below:
Claims 1, 3, 5-20 are pending.
Claims 1, 14, and 20 are independent claims.
In the amendment, claims 1, 3, 6, 14-15, 19-20 are amended.
Claims 2, 4 are cancelled.
The rejections to claim 20 based on 35 USC § 101 is respectfully withdrawn.
Response to Arguments
The examiner acknowledges the amendment made to claim(s) 1, 3, 6, 14-15, 19-20, and cancellation of claims 2 and 4 in the amendment filed on 4/28/2026.
Applicant’s arguments filed 4/28/2026 have been fully considered but they are directed to newly amended language which is now rejected in a new ground of rejection in light of newly cited art Reyes.
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.
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 of this title, 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.
Claim(s) 1, 3, 6-7, 14-17, 20 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over Tourne et al. (US Pub 2019/0205872, hereinafter Tourne), in view of Reyes et al. (US Pub 20170372292, hereinafter Reyes).
Per claim 1, Tourne teaches:
A computer system, comprising: (abstract: processing of data from multiple sources and augmenting with contextual data on transactions conducted by a user; Fig. 9);
a processor; ([0043] device includes a CPU);
a communications module coupled to the processor; and ([0043] device includes a communication device);
a memory coupled to the processor, the memory storing instructions that, when executed, configure the processor to: ([0043] device include memory and storage devices);
receive an indication to view a record of a data transfer on a device; ([0017] a data display interface 114 provides transaction and payment information (record of a data transfer) on a client device to view the transaction conducted);
collect metadata associated with the data transfer and device data associated with the data transfer from the device, wherein the metadata comprises one or more of a date and a time of the data transfer, wherein the device data comprises one or more of location data, image data, and email data of or on the device, … ; ([0032] Fig. 4 shows at third and fourth rows, bank transaction or other classified transaction may be correlated with emails or supplemented with context data (device data associated with data transfer) collected from other sources; [0016] Fig. 1: where transaction data 110 includes other meta data such as time (one of a data or a time), location, user identification, vendor, etc. (metadata associated with data transfer); [0018] Fig. 1 shows device data includes other information such as email 104, calendar 108 are collected to extract contextual data to augment the transaction data);
generate a context summary of the data transfer based on the metadata and the device data using a trained machine learning model; and ([0017] Fig. 1 shows the data processing system 102, after collecting data from transaction processing system 110, email system 104, social media account 106, and calendar 108, will present a data display interface (context summary) which provide a contextualized/augmented transaction data to the client device; [0034] a trained NLP classifier is used to identify emails related to the transaction);
transmit a signal to the device to display the context summary in association with the record of the data transfer. ([0017] Fig.1: a data display interface 114 is communicated to the client device 108 to display contextualized transactions conducted).
Although Tourne teaches a transaction matcher 304 that matches parsed email data with known transaction parameters such as date of the transaction to identify specific email data as context data for the transaction ([0027]), Tourne does not explicitly teach “wherein the location data, the image data and the email data are identified to be associated with the data transfer when a date and timestamp associated respectively with the location data, the image data and the email data are within a predetermined threshold of one or more hours relative to the date and the time of the data transfer”.
However, Reyes teaches:
… and wherein the location data, the image data and the email data are identified to be associated with the data transfer when a date and timestamp associated respectively with the location data, the image data and the email data are within a predetermined threshold of one or more hours relative to the date and the time of the data transfer; ([0006] meta data associated with user device such as geo-location and images within a predetermined time/hour of the transaction time stamp are identified and retrieved).
Reyes and Tourne are analogous art because Reyes also teaches generating summary with contextual data and for images. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Tourne and Reyes before him/her, to modify the teachings of Tourne to include the teachings of Reyes so that images within a transaction time threshold can be summarized with textual description as well. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide a more complete contextual summary including not only contextual text information, but also location and images, which would provide a fuller context for generated contextual summary.
Per claim 3, Tourne-Reyes teaches all the limitations of claim 1, and further teaches:
wherein the device data further comprises one or more of calendar data, and contact data of or on the device. (Tourne [0018] Fig. 1 shows other information such as calendar 108 are collected to extract contextual data to augment the transaction data).
Per claim 6, Tourne-Reyes teaches all the limitations of claim 1, and further teaches:
wherein the instructions, when executed, further configure the processor to obtain supplementary data based on the metadata and the device data by processing the metadata and the device data (Tourne [0019-0020] Fig. 2 shows information based on the metadata such as date/time can be correlated to retrieve additional info such as business address from geo-location 220, meetings 234 from calendar 222, social event 236 from social media 224, etc. (supplementary data).
Per claim 7, Tourne-Reyes teaches all the limitations of claim 6, and further teaches:
wherein the instructions, when executed, further configure the processor to obtain the supplementary data by querying third-party databases using the metadata and the device data. (Tourne [0019-0020] social profiles/events, GPS information, calendar info may be retrieved from different data sources such as social media platform (Facebook, Instagram, LinkedIn), map info based on GPS, and email/calendar from personal account stored on other platforms such as Google Calendar, Outlook).
Per claim 14, claim 14 is a method claim that include limitations that are substantially the same as claim 1, and is likewise rejected.
Per claim 15, claim 15 includes limitations that are substantially the same as claim 3, and is likewise rejected.
Per claim 16, Tourne-Reyes teaches all the limitations of claim 15, and further teaches:
generating supplementary data based on the metadata and the device data by processing the metadata and the device data. (Tourne [0018-0020] Fig. 2 shows supplemental data such as business name, address, product detail, and other context information can be extracted/generated after processing contextual data such as email, Geo-location, calendar, social media, derived from metadata of the transaction such as date of the transaction, amount, vendor, etc.)
Per claim 17, Tourne-Reyes teaches all the limitations of claim 16, and further teaches:
wherein generating the supplementary data comprises querying third-party databases with the metadata and the device data. (Tourne [0019-0020] third party databases such as Google calendar, Outlook, Facebook, Instagram, etc. are access to retrieve contextual data for the transaction based on known metadata about the transaction).
Per claim 20, claim 20 is a medium claim (Tourne [0043] device include memory and storage devices) that include limitations that are substantially the same as claim 1, and is likewise rejected.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over Tourne, in view of Reyes, and Park et al. (US Pub 20250291866, hereinafter Park).
Per claim 5, Tourne-Reyes teaches all the limitations of claim 3, but Tourne-Reyes does not explicitly teach “wherein the image data comprises an image, the trained machine learning model comprises an image processing neural network trained to analyze and describe the image, and the instructions, when executed, further configure the processor to collect the image data by generating a description of the image using the image processing neural network”.
However, Park teaches:
wherein the image data comprises an image, the trained machine learning model comprises an image processing neural network trained to analyze and describe the image, and the instructions, when executed, further configure the processor to collect the image data by generating a description of the image using the image processing neural network. ([0265] images may be pre-processed with image to text conversion that use captioning and object recognition algorithm such as CNN (image processing neural network) to generate text descriptions of an image).
Park and Tourne-Reyes are analogous art because Park also teaches generating summary with contextual data and for images. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Tourne-Reyes and Park before him/her, to modify the teachings of Tourne-Reyes to include the teachings of Park so that images can be summarized with textual description as well. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide a more complete contextual summary including not only contextual text information, but also textual descriptions of images, which would provide a fuller context for generated contextual summary.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over Tourne, in view of Reyes, and Dunjic et al. (US Pub 20220413735, hereinafter Dunjic).
Per claim 8, Tourne-Reyes teaches all the limitations of claim 7, but does not explicitly disclose “wherein the supplementary data comprises one or more of weather data and traffic data“.
However, Dunjic teaches:
wherein the supplementary data comprises one or more of weather data and traffic data. (Dunjic [0052-0053]: contextual data associated with a transfer instruction may include weather data based on the location of the recipient; also see [0157, 0162] Fig. 5).
Dunjic and Tourne-Reyes are analogous art because Dunjic also teaches retrieving contextual information related to a financial transaction. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Tourne-Reyes and Dunjic before him/her, to modify the teachings of Tourne-Reyes to include the teachings of Dunjic so that contextual data of a transaction would include weather data. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide additional contextual data related to user’s mood to add to the context of the transaction to help to provide a fuller picture for the context of a transaction ([0157, 0162]).
Claim(s) 9-11, 18 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over Tourne, in view of Reyes, and Li et al. (US Pub 20250315596, hereinafter Li).
Per claim 9, Tourne-Reyes teaches all the limitations of claim 6, and further teaches displaying the related contextual data based on the context of a financial data transfer from metadata, device data, and supplemental data (Fig. 1 [0016-0018]), but Tourne-Reyes does not explicitly disclose “generate the context summary by determining key points relating to context of the data transfer from the metadata, the device data, and the supplementary data using the trained machine learning model “.
However, Li further teaches generating key points based on the retrieved contextual data using a trained machine learning model:
wherein the instructions, when executed, further configure the processor to generate the context summary by determining key points relating to context of the data transfer from the metadata, the device data, and the supplementary data using the trained machine learning model. ([0043] a contextual summary component 47 may automatically present generated summary with a list of key points using a machine learning model (640 from Fig. 6), and present to the user as shown in Fig. 4A-4B [0062-0064]).
Li and Tourne-Reyes are analogous art because Li also teaches presenting a contextual summary to users based on resources and related contextual information. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Tourne-Reyes and Li before him/her, to modify the teachings of Tourne-Reyes to include the teachings of Li so that summary with key points may be automatically presented to the users based on contextual data of a transaction using a machine learning model. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide a context-aware summaries for resources to enhance focus and to express interested key points to the audience ([0006]).
Per claim 10, Tourne-Reyes-Li teaches all the limitations of claim 9, and further teaches:
wherein the trained machine learning model is a text summarizer that uses natural language processing (NLP) techniques. (Li [0052, 0073, 0077] Fig. 6: a contextual summary component may implement the machine learning model 630 to automatically suggest textual summary of the analyzed resources, that is trained on training data).
Per claim 11, Tourne-Reyes-Li teaches all the limitations of claim 10, and further teaches:
wherein the context summary is a listing of the key points. (Li [0043] Fig. 4A-4B shows a summary with a listing of key insights (key points)).
Per claim 18, claim 18 includes limitations that are substantially the same as claim 9, and is likewise rejected.
Claim(s) 12-13, 19 is/are rejected under 35 U.S.C. 103 as being as being unpatentable over Tourne, in view of Reyes, and Hudetz et al. (US Pub 20240370479, hereinafter Hudetz).
Per claim 12, Tourne-Reyes teaches all the limitations of claim 6, but does not explicitly teach using GenAI to generate a prompt to further generate the summary: “wherein the trained machine learning model is a generative artificial intelligence (GenAI) model, and wherein the instructions, when executed, further configure the processor to: generate a prompt to the GenAI model for generating the context summary, the prompt including the metadata, the device data, and the supplementary data; and obtain, from the GenAI model responsive to the prompt, the context summary, wherein the context summary is a natural language explanation of context of the data transfer “.
However, Hudetz teaches:
wherein the trained machine learning model is a generative artificial intelligence (GenAI) model, and wherein the instructions, when executed, further configure the processor to: ([0134] the search manager may prepare a prompt with both search query and search results/electronic document 706, to send to the GenAI model 728 to create a summary);
generate a prompt to the GenAI model for generating the context summary, the prompt including the metadata, the device data, and the supplementary data; and ([0134] the search manager may prepare a prompt with both search query and search results/electronic document 706, to send to the GenAI model 728 to create a summary);
obtain, from the GenAI model responsive to the prompt, the context summary, wherein the context summary is a natural language explanation of context of the data transfer. ([0182] Fig. 10 shows a context summary 148 generated using the prompt including a set of search results/documents (context data), the summary is a natural language explanation of the query result (data transfer)).
Hudetz and Tourne-Reyes are analogous art because Hudetz also teaches presenting a summary based on a set of resources. Therefore, it would have been obvious to one of ordinary skills in art before the effective filing date, having the teachings of Tourne-Reyes and Hudetz before him/her, to modify the teachings of Tourne-Reyes to include the teachings of Hudetz so that contextual summary may be generated via a GenAI further utilizing a generated prompt. One would be motivated to make the combination, with a reasonable expectation of success, because it would provide a contextual summary utilizing existing commercial GenAI model already available, saving resources to develop new tool to generate the summary.
Per claim 13, Tourne-Reyes-Hudetz teach all the limitations of claim 12, and further teach:
wherein the GenAI model is a large language model (LLM). (Hudetz [0050, 0084] the search manager may access a GenAI that uses a large language model to assist in summarizing the search result to produce the abstractive summary).
Per claim 19, claim 19 includes limitations that are substantially the same as claim 12, and is likewise rejected.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patents & Publications
US 20200349614 A1
Batcha; Mohamed Uvaiz Anwar et al.
Providing contextual summary of interactions for automating customer experience, involves processing text transcript of interaction to third party to obtain contextual summary and metadata to be given to third party for handling
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
The examiner requests, in response to this Office action, support by shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections, See 37 CFR 1.111(c).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHOEBE X PAN whose telephone number is (571)270-7794. The examiner can normally be reached M-F 9am-6pm.
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/PHOEBE X PAN/Examiner, Art Unit 2179
/IRETE F EHICHIOYA/Supervisory Patent Examiner, Art Unit 2179
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