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 .
Status of Claims
Claims 1-3, 9-10, 15, 21, and 23-25 are pending in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 9-10, 15, 21, and 23-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3, 9-10, 15, 21, and 23-25 are directed to a system or method, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method claim 1 as the claim that represents the claimed invention for analysis and is similar to independent system claim 9. Claim 1 recites the limitations of capturing and storing data/evidence of transaction (e.g., a receipt) for a customer/user. Displaying (vendor) webpages and operational pages on user device; detecting a completion page among the many transactional pages rendered on user device; applying character recognition algorithm to the pages, by scanning and converting data as machine-readable text; extracting recognized words, images; determining a position of the displayed page; inputting “character recognition algorithm” output into a machine learning model; data including (1) extracted features and (2) position of displayed page [representing order of representation]; classifying the completion page; triggering “data capturing module” to capture an “actual image of the completion page”; capturing actual image of the “completion page” rendered on the user device; determining metadata containing (a) time when the actual image was captured, (b) resource provider identifier, and (c) transaction amount and (4) confirmation for the transaction that are extracted from the completion page; storing “completion page image data” locally; storing metadata associated with the captured image; transmitting metadata to be matched to transaction data generated by the authorizing entity computer; transaction data generated by the authorizing entity computer may be time, amount, OR resource provider; matching (a) transaction amount, (b) resource provider identifier (c) time when the actual image was captured; receiving the transaction data matched metadata; matching metadata to the locally stored completion page image stored on the user device; displaying transaction data AND a selectable link to the locally stored completion page image; receiving a user selection input to the selectable link; generating captured image; and displaying the captured image., – specifically, the claim recites:
“controlling… to present a plurality of operational pages of a website of a resource provider computer during a plurality of user interactions with the website using the user device in a session, the plurality of operational pages comprising text and graphics… (b) automatically detecting… based on content currently rendered on the display and without user input, a completion page for a transaction among the plurality of operational pages, wherein the automatically detecting the completion page comprises: (c) applying character recognition algorithm on each page of the plurality of operational pages as each page is displayed, the applying the character recognition algorithm including by scanning each displayed page, and converting recognized content data comprising at least one from among the text and the graphics that are present on the displayed page into a machine- readable text, (d) extracting, from the recognized content data, features of each respective displayed page, the extracted features comprising words and graphical elements, (e) determining a position of the displayed page within a sequence of the plurality of operational pages based on a temporal order in which the displayed page is rendered during the session, (f) providing, as an input to a trained machine learning model comprising a neural network, feature data including (1) the extracted features and (2) the position of the displayed page, the position representing an order of a presentation of the displayed page within the session, and (g) classifying… each respective displayed page into one of a completion page class or a non-completion page class, and identifying one of the plurality of operational pages as the completion page based on a classification output, (h) in response to the classification output indicating the completion page, automatically triggering… a data capture module included in the user device and coupled to the hardware processor to initiate a capture of an image of the completion page; (i) automatically capturing… an actual image of the completion page as rendered on the display; (j) determining… metadata associated with the actual image, the metadata comprising a time when the actual image was captured and a resource provider identifier, and further comprising a transaction amount and a confirmation for the transaction that are extracted from the completion page; (k) storing… completion page image data corresponding to the captured image, wherein the captured image remains stored locally on the user device and is not shared with an authorizing entity computer; (l) storing… the metadata associated with the captured image; (m) transmitting… the metadata without transmitting the captured image, for matching the metadata to transaction data generated by the authorizing entity computer for the transaction, the transaction data comprising at least a time for the transaction, the transaction amount for the transaction, and the resource provider identifier, wherein, to determine a match the transaction amount and the resource provider identifier from the metadata are matched to, the transaction amount, and the resource provider identifier in the transaction data, and the time when the actual image was captured is within a predefined time window from the time of the transaction in the transaction data; (n) receiving… the transaction data matched to the metadata; (o) matching… the received transaction data to the locally stored completion page image data using the metadata; (p) controlling… the display to present the transaction data received from the authorizing entity computer together with a selectable link associated with the locally stored completion page image data; (q) receiving… a user selection input on the selectable link through the display; (r) in response to the receiving the user selection input, generating… the captured image using the locally stored completion page image data; and (s) controlling… the display to present the captured image, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “a user device”, “a hardware processor”, “a display”, “an antenna”, “a content determination module”, “a data capture module”, “a non-transitory computer readable medium”, “a resource provider computer”, “character recognition algorithm”, “a machine learning model”, “a neural network”, “a memory of the user device”, and “an authorizing entity computer”, in claim 9, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claim 1 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a user device, a hardware processor, a resource provider computer, and an authorizing entity computer; a communication device such as a display and an antenna; a storage unit such as a non-transitory computer readable medium and a memory of the user device; and software module and algorithm such as a content determination module, a data capture module, character recognition algorithm, a machine learning model, and a neural network. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1 and 9 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1 and 9 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 1 and 9 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of providing, by the user device, the completion page image data of the completion page to a server computer; and displaying the transaction data for the transaction along with the captured image of the completion page on a Website of an authorizing entity that operates the server computer, which further narrows the abstract idea. Note that the technical elements “the user device” and “a server computer” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 discloses the limitation of the transaction data and the selectable link to the locally stored completion page image data are displayed on an application that resides on the user device, which further narrows the abstract idea. Note that the technical element “the user device” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 discloses the limitation of providing the completion page image data of the completion page to a server computer, wherein the transaction data for the transaction and the captured image of the completion page are displayed on a Website of an authorizing entity that operates the server computer, which further narrows the abstract idea. Note that the technical element “a server computer” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 15 discloses the limitation of the metadata further comprises a date when the completion page image data was created, which further narrows the abstract idea.
Dependent claim 21 discloses the limitation of wherein the completion page image data is in a JPEG, PDF, GIF, or PNG file format, which further narrows the abstract idea.
Dependent claim 23 discloses the limitation of the machine learning model is a recurrent neural network, which further narrows the abstract idea. Note that the technical elements “the machine learning model” and “a recurrent neural network”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 24 discloses the limitation of the completion page comprises a user interface (UI) screen displayed after the authorizing entity computer authorizes the transaction and generates the transaction data, which further narrows the abstract idea. Note that the technical elements “a user interface (UI) screen” and “the authorizing entity computer” are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 25 discloses the limitation of wherein (a) to (s) are performed for a plurality of users, and the method further comprises using feedback data to update weights of the neural network, the feedback data comprising a plurality of indications whether a plurality of completion pages, which are classified as completion pages for the plurality of users, are true completion pages or not, which further narrows the abstract idea. Note that the technical element “the neural network” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-3, 9-10, 15, 21, and 23-25 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 3/5/26 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“35 U.S.C. § 101… Claim 1, as now amended, is directed to device level automated mechanism for detecting a dynamically rendered completion page during a live web session and reconciling the completion page with separately generated transaction data using a privacy preserving distributed architecture,”
the examiner respectfully disagrees. The examiner has determined that the claim recite the limitations of capturing and storing data/evidence of transaction (e.g., a receipt) for a customer/user, and that this abstract idea is carried out by a generic computer.
In comparison to the prior version, the added elements (see underlined) and deleted elements (if any, struck out with a line) are essentially:
(1) “(e) determining a position of the displayed page within a sequence of the plurality of operational pages based on a temporal order in which the displayed page is rendered during the session,”;
(2) “(f) providing, as an input trained machine learning model comprising a neural network, feature data including (1) the extracted features and (2) the position of the displayed page, the position representing an order of a presentation of the displayed page within the session”;
(3) “(j) determining, by the data capture module, metadata associated with the actual image, and further comprising a transaction amount and a confirmation for the transaction that are extracted from the completion page;”;
(4) “(k) storing, by the hardware processor in a memory of the user device, completion page image data corresponding to the captured image, wherein the captured image remains stored locally on the user device and is not shared with an authorizing entity computer”;
(5) “wherein, to determine a match data, and the time when the actual image was captured is within a predefined time window from the time of the transaction in the transaction data;”; and
(6) “(o) transaction data locally stored completion page image data using the metadata;”.
These changes are not sufficient to overcome the 35 U.S.C. § 101 rejections because: for 101 analysis purpose, this is just stating (corresponding to the numberings above):
that one of the datapoint used in the process is the position of the displayed page;
that training data includes (1) the extracted features and (2) the position of the displayed page, the position representing an order of a presentation of the displayed page within the session;
that metadata also includes transaction amount and a confirmation for the transaction;
that captured image remains stored locally;
that data matching also involves the time when the actual image was captured is within a predefined time window from the time of the transaction; and
that data used in matching are locally stored.
These are abstract ideas. There is nothing technical about it.
Note that the technical element “machine learning model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“Similarly to the claims in DDR, claim 1 addresses a problem rooted in Internet architecture,”
the examiner respectfully disagrees. The claims do not address or improve Internet architecture.
In response to applicant's argument that:
“Claim 1 does not merely captures an image of a receipt, stores a receipt, and displays a receipt. Claim 1 recites complex technical processing steps of: applying character recognition to content currently rendered on the display, extracting textual and graphical features, determining the page position in the session, providing both content features and page position in the session as an input to neural network, classifying pages into completion and non-completion classes, and automatically triggering a capture module within user input based on classification output,”
the examiner respectfully disagrees. This is the normal process of document management – i.e., recognizing the words in the document and matching/comparing them to references. There is no technical improvement or innovation. As stated in the prior office action:
“the steps recited in the above (applying character recognition, determining the content, etc.) are part of a business process that is being carried out by “generic computer” and its components. There is no technical improvement.”
In response to applicant's argument that:
“The neural network operates on both extracted content features and a value corresponding to a position representing temporal order in the session pages. This sequential modeling is a specific technical mechanism for improving page-type detection and multi-page web navigation,”
the examiner respectfully disagrees. This sequence is natural order – i.e., that the content must be extracted before it can be analyzed. Why is it an innovation?
Also, the “machine learning model” and the “neural network” are recited at such a high level of generality that they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“The classification output automatically triggers a data capture module without user input. This modifies device behavior in response to machine inference, reflecting the technical control flow improvement rather than a business practice. The claim recites a control loop where content is presented on the display, the features are extracted from the content, the page is classified as completion page by ML, which leads to automatic image capture. This is event driven device control and not any abstract idea,”
the examiner respectfully disagrees. These are business process programed into a generic computer. They are not patentable.
In response to applicant's argument that:
“The captured image remains locally stored, is not shared with the authorizing entity computer, and is not transmitted. Only metadata (including time, amount, resource provider identifier, and confirmation) is transmitted. Matching is performed with the transaction data, using a predefined time window relative to the image capture time. The separation of local image storage and remote metadata matching constitutes a specific distributed system architecture that addresses privacy and interoperability constraints between independent computing systems,”
the examiner respectfully disagrees. While storing data locally and not transmitting the data may improve privacy, it is a business decision. The improvement is due to the business procedure/idea being implemented. It is not due to any technological improvement. Technologically, nothing has changed.
In response to applicant's argument that:
“the claim does not preempt receipt capture, storage, and display,”
the examiner respectfully disagrees. The argument is not found persuasive because preemption is but one of the factors in the 35 USC 101 analysis. The preemption doctrine is a policy that shields the public against monopoly of ideas by individuals. It is less effective as an applicant’s sword against patent prohibitions. This is reflected by the Office’s guidance, which states “the absence of complete preemption does not guarantee that a claim is eligible.” See the July 2015 Update.
In response to applicant's argument that:
“The ordered combination recited in claim 1 provides significantly more. Under Step 2B, the ordered combination recited in claim 1 is not conventional. The claim requires:
-real time OCR of rendered display content,
-feature extraction from text and graphical elements,
-neural network classification based on page position within the session,
-automatic triggering of capture hardware based on classification output,
-metadata generation from the rendered page,
-time window matching to the transaction data,
-local only retention of captured image data with metadata only transmission,”
the examiner respectfully disagrees. Again, these are business process programed into a generic computer. They are not patentable.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mike Anderson can be reached on 571 270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK H GAW/Examiner, Art Unit 3693
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