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 1-20 are pending in this application.
Claim Rejections - 35 USC §112(b)
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention
The following is a quotation of pre-AIA 35 U.S.C. 112, second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 8 and 15 recites the limitation "machine-learning model". There is insufficient antecedent basis for this limitation in the claim. Thus, these claims and their dependent claims are rejected under as being indefinite for failing to particularly point out and distinctly claim.
Claims 9-14 and 16-20 are rejected by virtue of dependency on a rejected based claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 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 8 and method claim 15. Claim 1 recites the limitations of determining fraudulent recurring operations/transactions by machine learning algorithm that relies on specific data such as customer feedbacks on the company and related transactions.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Receiving user feedback contesting recurring operation; Identifying the operating and related entity; obtaining entity’s information; training machine learning model with the operation, the entity, and the entity’s information; receiving subsequent operation associated with a user; obtaining transaction/merchant information (via a transceiver); the information described relationship between (1) merchant, (2) merchant’s location, and (3) either merchant customer data or merchant ownership; analyzing the subsequent operation using the machine learning algorithm; analysis includes: (i) transaction periodicity (= frequency, see specification’s paragraphs 22 & 23); (ii) customer feedback on company; (iii) similarity of company names (current vs prior transaction, see specification’s paragraph 33); (iv) customer feedback on related transaction AND (v) third party information; weighting review as: as favoring or disfavoring recurring charge; AND as favoring or disfavoring fraudulent charge; determining that the operation is fraud; and transmitting fraud notification to user and request user feedback on operation legitimacy; receiving user’s feedback; terminate operation is user confirms fraud determination; and retraining machine-learning model based on the user’s feedback – specifically, the claim recites “receiving… user feedback relating to a contested recurring operation; identifying… an operation instance and an entity associated with the operation instance; obtaining… information relating to the entity; training… a machine learning model with the operation instance, entity, and information relating to the entity; receiving… a subsequent operation instance associated with a user; obtaining information from a third party server relating to a transaction or merchant associated with the subsequent operation instance via a transceiver over a network, the information describing relationships between business entities, geolocation of a business, and at least one of merchant customer data or merchant ownership; analyzing… the subsequent operation instance using the machine learning model and the obtained information from the third party server, the analyzing including: processing a plurality of factors, including a periodicity of related transactions, customer feedback relating to a company associated with the subsequent operation, a similarity between a company name associated with the subsequent operation and company names of prior transactions, customer feedback associated with the related transactions, and the information obtained from the third party server; and weighting each of the reviewed plurality of factors as favoring or disfavoring a recurring charge, and as favoring or disfavoring a fraudulent charge; determining… based on the analyzing, that the subsequent operation instance is at least one of a recurring operation or the result of fraud or deceit; and transmitting, in response to the determining, a notification to the user that informs the user of the determination result and requests feedback as to whether the operation instance is legitimate; receiving responsive user feedback from the user confirming or disputing the determination result; automatically terminating the subsequent operation instance in response to the user feedback confirming the determination result; and retraining the machine-learning model based on the user feedback”, recites a fundamental economic practice, directed to mitigating risk (of fraudulent recurring operations/transactions).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial 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 system”, “a memory”, “a transceiver”, “external devices”, “a network”, “a communication interface”, “one or more processors”, “a machine learning algorithm”, “a machine learning model”, and “a third party server”, in claim 8; the additional technical element of “a cloud-based management service” and “an electronic communication channel” in claim 1; and the additional technical element of “a management service” in claim 15, 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. Claims 1 and 15 are 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 system, external devices, one or more processors, and a third party server; a communication device such as a network, a communication interface, and an electronic communication channel; a storage unit such as a memory; an electronic/electrical device such as a transceiver; and software module and algorithm such as a machine learning model, a machine learning algorithm, a cloud-based management service, and a management service. 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.
The examiner notes that although the claims recite “a machine learning model”, it is recited at a high level. See claims 1, 8 and 15. For example, the claims simply state what the “a machine learning model” will do in the claimed business process – i.e. based on user feedback on contesting recurring operations/transactions, analyzes subsequent operations/transaction using transaction frequency, company’s name, and customer feedback on the company and transactions. Similarly the specification recites the “machine-learning model” a high level – see, for examples, “a machine-learning algorithm… for carrying out the recurring charge detection” and “the machine learning algorithm… analyzes the transaction against previous transactions and knowledge of different merchants, and determines whether the transaction is a recurring charge associated with a bad actor”, and “Machine-learning algorithm… is trained according to previous transactions and their identifications of being either legitimate or suspicious.”, at paragraphs 12, 13, and 17. These are nominal recitations. The examiner notes that the applicant is not improving “machine learning model”. Rather the applicant is using “machine learning model” in a business process.
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, 8, and 15 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, 8, and 15 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, 8, and 15 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 the information relating to the entity is obtained from an external source, which further narrows the abstract idea.
Dependent claim 3 discloses the limitation of the analyzing includes extracting operation information and an entity identifier from the subsequent operation instance, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of retrieving account information of a user, the account information associated with the subsequent operation instance; and retrieving related operations that share at least one data point with the subsequent operation instance, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of comparing operation data of the subsequent operation instance to the related operations; and determining that the subsequent operation instance is the recurring operation based on the comparing, which further narrows the abstract idea.
Dependent claim 6 discloses the limitation of determining that the recurring operation originates from a bad actor entity based on the comparing; and in response to the determining: transmitting, by the management service of the cloud server, a notification signal to the user associated with the recurring operation; receiving, by the management service of the cloud server, a response message from the user disputing the recurring operation; and terminating the recurring operation in response to the receiving of the response message, which further narrows the abstract idea. Note that the technical element “the management service of the cloud server” 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 7 discloses the limitation of performing additional training of the machine learning algorithm using the subsequent operation instance, which further narrows the abstract idea. Note that the technical element “of the machine learning algorithm” 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 9 discloses the limitation of wherein the one or more processors are further configured to cause the transceiver to communicate with at least one external source to obtain the information relating to the entity, which further narrows the abstract idea. Note that the technical elements “the one or more processors” and “the transceiver” 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 10 discloses the limitation of the one or more processors are further configured to extract operation information and an entity identifier from the subsequent operation instance, which further narrows the abstract idea. Note that the technical element “the one or more processors” 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 11 discloses the limitation of retrieve account information of a user, the account information associated with the subsequent operation instance; and retrieve related operations that share at least one data point with the subsequent operation instance, which further narrows the abstract idea.
Dependent claim 12 discloses the limitation of comparing the operation data of the subsequent operation instance to the related operations; and determine that the subsequent operation instance is the recurring operation based on the comparing, which further narrows the abstract idea.
Dependent claim 13 discloses the limitation of the one or more processors are further configured to determine that the recurring operation originates from a bad actor entity based on the comparing; and in response to the determining: cause the transceiver to transmit a notification signal to the user associated with the recurring operation; receive, via the transceiver, a response message from the user that disputes the recurring operation; and canceling the recurring operation in response to the receiving of the response message, which further narrows the abstract idea. Note that the technical elements “the one or more processors” and “the transceiver” 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 14 discloses the limitation of perform additional training of the machine learning algorithm using the subsequent operation instance, which further narrows the abstract idea. Note that the technical element “the machine learning algorithm” 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 16 discloses the limitation of transmitting a notification message to the user, the notification message informing the user of the flagged operation instance, wherein the notification message includes a request for confirmation or rejection of the flagged operation instance, which further narrows the abstract idea.
Dependent claim 17 discloses the limitation of receiving, by the management service of a cloud server, a response message from the user that confirms the flagged operation instance as being a legitimate recurring operation; processing, by the management service of a cloud server, the flagged operation instance; and updating, by the management service of a cloud server, a model that performs the analyzing based on the response message, which further narrows the abstract idea. Note that the technical element “the management service of a cloud server” 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 18 discloses the limitation of receiving, by the management service of a cloud server, a response message from the user that rejects the flagged operation instance; and terminating, by the management service of a cloud server, the flagged operation instance in response to the receiving of the response message, which further narrows the abstract idea. Note that the technical element “the management service of a cloud server” 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 19 discloses the limitation of the flagging of the operation instance includes: first identifying the operation instance as a recurring operation; and second identifying the recurring operation as being at least one of associated with a bad actor entity, the result of fraud on the part of the entity, or the result of deceit on the part of the entity, which further narrows the abstract idea.
Dependent claim 20 discloses the limitation of the first identifying is based on an analysis of the related operations and the account information, and wherein the second identifying is based on the related operations and the entity information, which further narrows 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 1-20 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 11/25/25 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“35 U.S.C. § 101… Claims 1, 8, and 15 do not recite any method of organizing human activity such as a fundamental economic concept… Rather, the independent claims recite an improved recurring and fraudulent transaction detection system that enables a user to be made aware of potentially unauthorized recurring transactions and to automatically take remedial actions to remedy such transactions,”
the examiner respectfully disagrees. The claims recite fraudulent transaction detection and management process, which is often part of retail transactions. It is part of a fundamental economic concept in commerce.
In response to applicant's argument that:
“Example 39, which found that claims directed to training a neural network did not involve a mental process because the steps "are not practically performed in the human mind",”
the examiner respectfully disagrees. The claimed invention is not the same as Example 39. Determining fraudulent recurring operations/transactions by machine learning algorithm based on customer feedbacks and related transactions is not the same as training a neural network for facial detection by the use of an expanded training set of facial that is developed by applying mathematical transformation functions on an acquired set of facial images, with the transformations that can include offline transformations; and then training the neural networks further using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network. Also, Example 39 deals with false positives when classifying non-facial images by performing an iterative training algorithm. The claimed invention does not have the elements and the steps recited in Example 39. One must read Example 39 narrowly in deference to the Alice Court’s emphatic prohibition against patenting abstract ideas that lack genuine innovation beyond the use of generic computers. Implementing a business process/idea by processing data using generic computers is not patentable.
In response to applicant's argument that:
“The technological improvements are embodied in several of the limitations of independent claim 1, including:
1. "transmitting, in response to the determining, a notification to the user that informs the user of the determination result and requests feedback as to whether the operation instance is legitimate";
2. "receiving responsive user feedback from the user confirming or disputing the determination result";
3. "automatically terminating the subsequent operation instance in response to the user feedback confirming the determination result"; and
4. "retraining the machine-learning model based on the user feedback,”
the examiner respectfully disagrees. The cited steps in the quotation above are newly added elements.
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):
transmitting fraud notification to user and request user feedback on operation legitimacy. This is a business procedure;
receiving user’s feedback. This is another business procedure;
terminate operation is user confirms fraud determination. Another business procedure; and
retraining machine-learning model based on the user’s feedback. A business procedure.;
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.
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