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 .
This application claims foreign priority of JAPAN 2022-145424 filed on 09/13/2022.
Status of Claims
Claim 1-8 and 10-12 are currently pending and rejected.
Claim 9 is canceled.
Claim Rejection – 35 U.S.C. 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 1-8 and 10-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below. In the instant case, the claims are directed towards targeted advertising. The concept is clearly related to longstanding economic practice and managing human economic activity behavior, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. The present claims can also be performed in the human mind, thus the present claims also fall within the Mental Processes grouping. The claims do not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Note that the limitations, in the instant claims, are done by the generically recited computer device. The limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Therefore, claims 1-12 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1-8 and 10-12 are directed to a process, machine, manufacture, or composition matter.
In Alice Corp. Pty. Ltd. v. CLS Bank Intern., 134 S. Ct. 2347 (2014), the Supreme Court applied a two-step test for determining whether a claim recites patentable subject matter. First, we determine whether the claims at issue are directed to one or more patent-ineligible concepts, i.e., laws of nature, natural phenomenon, and abstract ideas. Id. at 2355 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1296–96 (2012)). If so, we then consider whether the elements of each claim, both individually and as an ordered combination, transform the nature of the claim into a patent-eligible application to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.
Claims 1-8 and 10 are directed to a machine (i.e., device claims)
Claim 11 is directed to a process (i.e., a method claim).
Claim 12 is directed to a manufacture (i.e., a non-transitory computer readable storage medium claim).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards targeted advertising. The concept comprises associate deposit and withdrawal history data with user behavior, extract an inducement destination candidate (i.e., candidates of the commodities and services recommended to user) according to behavioral characteristics of the user based on changes along a time series, and output the extracted inducement destination candidate. In other words, the claimed concept is related to selecting targeted offers of products and/or services based on user’s behaviors associated with deposit and withdrawal history. The concept is clearly related to longstanding economic practice and managing human economic activity behavior, thus the present claims fall within the Certain Method of Organizing Human Activity grouping. Moreover, similar to the ineligible claims in Electric Power Group v. Alstom, the present claims recite a process of obtaining data, analyzing data, and outputting the result of the analysis. These steps can be performed entirely in the human mind, or by a computer automating mental processes. As such, the present claims also fall within the Mental Processes grouping. The performance of the claim limitations using generic computer components (i.e., an information processing device, or a non-transitory computer readable medium) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping or the mental processes grouping. Accordingly, this claim recites an abstract idea.
Prong Two
Claims 1-10 recite a memory and a processor as additional elements. Claim 11 recites an information processing device as additional element. Claim 12 recites a non-transitory computer readable storage medium as additional element. The additional elements are claimed to perform basic computer functions, such as receiving and processing data, performing calculations to select targeting advertisement, and transmitting data over network. The recitation of the computer elements amounts to mere instruction to implement an abstract concept on computers. The present claims do not solve a problem specifically arising in the realm of computer networks. Rather, the present claims implement an abstract concept using existing technology in a networked computer environment. The present claims do not recite limitation that improve the functioning of computer, effect a physical transformation, or apply the abstract concept in some other meaningful way beyond generally linking the use of the abstract concept to a particular technological environment. As such, the present claims fail to integrate into a practical application.
The amended feature recites “extract the inducement destination candidate according to the behavioral characteristics of the user, by inputting data indicating changes along a time series in each of a plurality of behaviors associated with each of the series of pieces of the history data, to a learned model established based on supervised learning using teacher data where information indicating the inducement destination candidate used by a user as a subject of the plurality of behavior is associated with the data indicating the changes along the time series in each of the plurality of behaviors, as correct label”. The amended claim merely describes applying supervised learning using teacher/training data (which is a well-understood, routine, and conventional training method in machine learning) to a targeted advertising scenario. The machine learning model describes in page 23 and page 24 of the specification does not appear to be an improved machine learning model. Applicant appears to only apply an existing machine learning model trained with supervised learning to a new data environment. Examiner again points to the Recentive v. Fox decision, where the Federal Circuit stated “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101”.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, claims 1-10 recite a memory and a processor as additional elements. Claim 11 recites an information processing device as additional element. Claim 12 recites a non-transitory computer readable storage medium as additional element. The additional elements are claimed to perform basic computer functions, such as receiving and processing data, performing calculations to select targeting advertisement, and transmitting data over network. According to MPEP 2106.05(d), “performing repetitive calculations”, “receiving, processing, and storing data”, “electronically scanning or extracting data from a physical document”, “electronic recordkeeping”, “storing and retrieving information in memory”, and “receiving or transmitting data over a network, e.g., using the Internet to gather data” are considered well-understood, routine, and conventional functions of computer. The present claims do not improve the functioning of computer technology. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
Response to Remarks
Rejection under 35 U.S.C. 101
Applicant's arguments filed on 09/05/2025 have been fully considered but they are not persuasive.
Step 2A Prong 1
Applicant argued the recited operations “(i) associating each piece of deposit/withdrawal history with three defined economic-behavior vectors, (ii) generating time-series features set for each behavior, and (iii) inputting that multidimensional time-series data into a trained supervised-learning model to output an inducement-destination candidate” cannot be performed in the human mind, because the volume, dimensionality and temporal granularity of the required data far exceed what an ordinary mind could process. Applicant further argued the claimed technique “requires matrix operations and weight-vector multiplications” beyond observation, evaluation, judgement, or opinion. Examiner disagrees and points out that the claim language does not recite any particular formula or algorithm to show the complexity of calculation/analysis required. The calculation process is recited in high level of generality. Under the broadest reasonable interpretation, the claims cover situations where the amount of data is limited and calculations are simple enough for human to perform mentally. The Federal Circuit court have consistently rejected the argument human could not mentally perform the claimed process due to large number of calculations when the process uses nothing more than generic computer and existing machine learning. The court stated that “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101” (see Recentive v. Fox).
Examiner also reminds Applicant that the present claims are not only rejected under the Mental Processed grouping. The claimed concept is related to selecting targeted offers of products and/or services based on user’s behaviors associated with deposit and withdrawal history. The concept is clearly related to longstanding economic practice and managing human economic activity behavior, thus the present claims also fall within the Certain Method of Organizing Human Activity grouping, which Applicant did not address.
Step 2A Prong 2
Applicant argued that following additional elements integrate any abstract concept into practical application. Examiner disagrees.
Use of particular machine. Applicant argued the claims require a processor coupled to memory that is configured to execute a supervised-learning interference pipeline. Examiner points out that processor and memory configured to run machine learning model do not make a particular machine. Examiner also points out that any generic processor can be configured to receive three-channel time series data (i.e., receiving data over network), apply learned weight matrices (i.e., performing calculations), and generate an inducement-destination candidate (i.e., identifying targeted advertisement based on rules). Examiner also points to the Recentive v. Fox decision, where the Federal Circuit stated “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101”. Computer configured to run a generic machine learning model is not a particular machine and does not integrate an abstract concept into a practical application.
Technical improvement to computer functionality. Applicant argued that the present claim “automates inducement selection with a learned time-series model, thereby reducing processor cycles devoted to repeated rule evaluation and eliminating manual rule-upkeep operation”. Examiner again points to the Recentive v. Fox decision, where the Federal Circuit stated “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101”. Applicant did not explain how the machine learning model in the present claims improve upon existing machine learning technology. The present claims appear to merely apply existing machine learning technology to a new data environment (i.e., targeted advertising). Applicant also argued that the specification “describes shorter inference latency and higher prediction accuracy, mirroring the network-security improvement accepted as practical in Example 47”. Examiner cannot find the shorter inference latency and higher prediction accuracy improvement in the specification. Even if those benefits were written in the specification, they come solely from the capabilities of a generic machine learning model. Similar to “accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer” (FairWarning v. Iatric), the alleged improvements of the claimed invention are not sufficient to show an improvement in computer-functionality.
Other meaningful limitation. Applicant argued the claim “confines the model’s input to behavior specific time-series signals derived from deposit and withdrawal history, and confines the output to a single inducement-destination candidate”. Applicant further argued these limitations foreclose preemption of all behavioral marketing or all machine-learning predictions. Examiner again points to the Recentive v. Fox decision, where the Federal Circuit stated “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101”.
Examiner also points out that preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the examiner through the application of the two-step framework. Applicant’s attempt to show alternative uses of the abstract idea outside the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Similarly, applicant’s attempt to show that the recited abstract idea is a very narrow and specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim.
See the July 2015 Update: Subject Matter Eligibility that explains that questions of preemption are inherent in the two-part framework from Alice Corp and Mayo and are resolved by using this framework to distinguish between preemptive claims, and ‘those that integrate the building blocks into something more…the latter pose no comparable risk of preemption, and therefore remain eligible.” The absence of complete preemption does not guarantee the claim is eligible. Therefore, “[w]here a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). See also OIP Tech., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed Cir. 2015).
For these reasons, the present claims fail to integrate the abstract concept into a practical application.
Step 2B
Applicant argued that following additional elements amount to “significantly more”. Examiner disagrees.
Non-conventional data transformation. Applicant argued that associating every transaction with three quantified behavior channels and converting those associations into synchronized time-series vectors constitute more than mere “data gathering”. Indeed, that is more than data gathering, but it is considered “data processing”, which is also a conventional computer function. Examiner also questions the meaning of “associating every transaction with three quantified behavior channels”, since it is not clearly reflected in the actual claims. The claim language only recites “associate each of a series of pieces of history data related to deposits or withdrawals of a user with a plurality of behaviors related to an economic activity of the user, wherein the plurality of behaviors being a consumption behavior, a savings behavior, or an investment behavior”. As such, the claim language merely requires categorizing each transaction/withdrawal transaction into one of the three categories – consumption, saving, or investment. Such data processing can be easily performed mentally or automated by a programed computer to mimic mental processing. Categorizing transaction data is not unconventional data transformation.
Non-conventional model architecture. Applicant argued the claim’s learned model “is trained with labeled triplet time-series feature sets, a training regiment fundamentally different from the generic train on historical data approached criticized in Recentive”. Examiner points out that the claims do not reflect applicant’s characterization of the training method. Claim 1, for example, merely recites associate each deposit/withdrawal transaction to one of the categories of a consumption behavior, a savings behavior, or an investment behavior, extracting targeted advertisement candidate according to the behavior characteristic of the user. These features do not involve any training.
The amended feature recites “extract the inducement destination candidate according to the behavioral characteristics of the user, by inputting data indicating changes along a time series in each of a plurality of behaviors associated with each of the series of pieces of the history data, to a learned model established based on supervised learning using teacher data where information indicating the inducement destination candidate used by a user as a subject of the plurality of behavior is associated with the data indicating the changes along the time series in each of the plurality of behaviors, as correct label”. The amended claim merely describes applying supervised learning using teacher/training data (which is a well-understood, routine, and conventional training method in machine learning) to a targeted advertising scenario. The machine learning model describes in page 23 and page 24 of the specification does not appear to be an improved machine learning model. Applicant appears to only apply an existing machine learning model trained with supervised learning to a new data environment. Examiner again points to the Recentive v. Fox decision, where the Federal Circuit stated “patents that do no more than claim the applications of generic machine learning to new data environment, without disclosing improvements to the machine learning model to be applied, are patent ineligible under 101”. Simply implementing the abstract idea on a generic computer or using a computer as a tool to perform an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Therefore, the present claims are ineligible for patent.
For these reasons, the amended feature does not render the claims less abstract. Examiner maintains the ground of rejection under 35 U.S.C. 101.
Rejection under 35 U.S.C. 103
Examiner agrees that the best prior arts Lim (KR 20200055832 A) and KR 102430125 B1, whether individually or combined, fail to teach “associate each of a series of pieces of history data related to deposits or withdrawals of a user with a plurality of behaviors related to an economic activity of the user, wherein the plurality of behaviors being a consumption behavior, a savings behavior, or an investment behavior” and “extract the inducement destination candidate according to the behavioral characteristics of the user, by inputting data indicating changes along a time series in each of a plurality of behaviors associated with each of the series of pieces of the history data, to a learned model established based on supervised learning using teacher data where information indicating the inducement destination candidate used by a user as a subject of the plurality of behaviors is associated with the data indicating the changes along the time series in each of the plurality of behaviors, as a correct label”, as recited in the independent claims. Examiner has conducted updated search, but no relevant prior art was found. Therefore, Examiner withdraws the rejection under 35 U.S.C. 103.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
SEPT-2025