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 has PRO 63/400,683 08/24/2022
Claim Status
Claims 1, 4, 7-9, 14, 15, 19, and 20 are pending and rejected.
Claim 2, 3, 5, 6, 10-13, and 16-18 are 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, 4, 7-9, 14, 15, 19, and 20 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 mapping company identifiers to user’s purchases and displaying the company identifiers (to influence user to invest in the companies associated with the company identifiers). The concept is clearly related to managing human’s investment behavior, thus the present claims fall within the Certain Method of Organizing Human Activity 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, 4, 7-9, 14, 15, 19, and 20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Step 1: The claims 1, 4, 7-9, 14, 15, 19, and 20 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, 4, 7-9, 14, and 15 are directed to a process (method claims).
Claim 19 is directed to a machine (system claim).
Claim 20 is directed to a manufacture (non-transitory computer medium).
Step 2A: The claims are directed to an abstract idea.
Prong One
The present claims are directed towards mapping company identifiers to user’s purchases and displaying the company identifiers (to influence user to invest in the companies associated with the company identifiers). The concept comprises retrieving company identifier and transaction history from data repositories, performing data mapping, by a supervised machine learning model, to identify one or more companies represented by one or more company identifiers from which the user made one or more purchases, displaying the one or more company identifiers being associated with one or more feedback buttons, receiving use feedback input, and training the supervised machine learning model, based on the received feedback input. The claims are still directed to analyzing human purchase behavior to assist users “with identifying relevant information and then present that information to them in a manner that facilitates decision making” (see paragraph 0003 of the specification). As such, the amended claims still fall under the grouping of “certain method of organizing human activities”. The performance of the claim limitations using generic computer components (i.e., one or more servers and a supervised machine learning model) does not preclude the claim limitation from being in the certain methods of organizing human activity grouping. Accordingly, this claim recites an abstract idea.
Prong Two
Independent claim 1 recites one or more servers and a supervised machine learning model as additional elements. Dependent claims 4, 7-9, 14, and 15 do not recite any other additional element. Claims 19 and 20 recite a processor and a memory as additional elements. Paragraph 0045 of the specification suggests these computer elements are off-the-shelf computer components. The additional elements are claimed to perform basic computer functions, such as retrieving data from data repositories, performing data mapping using a supervised machine learning model, displaying result with feedback button, receiving user feedback via the feedback buttons, and training the supervised machine learning model based on the received user feedback. One skilled in the art would immediately recognize that supervised machine learning “can be significantly improved by incorporating human feedback…This approach, known as Reinforcement Learning from Human Feedback (RLHF), leverages human judgement to guide the learning process, leading to more accurate and aligned AI models”. Such approach was widely used at the effective filing date of the present application. For example, popular social media platforms, such as YouTube and X (formerly known as Twitter), provide “Like” and “Dislike” buttons attached to the content recommended by the platforms. The machine learning models of the platforms can then receive user feedback via the buttons to further understand the preference of the user. The “Like” and “Dislike” buttons are a form of feedback buttons.
To further support the argument that providing user feedback was well-known, Examiner cites the following prior arts. Venkateshwaran et al. (Pub. No.: US 2023/0177206) teaches “Reinforcement Learning can also be utilized. An end-user feedback loop in implemented in the user interface, using which the end user (claims adjuster/supervisor) can provide feedback on the suggestions provided by expert system, supervised or unsupervised machine learning. The user can provide a positive or negative feedback. Reinforcement learning learns patterns of when the user provided positive versus negative feedback, and accordingly tunes the system to provide more meaningful and targeted suggestions” (see paragraph 0079 and 0099). Horesh et al. (Pub. No.: US 2022/0138592) teaches “The feedback is provided as additional input to the supervised machine learning model operating in the background to predict features that the user will deem to be relevant. The more the user uses the My Insights Widget (422), and the more feedback the user provides, the more reliable the prediction of relevancy made by the supervised machine learning model” (see paragraph 0121). Igoe et al. (Patent No.: US 8,930,204) teaches “There are several well-known methods for training a neural network (i.e., selecting the values of the bias 126 and weights 122); one of the most common of these is ‘supervised learning’. In one embodiment using supervised learning, the user provides explicit feedback (not shown) indicating the correctness of the recommendations…implicit feedback is utilized to train the network to the user’s preferences” (see col 42 line 22-34). Karakotsios et al. (Patent No.: US 10,541,000) teaches “The feedback that corresponds to the data can take many different forms and can vary in granularity…the user 104 may specify that he/she does or does not generally like the first video summarization 134, which may be indicated by a thumbs up/down, actuating a like/dislike button, and so on” (see col 22 line 15-27). Such feedback buttons are common on social media platforms for machine learning algorithms to obtain user feedback on the recommended contents. Clearly, causing the one or more company identifiers (recommendations) to be displayed with one or more feedback buttons on a graphical user interface of a user device and receiving user feedback via the feedback buttons to train the supervised machine learning model was not an improvement to computer functionality of machine learning technology.
Examiner further points to the recent Federal Circuit decision – Recentive v. Fox. The Federal Circuit ruled the requirements that “the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement”. The Federal Circuit also suggested that using machine learning in a new environment does not improve the machine learning technology. As such, applying supervised machine learning with feedback buttons to an abstract concept of mapping company identifiers to user’s purchases and displaying the company identifiers, does not improve the machine learning technology or integrate the abstract concept into a practical application.
Moreover, Applicant’s specification states the benefit of the claimed invention is “to assist people with identifying relevant information and then present that information to them in a manner that facilitates decision making” (see Background section). Federal Circuit Court has ruled that “arranging transaction information on a graphical user interface in a manner that assists traders in processing information more quickly” (Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290) is insufficient to show an improvement in computer functionality. 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 computer and machine learning 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.
Step 2B: The claims do not recite additional elements that amount to significantly more than the abstract idea.
As discussed earlier, the present claims only recite a processor coupled with a memory as additional elements. The additional elements are claimed to perform basic computer functions, such as retrieving data from data repositories, performing data mapping using a supervised machine learning model, displaying result with feedback button, receiving user feedback via the feedback buttons, and training the supervised machine learning model based on the received user feedback. 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. Also as discussed earlier, applying supervised machine learning with feedback buttons to an abstract concept of mapping company identifiers to user’s purchases and displaying the company identifiers, does not improve the machine learning 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
In the response filed on 07/24/2026, Applicant amended independent claim 1 by adding the following limitations – “and wherein the electronic commerce transaction history comprises an online banking transaction history; wherein retrieving the company identifiers comprises retrieving the one or more stock tickers from an asset database via an application programming interface accessed through a wide area network, and wherein retrieving the online banking transaction history comprises accessing a transaction database controlled by a financial institution different from an organization that controls the asset database”. Examiner points out that electronic commerce transaction history comprising an online banking transaction history is a standard feature, since electronic commerce transaction is typically paid with online banking transaction. Recording online banking transaction is also a standard practice in the eCommerce and the banking industry. Moreover, retrieving data, such as stock tickers and banking transactions from different databases is a well-understood, routine, and conventional computer function according to MPEP 2106.05(d). Retrieving data via API (application programming interface) also does not improve computer function, since API is a standard set of rules and protocols that lets different software programs talk to each other. The addition of these limitations does not improve computer function or render the claims any less abstract.
Applicant's arguments filed on 07/24/2026 have been fully considered but they are not persuasive.
Step 2A
Applicant argued that claim 1 does not merely recite generic supervised machine learning with feedback, but “integrates three interdependent technical components: (1) feedback collection through one or more feedback buttons indicating a user preference regarding the company respected by the corresponding company identifier; (2) training the supervised machine learning model based on that feedback to refined subsequent data mapping operations; and (3) a particular GUI displaying company identifiers in ranked order”. Examiner disagrees and points out that these components together still amount to a generic supervised machine learning. Examiner have already cited prior art, which uses a dislike/negative feedback button and a like/positive feedback button to provide feedback to machine learning algorithm. For example, Karakotsios et al. (Patent No.: US 10,541,000) teaches “The feedback that corresponds to the data can take many different forms and can vary in granularity…the user 104 may specify that he/she does or does not generally like the first video summarization 134, which may be indicated by a thumbs up/down, actuating a like/dislike button, and so on” (see col 22 line 15-27). Such feedback buttons are common on social media platforms for machine learning algorithms to obtain user feedback on the recommended contents. Therefore, the recitation of this feature does not improvement supervised machine learning, graphical user interface, or computer function in general. Moreover, Venkateshwaran et al. (Pub. No.: US 2023/0177206) teaches “Reinforcement Learning can also be utilized. An end-user feedback loop in implemented in the user interface, using which the end user (claims adjuster/supervisor) can provide feedback on the suggestions provided by expert system, supervised or unsupervised machine learning. The user can provide a positive or negative feedback. Reinforcement learning learns patterns of when the user provided positive versus negative feedback, and accordingly tunes the system to provide more meaningful and targeted suggestions” (see paragraph 0079 and 0099). Horesh et al. (Pub. No.: US 2022/0138592) teaches “The feedback is provided as additional input to the supervised machine learning model operating in the background to predict features that the user will deem to be relevant. The more the user uses the My Insights Widget (422), and the more feedback the user provides, the more reliable the prediction of relevancy made by the supervised machine learning model” (see paragraph 0121). Igoe et al. (Patent No.: US 8,930,204) teaches “There are several well-known methods for training a neural network (i.e., selecting the values of the bias 126 and weights 122); one of the most common of these is ‘supervised learning’. In one embodiment using supervised learning, the user provides explicit feedback (not shown) indicating the correctness of the recommendations…implicit feedback is utilized to train the network to the user’s preferences” (see col 42 line 22-34). While not explicitly teach displaying one or more feedback buttons associated with one or more company identifiers, the cited prior arts provide a user interface for user to provide positive or negative feedback to indicate the correctness of recommendations by machine learning model. The present claims merely apply existing machine learning technology to new data environment (i.e., stock recommendation). Therefore, the present claims do not recite feature which indicates improvement to machine learning technology.
Furthermore, the amended independent claim 1, 19, and 20 recite supervised machine learning model and the human supervised feedback training in a high level of generality. The machine learning model merely mimics mental processes in identifying company identifiers associated with the user’s commerce transaction history. There is no indication of improvement in machine learning itself. In the Recentive v. Fox Corp decision, the Federal Circuit states that “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101”. The Federal Circuit also suggested that using machine learning in a new environment does not improve the machine learning technology. As such, applying supervised machine learning with user feedback to an abstract concept of mapping company identifiers to user’s purchases and displaying the company identifiers, does not improve the machine learning technology or integrate the abstract concept into a practical application or amounts to significantly more than the abstract concept.
Applicant further argued that the amended limitations – “wherein the electronic commerce transaction history comprises an online banking transaction history; wherein retrieving the company identifiers comprises retrieving the one or more stock tickers from an asset database via an application programming interface accessed through a wide area network, and wherein retrieving the online banking transaction history comprises accessing a transaction database controlled by a financial institution different from an organization that controls the asset database” – integrate the claims into a practical application, because these sources have different access and security requirements, necessitating separate management. Examiner disagrees and points out that retrieving data from various sources is a basic computer function. Retrieving data via API (application programming interface) also does not improve computer function, since API is a standard set of rules and protocols that lets different software programs talk to each other.
Applicant further argued that the limitations are analogous to claim 1 of Example 42. Examiner disagrees. Claim 1 of Example 42 addresses a technical problem of sharing updated information on patient’s medical condition with other medical providers due to format inconsistencies by converting non-standardized updated information from various sources into a standardized format. The present claims do not perform format conversion, and the specification does not state inconsistent format being a technical problem of prior systems at the time of filing.
Overall, the amended claims do not recite limitations sufficient to improve computer function or integrate the abstract idea into a practical application.
Step 2B
Applicant argued that even if each element of claim 1 were individually known, the particularly claimed combination that results in inventive functionality is not. Examiner disagrees and points out that Applicant did not explain what inventive functionality is provided as the result of combination of elements in claim 1. The combination of elements in claim 1 provides a generic supervised learning machine learning with human feedback loop. The machine learning technology has not been improved. Rather, it is merely being applied to a stock recommendation application. Patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101.
Examiner maintains the ground of rejection under 35 U.S.C. 101.
Rejection under 35 U.S.C. 103
Applicant’s arguments, see Remarks filed on 07/07/2025, with respect to rejection under 35 U.S.C. 102/103 have been fully considered and are persuasive. Examiner agrees the cited prior arts, in particular, Ghosh (Pub. No.: US 2016/0005126) and Mudgil et al. (Patent No.: US 11,410,085), do not teach every limitation in the amended claims. Examiner has conducted updated search, but cannot find a prior art to address the amended features, “(c) causing one or more company identifiers to be displayed on a graphical user interface of a user device, the one or more company identifiers being respectively associated with one or more feedback buttons displayed on the graphical user interface; (d) receiving, from the user device, feedback input via at least one of the one or more feedback buttons indicating a user preference regarding the company represented by the corresponding company identifier; and (e) training the supervised machine learning model, based on the received feedback input, to refine subsequent data mapping operations”. The rejection of claims 1-9 and 11-20 under 35 U.S.C. 103 has been withdrawn.
Examiner notes however, reinforcement learning from human feedback was a known technology in other applications. The present claims do not improve computer functionality or machine learning technology. Simply implementing machine learning in a new environment is not sufficient to improve computer technology. Therefore, the present claims are still ineligible for patent under 35 U.S.C. 101.
Conclusion
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/HAO FU/Primary Examiner, Art Unit 3695
JULY-2026
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