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 . Claims 1, 5-7, 9, 13-15, 17, and 19-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's RCE submission filed on 6/1/26 has been entered.
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, 5-7, 9, 13-15, 17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 9 to a system (i.e., a machine), and claim 17 to a computer program product (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
(Examiner notes that the computer readable storage medium, which is claimed as part of the computer program product in claim 17, is defined by applicant “not to be construed as storage in the form of transitory signals per se” in [0044] of the specification as filed. Accordingly, the claim, which describes a non-transitory computer readable storage medium, meets the threshold at Step 1.)
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 9, and 17 is (claim 1 being representative):
evaluating return data, received from a return channel, against a threshold,
wherein the return data includes attribute data of an originally-obtained product, attribute data of a new product, and a reasoning specifying that a return of the originally-obtained product is based on the new product having one or more attributes that are different from one or more attributes of the originally-obtained product;
validating, based upon the threshold being satisfied, the return data against multiple data sources associated with the originally-obtained product and the new product,
wherein the multiple data sources comprise publicly-available data, producer or manufacturer data, and retail data, and
the validating of the return data comprises:
determining whether the return data accurately reflects a difference between the originally-obtained product and the new product, and supplementing the return data with additional attributes that differentiate the originally-obtained product from the new product;
cognitive processing, based upon the threshold being satisfied, the return data to generate a return insight, wherein
the cognitive processing of the return data comprises comparing the attribute data of the originally-obtained product with the attribute data of the new product, and
the generated return insight comprises the one or more attributes of the new product that caused an exchange of the originally-obtained product with the new product;
generating, based upon the return insight, a corrective action, wherein the generating of the corrective action includes:
rearranging a presentation of the one or more attributes and the additional attributes of the originally-obtained product to highlight specific attributes of the originally-obtained product, wherein the one or more attributes and the additional attributes include the specific attributes, and
displaying the originally-obtained product in proximity to the new product for comparison between the originally-obtained product and the new product; and
associating a negative reward with the generated corrective action based on the generated corrective action that is not implemented within a specific period of time; and
updating the threshold based on the associating of the negative reward with the generated corrective action.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, evaluating return data against a threshold, validating it, generating a return insight and corresponding corrective action, and updating the threshold. These are steps an administrator could perform, using pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe a return process for a purchased product, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions pertaining to a return process for a purchased product, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: computer-implemented; training a neural network; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
Claim 9 recites the following additional elements: computer hardware system; hardware processor; training a neural network; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
Claim 17 recites the following additional elements: computer program product; computer readable storage medium having stored therein program code for training a neural network; computer hardware system; using the neural network; electronic display; training the neural network using feedback generated based upon the generated corrective action, wherein the training of the neural network includes.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0040]-[0057] of applicant’s specification as filed, for example.
Examiner interprets the neural network and associated training features, described in [0020] and [0035] of applicant’s specification as filed, as additional elements. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, the neural network and associated training features, which are merely being used to facilitate the tasks of the abstract idea, provide nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and are equivalent to the words “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 5-7, 13-15, and 19-20 include additional abstract steps and/or information that narrow the abstract idea. Some of the dependent claims include further additional elements (claims 5, 7, 13, 15, and 19-20: electronic and/or electronically). Similar to above, these additional elements are merely being used to facilitate the tasks of the abstract idea and equivalent to “apply it,” per MPEP 2106.05(f). Whether viewed alone or in combination, this does not integrate the narrowed abstract idea into practical application and/or add significantly more.
Accordingly, claims 1, 5-7, 9, 13-15, 17, and 19-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 6/1/26 have been fully considered. Examiner’s response follows.
Claim Rejections - 35 USC § 112
Applicant is thanked for their amendments overcoming the previous claim rejections under 35 USC § 112. Based on applicant’s amendments, which address the relative nature of the term “better,” these are withdrawn.
Claim Rejections - 35 USC § 101
On pages 12-18, applicant provides remarks regarding the rejections under 35 USC § 101. While well taken, they are not persuasive.
Applicant offers, after restating portions of the claim amendments:
The above-claimed features are directed a computer technology and inextricably tied to a machine. The claimed invention validates and enriches the data using diverse external data sources to ensure accuracy and depth. This enriched data enables cognitive analysis through a Neural Network (NN), allowing the claimed method to identify precise data insights and generate targeted corrective actions including product attribute presentation improvements. The incorporation of feedback-driven learning, including negative rewards and dynamic threshold updates, ensures continuous improvement, enabling the claimed method to refine its decisions over time.
Therefore, the claimed features cannot be considered as Mental Process or Certain Methods of Organizing Human Activity.
At least for the above-mentioned reasons, the Applicant respectfully submits that the amended independent claim 1 meets standards for patent eligibility under prong one of Step 2A of 2019 Revised Patent Subject Matter Eligibility Guidance. Therefore, the claims are not directed to the alleged abstract idea.
Examiner first notes that Step 2A Prong One asks the question: do the claims recite an abstract idea? The directed to inquiry is performed at Step 2A Prong Two. Examiner’s position is that an abstract idea is recited. Further, it appears that applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Examiner’s position is that the tasks of the abstract idea are merely being facilitated by generic computing elements, including a neural network. Applicant’s remarks to the contrary are not persuasive.
Applicant continues (bracketed text corresponds to citations to applicant’s specification that have been omitted for brevity):
Regarding Prong Two of Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance, even if one were to arrive at a conclusion satisfying the Prong one of such analysis, assuming arguendo, to which the Applicant does not concede, the Applicant submits that the alleged abstract idea is integrated into a practical implementation.
The Applicant's disclosure describes, for example, "[t]he present invention relates to neural network training, and more specifically, to using exchange data to train a neural network for improved threshold and action item determination ... what is needed is an improved neural network that could timely and correctly suggest corrective actions." See [0001] and [0003] of the Specification as originally filed (emphasis added).
The Applicant's disclosure further describes, for example […]
As shown in the Specification above, the present invention provides a solution to the technical problems:
Technical Problem: Neural networks that generate corrective actions without feedback on real-world implementation effectiveness cannot self-optimize. When a neural network generates a corrective action recommendation, there is no mechanism to determine whether that recommendation was actually effective in practice. Without this feedback, the neural network continues to generate potentially ineffective recommendations, and its decision thresholds remain static regardless of real-world outcomes. Additionally, neural networks trained on unvalidated or incomplete data produce unreliable outputs.
Technical Solution: The validation of return data against multiple data sources provides significant advantages in terms of accuracy, completeness, and reliability. By cross-referencing publicly available data, manufacturer specifications, and retail catalog information, the claimed method ensures that the return data accurately reflects real differences between products. Furthermore, the supplementation of additional attributes enhances the dataset by introducing objective and detailed feature comparisons that are not explicitly provided by the user, which ensures that the neural network is trained on accurate, verified, and enriched data.
Further, the closed-loop reinforcement learning feedback mechanism that comprises training the neural network using feedback generated based upon the generated corrective action; associating a negative reward with corrective actions not implemented within a specific period of time; and updating the threshold based on the negative reward association, creates a self-optimizing neural network system where real- world implementation outcomes directly influence the neural network's future decision- making. When corrective actions are not implemented (indicating they may be ineffective or impractical), the neural network receives negative feedback that causes it to adjust its thresholds, making it less likely to generate similar ineffective recommendations in the future.
Accordingly, the Applicant has shown teaching in the Specification that describes a practical implementation and how the technology is improved and has thus established a clear nexus between the claim language and the practical implementation of the alleged judicial exception, and improvements in the technology.
Regarding Step 2B of the 2019, the Applicant submits that taking all the claim elements of amended independent claim 1, individually, and in combination, as a while amount to significantly more than the alleged abstract idea of mental processes.
For example, amended independent claim 1 recites For example, amended independent claim 1 recites a combination of additional elements, "generating, using the neural network and based upon the return insight, a corrective action ... the generating of the corrective action includes ... rearranging a presentation of the one or more attributes and the additional attributes of the originally-obtained product to highlight specific attributes of the originally-obtained product ... the one or more attributes and the additional attributes include the specific attributes ... displaying the originally-obtained product in proximity of the new product for comparison,"
that are not well-understood, routine, conventional activity in the field. Therefore, the Applicant respectfully submits that taking all the claim elements of amended independent claim 1 individually, and in combination, amended independent claim 1 as a whole amounts to significantly more than the alleged abstract idea.
At least, for these reasons, the Applicant respectfully submits that the amended independent claim 1 meets the standard for patent eligibility under 35 U.S.C. § 101.
At Step 2A Prong Two and Step 2B, the questions being asked are 1) do the additional elements integrate the abstract idea into practical application or 2) add significantly more? Examiner’s position is that they do not. Applicant’s specification does not make the case for a technical improvement to neural networks, as suggested by applicant. Instead, applicant’s specification, for example at para. [0020] and [0035], describes generic, off-the-shelf machine learning models that are used in their ordinary capacity. Para. [0023] of applicant’s specification is also illustrative:
Examples of RL algorithms that may be used include Markov decision process (MDP) (i.e., the methodology illustrated in FIG. 2A), Monte Carlo methods, temporal difference learning, Q-learning, Deep Q Networks (DQN), State-Action-Reward-State-Action (SARSA), a distributed cluster-based multi-agent bidding solution (DCMAB), and the like. FIG. 2B illustrates one example of the operation of a DQN model. DQN is a combination of deep learning (i.e., neural network based) and reinforced learning. Deep learning is another subfield of machine learning that involves artificial neural networks. An example of a computer system that employs deep learning is IBM's Watson. While the terms "neural network" and "deep learning" are oftentimes used interchangeably, by popular convention, deep learning (e.g., with a DNN), refers to a neural network with more than three layers inclusive of the inputs and the output. A neural network with just two or three layers is considered just a basic neural network.
The claims are reflective of this results-oriented description, and, consequently, fail to show a technological solution to a technological problem. See MPEP 2106.05(a), with reference to MPEP 2106.05(f).
Accordingly, examiner maintains the rejections under 35 U.S.C. § 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce” (NPL attached), which teaches: With the rapid growth in fashion e-commerce and customer-friendly product return policies, the cost to handle returned products has become a significant challenge. E-tailers incur huge losses in terms of reverse logistics costs, liquidation cost due to damaged returns or fraudulent behavior. Accurate prediction of product returns prior to order placement can be critical for companies. It can facilitate e-tailers to take preemptive measures even before the order is placed, hence reducing overall returns. Furthermore, finding return probability for millions of customers at the cart page in real-time can be difficult. To address this problem we propose a novel approach based on Deep Neural Network. Users' taste & products' latent hidden features were captured using product embeddings based on Bayesian Personalized Ranking (BPR).
US 20200175528, which teaches: Systems and methods for predicting and preventing returns using transformative data-driven analytics and machine learning is provided. The systems and methods may include data stores to store and manage data within a network, as well as servers to facilitate operations using information from the one or more data stores. The systems and methods may also include an analytics subsystem having a data access interface to: receive data associated with a plurality of customers; and receive data associated with a plurality of transactions associated with the plurality of customers, where the plurality of transactions are transactions comprising at least a purchase, return, an exchange, or refund of an item. The systems and methods may further include a processor to: perform pre-processing of the data; apply feature engineering and business logic to the transformed returns data; determine root cause analysis based on the applied feature engineering and business logic; apply a machine learning technique based on the root cause analysis; and provide, to a user, at least one recommendation based on the applied machine learning technique.
US 20230230132, which teaches: Systems and methods of training and deploying a machine learning neural network in digital advertising. The method comprises receiving a plurality of input datasets at respective ones of a plurality of input layers of the neural network, the neural network being instantiated in one or more processors. The neural network comprises an output layer interconnected to the plurality of input layers via a set of intermediate layers, each of the input datasets being associated with a respective digital ad input attribute, ones of the intermediate layers being configured in accordance with an initial matrix of weights; and training the neural network in accordance with the plurality of input layers based upon recursively adjusting the initial matrix of weights by backpropogation in generating, at the output layer, at least one digital ad output attribute in accordance with diminishment of an error matrix computed at the output layer of the neural network.
US 20230401528, which teaches: Disclosed embodiments pertain to systems and methods of facilitating product return. A product purchased by a user from a merchant can be determined from transaction data of the user. A return window for the product can be predicted from one or more of data from merchant websites, industry standards, or transaction data. Further, the likelihood that a product is a candidate for return can be predicted based on the transaction data. If the product is determined to be a candidate for return, the user can be notified of the return window for the product and a confidence score associated with the window. Refund timeliness can also be determined or inferred based on transaction data and provided to the user. Subsequently, transaction data can be monitored, and the user can be alerted if credit is not received after a predetermined time or the credit is less than expected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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