Prosecution Insights
Last updated: April 19, 2026
Application No. 17/379,580

ENVIRONMENTAL IMPACT AWARE PRODUCT REFURBISHING

Non-Final OA §101
Filed
Jul 19, 2021
Examiner
TC 3600, DOCKET
Art Unit
3600
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
International Business Machines Corporation
OA Round
5 (Non-Final)
4%
Grant Probability
At Risk
5-6
OA Rounds
1y 1m
To Grant
5%
With Interview

Examiner Intelligence

Grants only 4% of cases
4%
Career Allow Rate
5 granted / 142 resolved
-48.5% vs TC avg
Minimal +2% lift
Without
With
+1.5%
Interview Lift
resolved cases with interview
Fast prosecutor
1y 1m
Avg Prosecution
206 currently pending
Career history
348
Total Applications
across all art units

Statute-Specific Performance

§101
36.1%
-3.9% vs TC avg
§103
34.6%
-5.4% vs TC avg
§102
13.9%
-26.1% vs TC avg
§112
10.9%
-29.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 142 resolved cases

Office Action

§101
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 . Continued Examination Under 37 CFR 1.114 A request for continued examination 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 submission filed on 11/13/2025 has been entered. Status of the Claims Claims 1, 6, 11, 16 and 20 are currently amended. Claims 4, 10 and 14 have been cancelled. Claims 1-3, 5-7, 11-13, 15-23 are currently pending and have been examined below. Response to Amendment Applicant’s amendments to the claims are insufficient to overcome the 101 rejection set forth in the previous Office Action. The rejection has been maintained. See below. Response to Arguments Applicant’s arguments have been carefully considered and are responded to herein. In the remarks, Applicant specifically addresses the following: 35 USC §101 The applicant asserts (on Page 12 of the remarks) that the amended claim specifies using a concrete, multi-step algorithm to detail how a constrained search for new product designs is achieved. The applicant further assets that by amending the claims in this manner, it enables the system to efficiently search the vector embedding space to optimize refurbishment designs and provides a particular way to achieve that solution. To analyze the applicant’s argument, the examiner is directed to the claim limitations below that recite the following: “generating the recommendation comprises executing a constraint-based selection process performed in the vector embedding space, and wherein the constraint-based selection process comprises iteratively checking for design constraint violations by: (i) providing a candidate product embedding from the vector embedding space to the decoder component to generate a corresponding candidate digital image, and (ii) processing the candidate digital image using a product attribution network to determine whether attributes of the candidate digital image violate the one or more design constraints, wherein the product attribution network is trained separately from the machine learning framework;” The examiner respectfully disagrees that the amended claims provide a technical solution because these claim limitations are recited at a high level of generality and do not provide a meaningful limitation to integrate the abstract idea into a practical application. Rather, the claim limitations recite “generating the recommendation” and “determine whether digital image attributes violate design constraints”, which are directed towards the abstract idea, a certain method of organizing human activity, specifically marketing or sales activities (see MPEP §2106.04(a)(2), subsection II). The additional elements in the claim limitations are interpreted to be the decoder component, the product attribution network as well as providing a candidate product embedding and processing the candidate digital image. These additional elements are recited at a high level of generality in ¶0018 of the current specification. Additionally, the specification recites utilizing “an autoencoder model (e.g., a GAN model and/or a VAE model) to generate product embeddings and to reconstruct images from product embeddings” (see ¶0045 in the current specification). The examiner interpreted these additional elements to recite only the idea of a solution or outcome, to determine product embeddings of products based on images to generate recommendations for refurbished products, and therefore fails to recite details of how a solution to a problem is accomplished. This is equivalent to the words “apply it”, see MPEP §2106.05(f). The examiner also referenced the specification figures and paragraphs (Figures 2 and 5 as well as ¶0023-24 and ¶0039-44) as referenced on Page 10 of the remarks dated 11/13/2025. Figures 2 and 5 recite the process steps at a high level and the examiner could not find additional details in the referenced paragraphs which lacked the technical steps to integrate the invention into a practical application. For guidance regarding how to integrate the invention into a practical application, see MPEP §2106.05, specifically subsections (a-e)). Therefore, the examiner finds the argument to be unpersuasive and the rejection is maintained. The applicant asserts (on Page 12-13 of the remarks) that adding the specific, iterative constraint-checking process, represents a clear improvement to the functioning of a computer and integrates the abstract idea into a practical, patent-eligible application because the amended claims recite how the solution is accomplished. The examiner respectfully disagrees because the decoder and the product attrition network are recited in the claims at a high level of generality because nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea, like checking for design constraint violations, arrive at an intended result and lacks the details as to how the computer performed the actions. The examiner interprets that the limitations recite merely providing a candidate product embedding to the encoder or decoder components and using the product attribution network to determine the abstract idea. These limitations do not demonstrate a clear improvement of the functioning of a computer and amount to nothing more than instructions to apply the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself, see MPEP §2106.05(f). Therefore, the examiner finds the argument to be unpersuasive and the rejection is maintained. Applicant asserts (on Page 13 of the remarks) that the office action does not follow the guidance in the Desjardin decision, Appeal 2024-000567, because the office action applies a high level of generality which the Desjardin decision cautioned against. The applicant further asserts that the claims include a specific machine learning architecture with a decoder component and a separately trained attribution network and that type of improvement to how the machine learning model itself operates should be patent-eligible. The examiner respectfully disagrees because, unlike the Desjardin decision, the current application recites these improvements with a high level of generality (see ¶0018 in the current specification) as mere instructions and lack specific limitations that would confine the abstract idea into a practical application. The recited limitations generally link providing a candidate product embedding to the decoder component and using a product attribution network (see ¶0018 in the current specification) to determine whether attributes violate design constraints. These limitations do not reflect an improvement to the machine learning model itself because the limitations do not adjust the machine learning to improve the machine learning model. Rather, the recite--d machine learning model improves the data in the model, specifically to determine the constraint-based optimization, through iterative experimentation, which reflects how one of ordinary skill in the art would interpret how a machine learning model functions. Thus, the limitations amount to nothing more than instructions to apply the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself, see MPEP §2106.05(f). Therefore, the examiner finds the argument to be unpersuasive and the rejection is maintained. Applicant asserts (on Page 14 of the remarks) that the specific, ordered combination of elements in the amended independent claim is not well-understood, routine, or conventional activity and provides an inventive concept under Step 2B. The examiner respectfully disagrees because the previous final rejection dated 08/13/2025 did not reference 2106.05(d) or 2016.05(g) for which the well-understood, routine, or conventional argument would apply. Therefore the argument is irrelevant and the examiner finds the argument to be unpersuasive. The rejection is maintained. Please see below for complete 35 USC §101 rejection of the amended claims. In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the applicant in regards to distinctly and specifically pointing out the supposed errors in examiner's prior office action (37 CFR 1.111). Examiner asserts that the applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art. Claim Rejections - 35 USC § 101 35 U.S.C. § 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-3, 5-7, 11-13, 15-23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Independent Claims 1, 11 and 20 Step 2A – Prong One: The claim limitations recite steps, specifically generating product embeddings of a plurality of products, checking for design constraint violations, determining whether an image violated design constraints, and generating a recommendation to refurbish products, that are directed to an abstract idea (see MPEP §2106.04(a)) because these steps are performed by an advertising professional to improve marketing effectiveness. Therefore, these steps fall within the abstract idea grouping certain methods of organizing human activity (see MPEP §2106.04(a)(2), subsection II), more specifically commercial interactions. The claim limitations recite steps, specifically performing vector operations and calculating an environmental/demand impact score, that are directed to an abstract idea (see MPEP §2106.04(a)) because performing vector operations and calculating impact scores involve mathematical calculations. Therefore, these steps falls under the abstract idea grouping mathematical concepts (see MPEP §2106.04(a)(2), subsection I). Claims 1, 11 and 20 recite an abstract idea. Step 2A – Prong Two: The scope of the independent claim limitations incorporate the following additional elements: a memory configured to store program instructions; a processor operatively coupled to the memory to execute the program instructions a machine learning framework a vector embedding space an encoder component a decoder component a product attribution network generate a second set of digital images by processing the additional product embeddings provide a candidate product embedding process the candidate digital image output the recommendation and generated image These additional elements listed above, or combination of these elements, amount to nothing more than simply reciting the abstract idea while adding the words ‘apply it’, see MPEP 2106.05(f). The system elements, including the processor, and the memory, to implement the abstract idea amount to components found in a general purpose computer. Further additional elements that recite generic computer-implemented steps to implement a machine learning framework, including an encoder, a decoder component, and a product attribution network, are recited at a high level of generality and amount to nothing more than instructions to apply the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself. The additional generic-computer implemented steps to generate a second set of images, to process the candidate digital image, to output the recommendation and generated image lack details about how the additional elements operates and confines the abstract idea into a practical application. The additional elements, whether considered individually or in combination, are recited at a high level of generality using general computer components and amount to nothing more than instructions to apply the abstract idea without any improvement to technology, technical field, or to the functioning of the computer itself. Therefore, the additional elements, whether evaluated individually or in combination, fail to integrate the recited abstract idea into a practical application. The claimed invention is directed to an abstract idea. Step 2B Under Step 2B of the patent eligibility analysis, the combination of additional elements is evaluated to determine whether they amount to something “significantly more” than the recited abstract idea to determine product embeddings of products based on images to generate recommendations for refurbished products. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a general purpose computer and/or its components. Mere instructions to apply an exception using a general purpose computer and/or its components cannot provide an inventive concept. Claims 1, 11 and 20 are not patent eligible. Dependent Claims – Claims 2-3, 5-7, 12, 13, 15-17 and 21-23 are merely reciting further embellishment of the abstract idea and do not amount to anything that is significantly more than the abstract idea itself. In other words, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limitations that, in an ordered combination provide “significantly more;” rather, the dependent claims are merely further reciting features that are just as abstract as independent claims 1, 11, 20. Therefore, Claims 1-3, 5-7, 11-13, 15-23 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 U.S.C. §101. Claim 8 and 18 claims an additional element of an autoencoder network. This is nothing more than generally linking the use of the judicial exception to the technical field of machine learning and deep learning. Claim 9 and 19 describes utilizing a generative adversarial network. This is nothing more than generally linking the use of the judicial exception to the technical field of generative modeling. Claim 10 describes wherein software is provided as a service on a cloud network. This is nothing more than generally linking the use of the judicial exception to the technical field of cloud computing. Therefore, Claims 1-3, 5-7, 11-13, 15-23 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 U.S.C. §101. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAHUL SHARMA whose telephone number is (571) 272-3058. The examiner can normally be reached Monday thru Friday 8:00am- 5:00pm ET. 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, Nathan C Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RAHUL SHARMA/Examiner, Art Unit 3626 /NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
Read full office action

Prosecution Timeline

Jul 19, 2021
Application Filed
Jan 25, 2024
Non-Final Rejection — §101
Apr 30, 2024
Response Filed
Jul 17, 2024
Final Rejection — §101
Sep 17, 2024
Interview Requested
Sep 24, 2024
Response after Non-Final Action
Oct 14, 2024
Response after Non-Final Action
Oct 24, 2024
Request for Continued Examination
Oct 25, 2024
Response after Non-Final Action
Feb 03, 2025
Non-Final Rejection — §101
May 06, 2025
Response Filed
Aug 11, 2025
Final Rejection — §101
Oct 14, 2025
Response after Non-Final Action
Nov 13, 2025
Request for Continued Examination
Nov 22, 2025
Response after Non-Final Action
Jan 23, 2026
Non-Final Rejection — §101 (current)

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Prosecution Projections

5-6
Expected OA Rounds
4%
Grant Probability
5%
With Interview (+1.5%)
1y 1m
Median Time to Grant
High
PTA Risk
Based on 142 resolved cases by this examiner. Grant probability derived from career allow rate.

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