Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 7/13/2026. Claims 1, 5, 7-8, 12, 14-15, 19-22, 25-29, and 31 are currently pending and have been examined. Claims 24 and 30 are newly cancelled; claims 2-4, 6, 9-10, 13, 16-18, 23-24, 30 stand cancelled. Claims 1, 8, and 15 have been amended.
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities:
“one or more AI based algorithms” should read “a plurality of AI based algorithms,” as the claims now require at least 2 algorithms in this group.
The algorithms including “at least a one-dimensional clustering algorithm … and at least one multi-dimensional clustering algorithm utilized based on a type of product” should read “... chosen based on a type of product”
“peaks…is utilized” should read “peaks…are utilized”
“the user actions” should read “user actions”
Appropriate correction is required.
Claim Rejection - 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, 7-8, 12, 14-15, 19-22, 25-29, and 31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1, 5, 7, 21-22, 25-29, are directed to a process, claims 8, 12, 14 are directed to a machine, and claims 15, 19-20, 31 are directed to an article of manufacture. Therefore, claims 1, 5, 7-8, 12, 14-15, 19-22, 25-29, and 31 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 8, and 15 recite at least the following limitations that are believed to recite an abstract idea:
receiving data from a plurality of users, wherein the data is anonymized by refining a procedure with inputs from at least two of the plurality of users and adding noise until the at least two users are indistinguishable by the procedure;
categorizing the data into one or more cohort groups according to attributes of the plurality of users;
utilizing the one or more cohort groups to define a plurality of new products using one or more algorithms, wherein the one or more algorithms are refined using both the data and additional data, wherein the one or more algorithms include at least a one-dimensional clustering algorithm utilizing either Jenks optimization-based clustering or Kernel Density Estimation (KDE) and at least one multi-dimensional clustering algorithm utilized based on a type of product, wherein peaks produced by the one-dimensional clustering algorithm is utilized in determining at least a quantity of one or more of the plurality of new products a user requires, wherein the at least one multi-dimensional clustering algorithm uses either density-based clustering or mixture models, and wherein the at least one multi-dimensional clustering algorithm is utilized in determining dimensions of the plurality of new products based on the data and a number of users in each of the one or more cohort groups; and
presenting, using a visual overlay within a supply chain display, the one or more of the plurality of new products to the user based on at least one of the one or more cohort groups to which the user belongs, wherein the visual overlay enables the user to move through features associated with each of the one or more new products;
transmitting, to a manufacturer, manufacturing instructions for at least one of the plurality of new products based on the user actions within the visual overlay; and
manufacturing, by the manufacturer, the at least one of the plurality of new products according to specific requirements of the user including size and dimension.
The above limitations recite the concept of personalized product design. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1, 5, 7-8, 12, 14-15, 19-22, 25-29, and 31 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
Augmented reality data
A Generative Adversarial Network (GAN)
Training a system
AI based algorithms
Training algorithms using AR data and non-Metaverse generated data
either Density-based clustering of applications with noise (DBSCAN) or Gaussian Mixture Models
A user interface
Swiping
A 3D printer
Manufacturing constituting printing
A computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method
A computer program product for supply chain optimization, comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 20 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 5, 7, 12, 14, 19, 21-22, 25-29, and 31, these claims are similar to the independent claims except that they recite the further additional elements of a machine learning model, retraining algorithms, AR or VR interactions, a digital twin environment, virtual representations, VR images corresponding to a digital twin, images, videos, and 3D scans, EUD or IoT devices, an eCommerce platform. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
Augmented reality data
A Generative Adversarial Network (GAN)
Training a system
AI based algorithms
Training algorithms using AR data and non-Metaverse generated data
either Density-based clustering of applications with noise (DBSCAN) or Gaussian Mixture Models
A user interface
Swiping
A 3D printer
Manufacturing constituting printing
A computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method
A computer program product for supply chain optimization, comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable over Prior Art of Record
Claims 1, 5, 7-8, 12, 14-15, 19-22, 25-29, and 31 are allowable over prior art though rejected on other grounds (e.g.35 USC § 101) as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1, 5, 7-8, 12, 14-15, 19-22, 25-29, and 31 would be allowable over prior art if rewritten to overcome the rejections above and to include all of the limitations of the base claim and any intervening claims. Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1, 5, 7-8, 12, 14-15, 19-22, 24-31 are allowable over prior art. The most related prior art patent of record include Goncalves et al (US20230077278A1), Cao et al (US20220108213A1), Duplessis et al (US20210342744A1), and Reference U (NPL -see attached).
Goncalves teaches a system which logs user activity, including activities engaged in through an AR headset [0102]. The user’s context, e.g. shopping, engaging in an AR environment, is also stored [0087]. Based on this information, the user’s profile is compared to other users’ profiles to assess similarity [0097], resulting in a determination of anonymous users in a similar context to the user [0011]. Products are selected based on the similar users [0105-0106] and presented to the user [1007], such as through an AR overlay [0034].
Cao teaches a neural network acting as a recommender system [0065], in which training data is used to train GANs [CLM 10]. To maintain privacy, this data has Gaussian noise added, with privacy measured by the degree to which the model can deviate between adjacent data sets [0072]. This nose, added to prevent exposure of the data set [0086], is added until convergence is reached, at which point the model is trained [0059].
Duplessis teaches a machine learning recommendation system [Abstract] in which users having similar information are grouped together [0135]. Recommendations are made based on information about this group [0142]. Recommendation are calculated using a metric score that includes user of a KDE or other estimation technique [0074]. A query is directed to a specific feature, and is used to obtain information that causes a peak in the estimation distribution [0078]. The ML model is trained by adding noise to inputs to simulate uncertainty [0035].
Agarwal teaches automated product design and recommendation systems [Abstract] that use a GAN and random noise added to image data [0078], as part of the training process for an AI/neural network [0079]. The system may use an AR display [0038], and modifies the suggested design based on comparison to similar user profiles, which are used for training the ML models [0010].
Reference U teaches recommendation systems that include utilizing AR image inputs and similarities to other customers to make product recommendations to a user, including tailor-made offers unique to a user.
However, each of these references fail to disclose or render obvious at least the limitations of: receiving augmented reality data from a plurality of users, wherein the augmented reality data is anonymized by training a Generative Adversarial Network (GAN) system with inputs from at least two of the plurality of users and adding noise until the at least two users are indistinguishable by the GAN; categorizing the augmented reality data into one or more cohort groups according to attributes of the plurality of users; utilizing the one or more cohort groups to define a plurality of new products using one or more Artificial Intelligence (Al) based algorithms, wherein the one or more Al based algorithms are trained using both the augmented reality data and non-Metaverse generated data, wherein the one or more Al based algorithms include at least a one-dimensional clustering algorithm utilizing either Jenks optimization-based clustering or Kernel Density Estimation (KDE) and at least one multi-dimensional clustering algorithm utilized based on a type of product, wherein peaks produced by the one-dimensional clustering algorithm is utilized in determining at least a quantity of one or more of the plurality of new products a user requires, wherein the at least one multi-dimensional clustering algorithm utilizes either Density-based clustering of applications with noise (DBSCAN) or Gaussian Mixture Models, and wherein the at least one multi- dimensional clustering algorithm is utilized in determining dimensions of the plurality of new products based on the augmented reality data and a number of users in each of the one or more cohort groups.
Each of these references fail to disclose or render obvious the combination of limitations in the independent claims 1,8, and 15, alone or in obvious combination. Therefore, at least for the combination of elements recited in the independent claims, the independent claims and those that depend thereon are allowable over prior art if rewritten to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 7/13/2026 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant argues that “the invention provides an improvement in computer-related technology as well as an improvement to at least the field of supply chain optimization by leveraging underutilized anonymized augmented reality data to identify new products specific to identified cohort groups and adjust supply chains according to the needs of those cohort groups.” Applicant argues that the claimed “invention further enables platforms to centralize the production and/or manufacturing of particular parts and/or components of the new products in geographic regions proximate to an associated cohort group which may reduce product costs, shipping costs, and/or other expenses.”
Examiner respectfully disagrees, and notes that several of these steps are not reflected in the claims, including positioning production in particular regions based on a cohort group. The pending claims recite an abstract idea, including the argued anonymization of data and the use of it to identify new products specific to user cohorts and to support a supply chain/manufacturing of those products. The additional elements in the claims, such as AR data and a 3D printer, are recited at a high level of generality and are invoked as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)]. At best, the claims offer business improvements stemming solely from the abstract idea itself.
Applicant further argues that “the invention improves supply chain requirement predictions collecting of permanent data that is generated during a transient virtual reality-based interaction of end users with eCommerce platforms by receiving augmented user-generated data and categorizing the anonymized augmented user-generate [sic] data into cohort groups using attributes of a plurality of users wherein the categorizing uses the same analytics that may be used on non-virtual reality-based augmented user generated data.” Applicant argues that the system can “leverage the categorizing to define new groups of products and feed the categorized augmented user-generated data into one or more AI-based algorithms for richer training and more accurate prediction of supply chain requirements for the new groups of products.”
Examine disagrees, and notes that the independent claims do not recite virtual reality, or that the AR data is user-generated. None of the claims recite state that categorizing AR data “uses the same analytics that may be used on non-virtual reality-based augmented user generated data.” The argued improvements stemming from language in the claims themselves are at best a business improvement stemming from the abstract idea; for instance, the alleged improvement to data quality and prediction accuracy are rooted solely in the abstract idea, except for additional elements which create only a general linking to computer technology, without integrating the abstract idea into a technological improvement [MPEP 2106.05(f)].
Applicant further argues that the system “may determine the local preferences of one or more geographic regions which may be utilized in further specifying categorization of the augmented user-generated data into the one or more cohort groups and retraining the one or more AI-based algorithms to further define one or more new products…to more specifically address the preferences of specific cohort groups,” which “may provide insights which enable the eCommerce platform to centralized the production…in geographic regions proximate to those cohort groups to reduce production costs.”
Examiner disagrees. Similar to the discussion above, geographic regions are not discussed in the independent claims as argued. Dependent claim 29 discusses determining preferences based on cohort regions and providing insights, including a bare assertion that this would reduce costs. Similarly, dependent claims discuss retraining with feedback to refine the algorithm. Each of these are at best a business improvement stemming solely from the abstract idea; the ability to perform unspecified data analysis to produce insights, and the ability to utilize feedback to improve the process for future iterations, are each abstract steps rooted in the abstract idea. The additional elements, such as the data being presented via an “eCommerce platform,” and the process being an AI algorithm, are invoked as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)].
Applicant argues that “several of the new dependent claims …directly claim improvements related to manufacturing and production,” with reference to “new claims 26-31.”
Examiner disagrees, and notes that these claims reflect several previously-presented claims and one cancelled claim. The claims themselves, as discussed in the rejection above, recite additional elements at a high level of generality that are invoked as mere instructions to apply this abstract idea to a technological environment [MPEP 2106.05(f)]. To the extent that any of the still-pending claims argued are related to manufacturing or production, these claims are directed to the abstract idea, reciting steps for data analysis and feedback-based updates to the process which amount to, at most, a business improvement rooted solely in the abstract idea.
Applicant argues that the dependent claims are allowable “for at least the same reasons” as the independent claims on which they depend.”
Examiner disagrees for the reasons addressed in the rejection and response above.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS J SULLIVAN whose telephone number is (571)272-9736. The examiner can normally be reached Mon - Fri 9-5 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, Marissa Thein can be reached at 571-272-6764. 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.
/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
