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 2/13/2026.
Claims 1, 5, 7-8, 12, 14-15, 19-22, 24-31 are currently pending and have been examined. Claims 2-4, 6, 9-11, 13, 16-18, 23 stand cancelled. Claims 1, 7-8, 14-15, 24 have been amended. Claims 26-31 have been newly entered. Prior Art rejection has been overcome by amendment.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/13/2026 has been entered.
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informality: “peaks…is utilized” should read “peaks…are utilized.” 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, 24-31 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, 24-30, 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, 24-31 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 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; 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.
The above limitations recite the concept of personalized recommendations. 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, 4-8, 11-15, and 18-25 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
A user interface
Swiping
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, 24-31, these claims are similar to the independent claims except that they recite the further additional elements a machine learning model, retraining algorithms, AR or VR interactions, a digital twin environment, virtual representations, multidimensional clustering algorithm utilizing either Density-based clustering of applications with noise (DBSCAN) or Gaussian Mixture Models, VR images corresponding to a digital twin, images, videos, and 3D scans, EUD or IoT devices, an eCommerce platform, a 3D printer. 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
A user interface
Swiping
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, 24-31 are allowable over prior art though rejected on other grounds (e.g. 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, 24-31 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 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.
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 2/13/2026 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that the claims provide “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 claims further improve “supply chain requirement predictions.”
Examiner respectfully disagrees. With reference to the rejection above, the alleged improvements are at best a business improvement stemming solely from the abstract idea itself; the ability to anonymize data and utilize it in placing a user in a cohort and to determine a quantity of products needed is part of the abstract idea, except for the recitation of additional elements at a high level of generality, which amount to mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. Similarly, the alleged improvements to the ability to identify needs of users in a cohort and predict supply chain requirements also stem solely from the abstract idea except for a general linking to computer technology.
Applicant argues that “claims 26-31 …directly claim improvements related to manufacturing and production…and 3D printing,” stating that “these claim limitations directly tie the claims to the improvements described through the specification.
Examiner disagrees. Similar to the discussion above with respect to the independent claims, the additional elements of these dependent claims amount to mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], providing only a general linking to computer technology. Alleged improvements to manufacturing and production streams at best stem solely from the abstract idea. For instance, recitations of feedback being used to further improve future results, and geographically localized recommendations are rooted in the abstract idea, with additional elements such as 3D scans, IoT devices, and the concept of eCommerce providing only a general linking to computer technology. Rather than improving 3D printing as alleged, Claim 30 merely invokes the concept of 3D printing as mere instructions to apply the abstract idea, e.g. the concept of custom, on-demand product manufacturing, to computer technology [MPEP 2106.05(f)]. The claim does not recite, and the Specification does not support, any improvements to the actual functionality of a 3D printer or the technical field of 3D printing technology or techniques.
Conclusion
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 8-5 PT.
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 on (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
Prosecution Insights