DETAILED ACTION
This Final Office Action is in response to the arguments and amendments filed June 11, 2026.
Claims 1, 5, and 13 have been amended.
Claims 1-20 are currently pending and have been considered below.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application.
In terms of Step 1, claims 1-20 are directed towards one of the four categories of statutory subject matter.
In terms of Step 2(a)(1), Independent claim 1 is directed towards, “generating first data including a vector representation of a trademark associated with at least one of a good or service, the vector representation of the trademark generated based on attributes of the trademark; generating second data including a vector representation of a description of the at least one of the good or service, the vector representation of the description of the at least one of the good or service generated based on attributes of the description of the at least one of the good or the service; determining a subset of trademarks to be analyzed utilizing the [specific, computer-centric] vector space, the subset of trademarks determined based on vector representations of the goods or services of the trademarks having at least a threshold similarity to the vector representation of the description of the at least one of the good or service of the trademark; determining a similarity metric indicating a degree of similarity between the vector representation of the trademark and vector representations of the trademarks from the subset of the trademarks; determining context data associated with the trademark, the context data indicating information other than the trademark and description that is related to the trademark; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; and determining, utilizing a trained machine learning model configured to predict distinctiveness of the trademark, a trademark distinctiveness score to associate with the trademark, wherein the trained machine learning model utilizes at least the first data, vector representations of the subset of trademarks, the similarity metric, and the context data to predict the trademark distinctiveness score”. The claims are describing a similarity model based on a vector representation of a first and second data and subset threshold value to determine a similarity metric utilizing a trained machine learning model. This includes the training step as the training is providing the input data to provide ground truth, but that is merely describing the input values towards the model. The claims are directed towards a mathematical relationship and formula for calculating similarity between a trademark description and a subset of analyzed trademark elements to provide a similarity metric. The claim is directed towards an abstract idea under the mathematical concept grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claim 1 is, “a system, comprising: one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising; the vector representation of the trademark corresponding to a computer-generated representation of the trademark that is less computationally extensive than data representing the trademark itself; the vector representation of the description corresponding to a computer-generated representation of the description that is less computationally extensive than data representing the description itself; generating a specific, computer-centric vector space that includes at least the vector representation of the trademark and the vector representation of the description; generating a machine learning model configured to predict trademark distinctiveness; generating feedback data indicating performance of the machine learning model over time; transforming the feedback data into a training dataset configured to be utilized for training the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; generating, in real time, a generated user interface (GUI) configured to display at least the trademark distinctiveness score to a user on a user device, wherein the GUI dynamically updates based at least in part on receiving user input”. The system and computer elements are described in the originally filed specification paragraph [34] and figure 1. The computer elements are merely generic technology to implement the abstract idea. In terms of the displaying additional elements, the display and user input are described in the originally filed specification [62, 52, 112]. The display and updating elements based on user input is merely providing the information to the user based on the identified abstract idea above. In terms of the limitations regarding the vector representation and computer-centric vector space, the specification describes the vector elements in paragraphs [46-54, 71-75, and 85-87]. The vector elements are further describing mathematical techniques to implement the abstract idea. The vector aspects are described in terms of ANN and other mathematical techniques to describe a vector with which to implement the calculation for the trademark similarity score. Additionally, the specification does not describe a “computer-centric” vector space. The additional elements further include machine learning and ML model feedback elements. The machine learning elements are taught within paragraphs [21, 31, and 37-40]. The machine learning is merely generic technology to implement the abstract idea. The specification merely lists provided techniques as tools and are not improving the machine learning itself as a technical improvement {further discussed in paragraphs [56-58 and 124]}. The training step specifically is provided in paragraphs [165-168], however, the training step is describing implementing the abstract idea using generic technology. This further includes the limitations that describe the ground truth data, but the claims merely provide the input information with respect to the training dataset and not describing a technical improvement or otherwise technical interaction beyond stating the data used in the generic training step. The use of generic mathematical or ML techniques to provide vector elements is implementing the abstract idea using generic technology as a tool. The additional elements are not technical improvements and therefore are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claim 1 are, “a system, comprising: one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising; the vector representation of the trademark corresponding to a computer-generated representation of the trademark that is less computationally extensive than data representing the trademark itself; the vector representation of the description corresponding to a computer-generated representation of the description that is less computationally extensive than data representing the description itself; generating a specific, computer-centric vector space that includes at least the vector representation of the trademark and the vector representation of the description; generating a machine learning model configured to predict trademark distinctiveness; generating feedback data indicating performance of the machine learning model over time; transforming the feedback data into a training dataset configured to be utilized for training the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; generating, in real time, a generated user interface (GUI) configured to display at least the trademark distinctiveness score to a user on a user device, wherein the GUI dynamically updates based at least in part on receiving user input”. The system and computer elements are described in the originally filed specification paragraph [34] and figure 1. The computer elements are merely generic technology to implement the abstract idea. In terms of the displaying additional elements, the display and user input are described in the originally filed specification [62, 52, 112]. The display and updating elements based on user input is merely providing the information to the user based on the identified abstract idea above. In terms of the limitations regarding the vector representation and computer-centric vector space, the specification describes the vector elements in paragraphs [46-54, 71-75, and 85-87]. The vector elements are further describing mathematical techniques to implement the abstract idea. The vector aspects are described in terms of ANN and other mathematical techniques to describe a vector with which to implement the calculation for the trademark similarity score. Additionally, the specification does not describe a “computer-centric” vector space. The additional elements further include machine learning and ML model feedback elements. The machine learning elements are taught within paragraphs [21, 31, and 37-40]. The machine learning is merely generic technology to implement the abstract idea. The specification merely lists provided techniques as tools and are not improving the machine learning itself as a technical improvement {further discussed in paragraphs [56-58 and 124]}. The training step specifically is provided in paragraphs [165-168], however, the training step is describing implementing the abstract idea using generic technology. This further includes the limitations that describe the ground truth data, but the claims merely provide the input information with respect to the training dataset and not describing a technical improvement or otherwise technical interaction beyond stating the data used in the generic training step. The use of generic mathematical or ML techniques to provide vector elements is implementing the abstract idea using generic technology as a tool. The additional elements are not technical improvements and therefore are not significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2-4 are further describing the abstract idea without further additional elements beyond those identified above. The claims are directed towards, “the training dataset includes at least: first reference vector representations of reference trademarks; second reference vector representations of reference goods or services associated with the reference trademarks; reference similarity metrics indicating similarity between individual ones of the first reference vector representations; reference context data associated with the reference trademarks; and third data indicating known distinctiveness outcomes associated with the reference trademarks”, “receiving third data indicating that another trademark has been included in a dataset from which the first data was received; determining to retrain the trained machine learning model based on receiving the third data; and retraining the trained machine learning model utilizing at least the third data”, and “generating an aggregated vector representation of the vector representations of the trademarks from the subset of the trademarks, the aggregated vector representation indicating a centroid of the vector representations of the trademarks from the subset of the trademarks; and wherein determining the similarity metric is performed utilizing the vector representation of the trademark and the aggregated vector representation”. The claims are further providing elements of the mathematical model in terms of the machine learning training dataset, retraining the model, and generating an aggregated vector representation based on the similarity metric. The claims are directed towards elements of the machine learning and vector representation that fall under the mathematical elements of the independent claim. The machine learning training and retraining is describing the dataset and not a specific technical improvement in terms of additional element consideration. The vector representation is further describing the similarity metric in terms of providing the information and determination of the trademark metric. The claims are not directed towards additional elements that are significantly more or transformative into a practical application.
Independent claims 5 and 13 are directed towards, “generating first data including a vector representation of a trademark associated with at least one of a good or service; generating second data including a vector representation of a description of the at least one of the good or service; determining a similarity metric indicating a degree of similarity between the vector representation of the trademark and vector representations of a subset of trademarks utilizing the [specific, computer-centric] vector space; determining context data associated with the trademark; generating a machine learning model configured to predict trademark distinctiveness; generating feedback data indicating performance of the machine learning model over time; transforming the feedback data into a training dataset configured to be utilized for training the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; determining, utilizing the trained machine learning model configured to predict distinctiveness of the trademark, a trademark distinctiveness score to associate with the trademark, wherein the trained machine learning model utilizes at least the first data, the similarity metric, and the context data to predict the trademark distinctiveness score”. The claims are describing a similarity model based on a vector representation of a first and second data to determine a similarity metric utilizing a trained machine learning model. The claims are directed towards a mathematical relationship and formula for calculating similarity between a trademark description and a subset of analyzed trademark elements to provide a similarity metric. The claim is directed towards an abstract idea under the mathematical concept grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claim 5 and 13 are, “a system, comprising: one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising {claim 13}; the vector representation of the trademark corresponding to a computer-generated representation of the trademark that is less computationally extensive than data representing the trademark itself; the vector representation of the description corresponding to a computer- generated representation of the description that is less computationally extensive than data representing the description itself; generating a specific, computer-centric vector space that includes at least the vector representation of the trademark and the vector representation of the description; generating a machine learning model configured to predict trademark distinctiveness; generating feedback data indicating performance of the machine learning model over time; transforming the feedback data into a training dataset configured to be utilized for training the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; and generating, in real time, a generated user interface (GUI) configured to display at least the trademark distinctiveness score to a user on a user device, wherein the GUI dynamically updates based at least in part on receiving user input”. The system and computer elements are described in the originally filed specification paragraph [34] and figure 1. The computer elements are merely generic technology to implement the abstract idea. In terms of the displaying additional elements, the display and user input are described in the originally filed specification [62, 52, 112]. The display and updating elements based on user input is merely providing the information to the user based on the identified abstract idea above. In terms of the limitations regarding the vector representation and computer-centric vector space, the specification describes the vector elements in paragraphs [46-54, 71-75, and 85-87]. The vector elements are further describing mathematical techniques to implement the abstract idea. The vector aspects are described in terms of ANN and other mathematical techniques to describe a vector with which to implement the calculation for the trademark similarity score. Additionally, the specification does not describe a “computer-centric” vector space. The additional elements further include machine-learned elements. The machine learning elements are taught within paragraphs [21, 31, and 37-40]. The machine learning is merely generic technology to implement the abstract idea. The specification merely lists provided techniques as tools and are not improving the machine learning itself as a technical improvement {further discussed in paragraphs [56-58 and 124]}. The training step specifically is provided in paragraphs [165-168], however, the training step is describing implementing the abstract idea using generic technology. This further includes the limitations that describe the ground truth data, but the claims merely provide the input information with respect to the training dataset and not describing a technical improvement or otherwise technical interaction beyond stating the data used in the generic training step. The use of generic mathematical or ML techniques to provide vector elements is implementing the abstract idea using generic technology as a tool. The additional elements are not technical improvements and therefore are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claim 5 and 13 are, “a system, comprising: one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising {claim 13}; the vector representation of the trademark corresponding to a computer-generated representation of the trademark that is less computationally extensive than data representing the trademark itself; the vector representation of the description corresponding to a computer- generated representation of the description that is less computationally extensive than data representing the description itself; generating a specific, computer-centric vector space that includes at least the vector representation of the trademark and the vector representation of the description; generating a machine learning model configured to predict trademark distinctiveness; generating feedback data indicating performance of the machine learning model over time; transforming the feedback data into a training dataset configured to be utilized for training the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated, wherein the training dataset comprises ground truth data including at least one of: (i) data indicating whether reference trademarks are associated with a principal register or a supplemental register, (ii) data indicating whether disclaimers are associated with reference trademarks, (iii) data indicating whether affidavits of incontestability are associated with reference trademarks, or (iv) data indicating distinctiveness findings from litigation of reference trademarks; and generating, in real time, a generated user interface (GUI) configured to display at least the trademark distinctiveness score to a user on a user device, wherein the GUI dynamically updates based at least in part on receiving user input”. The system and computer elements are described in the originally filed specification paragraph [34] and figure 1. The computer elements are merely generic technology to implement the abstract idea. In terms of the displaying additional elements, the display and user input are described in the originally filed specification [62, 52, 112]. The display and updating elements based on user input is merely providing the information to the user based on the identified abstract idea above. In terms of the limitations regarding the vector representation and computer-centric vector space, the specification describes the vector elements in paragraphs [46-54, 71-75, and 85-87]. The vector elements are further describing mathematical techniques to implement the abstract idea. The vector aspects are described in terms of ANN and other mathematical techniques to describe a vector with which to implement the calculation for the trademark similarity score. Additionally, the specification does not describe a “computer-centric” vector space. The additional elements further include machine-learned elements. The machine learning elements are taught within paragraphs [21, 31, and 37-40]. The machine learning is merely generic technology to implement the abstract idea. The specification merely lists provided techniques as tools and are not improving the machine learning itself as a technical improvement {further discussed in paragraphs [56-58 and 124]}. The training step specifically is provided in paragraphs [165-168], however, the training step is describing implementing the abstract idea using generic technology. This further includes the limitations that describe the ground truth data, but the claims merely provide the input information with respect to the training dataset and not describing a technical improvement or otherwise technical interaction beyond stating the data used in the generic training step. The use of generic mathematical or ML techniques to provide vector elements is implementing the abstract idea using generic technology as a tool. The additional elements are not technical improvements and therefore are not transformative into a practical application. Refer to MPEP 2106.05(f).
Dependent claims 6-12 and 14-20 are further describing the abstract idea without further additional elements beyond those identified above. The claims are directed towards, “the training dataset includes at least: first reference vector representations of reference trademarks; second reference vector representations of reference goods or services associated with the reference trademarks; reference similarity metrics indicating similarity between individual ones of the first reference vector representations; reference context data associated with the reference trademarks; and third data indicating known distinctiveness outcomes associated with the reference trademarks; and training the machine learning model utilizing the training dataset such that the trained machine learning model is generated”, “receiving third data indicating that another trademark has been included in a dataset from which the first data was received; determining to retrain the trained machine learning model based on receiving the third data; and retraining the trained machine learning model utilizing at least the third data”, “generating an aggregated vector representation of the vector representations of the trademarks from the subset of the trademarks, the aggregated vector representation indicating a centroid of the vector representations of the trademarks from the subset of the trademarks; and wherein determining the similarity metric is performed utilizing the vector representation of the trademark and the aggregated vector representation”, “further comprising determining the subset of trademarks to be analyzed, the subset of trademarks determined based at least in part on vector representations of the goods or services of trademarks having at least a threshold similarity to the vector representation of the description of the at least one of the good or service”, “wherein the trained machine learning model is trained based at least in part on at least one of third data indicating that a reference trademark is associated with a principal register of trademarks or a supplemental register of trademarks; fourth data indicating whether a disclaimer is associated with the reference trademark; fifth data indicating whether an affidavit of incontestability is associated with the reference trademark; or sixth data indicating whether an affidavit of continuous use for a predetermined time is associated with the reference trademark”, “wherein the trained machine learning model is trained based at least in part on at least one of third data indicating distinctiveness findings associated with litigation of a reference trademark; fourth data indicating findings of famousness associated with the litigation; or fifth data indicating outcomes of cancellation proceedings associated with the reference trademark”, and “wherein the subset of trademarks comprises a first subset of trademarks, and the method further comprises: determining a second subset of trademarks, the second subset of trademarks associated with goods or services having a similarity to the vector representation of the description of the at least one of the good or service that does not satisfy the first threshold similarity but that does satisfy a second threshold similarity; and weighting the first subset of trademarks more than the second subset of trademarks”. The dependent claims are further providing elements of the mathematical model in terms of the machine learning training dataset, retraining the model, generating an aggregated vector representation based on the similarity metric, and threshold analysis. The claims are directed towards elements of the machine learning based on different training data sets, vector representation, and threshold analysis that fall under the mathematical elements of the independent claim. The machine learning training and retraining is describing the dataset and not a specific technical improvement in terms of additional element consideration. The vector representation is further describing the similarity metric in terms of providing the information and determination of the trademark metric. The threshold analysis merely provides a further aspect of the metric in terms of the similarity based on the threshold. The claims are not directed towards additional elements that are significantly more or transformative into a practical application.
The claimed invention are describing an abstract idea without additional elements that are significantly more or transformative into a practical application. As such, claims 1-20 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed June 11, 2026 on pages 12-16 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments discuss the aspects in terms of being similar to Example 47. The arguments allege that the claimed invention is similar in terms of the utilization of neural networks to provide remedial actions based on the analysis. Examiner notes that the example provides specific technical interactions for the network protocols based on the anomaly detection including dropping packets and blocking traffic. The claims, as currently written, provide an output based on the analysis. The arguments allege that the display and updated GUI is similar to the Example 47 mitigation actions. The example provides technical interactions in terms of network access based on the analysis. Merely presenting the trademark information on a GUI is not directed towards a specific technical interaction and improvement based on the analysis. The arguments further discuss the amended claim limitations regarding the ground truth as being towards a technical solution. The claims regarding the ground truth are not describing a technical improvement. These limitations are merely providing the data input into the ground truth training, but is not describing an improvement to training or machine learning itself. There is no specific technical improvement providing the types of information into a model or generic training step that would be directed towards additional elements that are transformative or significantly more. As such, claims 1, 5, and 13 are maintaining the 35 USC 101, as considered above in light of the amended claim limitations.
Lacking any further arguments, claims 1-20 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Stojanovic et al [2019/0138538] (trademark similarity analysis);
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
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