DETAILED ACTION
This Non-Final Office Action is in response to the arguments, amendments, and Request for Continued Examination filed December 9, 2025.
Claims 1 and 20 have been amended.
Claims 1-11 and 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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 9, 2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 20 are rejected under 35 U.S.C. 101 because
In terms of step 1, Claims 1-11 and 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, “A method of docketing an incoming electronic communication for managing intellectual property matters, the method comprising: receiving the incoming electronic communication, wherein the electronic communication comprises unstructured text; identifying a first pre-code for the incoming electronic communication; analyzing the unstructured text using a machine learning model […] to generate annotations; based on the generated annotations, selecting a second pre-code for associating with the electronic communication based on the type of the electronic communication, wherein the second pre-code comprises structured text and wherein the second pre-code is more specific than the first pre-code by indicating at least one of: (i) a communication from an international associate, (ii) a communication from a governmental agency, (iii) a reminder, or (iv) an upcoming deadline; automatically associating the second pre-code with the electronic communication by adding the structured text denoting the second pre-code to the electronic communication; and sending the electronic communication with the second pre-code to an automated docketing system”. The claims are describing collecting incoming communications, analyzing in terms of selecting pre-codes (which describes a machine learning model that is considered under 2(a)(II) and 2(b) below), and presenting the communication (in terms of sending to the docketing system). The claim recites an abstract idea under the mental process grouping. Examiner notes that while the claims are directed towards a machine learning model, the specification merely describes the ML in terms of the techniques and training elements which fall into the consideration of high level analysis. With respect to compact prosecution, the ML will be considered as an additional element, but also notes that the ML falls into high level analysis within the mental process consideration. In terms of the mental process consideration, the claims provide aspects that are within a computer environment. The claim elements are directed towards aspects within a computer environment in terms of the communication, unstructured and structured text, and pre-code. The elements are describing data points that are provided in the collection, analysis, and display for the considered mental process.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claim 1 are, “analyzing the unstructured text using a machine learning model trained on historical docketing data from a plurality of previously processed intellectual property communications to generate annotations, wherein the annotations comprise metadata associated with the electronic communication wherein training the machine learning model on historical docketing data comprises creating a training set from previously classified legal documents that have been manually tagged with correct pre-code assignments; and sending the electronic communication with the second pre-code to an automated docketing system configured to interpret the structured text of the second pre-code and execute one or more automated docketing actions based on the second pre-code”. The automated docketing system is described in the originally filed specification [35-40 and 46-50]. The docketing system is merely described in terms of generic technology. Further, the independent claim provides aspects of receiving and identifying that while not specifically directed towards additional elements are also generic technology to implement the abstract idea. The claim provides the collection, receiving, and sending towards a general purpose computer. The claims provide an additional element in terms of the machine learning model and training based on historical information. The ML model and training techniques are disclosed in the originally filed specification [39 and 45]. The specification and claims merely describe the training and ML models as generic technology and analysis to apply the abstract idea. The ML and training steps are not directed towards a technical improvement and thus are not transformative into a practical application. The additional elements 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, “analyzing the unstructured text using a machine learning model trained on historical docketing data from a plurality of previously processed intellectual property communications to generate annotations, wherein the annotations comprise metadata associated with the electronic communication wherein training the machine learning model on historical docketing data comprises creating a training set from previously classified legal documents that have been manually tagged with correct pre-code assignments; and sending the electronic communication with the second pre-code to an automated docketing system configured to interpret the structured text of the second pre-code and execute one or more automated docketing actions based on the second pre-code”. The automated docketing system is described in the originally filed specification [35-40 and 46-50]. The docketing system is merely described in terms of generic technology. Further, the independent claim provides aspects of receiving and identifying that while not specifically directed towards additional elements are also generic technology to implement the abstract idea. The claim provides the collection, receiving, and sending towards a general purpose computer. The claims provide an additional element in terms of the machine learning model and training based on historical information. The ML model and training techniques are disclosed in the originally filed specification [39 and 45]. The specification and claims merely describe the training and ML models as generic technology and analysis to apply the abstract idea. The ML and training steps are not directed towards a technical improvement and thus are not significantly more than the identified abstract idea. The additional elements are not significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claim 2-11 are further describing the abstract idea and not further describing additional elements beyond those identified above. The dependent claims are directed towards, “wherein the secondary pre-code is more specific than the first pre-code”, “wherein at least one of the first and second pre-codes denotes a communication from an international associate or a communication from a governmental agency”, “wherein at least one of the first and second pre-codes denotes a reminder or an upcoming deadline”, “wherein associating the second pre-code comprises adding structured text to the electronic communication, the structured text denoting the pre-code”, “wherein selecting a second pre-code is further based on the unstructured text of the electronic communication”, “further comprising sending the electronic communication with the associated pre-code to a queue for review prior to sending to the automated docketing system”, “further comprising reviewing the electronic communication with the second pre-code for accuracy prior to sending to the automated docketing system”, “further comprising determining whether a pre-code is applicable after identifying a type of the incoming electronic communication”, “wherein, if a pre-code is not applicable, sending the electronic communication for review”, and “further comprising applying more than one pre-code to the electronic communication”. The claims are further describing the type of information that the pre-code provides, applying pre-codes to the communication, sending the communication for review (further providing aspects of mental process needing a user/person’s observations to complete the process), and other elements that are within the collection, analyzing, and displaying results. The claims fall within the abstract idea of mental process and are not directed towards additional elements that are significantly more or transformative into a practical application.
Independent claim 20 is directed towards, “receive an incoming electronic communication, wherein the electronic communication comprises unstructured text; analyze the unstructured text […] to generate annotations; based on the generated annotations, selecting a second pre-code for associating with the electronic communication based on the type of the electronic communication, wherein the second pre-code comprises structured text and wherein the second pre-code is more specific than the first pre-code by indicating at least one of. (i) a communication from an international associate, (ii) a communication from a governmental agency, (iii) a reminder, or (iv) an upcoming deadline: identify a pre-code for associating with the electronic communication based on the generated annotations, wherein the pre-code comprises structured text; automatically associate the pre-code with the electronic communication by adding the structured text denoting the second pre-code to the electronic communication; send the electronic communication with the pre-code to an automated docketing system”. The claims are describing collecting incoming communications, analyzing in terms of selecting pre-codes (which describes a machine learning model that is considered under 2(a)(II) and 2(b) below), and presenting the communication (in terms of sending to the docketing system). The claim recites an abstract idea under the mental process grouping. Examiner notes that while the claims are directed towards a machine learning model, the specification merely describes the ML in terms of the techniques and training elements which fall into the consideration of high level analysis. With respect to compact prosecution, the ML will be considered as an additional element, but also notes that the ML falls into high level analysis within the mental process consideration. In terms of the mental process consideration, the claims provide aspects that are within a computer environment. The claim elements are directed towards aspects within a computer environment in terms of the communication, unstructured and structured text, and pre-code. The elements are describing data points that are provided in the collection, analysis, and display for the considered mental process.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claim 20 are, “A machine readable medium, comprising a processor and a memory with instructions, which when executed, cause the processor to: analyze the unstructured text using a machine learning model trained on historical docketing data from a plurality of previously processed intellectual property communications to generate annotations, wherein the annotations comprise metadata associated with the electronic communication wherein training the machine learning model trained on historical docketing data comprises creating a training set from previously classified legal documents that have been manually tagged with correct pre-code assignments; send the electronic communication with the pre-code to an automated docketing system configured to interpret the structured text of the second pre-code and execute one or more automated docketing actions based on the second pre-code”. The automated docketing system is described in the originally filed specification [35-40 and 46-50]. The docketing system is merely described in terms of generic technology. Further, the independent claim provides aspects of receiving and identifying that while not specifically directed towards additional elements are also generic technology to implement the abstract idea. The claim provides the collection, receiving, and sending towards a general purpose computer. In terms of the computer elements, the specification describes the processor and other computer elements in paragraphs [55-58]. The computer elements are merely generic computer elements to implement the abstract idea. The claims provide an additional element in terms of the machine learning model and training based on historical information. The ML model and training techniques are disclosed in the originally filed specification [39 and 45]. The specification and claims merely describe the training and ML models as generic technology and analysis to apply the abstract idea. The ML and training steps are not directed towards a technical improvement and thus are not transformative into a practical application. The additional elements 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 20 are, “A machine readable medium, comprising a processor and a memory with instructions, which when executed, cause the processor to: analyze the unstructured text using a machine learning model trained on historical docketing data from a plurality of previously processed intellectual property communications to generate annotations, wherein the annotations comprise metadata associated with the electronic communication wherein training the machine learning model trained on historical docketing data comprises creating a training set from previously classified legal documents that have been manually tagged with correct pre-code assignments; send the electronic communication with the pre-code to an automated docketing system configured to interpret the structured text of the second pre-code and execute one or more automated docketing actions based on the second pre-code”. The automated docketing system is described in the originally filed specification [35-40 and 46-50]. The docketing system is merely described in terms of generic technology. Further, the independent claim provides aspects of receiving and identifying that while not specifically directed towards additional elements are also generic technology to implement the abstract idea. The claim provides the collection, receiving, and sending towards a general purpose computer. In terms of the computer elements, the specification describes the processor and other computer elements in paragraphs [55-58]. The computer elements are merely generic computer elements to implement the abstract idea. The claims provide an additional element in terms of the machine learning model and training based on historical information. The ML model and training techniques are disclosed in the originally filed specification [39 and 45]. The specification and claims merely describe the training and ML models as generic technology and analysis to apply the abstract idea. The ML and training steps are not directed towards a technical improvement and thus are not significantly more than the identified abstract idea. The additional elements are not significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
The claimed invention is describing an abstract idea without additional elements that are significantly more or transformative into a practical application. As such, claims 1-11 and 20 are rejected under 35 USC 101 being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed December 9, 2025 on pages 15-29 regarding the 35 USC 103 rejection, specifically that the combination of prior art elements does not teach the amended claim limitations. Examiner agrees. The combination of elements provide aspects of pre-code that associates incoming communication with automated docketing based on a machine learning analysis that is trained on historical docketing data. The combination of prior art elements does not specifically teach the combination of elements. As such, the 35 USC 103 rejection has been withdrawn.
In response to the arguments filed December 9, 2025 on pages 5-15 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
In terms of the arguments regarding the technical improvement through the machine learning, the arguments cite and discuss specific decisions (SRI, ADASA, McRo, XY, LLC) with respect to the machine learning of the claimed invention. The difference between the pending application and the decisions and 35 USC 101 consideration is that the pending application describes the machine learning at a high level of generality and does not provide the specificity in the specification. The machine learning is merely provided in terms of both high level of generality and merely using the ML as a tool to implement the abstract idea. The machine learning is not describing or directed towards an improvement to the machine learning model but providing aspects of input and output data that is utilizing machine learning to analyze. The arguments discuss scale and complexity, but the specification does not describe the scale or complex calculations in terms of using machine learning to analyze and implement the abstract idea. A person would be able to provide with pen and paper analysis to provide a docketing action including deadlines, reminders, and other legal docketing annotations based on incoming communication.
In terms of the arguments with respect to Desjardins, the arguments discuss and allege that the claims are directed towards a technical improvement with the ML training and IP docketing. Examiner notes that while the arguments allege that the claims and Desjardins are similar, the claims in the pending application and the claims in Desjardins are directed towards different subject matter. Desjardins provides specific technical improvement discussion and description with respect to artificial intelligence that specifically provides a technical improvement. This is further discussed and considered with respect to the subsequent Memo and APR. The pending application is directed towards an IP docketing system that merely utilizes ML as a tool to implement the analysis for project management. The machine learning analysis is described at a high level in the specification and is not providing a technical improvement with respect to the additional elements. The ML is not describing a specific model or improvement to the modeling, but rather utilizing ML as a tool for analysis to implement the abstract idea. As such, the claims are directed towards an abstract idea without additional elements that are transformative or significantly more.
In terms of the arguments that the claimed invention is eligible with respect to the ML/AI examples (claim 2 and claim 3 specifically). In terms of the example claims, the examples provide consideration in terms of a neural network and a consideration for eligible and ineligible claim limitation. With respect to the pending application, there is no discussion or description in terms of technical aspects that are similar to claim 3 that was considered eligible in the example consideration. Claim 3 provides system elements that are based on the output of the NN that attributes to technical improvements for network remediation based on dropping packets, blocking traffic, and detecting network anomalies. Claim 2 was considered ineligible in the examples as the claims utilize a neural network to display the results of analysis. The pending application is not describing network analysis and is directed towards IP docket management utilizing generic ML to analyze information for docketing actions. The claims are not directed towards technical improvements and thus the additional elements are not significantly more or transformative into a practical application.
The arguments continue in terms of alleging that the consideration for the ML is provided for technical improvements and that the rejection with respect to generic technology was inadequately supported. This is further alleged with respect to arguments regarding the Desjardins memo further in terms of the AI criticality and emerging technology that the claimed invention provides a technical solution. The claims as a whole are not directed towards a technical improvement. The consideration was based on the originally filed specification that merely describes the machine learning at a high level without providing support for the alleged improvement within the arguments. There is no discussion or description in terms of scale or complexity. The specification does not even provide specific models or examples of models for the implanted machine learning. This is not a consideration with respect to enablement or written description as one of ordinary skill would be able to recognize how to implement ML for the aspects, however, the specification does not provide support for an improvement as the BRI and specification description is that the machine learning and training steps are utilizing generic technology to implement IP docket management. The claimed invention is directed towards an abstract idea and the additional elements are not significantly more or transformative into a practical application. Refer to MPEP 2106.05(f). This consideration was based on the combination of elements of the claimed invention, individually and as a whole, and the originally filed specification support for the claimed additional elements. As such, claims 1-11 and 20 are maintaining the 35 USC 101 rejection, as considered above, in light of the amended claim limitations.
Lacking any further arguments, claims 1-11 and 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:
Lundberg et al [2020/0117718] (patent docketing);
Shanahan [2018/0293678] (automated docketing and ML analysis for IP analysis);
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