DETAILED ACTION
This Final Office Action is in response to the arguments and amendments filed April 8, 2026.
Claim 2 and 12-19 are cancelled.
Claims 1, 7, 9, 11, and 20 have been amended.
Claims 1, 3-11, 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 .
Response to Amendment
Examiner notes that claim 7 has an inadvertent missed indicator for amended claim language. Examiner has entered the amendments and that claim 7 has been considered in terms of the following: The method of claim 1, further comprising sending the electronic communication with the second pre-code to a queue for review prior to sending to the automated docketing system.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-11 and 20 are rejected under 35 U.S.C. 101 because
In terms of step 1, Claims 1, 3-11 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; using the […] model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; based on the generated annotations and the predicted first and second pre-codes, selecting a second pre-code for associating with 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 first and second 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. This further includes the ML model to predict and first and second pre-code for the communication based on patterns. 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 comprising 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 first and second assignments; using the machine learning model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; 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 comprising 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 first and second assignments; using the machine learning model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; 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 3-11 are further describing the abstract idea and not further describing additional elements beyond those identified above. The dependent claims are directed towards, “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 the second pre-code is further based on the unstructured text of the electronic communication”, “further comprising sending the electronic communication with the second 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 first 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 multiple first and second pre-codes 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; use the machine learning model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; based on the generated annotations and the predicted first and second pre-codes, selecting a second pre-code for associating with 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 comprising 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 first and second pre-code assignments; use the machine learning model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; 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 comprising 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 first and second pre-code assignments; use the machine learning model to predict a first and second pre-code for each incoming electronic communication based on patterns learned from the training set; 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, 3-11, and 20 are rejected under 35 USC 101 being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed April 8, 2026 on pages 5-6 regarding the 35 USC 101 rejection, specifically that the claims are not directed towards an abstract idea and are eligible subject matter.
Examiner respectfully disagrees.
The arguments allege that the rejection is providing a personal opinion with respect to the Step 2(a)(1) mental process abstract idea consideration. The arguments allege that the machine learning and training elements are beyond a mental process consideration for high level analysis. Examiner notes that the consideration provides the machine learning elements as additional elements, and has noted that the ML is merely described at a high level that it falls into high level analysis. Also, mental process consideration provides steps within a technical/computer environment. The claims provides steps in terms of collecting information (received communications), analyzing to determine pre-codes, and providing/displaying communications based on the pre-code for docketing actions. The arguments allege that the ML and training elements provide required layers of pattern extraction, iterations of optimization across parameter sets, and generation of a statistical model based around a large corpus of communications. Examiner notes that none of the these aspects are provided or technologically described in the specification and claim language. The machine learning and training/learning elements are described as generic modeling techniques to provide analysis. There is no improvement to the technology of machine learning, but rather utilizing the ML as a tool. Further, the mental process consideration is that the collection, high level analysis, and display of the results is within the specified consideration under mental process. The claims are describing aspects of docketing actions based on the collection and analysis of incoming communication that a person would be able to perform based on the collection and high level analysis for the communication received. The arguments further continue in terms of alleging that the claimed invention with respect to Desjardins provides a technical improvement. Desjardins provides specific technical claim limitations and written support for improving the technology of machine learning, specifically for learning techniques for catastrophic forgetting. The claims are merely describing generic technology to implement the abstract idea. The specification, including the cited passages within the arguments, describe the machine learning in terms of providing an output based on the model but there is no improvement to the machine learning model itself. Paragraph [39] provides the technical description for support of the machine learning, however, there is no description or discussion in terms of techniques, models, or other technical aspects besides stating that the system provides machine learning techniques and training set for the model. As such, the ML is merely a tool to implement the abstract idea. There is no specific technical improvement described, but rather generic technology to implement the abstract idea. As such, the additional elements are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f). Lacking any further arguments, claims 1 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, 3-11, 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:
Kenney et al [2010/0223557];
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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