Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Non-Final Office action is in response to the application filed on June 3rd, 2024. Claims 16-38 are pending.
Examiner Note
3. Examiner Michael Young is no longer continuing prosecution on application number 18/731,864. It has been transferred to Examiner Jessica Lemieux.
Continued Examination Under 37 CFR 1.114
4. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 11th, 2026 has been entered.
Priority
5. Application 18/731,864was filed on June 3rd, 2024 which is a continuation of 15/934,917 riled on March 23rd, 2018, which is a continuation in part of 15/693,212 filed on August 31st, 2017 which has provisional applications 62/475,394 and 62/382,296 filed on March 23rd, 2017 and September 1st, 2016 respectively.
Examiner Request
6. The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Response to Arguments
7. Applicant’s arguments regarding subject matter eligibility have been fully considered but are not persuasive.
Applicant argues that the claims are directed to improved computer technology in the field of document research and recommendations and that the claimed technology improves the accuracy, efficiency, and usefulness of research results. Applicant relies, in part, on Specification paragraph [0051]. The argument is not persuasive. Paragraph [0051] generally states that the disclosed systems may improve the manner in which a computer serves a user’s research goals and improves the accuracy, efficiency, and usefulness of returned search results. However, the identified benefits concern the quality and usefulness of the information produced by the computer rather than an improvement to the operation of the computer itself. The claims recite use of computer, database, and machine-learning technology to determine which research documents are relevant and how those documents should be ranked, but do not recite an improvement to the underlying computer, database, or machine-learning technology.
Applicant further argues that the amended claims identify a set of relevant documents including both documents selected by the user and documents inferred to be relevant based on document interactions and determine conceptual issue topics in the relevant documents. Applicant contends that these amendments contribute to a particular improved algorithm for determining recommended documents. These limitations have been considered. Identifying documents inferred to be relevant based on prior document interactions further specifies the information evaluated in determining relevance. The claim does not, however, recite a particular technological mechanism for performing the inference that improves the operation of a computer or another technology. Rather, the limitation further defines the claimed document-relevance analysis.
Applicant further argues that the claimed processing cannot effectively be performed in the human mind. The Examiner acknowledges that not every limitation of the claims, including implementation using a trained machine-learning model and other computer components, can practically be performed in the human mind. The present rejection does not characterize those technological implementation limitations as mental processes. Rather, the identified mental-process limitations concern evaluating research activity and document information to identify subject matter and determine relevant documents, while the scoring limitations additionally recite mathematical concepts. The remaining computer and machine-learning limitations are separately evaluated as additional elements under Step 2A, Prong Two and Step 2B.
Applicant also relies on Enfish, LLC v. Microsoft Corp. and the USPTO memorandum addressing Ex Parte Desjardins arguing that the claimed logical structures and processes constitute a software-based improvement to computer technology and the claims must be considered as a whole and as an ordered combination. Applicant further asserts that the claims recite a particular solution and particular manner of achieving the desired result rather than merely claiming the result itself. The Examiner agrees that software-based improvements may constitute improvements to computer technology and that the claims must be evaluated as a whole and as an ordered combination. Those considerations have been applied in the present rejection. However, the claimed combination does not recite an improvement to the operation of the computer, database, or machine-learning technology itself. Rather, the recited issue-topic database, metadata, feature sets, and trained machine-learning model are used together to identify and rank research documents. The Specification confirms that the metadata includes substantive information concerning legal documents, such as filing date, court level, jurisdiction, headnotes, and citation information. See Spec. paragraph [0123]. Thus, the claimed combination provides a particular manner of performing the document-recommendation analysis, but the particularity of that implementation does not establish the asserted improvement to computer technology.
Applicant further argues that the amended claims recite a particular technological solution and therefore integrate the alleged abstract idea into a practical application. The Examiner has considered the claimed combination as a whole but does not find that the claims solve a problem arising in computer technology. The identified problem is determining which documents are relevant to a user’s research and how those documents should be ranked. The recited issue-topic database, metadata, feature sets, trained machine-learning model, API, and communication components are used to carry out that analysis, but the claims do not recite a deficiency in the operation of the computer or other technology that is corrected by the claimed arrangement. Thus, the claimed advance concerns the quality and usefulness of the research recommendations produced, rather than an improvement in the functioning of the underlying computer, database, communication, or machine-learning technology.
With respect to claims 36–38, Applicant argues that training the machine-learning model based on the generated feature set provides an additional technological improvement. The argument is not persuasive. Claims 36–38 broadly require training the machine-learning model based on the feature set, but do not recite an improvement to the training process or operation of the model itself. Rather, the training configures the model to perform the claimed document-relevance and ranking analysis. Thus, the limitation allegedly improves, at most, the resulting research recommendations, not the underlying machine-learning technology.
Accordingly, Applicant's arguments do not overcome the rejection under 35 U.S.C. § 101, and the rejection of claims 16-38 is maintained.
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.
8. Claims 16-38 are rejected under 35 U.S.C. §101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
In the instant case, claims 16-38 are directed to a system, method, and computer product. Thus, each of the claims falls within one of the four statutory categories (Step 1: YES).
The analysis proceeds to Step 2A to determine whether the claims are directed to a judicial exception. Examiner notes that while claims 16, 23, and 30, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
Independent claims 16, 23, and 30 recite, among other limitations, recording activity information associated with research sessions; identifying a set of relevant documents based on document interactions, including documents selected by a user and documents inferred to be relevant based on the document interactions; determining conceptual issue topics in the relevant documents; associating the conceptual issue topics with predefined conceptual issue topics and corresponding metadata; and determining recommended documents associated with the conceptual issue topics and a respective relevancy of the recommended documents. The claim further recites generating results comprising recommended documents associated with the determined conceptual issue topics and ranked according to a respective relevancy score indicating how relevant each recommended document is to the associated conceptual issue topic. These limitations recite a mental process involving observations, evaluations, and judgments. For example, a researcher may review information concerning documents consulted during research, identify subject matter or issues associated with the documents, determine other documents associated with those issues, and evaluate which documents are relevant to the identified issues. Further determining which documents are relevant and ranking documents according to their relative relevance constitutes an evaluation or judgment and therefore recites a mental process.
The independent claims further recite ranking the recommended documents according to a respective relevancy score, wherein the relevancy score is based on a weighted aggregation of at least a recency score and a recommendation-frequency score, and wherein the recommendation-frequency score indicates a degree of similarity between identified headnotes. These limitations recite mathematical concepts, including mathematical calculations and relationships used to determine a score and ranking. The Specification similarly describes weighting and aggregating scores to determine a ranking (paragraph [0133]).
Accordingly, claims 16, 23, and 30 recite an abstract idea falling within the mental-process and mathematical-concept groupings identified in MPEP § 2106.04(a) (Step 2A- Prong 1: YES. The claims recite an abstract idea).
The Examiner does not determine that every limitation of claims 16, 23, and 30 can practically be performed in the human mind. Limitations relating to implementation using databases, feature sets, application of a trained machine-learning model, an API, encoded signals, and a user computing device are evaluated below as additional elements under Step 2A, Prong Two. Step 2A, Prong Two.
Claims 16, 23, and 30 further recite additional elements including a memory and one or more processors; a user computing device and user interface; a database configured to store documents; documents configured as logical data containers, portions of which may be modified by document interactions; receipt, using an API, of a first binary encoded signal associated with the document interactions; an issue-topic database storing predefined conceptual issue topics and associated metadata; accessing the issue-topic database to obtain the metadata; generating a feature set for each determined conceptual issue topic based on the obtained metadata; applying a trained machine-learning model to each feature set; and transmitting, using the API, a second binary encoded signal configured to render the resulting recommendations via the user interface.
The memory, processors, user computing device, user interface, database, API, binary encoded signals, and logical data containers provide the technological environment through which the claimed research-information analysis is carried out. These limitations are used to receive, store, modify, process, communicate, and display the information involved in the claimed evaluation and ranking process. The claims do not recite an improvement to the operation of the processor, memory, API, signal transmission, user interface, or other underlying computing technology.
The recitation that documents are configured as logical data containers and that document interactions modify portions of the logical data containers has also been considered. The claim uses the logical data containers to represent and modify information associated with the user’s document interactions. The claim does not recite a particular improved data structure or an improvement to the manner in which the computer stores, retrieves, or otherwise processes data.
The recited issue-topic database stores predefined conceptual issue topics and corresponding metadata and is accessed according to the determined conceptual issue topics to obtain that metadata. The Specification explains that the metadata may include substantive information relating to legal documents, including case title, filing date, court level, jurisdiction code, headnotes, and citation information. See Spec. (paragraph [0123]). Thus, accessing the database supplies information used in evaluating and ranking legal-research documents, but does not recite an improved database structure, storage technique, or database-retrieval technology.
The claims further require generating feature sets based on the metadata and applying a trained machine-learning model to the feature sets to generate ranked recommended documents. These limitations use machine-learning technology as a tool for carrying out the claimed evaluation of research information. The claims do not recite an improvement to the architecture or operation of the machine-learning model itself, an improvement in computer-resource utilization, or another technological improvement to the underlying computing technology. Instead, the trained model is used to determine which documents are relevant and how those documents should be ranked.
Under MPEP § 2106.05(f), merely instructing that a judicial exception be implemented using a computer, or using a computer as a tool to perform the abstract idea, does not integrate the exception into a practical application. Here, the recited computer, database, API, feature-set, and machine-learning limitations specify technological tools used to perform the claimed collection, evaluation, ranking, and presentation of research information. They do not impose a meaningful limit on the judicial exception by improving the functioning of the computer or another technology or technical field or change the manner in which the underlying computer technology operates.
The Specification states generally that the disclosed systems may improve searching by improving how a computer serves a user’s research goals and by improving the accuracy, efficiency, and usefulness of search results. See Spec. (paragraph [0051]). However, the identified benefits concern the quality and usefulness of the research results produced rather than an improvement in the technological operation of the computer itself. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 16, 23, and 30 are directed to an abstract idea without a practical application (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims are further evaluated under Step 2B to determine whether any additional element, alone or in combination, amounts to significantly more than the judicial exception. The additional elements include the recited processors and memory, user computing device and interface, databases and logical data containers, API and encoded signals, generation of features sets, and application of a trained machine-learning model. These elements perform computer functions such as receiving, storing, retrieving, processing, transmitting, and displaying information and using a machine-learning model to perform the claimed information analysis. The independent claims recite the machine-learning model functionally as a “trained machine learning model” that is applied to feature sets to generate ranked results. The claims do not require a particular technological modification to the model or computer system that supplies an inventive concept separate from the abstract idea. Similarly, when considered as an ordered combination, the additional elements automate the claimed evaluation, ranking, and recommendation of research documents using computer, database, communication, and machine-learning components. The additional elements have also been considered as an ordered combination. In combination, the additional elements automate the claimed evaluation, ranking, and recommendation of research documents using the recited computer, database, communication, feature-set, and machine-learning components. The ordered combination does not provide an inventive concept separate from the abstract information-evaluation and ranking process. Rather, the additional elements provide the technological tools through which the judicial exception is implemented. Mere instructions to implement a judicial exception using a computer or other technological tool, without more, do not amount to significantly more than the judicial exception. Accordingly, claims 16, 23, and 30 do not recite significantly more than the judicial exception and are ineligible under 35 U.S.C. § 101 (Step 2B: NO. The claims do not provide significantly more).
Dependent claims 17, 24, and 31 further specify that the trained machine-learning model is a support vector machine model. These limitations further specify the machine-learning tool used to perform the claimed analysis, but do not recite an improvement to support-vector-machine technology itself.
Claims 18–20, 25–27, and 32–34 further specify additional factors used to calculate the relevancy score, including legal-jurisdiction and authority scores. These limitations further refine the mathematical and informational evaluation used to rank documents and therefore remain part of the abstract idea.
Claims 21, 28, and 35 further define the recommendation-frequency calculation by determining frequencies associated with headnotes and summing such frequencies. These limitations further specify mathematical calculations used in ranking the documents and therefore remain part of the judicial exception.
Claims 22 and 29 further require modifying a feature set based on user input, applying the trained machine-learning model to the modified feature set, and transmitting a signal configured to render updated results. These limitations further apply the same informational analysis to modified input and communicate the resulting information to the user, but do not improve the underlying computing technology.
Claims 36–38 further require training the machine-learning model based on the feature set. These claims do not require a particular improvement to machine-learning training technology. Thus, the claims use machine-learning training to configure the model to perform the claimed document-ranking analysis, but do not recite an improvement to machine-learning technology itself.
Therefore, the dependent claims do not recite additional limitations that integrate the judicial exception into a practical application or that, individually or in combination, amount to significantly more than the judicial exception. Accordingly claims 17-22, 24-29 and 31-38 are also rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. See MPEP 2106.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LEMIEUX whose telephone number is (571)270-3445. The examiner can normally be reached Monday-Friday 7AM-3PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TARIQ HAFIZ can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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