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 .
DETAILED ACTION
This communication is a Non-Final Office Action in response to communications received on 3/4/26.
Claims 2, 9, 16 have been previously cancelled.
Therefore, Claims 1, 3-8, 10-15, 17-21 are now pending and have been addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/4/26 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, 3-8, 10-15, 17-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1,3-7 are directed to a method, claims 8,10-14 are directed to a non-transitory medium and claims 15,17-21 are directed to a system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1, 3-8, 10-15, 17-21 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 8 and 15 recite methods that detecting that a trigger event associated with a case has occurred, wherein a current label for the case indicates that the case has not been solved; generate a current solvability score for the case based on a set of features extracted from an electronic data collection associated with the case; upon determining that the current solvability score causes a condition to be satisfied, (1) determining, from the set of training instances, a correlation between a first feature that indicates a presence of a first type of evidence and a second feature that indicates a presence of a second type of evidence, and (11) determining that the first type of evidence is present in the electronic data collection associated with the case and that the second type of evidence is missing from the electronic data collection associated with the case; rendering, a graph that illustrates a solvability-score trend, wherein at least the current solvability score and a prior solvability score for the case are plotted against time in the graph; and based on the determination that the first type of evidence is present in the case and that the second type of evidence is missing from the case, presenting, a message that recommends obtaining the second type of evidence in order to influence the solvability-score trend in a manner that will increase a probability of the case being solved. detecting that the current label for the case has been updated to indicate that the case has been solved, based on the current label indicating that the case has been solved, creating a new training instance that includes features extracted from the electronic data collection associated with the case; updating the set of training instances to include the new training instance.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), including interaction between person and computer) and mathematical calculations (generating a solvability score; determining from the set of training instance, a correlation between features), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as (computing system, using a machine-learning model that is trained using a set of training instances, user interface presented on an electronic display, retraining the machine learning model (claim 1, 8, 15 ), non-transitory medium, one or more processor (Claim 8, 15)), the claims are directed to generating solvability score for case and presenting action to be performed in order to influence the solvability score. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and recommending action to be performed. In particular, the claims only recites the additional element – computing system, using a machine-learning model that is trained using a set of training instances, user interface presented on an electronic display, retraining the machine learning model (claim 1, 8, 15 ), non-transitory medium, one or more processor (Claim 8, 15). The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. a) The limitations of a using a machine-learning model that is trained; retraining the machine learning model merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “retraining the machine learning system data” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. (RECENTIVE ANALYTICS, INC. v. FOX CORP.). In addition, limitations reciting “presenting in the user interface a message that recommends….” is merely a post-solution step of transmitting/displaying data output—a nominal addition to the claim that does not meaningfully limit the claim. Therefore, presenting step is an insignificant extra-solution activity. See MPEP 2106.05(g). Further, the limitation of “ based on the determination that the first type of evidence is present in the case and that the second type of evidence is missing from the case , presenting a message/recommendation..” is recited at high level of generality. The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; generating solvability score for case and presenting action to be performed in order to influence the solvability score. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the computing system, using a machine-learning model that is trained using a set of training instances, user interface presented on an electronic display, retraining the machine learning model (claim 1, 8, 15 ), non-transitory medium, one or more processor (Claim 8, 15)), these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0066], [0069] details “ processor of a general purpose computer, [0067] memory. [0016] the score generator 117 may leverage a machine-learning model 118 to accomplish these tasks. [0018] machine learning model. [0029] There are many different types of inductive machine-learning models that can be used for the machine-learning model 118. Neural networks, support vector machines, Bayesian belief networks, association-rule models, decision trees, nearest-neighbor models (e.g., k-NN), regression models, deep belief networks, and Q-learning models are a few examples of model types that may be used.” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. As discussed in Step 2A, Prong Two above, the recitations of “presenting in the user interface a message that recommends…” amount to transmitting/displaying data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Further, the limitation of “ based on the determination that the first type of evidence is present in the case and that the second type of evidence is missing from the case , presenting a message/recommendation..” is recited at high level of generality and similar to Mostaert (US 2023/0196031) Fig 2 # 220 and [0032]-[0034] recommendation provided based on correlation between features. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amounts to mere instructions to apply the exception. See MPEP 2106.05(f). Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself.
Dependent claims 3-7, 10-14, and 17-21 add additional limitations, for example but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1, 8 and 15.
Claims 3, 10, 17 recites wherein the trigger event is that a predefined amount of time has elapsed since the prior solvability score was generated. These limitations further narrow the abstract idea and limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d.
Claims 4-6, 11-13 and 18-20 recites wherein the condition is that the current solvability score meets a predefined threshold value; wherein the condition is that a difference between the prior solvability score and the current solvability score meets a predefined threshold value; wherein the condition is that a series of N solvability scores determined for the case has been monotonically non-increasing, wherein N is a positive integer, the prior solvability score is a penultimate solvability score in the series, and the current solvability score is an Nth solvability score in the series. These limitations further narrow the abstract idea and limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d.
Claims 7, 14, 21 recites wherein the trigger event comprises detecting that the set of training instances used to train the machine-learning model has been changed by at least a threshold quantity of training instances since the prior solvability score for the case was generated. Further, the limitation of “train a machine learning system” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions.
These limitations merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Response to Arguments
Applicant's arguments filed 3/4/26 have been fully considered but they are not persuasive.
The affidavit under 37 CFR 1.132 filed 3/4/26 is insufficient to overcome the rejection of claims based upon 35U.S.C 101 as set forth in the last Office action because: the affidavit submitted does not provide any details regarding improvements to computer or technology and how these improvements are achieved. Applicant on pages 2-5 of the affidavit state that ‘342 application provides a more comprehensive solution that amounts to a technical improvement over conventional methods, however the claims/affidavit do not provide details on how the solution/improvement is achieved. Further, claims recites ‘using machine learning technique to generate case solvability score” at a high level of generality as noted in final office action. The limitations of a using a machine-learning model that is trained; retraining the machine learning model merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Furthermore, such training/retraining and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. (RECENTIVE ANALYTICS, INC. v. FOX CORP.). The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a)
Regarding 101 rejection, applicant on page 9, states that claims recite steps that amount to a technical improvement over conventional machine learning system and therefore, integrate abstract idea into practical application.
Examiner has considered all arguments and respectfully disagrees. With regards to new additional element (computing system), they are recited at “apply it “ level and therefore do not integrate the abstract idea into practical application as noted in rejection above. The limitations of a using a machine-learning model that is trained using a set of training instance..; retraining the machine learning model using the updated set of training instance” merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Further, the limitation of “retraining the machine learning system” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The requirement that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. (RECENTIVE ANALYTICS, INC. v. FOX CORP.). The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Further, regarding improvement to technology argument, to evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a). Applicant points to specification [0009]-[0013] for improvement, however spec. does not specifically point out the improvement and how it is achieved. The claimed invention does not reflect improvement in the technical field or computer functioning. To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Applicant remarks regarding Ex parte Desjardins have been considered, but they are not persuasive. The claims in Desjardins were directed to training a machine learning model, however current claims recite using ML at apply it level. In Desjardins specification [0021] recited that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." However, current specification does not provide any details for improvement to technology or computer functionality. The instant claims recite apply one or more models, thus the limitations are recited at high level of generality without any details regarding training or model or how the model is used. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CN115577077A discloses an automatic recommending method, which can automatically recommend the evidence collecting content of the case according to the case type and the history same type case, so as to help the criminal police to make decision in the evidence collecting process, collecting what kind of evidence, and what kind of investigation is carried out, identifying and so on, how to carry out the way recommendation of such evidence collection, so as to avoid the important fact in the process of taking evidence.
Courson (US 7,974850) discloses a tool which provides counsel with a data collection mechanism to guide them through various steps in the litigation process and directs counsel and/or legal assistants to determine what information is required. The tool provides a "Discovery Generator" that is available to capture counsel's potential discovery requests, which are linked to existing document and form production tools for facilitated production of discovery.
Mostaert (US 2023/0196031) discloses generate adaptive templates for different case types and leverage those templates to ascertain whether case files are sufficiently complete to support desired outcomes. The systems described herein can detect patterns in cases that have been marked as complete, add indicia of those patterns to the adaptive templates, and compare as-yet incomplete case files to the templates to identify and suggest specific types of data that can be added to the incomplete case files to increase the likelihood that those incomplete case files will contain sufficient data to support a desired outcome. (Fig 2 #202 case suggestion) [0024] Features that are extracted from natural-language data can be used as input for machine-learning components
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626