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
In the previous Office Action issued October 23, 2025 (hereinafter “the previous Office Action”), claims 1-20 were pending.
This action is in response to the amendment and remarks filed February 23, 2026. In the amendment, claims 1, 4-6, 8, 11-13, 15, and 18-20 were amended, no claims were canceled, and no claims were added. Thus, claims 1-20 are pending.
The objections to the specification, set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The interpretations of claims 6, 13, and 20 under 35 U.S.C. § 112(f) and the subsequent rejects under 35 U.S.C. § 112(a) and 35 U.S.C. § 112(b), set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 4, 6, and 8-20 under 35 U.S.C. § 112(b), set forth in the previous Office Action, have been withdrawn in view of Applicant’s amendments and remarks.
The rejections of claims 2 and 9 under 35 U.S.C. § 101, set forth in the previous Office Action, have been withdrawn.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 16, 2026, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-8, and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a computing system [machine].
Claims 8-14 are directed to a method [process].
Claims 15-20 are directed to a computer-readable storage medium [machine].
Claim 1:
Step 2A Prong 1: The claim recites the limitations:
map a textual portion and a numerical portion of the characteristic information to respective inputs
perform word embedding on the mapped textual portion to generate embedded text features
determine the predicted likelihood of the state based at least on the output of the neural network
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “at least one memory that stores program code; and a processing system, comprising one or more processors, configured to receive the program code from the at least one memory and, in response to at least receiving the program code”; “a processing system”; “a neural network that comprises a plurality of layers” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). The claim recites “receive characteristic information, including local and external information, which corresponds to a predicted likelihood of a state that is associated”, “provide a plurality of inputs… the plurality of inputs including at least the embedded text features and numerical features based on the numerical portion”, which recite insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does 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 element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “receive…”; “provide…” are directed to insignificant extra-solution activities that are well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3:
Step 2A Prong 1: Please see analysis of an independent claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein at least one of the plurality of inputs to the neural network also includes an external machine learning model” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does 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 element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 4:
Step 2A Prong 1: The claim recites the limitations:
wherein the embedded text has less input dimensionality than the textual portion and has a mapping scope, via an embedding size, that is greater than the textual portion
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 5:
Step 2A Prong 1: The claim recites the limitations:
wherein to map the textual portion and the numerical portion of the characteristic information to the inputs…where the textual portion and the numerical portion are otherwise incompatible inputs…without said mapping
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “of the neural network includes mapping via a mapping layer… for the neural network” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does 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 element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 6:
Step 2A Prong 1: The claim recites the limitations:
maps available features of the textual portion and the numerical portion to the embedded text features and the numerical features respectively
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the mapping layer – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). See MPEP § 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does 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 element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 7:
Step 2A Prong 1: The claim recites the limitations:
determine respectively…variables to the predicted likelihood of the state
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “wherein the processing system, in response to at least receiving the program code, is configured to”; “via an interpretability layer, an incremental contribution of neural network” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f). The claim recites “provide each incremental contribution to at least one of a user interface or a prediction log file”, which recite insignificant extra-solution activity of mere data gathering. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does 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 element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “provide…” is directed to insignificant extra-solution activities that are well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Regarding Claims 8 and 10-14:
Claims 8 and 10-14 correspond to claims 1 and 3-7. In particular, 8:1, 10:3, 11:4, 12:5, 13:6, 14:7.
Step 2A, Prong 1: Claims 8 and 10-14 recite the same abstract ideas as in claims 1 and 3-7.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 8 and 10-14 at this step mirror that of claims 1 and 3-7.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 8 and 10-14 at this step mirror that of claims 1 and 3-7.
Regarding Claims 15-20:
Claims 15-20 correspond to claims 1 and 3-7. In particular, 15:1, 16:7, 17:3, 18:4, 19:5, 20:6. More specifically, claim 16 recites limitations of claim 2 or claim 7 and is being analyzed as corresponding to claim 7.
Step 2A, Prong 1: Claims 15-20 recite the same abstract ideas as in claims 1 and 3-7.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application. The analysis of claims 15-20 at this step mirror that of claims 1 and 3-7.
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception. The analysis of claims 15-20 at this step mirror that of claims 1 and 3-7.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Durvasula (US 11481553 B1) in view of Keng (US 20210125031 A1).
Regarding Claim 1:
Durvasula discloses:
A computing system, comprising: at least one memory that stores program code; and a processing system, comprising one or more processors, configured to receive the program code from the at least one memory and, in response to at least receiving the program code, to:
Durvasula, col. 6, ll. 50-60, “non-transitory computer-readable medium (e.g., standard random access memory (RAM), an optical disc, a universal serial bus (USB) drive, or the like) having such computer-readable program code or computer instructions embodied therein, wherein the computer-readable program code or computer instructions may be installed on or otherwise adapted to be executed by the processor(s) 120 (e.g., working in connection with the respective operating system in memory 122) to facilitate, implement, or perform the machine readable instructions”
Durvasula teaches a computer system comprise processor and memory.
receive characteristic information, including local and external information, which corresponds to a predicted likelihood of a state of a processing system
Durvasula, col. 15, ll. 35-38, “The predictive knowledge machine learning model may predict future outcomes based on data inputs (block 3210). The predictive knowledge machine learning model may generate predictions/forecasts” and Column 13 & lines 28-35 “FIG. 2C depicts an exemplary block flow diagram depicting a detail view of receiving information in the knowledge management environment at block 2000 of the method 200 of FIG. 2A, according to some aspects. The method 200 may include continuously ingesting data from internal and external data sources including an internal knowledge portal (block 2002), internal documentation (block 2004), a web portal (block 2006) and one or more blogs (block 2008)”
Durvasula teaches receiving information from external and internal data source for predicting future outcomes.
FIG. 2A more clearly depicts the machine learning models including a descriptive knowledge model, predictive knowledge model, diagnostic knowledge model, and a prescriptive knowledge model. Paragraphs [62]-[65] describe using the models to diagnose any issues with a system and the prescriptive knowledge model may prescribe a recommended action based on the data from the descriptive, diagnostic, and predictive model. Examiner has interpreted this as “likelihood of a state of a system” because in generating predictions using the internal/external data from their models including the diagnostic knowledge model, the model is predicting the likelihood of the system’s state.
map a textual portion and a numerical portion of the characteristic information to respective inputs of a neural network that comprises a plurality of layers
Durvasula, col. 8, ll. 20-29, “The data used to train the ANN may include heterogeneous data (e.g., textual data, image data, audio data, etc.)…an ML model, as described herein, may be trained using a supervised or unsupervised machine learning program or algorithm”
Col. 8-9, ll. 61-67, “In supervised machine learning, a machine learning program operating on a server, computing device, or otherwise processor(s), may be provided with example inputs (e.g., “features”) and their associated, or observed, outputs (e.g., “labels”) in order for the machine learning program or algorithm to determine or discover rules, relationships, patterns, or otherwise machine learning “models” that map such inputs (e.g., “features”) to the outputs (e.g., labels)…In unsupervised machine learning, the server, computing device, or other processor(s), may be required to find its own structure in unlabeled example inputs”
Durvasula teaches map a label of input e.g. features wherein inputs comprising heterogeneous data which comprising numerical and textual data.
[31], “In general, training an ANN may include establishing a network architecture, or topology, adding layers including activation functions for each layer (e.g., a “leaky” rectified linear unit (ReLU), softmax, hyperbolic tangent, etc.), loss function, and optimizer. In an aspect, the ANN may use different activation functions at each layer, or as between hidden layers and the output layer.”
In para. 31, Durvasula further specifies artificial neural networks with multiple layers [that comprises a plurality of layers].
determine the predicted likelihood of the state based at least on the output of the neural network
Durvasula, col. 15, ll. 35-38, “The predictive knowledge machine learning model may predict future outcomes based on data inputs (block 3210)”
Col. 9, ll. 5-9 “Such rules, relationships, or otherwise models may then be provided subsequent inputs in order for the model, executing on the server, computing device, or otherwise processor(s), to predict, based on the discovered rules, relationships, or model, an expected output”
Durvasula teaches determine the prediction based on the generated output from the inputs.
Durvasula does not explicitly disclose:
perform word embedding on the mapped textual portion to generate embedded text features
provide a plurality of inputs to the neural network, the plurality of inputs including at least the embedded text features and numerical features based on the numerical portion
However, in the same field, analogous art Keng teaches:
perform word embedding on the mapped textual portion to generate embedded text features
Keng, [0065]-[0066], “a word2vec skipgram model can be trained on this corpus; for example, using a context window size of 5 and an embedding dimensionality of 128…the RNN module 122 can learn subject embedding representations from historical data. For example, in the retail datasets example, customers can be characterized by their purchase habits by learning customer embedding representations from the customer's transactional data”
Keng teaches perform word embedding on the corpus (corresponds to textual portion) to generate feature of customer’s purchase habits.
provide a plurality of inputs to the neural network, the plurality of inputs including at least the embedded text features and numerical features based on the numerical portion
Keng, [0065], “In the example experiments of the retail datasets example, described herein, an example corpus can contain 11,443 products (as the aspect) that are transacted (as the future event), which has a vocabulary size of 21,894 words”
[0051] “Given a product embedding, some approaches generate a tuple containing a product embedding, customer embedding, price, and date of purchases, which summarizes a typical order”
[0053] “The inputs to the RNN are embeddings of one or more aspects of a future event derived from textual descriptions of the aspects”
Keng teaches provide input comprising retail datasets wherein retail datasets comprising a price (numerical), products (text features)).
Durvasula, Keng, and the instant application are analogous art because they are all directed to techniques and systems involving prediction based on provided input.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Keng into the disclosed invention of Durvasula. One of ordinary skill in the arts would have been motivated to make this modification because of the following, “An RNN is trained to generate a subject embedding by using a multi-task learning approach. The inputs to the RNN are embeddings of one or more aspects of a future event derived from textual descriptions of the aspects…Advantageously, this provides a prediction for multiple aspects of a single subject-level event. In some cases, the predicted aspect values can be fed back into the RNN to generate a prediction for a subsequent event associated with the subject by then repeating the above steps” (Keng, [0053]).
Claim 2.
Durvasula in view of Keng teaches the computing system of claim 1,
Durvasula further teaches wherein the processing system, in response to at least receiving the program code, is configured to: perform a mitigating action based at least on the predicted likelihood of the state indicating an adverse state, the mitigating action including one or more of predictive maintenance, load balancing, altered scheduling, an upgrade, or a resource capacity increase (Column 15 & lines 38-42 “The predictive knowledge machine learning model may determine frequency of data updates and volume of data (block 3220). The predictive knowledge machine learning model may classify data (block 3230) and classify different patterns (block 3240)” and Column 16 & lines 44-48 “The continuous updates may include updates from various types of data, including various internal data (e.g., proprietary knowledge, engineering data, etc.) as well as external data (e.g., blogs, videos, news, etc.)” teaches based on prediction update an internal and external data corresponding to the mitigating action).
Claim 3.
Durvasula in view of Keng teaches the computing system of claim 1,
Durvasula further teaches wherein at least one of the plurality of inputs to the neural network also includes an external machine learning model (Column 8 & lines 27-35 “In various aspects, an ML model, as described herein, may be trained using a supervised or unsupervised machine learning program or algorithm. The machine learning program or algorithm may employ a neural network, which may be a convolutional neural network, a deep learning neural network, and/or a combined learning module or program that learns in two or more features or feature datasets (e.g., structured data, unstructured data, etc.) in a particular areas of interest” teaches machine learning employ a neural network corresponding to an external machine learning model).
Claim 4.
Durvasula in view of Keng teaches the computing system of claim 1,
Keng further teaches wherein the embedded text has less input dimensionality than the textual portion and has a mapping scope, via an embedding size, that is greater than the textual portion (Para [0054] “if there are 100,000 products, it may not be scalable to put this many one-hot binary variables into a model. However, the 100,000 products can be “embedded” into a smaller (e.g. 100) dimensional real-valued vector; which is much more computationally efficient. Generally, the system tries to ensure that each product is placed in a reasonable location in the 100-Dimesional vector space” teaches reducing dimension using embedded for the larger products).
Durvasula and Keng are analogous art because they are both directed to techniques and systems involving prediction based on provided input.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Keng into the disclosed invention of Durvasula. One of ordinary skill in the arts would have been motivated to make this modification because of the following, “An RNN is trained to generate a subject embedding by using a multi-task learning approach. The inputs to the RNN are embeddings of one or more aspects of a future event derived from textual descriptions of the aspects… Advantageously, this provides a prediction for multiple aspects of a single subject-level event. In some cases, the predicted aspect values can be fed back into the RNN to generate a prediction for a subsequent event associated with the subject by then repeating the above steps” (Keng, [0053]).
Claim 5.
Durvasula in view of Keng teaches the computing system of claim 1,
Durvasula further teaches wherein to map the textual portion and the numerical portion of the characteristic information to the inputs of the neural network includes mapping via a mapping layer where the textual portion and the numerical portion are otherwise incompatible inputs for the neural network without said mapping (Column 12 & lines 60-67 “The user may preview the generated details and confirm whether the details correspond to the user's requirements (block 1012). When the user does not approve of the generated details, flow control pay pass to blocks 1002, 1004 and/or block 1006 to allow the user to revise the input problem statement, input parameters and/or input variables, respectively. When the user approves, the method 200 may include generating an input template (block 1014)” teaches the user does not approve of the generated detailed (corresponding to incompatible inputs) for the input data).
Claim 6.
Durvasula in view of Keng teaches the computing system of claim 5,
Keng further teaches wherein the mapping layer maps available features of the textual portion and the numerical portion to the embedded text features and the numerical features respectively (Column 12 & lines 60-67 “The user may preview the generated details and confirm whether the details correspond to the user's requirements (block 1012). When the user does not approve of the generated details, flow control pay pass to blocks 1002, 1004 and/or block 1006 to allow the user to revise the input problem statement, input parameters and/or input variables, respectively. When the user approves, the method 200 may include generating an input template (block 1014)” teaches the user does not approve of the generated detailed (corresponding to incompatible inputs) for the input data).
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Keng into the disclosed invention of Durvasula. One of ordinary skill in the arts would have been motivated to make this modification because of the following, “An RNN is trained to generate a subject embedding by using a multi-task learning approach. The inputs to the RNN are embeddings of one or more aspects of a future event derived from textual descriptions of the aspects… Advantageously, this provides a prediction for multiple aspects of a single subject-level event. In some cases, the predicted aspect values can be fed back into the RNN to generate a prediction for a subsequent event associated with the subject by then repeating the above steps” (Keng, [0053]).
Claim 7.
Durvasula in view of Keng teaches the computing system of claim 1,
Durvasula further teaches wherein the processing system, in response to at least receiving the program code, is configured to: determine respectively, via an interpretability layer, an incremental contribution of neural network variables to the predicted likelihood of the state; and provide each incremental contribution to at least one of a user interface or a prediction log file (Column 12 & lines 60-67 “The user may preview the generated details and confirm whether the details correspond to the user's requirements (block 1012). When the user does not approve of the generated details, flow control pay pass to blocks 1002, 1004 and/or block 1006 to allow the user to revise the input problem statement, input parameters and/or input variables, respectively. When the user approves, the method 200 may include generating an input template (block 1014)” and Column 19 & lines 21-57 “This information can be input into block 3000 to determine what happened in past, to determine best solutions/next actions for a given solution, and diagnostics such as accuracy, bias and fairness. The one or more living documents may be repeatedly predicted, creating cycle of innovation and maintaining the latest information for consultants and users” teaches determine an incremental of neural network to the prediction and provide to user for on each incremental).
Regarding Claims 8-14:
Claims 8-14 correspond to claims 1-7 and are rejected for at least the same reasons as given in the rejections of claims 1-7. In particular, 8:1, 9:2, 10:3, 11:4, 12:5, 13:6, 14:7.
Regarding Claims 15-20:
Claims 15-20 correspond to claims 1-7 and are rejected for at least the same reasons as given in the rejections of claims 1-7. In particular, 15:1, 16:2 or 16:7, 17:3, 18:4, 19:5, 20:6.
Response to Arguments
Applicant's arguments filed February 23, 2026 (“Remarks”) have been fully considered but they are not persuasive.
35 U.S.C. § 101:
Remarks, pp. 12-14. Applicant argues with respect to independent claims 1, 8, and 15, that the claimed embodiments are not directed to an abstract idea. In particular, Applicant argues that the instant application’s independent claims are similar to Ex Parte Hannun. Examiner respectfully disagrees. As stated by Applicant, Hannun recited a specific implementation involving a trained neural network which cannot be performed mentally. On the other hand, the present claims recite more generic/high level limitations. For example, claim 1 recites a generic high-level neural network with a plurality of layers, which receives a plurality of inputs and has an output. Furthermore, the neural network is not positively recited. In other words, there is an implied usage of the neural network in “provide a plurality of inputs to the neural network…” and “determine the predicted likelihood of the state based at least on the output of the neural network”, but the claim does not recite any specific implementation of the neural network. Lastly, limitations involving mapping, performing embedding, and determining a predicted likelihood are all mentally performable. For at least these reasons, the claim does not recite and is not directed to a specific implementation involving a trained neural network, and is directed to the abstract idea.
Remarks, pp. 14-15. Applicant further argues in view of Hannun that the claims integrate into a practical application of “determining the predicted likelihood of the state…”. Examiner respectfully disagrees. This limitation does not integrate into a practical application because the limitation has been analyzed as mentally performable.
35 U.S.C. § 103:
Remarks, pp. 15-18. Applicant argues Durvasula does not teach making a prediction related to a likelihood of state of a processing system. In particular, Applicant argues Durvasula trains their model to generate a living document, whereas in instant claim 1, characteristic information is received corresponding to a predicted likelihood of a processing system state. Examiner respectfully disagrees. As discussed under section 103, data is ingested from internal and external sources into the machine learning models. FIG. 2A more clearly depicts the machine learning models including a descriptive knowledge model, predictive knowledge model, diagnostic knowledge model, and a prescriptive knowledge model. Paragraphs [62]-[65] describe using the models to diagnose any issues with a system and the prescriptive knowledge model may prescribe a recommended action based on the data from the descriptive, diagnostic, and predictive model. Examiner has interpreted this as “likelihood of a state of a system” because in generating predictions using the internal/external data from their models including the diagnostic knowledge model, the model is predicting the likelihood of the system’s state.
Conclusion
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.
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/S.H.P./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
Prosecution Insights