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 Final Office Action in response to communications received on 6/8/26.
Claims 1-5, 10-14, 19-23 have been amended.
Claims 28-29 have been added.
Therefore, Claims 1-29 are now pending and have been addressed below.
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 1-29 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 1-9, 28-29 are directed to a non-transitory medium, claims 10-18 are directed to a method and claims 19-27 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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 10 and 19 recite methods for processing service requests that includes receiving an initial service request comprising an incomplete feature set; identifying a plurality of reference service requests in past service requests that meet a first similarity criteria with relation to the initial service request based on (a) the incomplete feature set of the initial service request and (b) a plurality of feature sets corresponding respectively to the plurality of reference service requests; determine an enhanced service request by predicting values for augmenting the incomplete feature set of the initial service request to synthesize data for augmenting service requests and the plurality of reference service requests and generating the enhanced service request with an updated feature set by augmenting the incomplete feature seat of the initial service request with the predicted values; identifying a subset of reference service requests, of the plurality of reference service requests, meeting a second similarity criteria with relation to the enhanced service request based on (a) the updated feature set of the enhanced service request and (b) a subset of the plurality of features sets corresponding respectively the subset of reference service requests; and processing the initial service request based on the subset of the reference service requests
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), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as computer readable medium, database, using an adversarial domain adapter trained, a hardware processor ), the claims are directed to processing incomplete service request based on subset of reference service requests. 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 service request data, analyzing it, and providing resolution. In particular, the claims only recites the additional element – computer readable medium, database, using an adversarial domain adapter, a hardware processor. 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 or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). 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.
In addition, limitations reciting data gathering such as “receiving an initial service request “ is also insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en bane), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371-72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
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; processing service request based on subset of reference service requests. 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 computer readable medium, database, using an adversarial domain adapter, a hardware processor, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0017] details “ the client device 101 can be, but is not limited to, a personal computer system, server computer system, thin client, thick client, hand-held or laptop device, multiprocessor system, microprocessor-based system, set top box, programmable consumer electronic, network PC, minicomputer system, mainframe computer system, and the like..[0033] the computing device 205 can comprise any general-purpose computing article of manufacture capable of executing computer program instructions installed thereon (e.g., a personal computer, server, etc., [0022] The adversarial domain adapter 135 uses information from the reference service requests 119, which have complete data, to generate synthetic data for enhancing new service requests, which lack complete data. Some embodiments of the adversarial domain adapter 135 are deep learning models trained to perform natural language processing that generate synthetic data from the data in the reference service requests 119)”. 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 “receiving step” amount to receiving data over a network. See MPEP 2106.05(d), subsection II. 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); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. 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 amount 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. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. 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 claims do not amount to significantly more than the abstract idea itself.
Dependent claims 2-9, 11-18, and 20-29 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as Independent claims.
Claims 2-5, 8,11-15, 17, 20-23, 26 recite wherein the feature set of the enhanced service request includes: a first subset of features included in the feature set of the initial service request, and a second subset of features generated by the adversarial domain adapter; wherein identifying a plurality of reference service requests comprises: determining a cosine similarity between the incomplete feature set into the initial service request and the respective features sets of the plurality of reference service requests; wherein identifying a subset of reference service requests: determining similarities between the feature set of the enhanced service request and the respective features sets of the plurality of reference service requests using a probability-based similarity function; wherein selecting the subset of the reference service requests comprises: ranking the plurality of reference service requests based on the similarities between the updated feature set of the enhanced service request and the subset of the plurality of features sets corresponding respectively the subset of reference service requests; and selecting a subset of the reference service requests based on the ranking; wherein the adversarial domain adapter comprises an adversarial domain adapter trained using a generative adversarial network. These limitations further limit the abstract idea of independent claims. The additional element including generative adversarial network is recited at “apply it” level. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 6-7, 16, 24-25 recites the initial service request comprises an unresolved service request submitted to a provider of a product or a service; wherein the plurality of reference service requests comprise service requests previously resolved by the provider. These limitations further limit the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 9, 18 and 27 recites transform the initial service request to a feature vector; obtaining feature vectors of the plurality of reference service requests; and generating the features that complete the feature set of the initial service request by executing the adversarial domain adapter on the feature vector of the initial service request and the feature vectors of the plurality of reference service requests These limitations further limit the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claim 28 recite the incomplete feature set comprises a record storing a set of fields corresponding to service request features, wherein:(a) the set of fields includes a plurality of fields populated with corresponding data, and(b) the set of fields includes at least one field lacking corresponding data; and predicting values for augmenting the incomplete feature set of the initial service request comprises: transforming the initial service request to a first feature vector; obtaining a plurality of reference feature vectors corresponding to the plurality of reference service requests; executing the adversarial domain adapter on the first feature vector and the plurality of reference feature vectors; and generating, using the adversarial domain adapter, the predicted values for augmenting the incomplete feature set of the initial service request by generating synthesized data for the at least one field lacking corresponding data. These limitations further limit the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claim 29 recites training the adversarial domain adapter by iteratively performing operations comprising: obtaining a plurality of training service requests from a database of past service requests; generating a sample service request comprising an incomplete sample feature set from the plurality of training service requests; transforming the sample service request and the plurality of training service requests to a sample feature vector and a plurality of training feature vectors; generating a synthesized feature set for the sample service request by executing a feature generator on the sample feature vector and the plurality of training feature vectors; generating a first prediction of whether the synthesized feature set comprises synthetic features using a domain discriminator model trained to discriminate between synthetic and real features based on statistical properties of the plurality of training feature vectors; generating a second prediction of whether the synthesized feature set comprises synthetic features using a class discriminator model trained to discriminate between synthetic and real features based on class-specific properties of the plurality of training feature vectors; updating parameters of the feature generator based on a correctness of the first prediction and a correctness of the second prediction; and updating parameters of the domain discriminator model and the class discriminator model based on the correctness of the first prediction and the correctness of the second prediction,respectively. These limitations are part of the abstract idea of organizing of human activity as they further limit the abstract idea of independent claims. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
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 are merely 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.).
Subject Matter distinguished from prior art
Regarding Claims 1, 10 and 19, Lee et al. (US 2022/0138598 A1) discloses the one or more non-transitory computer-readable media method/system storing program instructions that, when executed by one or more hardware processors (([0090] a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out instructions), cause performance of operations comprising:
Lee discloses receiving an initial service request comprising an feature set ([0005] collecting datasets, each dataset including: a previously submitted service request, and a team that satisfied the respective previously submitted service request. A newly submitted service request is received, and features are extracted from the newly submitted service request.)
Lee discloses identifying a plurality of reference service requests in a database of past service requests that meet a first similarity criteria with relation to the initial service request ([0008] determining the dataset that most closely matches the newly submitted service request, by applying the model to the features extracted from the newly submitted service request includes: calculating a similarity score between the newly submitted service request and each of the datasets) based on (a) the feature set of the initial service request ([0008] determining the dataset that most closely matches the newly submitted service request, by applying the model to the features extracted from the newly submitted service request includes: calculating a similarity score between the newly submitted service request and each of the datasets) and (b) a plurality of features sets corresponding respectively the plurality of reference service requests; ([0008] determining a dataset that most closely matches the newly submitted service request by applying the model to the features extracted from the newly submitted service request. selecting the dataset (reference service request) that produced the highest similarity score as the dataset that most closely matches the newly submitted service request, [0073] identifying one of the remaining datasets in the hidden layer as a closest match to the acting target, while sub-operation 560 includes comparing the features of the acting target and the features of the identified one of the remaining datasets. In some approaches, the service request determined as having a highest similarity score with the target service request may be identified as the closest match to the acting target.)
Lee discloses identifying a subset of reference service requests, of the plurality of reference service requests, meeting a second similarity criteria ([0073] identifying one of the remaining datasets in the hidden layer as a closest match to the acting target, while sub-operation 560 includes comparing the features of the acting target and the features of the identified one of the remaining datasets. In some approaches, the service request determined as having a highest similarity score with the target service request may be identified as the closest match to the acting target.) based on (b) a subset of the plurality of feature sets corresponding respectively the subset of reference service requests ([0072] the similarity between two given service requests may be determined by comparing one or more predetermined features (second similarity criteria) associated with each of the service requests. For example, the service request types, any comments included with the service requests, corresponding market information, TCV, a country or region which the service requests correspond to, relevant sector information, (second similarity criteria) etc. may be compared to determine a similarity between two given service requests. Thus, by comparing one or more predetermined features of the target service request with corresponding features of the remaining service requests in the hidden layer, a service request which most closely matches the target service request may be identified.) ; and
Lee discloses processing the initial service request based on the subset of the reference service requests ([0081] In response to identifying one of the datasets as most closely matching the newly submitted service request, method 500 proceeds to operation 514, whereby the identified dataset is used to process the newly received service request. By determining that the service request portion of the identified dataset closely matches the newly submitted service request, the corresponding team information in the identified dataset is known to be a desirable method of satisfying the new service request.).
Lee does not specifically teach service request comprising an incomplete feature set, identifying a plurality of reference service requests meet a first similarity criteria with relation to the initial service request based on the incomplete feature; determine an enhanced service request by predicting value for augmenting the incomplete feature set of the initial service request using an adversarial domain adapter and the plurality of reference service requests; identifying a subset of reference service requests based on (a) the updated feature set of the enhanced service request
Qi (US 8,117,178 B2), teaches service request comprising an incomplete feature set (Col1 lines 21-25 If the user enters an incomplete query, that is, the query lacks some essential parameters, the system has difficulty in effectively handling such query, especially in finding the lost part of the query. Col 1 lines 31-35 process effectively an incomplete query. A query entered by the user is not complete, the invention can process it accordingly to obtain a selected service and thus a query answer.), identifying a plurality of reference service requests meeting a first similarity criteria with relation to the initial service request based on the incomplete feature (Col 8 lines 54-62 a similar query detecting unit 74 for searching the history query that contains the lost parameter in the current query as a similar query from the current user query history base 77 when there is no parameter value extracted by the latest query detecting unit 73, and extracting the parameter value corresponding to the lost parameter in current query from the similar query if it contains the parameter lost in the current query, a query complementing unit 75 for adding the service type, the lost parameter and the parameter value into the semantic analysis query so as to obtain the selected service, and an output unit 76 for outputting the selected service.) and the plurality of reference service requests to predict values for augmenting the incomplete feature set of the initial service request to generate an enhanced service request with an updated feature set (Col 8 lines 54-62 a similar query detecting unit 74 for searching the history query that contains the lost parameter in the current query as a similar query from the current user query history base 77 when there is no parameter value extracted by the latest query detecting unit 73, and extracting the parameter value corresponding to the lost parameter in current query from the similar query (predict value augmenting incomplete feature) if it contains the parameter lost in the current query, a query complementing unit 75 for adding the service type, the lost parameter and the parameter value into the semantic analysis query (enhanced service request with updated feature) so as to obtain the selected service, and an output unit 76 for outputting the selected service., Col 9 lines 1-8 receives a semantically-analyzed query in natural language from the user. The lost content searching unit 72 at S702 matches the semantic analysis result with the service mapping rule in the service mapping rule base 160, finds a matched service mapping rule and determines the lost service parameter (incomplete feature). Then, it extracts from the matched service mapping rule the service type to which the query belongs and the service parameter lost in the query, Col 9 lines 34-37 At S705, the service type, the lost parameter and the parameter value are added into the semantic analysis query (enhanced service request with updated feature ) to obtain the selected service.); identifying a subset of reference service requests (Col 10 lines 60-67 detecting the similar query: there exists a query made by another user John, "how high is the temperature in Beijing today?", in which the service type is "weather", the query parameter "place: Beijing; date: today" contains the parameter "place" in the semantic analysis result, and both of the parameter values are "Beijing", this query is thus regarded as the similar query; Col 11 lines 1-8 since the similar query contains the lost parameter "date", the corresponding parameter value "today" is extracted; Final step of complementing the query: adding the service type "weather", the lost parameter "date" and the lost parameter value "today" into the semantic analysis result to obtain a selected service "service type: weather; place: Beijing; date: today". ); processing the initial service request based on the subset of the reference service requests (Col 12 lines 64-67 service query system can process both an incomplete query and a complete query, and thus find out an answer corresponding to the incomplete or complete query., Fig 11b generate answer and output relevant answer Col 5 lines 15-16 The retrieved answer is sent to the user terminal by the answer sender 102 at S105.)
Gaber (US 2022/0405386 A1) teaches using an adversarial domain adapter and the plurality of reference service requests to predict values for augmenting the incomplete feature set of the initial service request to generate an enhanced service request with an updated feature set ([0015] the trained model data sets each include a trained ML model, a list of features important to the model's performance, and an algorithm (e.g., a generative adversarial network (GAN)) suitable for use in generating feature data when features are missing that the ML model expects to receive as input when making a prediction [0016] when a prediction is to be made (e.g., whether a given file is malicious in some way), data corresponding to the prediction request is used as input for each of the trained ML models received from the model source nodes. Any features that a given model expects as input that are missing (incomplete feature) from the data associated with the prediction request are filled in with data (augmenting the incomplete feature) using the missing feature generator associated with the ML model. [0021] generate features needed as input for the trained ML model in situations where such features are missing from a dataset to be analyzed. Such a missing feature generator may, for example, generate missing feature data based on the data structures and correlations. As an example, GANs may be used as missing feature generator s. In one or more embodiments, the missing feature generator may be used to generate any feature required as input for a trained model that is not included in the important feature list associated with the model. [0044] a missing feature generator is an algorithm for generating any missing feature data. In one or more embodiments, missing feature data is any data or values required as input for a given ML model that are not present in a data set intended as input to the ML model. The missing feature generator may be a GAN, which may use feature statistics and correlations from the data set used to train an ML model to impute feature data values to fill in missing feature data required for input to an ML model.[0048] when a missing feature needed as input for one of the ML models is identified, the missing feature generator provided by the model source node that provided the ML model is used to generate a value for the missing feature. [0049] if missing features were identified in Step 204, and values for the same were generated in Step 206, then the trained ML models are executed using the ensemble model training data set and the missing feature values. (enhanced service request with an updated feature). [0052] In Step 214, a determination is made as to whether the prediction request data set has any missing features for any of the trained ML models from the model source nodes, [0054] if missing features were identified in Step 214, and values for the same were generated in Step 216, then the trained ML models are executed using the prediction request data set and the missing feature values., [0063] an entity seeking an answer to whether a given URL is a phishing attempt submits a prediction request data set (service request) to the central hub having the ensemble model. Any features needed as inputs for the trained ML models from the participant entities are imputed using the GANs associated with the models. Then, the prediction request data set and any imputed missing feature values are used to execute the trained ML models. The results from each model are then used as inputs to the ensemble model, which produces a single answer based on the inputs. The single answer may, for example, be that the URL for which the prediction was requested is, or is not, likely to be part of a phishing attack.)
Taylert (US11,960514) discloses receiving an initial service request feature set (Col 5 lines 41-50 the nature of the query may vary, but typically the query is an utterance, or perhaps some ungrammatical collection of words. The user queries the service using a browser, the event-based semantic search is carried out against the event-level metadata, and the response is returned to the user over the network. The utterance is passed into the vector database to return a semantically-similar context.)
Arneault (US 2023/0350874) teaches request comprising an incomplete feature set ([0047] In step 305, a computing device (e.g., record system 140) may receive a request for information associated with a first record. The request for information associated with the first record may be a request for a source record. The computing device may determine that a record is incomplete.), using an adversarial domain adapter and the plurality of reference service requests to predict values for augmenting the incomplete feature set to generate an updated feature set; ([0013] create, using a machine learning model, one or more temporary records to replace missing information in the chain of records associated with the first record; send, to a user device, a first temporary record of the one or more temporary records [0048] The discoverative process may be used to identify missing information, such as first missing information 560 and second missing information 570 shown in FIG. 5. Alternatively, the missing information requested may correspond to an origin source, such as origin records 502 or 510 in FIG. 5 or item properties, [0052] the computing device may utilize one or more machine learning models or artificial intelligence techniques to determine the probabilities of possible information which would enhance, “fill in,” correct or otherwise inform the request when such information in the ledger is missing, inaccurate, or determined to be unreliable, [0026] The one or more machine learning models may comprise a neural network, such as a convolutional neural network (CNN), a recurrent neural network a generative adverserial network (GAN), or a consistent adverserial network (CAN), Claim 1 using a machine learning model, one or more temporary records to replace missing information in the chain of records associated with the first record; sending, by the computing device to a user device, a first temporary record of the one or more temporary records)
Bellegarda (US 2020/0104357) discloses answers are received from the user, dialogue flow processing module 734 then populates the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.
Caldwell (US 2017/0220670) discloses the supplementation module 104 may make a prompt and/or request based on a user's expertise, based on a user's previous votes, to fill in missing or low quality data, or the like.
QI (US20090089282) discusses a semantic analyzing device (20) provided for analyzing an incomplete query from a user semantically. A service selecting device (30) complements the incomplete query based on the semantic-analyzed query to acquire corresponding selected service.
Truong et al. (US 10,460,235 B1) teaches using a probability-based similarity function. (Col 4 lines 2-9 The similarity metric can depend on a joint probability distribution of elements in the output data and a joint probability distribution of elements in the reference dataset, claim 18)
NPL, Li, “ MisGAN: Learning from incomplete data with generative adversarial networks” 2019, discloses a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. How to impute missing data by equipping our framework with an adversarially trained imputer. We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption
However, the prior art fails to teach or suggest at least “determining an enhanced service request by: predicting values for augmenting the incomplete feature set of the initial service request using (a)an adversarial domain adapter trained to synthesize data for augmenting service requests and (b)the plurality of reference service requests; generating the enhanced service request with an updated feature set by augmenting the incomplete feature set of the initial service request with the predicted values; identifying a subset of reference service requests, of the plurality of reference service requests meeting a second similarity criteria with relation to the enhanced service request based on (a) the updated feature set of the enhanced service request and (b) a subset of the plurality of feature sets corresponding respectively to the subset of reference service requests”. The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention.
Thus, the aforementioned combination of features claimed, as a whole, are not anticipated nor rendered obvious for any sufficient rationale by any of the prior art teachings. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims 1, 10 and 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gorny (US11,676,093) discloses analyze the ticket for at least one topic and customer data, search the one or more databases for missing customer data, and in response to detecting missing customer data, identify one or more relationship models that predicts a value for the missing customer data based on known customer data.
McKenna (US12530384) discloses system uses multiple models to retrieve data relevant to queries and appropriate for the requesting user and to output the data in a certain style. In particular, the system uses a first model to determine a type of user submitting a query, a second model to retrieve data relevant to the query, subject to constraints based on the type of user, and a third model to formulate a response to the query, based on the retrieved data
Leon (US 20240006080) discloses service request comprising an incomplete feature set ([0217] query the stored set of subject records to identify an incomplete subset of the set of subject records. Each subject record of the incomplete subset may be identified and included in the incomplete subset)
Troung (US10460235) discloses the reference dataset can include at least one of missing values or not-a-number values. Generating the normalized training dataset by normalizing the categorical data can include converting the at least one of the missing values or the not-a-number values to corresponding predetermined numerical values outside the predetermined range.
Bellegarda (US 2020/0104357 A1) discloses a GAN can be helpful in generating a data set including realistic typographical errors for providing an enhanced typographical error model.([0041])
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).
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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