DETAILED ACTION
This Office Action is in response to communications filed on June 16th, 2026 for Application No. 19/427,696, in which claims 1, 4, 6-8, 11, 13-15, 18, and 20-27 are presented for examination. The amendments filed on June 16th, 2026 have been entered, where claims 1, 4, 6-8, 11, 13-15, 18, and 20 are amended, claims 2-3, 5, 9-10, 12, 16-17 and 19 are canceled, and claims 21-27 are newly added.
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 .
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, 4, 6-8, 11, 13-15, 18, and 20-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a process claim. Therefore, claims 1, 4, 6-7, and 21-23 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed subject matter are mental processes. Specifically, the claim recites
“classifying an event . . . wherein classifying the event . . . provides an initial classification for the event” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, which may be aided by pen and paper);
“based on the initial classification for the event, obtaining an intermediate classification for the event and classifying the event . . . ” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper);
“providing the intermediate classification for the event . . . providing a first set of one or more unstructured data items comprising the one or more sentiments . . . providing a confidence for the intermediate classification . . . providing the second set of one or more unstructured data items” (mental process – amounts to observing information and exercising judgement to form an opinion, with reference to the output of previous mental processes, which may be aided by pen and paper);
“extracting one or more sentiments from a first record related to the event . . . extract the one or more sentiments from the first record related to the event . . . extracting a second set of one or more unstructured data items from a second record related to the event” (mental process – apart from the “extracting” itself, which could arguably require a particular technological environment, amounts to exercising judgment to for an opinion on a set of unstructured data items, with reference to a known or observed record of an event, which may be aided by pen and paper);
“wherein classifying the event . . . provides a . . . classification for the event” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper);
“based on the . . . classification for the event, classifying the event . . . wherein classifying the event . . . provides an output classification for the event” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper); and
“processing the event based on the output classification for the event” (mental process – amounts to exercising judgment to evaluate a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the event is a request . . . comprising an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea) and
“using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the event is a request . . . comprising an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept) and
“using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 4, 6-7, and 21-23. The additional limitations of the dependent claims are addressed below.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 4 depends on. Here, the claim recites additional elements that are mental processes. Specifically,
“wherein: the first record relating to the event comprises a factual summary created in connection with the intermediate classification for the event” (mental process – amounts to exercising judgment to form an opinion on a summary, which is created in connect with other mental processes and may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the second record related to the event is used as a basis for the intermediate classification for the event and for the first record related to the event” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to known or observed records, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 6 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein processing the event based on the output classification for the event comprises: obtaining a final classification for the event based on the output classification for the event; and processing the event based on the final classification for the event” (mental process – amounts to exercising judgment to evaluate a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 7 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 8:
Step 1: Claim 8 is a machine claim. Therefore, claims 8, 11, 13-14, and 24-27 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes. Additionally, the claim recites new limitations that are mental processes. Specifically,
“terminating the method prior to classifying the event . . . based on the initial classification for the event” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A system comprising: a first explicit classifier; a neural network classifier; a second explicit classifier; a processor; and a non-transitory computer readable medium storing instructions to perform a method . . . using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier . . . wherein the non-transitory computer readable medium stores instructions for” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“wherein the event is a request . . . wherein the neural network classifier comprises an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting . . . prior to . . . using the neural network classifier” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A system comprising: a first explicit classifier; a neural network classifier; a second explicit classifier; a processor; and a non-transitory computer readable medium storing instructions to perform a method . . . using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier . . . wherein the non-transitory computer readable medium stores instructions for” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“wherein the event is a request . . . wherein the neural network classifier comprises an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting . . . prior to . . . using the neural network classifier” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 8 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 11, 13-14, and 24-27. The additional limitations of the dependent claims are addressed below.
Regarding Claim 11, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 11 is rejected under the same rationale.
Regarding Claim 13:
Step 2A Prong 1: See the rejection of Claim 8 above, which Claim 13 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the instructions are operable to, when executed, make the second record related to the event available as a basis for the intermediate classification for the event and for the first record related to the event from which the first set of unstructured data items are extracted” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to known or observed records that are used to update other known or observed records, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 13 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 14, the claim recites limitations that are all substantially the same as limitations of Claim 7, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 14 is rejected under the same rationale.
Regarding Claim 15:
Step 1: Claim 15 is a machine claim. Therefore, claims 15, 18, and 20 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, the claim recites limitations that are substantially the same as the limitations of Claim 1. As a result, and as elaborated above, these limitations are abstract ideas because they are mental processes.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A non-transitory computer readable medium having stored thereon instructions to perform a method . . . using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“wherein the event is a request . . . wherein the neural network classifier comprises an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A non-transitory computer readable medium having stored thereon instructions to perform a method . . . using a first explicit classifier . . . using the first explicit classifier . . . using a neural network classifier . . . to a first input node of the neural network classifier . . . using a neural network . . . wherein the neural network used to . . . to one or more corresponding input nodes of the neural network classifier . . . to corresponding input nodes of the neural network classifier . . . to a second input node of the neural network classifier . . . using the neural network classifier . . . neural network . . . neural network . . . using a second explicit classifier . . . using the second explicit classifier” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“wherein the event is a request . . . wherein the neural network classifier comprises an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer by performing acts comprising . . . extracting . . . other than the neural network classifier . . . extract . . . is a transformer or a recurrent neural network . . . extracting” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 18 and 20. The additional limitations of the dependent claims are addressed below.
Regarding Claim 18, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 18 is rejected under the same rationale.
Regarding Claim 20, the claim recites limitations that are all substantially the same as limitations of Claim 13, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 20 is rejected under the same rationale.
Regarding Claim 21:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 21 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“provide the initial classification from the event from a set of potential initial classifications . . . provide the . . . classification for the event from a set of potential . . . classifications . . . provide the output classification for the event from a set of potential output classifications” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to know sets of classification outputs, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“the first explicit classifier is configured to . . . the neural network classifier is configured to . . . neural network . . . neural network . . . the second explicit classifier is configured to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“the set of potential initial classifications, the set of potential neural network classifications, and the set of potential output classifications each comprise: a classification for approving the request; and a classification for denying the request” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“the first explicit classifier is configured to . . . the neural network classifier is configured to . . . neural network . . . neural network . . . the second explicit classifier is configured to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“the set of potential initial classifications, the set of potential neural network classifications, and the set of potential output classifications each comprise: a classification for approving the request; and a classification for denying the request” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 21 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 22:
Step 2A Prong 1: See the rejection of Claim 21 above, which Claim 22 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein, in the set of potential initial classifications:- the classification for approving the request is a classification for approving the request after further processing by the neural network classifier and the second explicit classifier; and- the classification for denying the request is a classification for denying the request and terminating processing prior to processing by the neural network classifier” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein, in the set of potential initial classifications:- the classification for approving the request is a classification for approving the request after further processing by the neural network classifier and the second explicit classifier; and- the classification for denying the request is a classification for denying the request and terminating processing prior to processing by the neural network classifier” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 22 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 23:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 23 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the first explicit classifier is an exclusionary rule based classifier, and wherein the second explicit classifier is a balancing classifier” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the first explicit classifier is an exclusionary rule based classifier, and wherein the second explicit classifier is a balancing classifier” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 23 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 24:
Step 2A Prong 1: See the rejection of Claim 8 above, which Claim 24 depends on. Here, the claim recites additional elements that are mental processes. Specifically,
“terminating the method prior to classifying the event . . . based on the initial classification for the event . . . terminating the method for prior to classifying the event . . . when the initial classification for the event is a classification denying the request” (mental process – amounts to exercising judgment to form an opinion on a known or observed event, with reference to previous mental determinations, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the instructions for . . . using the neural network classifier . . . are instructions for . . . using the neural network classifier” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the instructions for . . . using the neural network classifier . . . are instructions for . . . using the neural network classifier” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Accordingly, Claim 24 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 25, the claim recites limitations that are all substantially the same as limitations of Claim 21, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 25 is rejected under the same rationale.
Regarding Claim 26, the claim recites limitations that are all substantially the same as limitations of Claim 22, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 26 is rejected under the same rationale.
Regarding Claim 27, the claim recites limitations that are all substantially the same as limitations of Claim 23, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 27 is rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 6-8, 11, 13-15, 18, and 20-27 are rejected under 35 U.S.C. 103 as being unpatentable over Eledath et al. (hereinafter Eledath) (Pat. No. US 11,869,065 B1) in view of Bocamazo et al. (hereinafter Bocamazo) (Pat. Pub. No. US 2026/0141721 A1) and Nizamani et al. (hereinafter Nizamani) (“Automatic approval prediction for software enhancement requests”).
Regarding Claim 1, Eledath teaches a method comprising (Pg. 23, Col. 25, Ln. 37-42, “Embodiments may be provided as a software program or computer program product . . . to perform processes or methods described herein”; see also Fig. 1 and Fig. 3, where relevant components of the “methods described herein” are depicted):
classifying an event, wherein the event is . . . [data], using a first explicit classifier, wherein classifying the event using the first explicit classifier provides an initial classification for the event (Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”; see also Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306” that is used to generate the “INTERACTION DATA 306”);
based on the initial classification for the event, obtaining an intermediate classification for the event (Pg. 16, Col. 11, Ln. 47-50, “The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308”, where the initial classification, “feature data 306”, is used as a basis to obtain the intermediate classification for the event, “interaction data”; see also Fig. 3)
and classifying the event using a . . . classifier . . . by performing acts comprising: providing the intermediate classification for the event to . . . the . . . classifier (Pg. 16, Col. 11, Ln. 54-67, “The interaction data 308 may be provided to the hypothesis-generation component 120 for determining whether the interaction data 308 represents one or more events . . . the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more event”, where the “first classifier” classifies the event using the interaction data, “whether the interaction data 308 represents one or more event”, which, as discussed above, is the intermediate classification that is obtained based on the initial classification of the event, “FEATURE DATA 306”, see Fig. 3);
extracting one or more sentiments from a first record related to the event (Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”, which is operationalized by use of a “feature-extraction component” to extract a set of data items, comprised in the “feature data 306” from the unstructured data, “image data”, which is within the broadest reasonable interpretation one or more sentiments because it is a specific view or notion that the features are contained in the image data; see also Fig. 3, where the data from one of the “CAMERA[S] 102”, either alone or in combination with the “ADDITIONAL DATA 310”, is within the broadest reasonable interpretation of a record related to the event; see also Pg. 16, Col. 11, Ln. 57-63, “the hypothesis-generation component 120 makes this determination using the interaction data 308 and additional data 310, such as a current state of a shelf, aisle, or lane corresponding to the interaction data, identit(ies) of any user(s) at or near the location corresponding to the interaction data, a state of a virtual cart of any proximate users, and/or the like”, where the “additional data 310”, which is a comprising component of the record of the event, comprises a collection of sentiments as a factual summary, “current state of a shelf, aisle, or lane corresponding to the interaction data, identit(ies) of any user(s) . . . a state of a virtual cart of any proximate users, and/or the like”, created in connection with the intermediate classification for the event, “at or near the location corresponding to the interaction data”; see also Fig. 3, where the “ADDITIONAL DATA 310” comprises “SHELF STATE”, “USER IDENTITY”, and “CART STATE”, which is within the broadest reasonable interpretation of a factual summary),
using a neural network other than the . . . classifier, wherein the neural network used to extract the one or more sentiments from the first record related to the event is a . . . neural network (Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”, which is operationalized by use of a “feature-extraction component” to extract a set of data items, comprised in the “feature data 306” from the unstructured data, “image data”, which, as discussed above, is within the broadest reasonable interpretation of the one or more sentiments from the unstructured data items; see also Fig. 3, where the data from one of the “CAMERA[S] 102”, either alone or in combination with the “ADDITIONAL DATA 310”, is within the broadest reasonable interpretation of a record related to the event; and where, as discussed above, the unstructured data items, “FEATURE DATA 306”, are extracted from the record using the “FEATURE-EXTRACTION COMPONENT 116”; and where the “CLASSIFIER 304” is different from the first classifier in the “HYPOTHESIS-GENERATION COMPONENT 120”, but can also be a “neural network” classifier, see Pg. 15, Col. 9, Ln. 12-14, “the feature data may be generated from the image data using one or more trained classifiers, such as artificial neural networks”);
providing a first set of one or more unstructured data items comprising the one or more sentiments to . . . the . . . classifier (Pg. 16, Col. 11, Ln. 54-67, “The interaction data 308 may be provided to the hypothesis-generation component 120 for determining whether the interaction data 308 represents one or more events . . . the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more event”, where the “first classifier” classifies the event using the interaction data, “whether the interaction data 308 represents one or more event”, which indirectly provides the set of unstructured data items to the classifier as input, “FEATURE DATA 306”, which as discussed above, comprises the one or more sentiments, see Fig. 3; alternatively, see Pg. 24, Col. 28, Ln. 16-22, “inputting the feature data into a first classifier . . . to determine whether the image data represents the interaction between the user and the item; generating, using the first classifier and based at least in part on inputting the feature data to the first classifier, interaction data”, where the unstructured data items, “feature data” can also be directly provided to the “first classifier” as “input”);
extracting a second set of one or more unstructured data items from a second record related to the event (Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”, which is operationalized by use of a “feature-extraction component” to extract a set of data items, comprised in the “feature data 306” from the unstructured data, “image data”, which is within the broadest reasonable interpretation of unstructured data items; see also Fig. 3, where the data from the “CAMERA[S] 102” are each within the broadest reasonable interpretation of a record related to the event, such that the data from any of “CAMERA 102(1)” through “CAMERA 102(N)” can be considered a second record, which is the basis for the collective record, data from the “CAMERA[S] 102” and “ADDITIONAL DATA 310”, which in turn means the “FEATURE DATA 306” comprises a set of unstructured data items, which has subsets of data, such as a second set of unstructured data)
and providing the second set of one or more unstructured data items to . . . the . . . classifier (Pg. 16, Col. 11, Ln. 54-67, “The interaction data 308 may be provided to the hypothesis-generation component 120 for determining whether the interaction data 308 represents one or more events . . . the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more event”, where the “first classifier” classifies the event using the interaction data, “whether the interaction data 308 represents one or more event”, which indirectly provides the sets of unstructured data items to the classifier as input, “FEATURE DATA 306”, see Fig. 3; alternatively, see Pg. 24, Col. 28, Ln. 16-22, “inputting the feature data into a first classifier . . . to determine whether the image data represents the interaction between the user and the item; generating, using the first classifier and based at least in part on inputting the feature data to the first classifier, interaction data”, where the unstructured data items, “feature data” can also be directly provided to the “first classifier” as “input”); and
. . . ;
wherein classifying the event using the . . . classifier provides a . . . classification for the event (Pg. 16, Col. 11, Ln. 54-67, “The interaction data 308 may be provided to the hypothesis-generation component 120 for determining whether the interaction data 308 represents one or more events . . . the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more event”, where the “first classifier” classifies the event using the interaction data, “whether the interaction data 308 represents one or more event”, which, as discussed above, is based on the initial classification of the event, “FEATURE DATA 308”, see Fig. 3);
based on the . . . classification for the event, classifying the event using a second explicit classifier, wherein classifying the event using the second explicit classifier provides an output classification for the event (Pg. 16, Col. 11-12, Ln. 63-5, “the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more events and, if so, a second classifier to parse out the different events and localize the different events. As illustrated, after determining that the interaction data 308 indicates the existence of one or more events, the hypothesis-generation component 120 may generate and output event data 312”, where the “second classifier”, which is within the broadest reasonable interpretation of a second explicit classifier, provides an output classification in order to generate the “output event data 312”; see also Fig. 3; see also Pg. 24, Col. 28, Ln. 21-27, “generating, using the first classifier and based at least in part on inputting the feature data to the first classifier, interaction data indicative of a first interaction between the user and the item . . . inputting the interaction data into a second classifier”, where the output of the “second classifier” is based on the output of the “first classifier”); and
processing the event based on the output classification for the event (Pg. 16, Col. 12, Ln. 10-12, “The virtual-cart component 122 may receive the event data 312 and may update the state of one or more virtual carts corresponding to the event data 312”, where “update[ing] the state of one or more virtual carts” is within the broadest reasonable interpretation of processing the event, which is based on the output classification for the event, “corresponding to” the output of the second classifier used to generate “the event data 312”; see also Fig. 3).
Eledath does not explicitly disclose . . . a request (where the event is not explicitly described as a request)
. . . neural network . . . comprising an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer . . . a first input node of . . . transformer or a recurrent . . . providing a confidence for the intermediate classification to a second input node of the neural network classifier . . . (where the classifier between the first explicit classifier and the second explicit classifier is not specifically described as a neural network classifier, therefore, specific neural network elements are not specifically described in regard to the classifier between the first explicit classifier and the second explicit classifier; redundant recitations of neural network classifier and input nodes omitted; but see Pg. 15, Col. 9, Ln. 12-14, “the feature data may be generated from the image data using one or more trained classifiers, such as artificial neural networks”, where use of “neural networks” as “classifiers” is explicitly taught).
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Bocamazo, Figure 4
However, Bocamazo teaches . . . [a multi-stage] neural network [classification method, wherein the output of a first neural network is provided to a second neural network] . . . (Abstract, “The system generates, using two or more neural networks, a plurality of predictions” and Fig. 4, wherein the output of a first neural network, “FIRST NEURAL NETWORKS 402”, is provided, as indicated by the connecting arrow, to a second neural network, “SECOND NEURAL NETWORKS 404”; see also Para. [0171] – [0173], “identify, using a first neural network, an identifier associated with the object . . . generate, using a second neural network, a first prediction of whether the object entered the tote” ; Para. [0111] – [0112], “At block 702, the one or more entities may identify, a structured identifier corresponding to an item (e.g., one or more first neural networks 402 illustrated in FIG. 4). After performing block 702, process 700 may move to block 702 and block 704 simultaneously. At block 704, the one or more entities may use one or more second neural networks (e.g., one or more second neural networks 404 illustrated in FIG. 4) to generate a first prediction on whether the item is properly stored within the tote”; and Para. [0047], “neural network inferencing may include . . . classification”)
[the second neural network] comprising an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer . . . (Fig. 4, where the “SECOND NEURAL NETWORKS 404” comprises an input layer having a plurality of input nodes, which is the first layer depicted, and a set of dense layers, which is the second layer depicted, and, as demonstrated by the arrows, applies operations on values from the previous layer; Para. [0036], “one or more neural networks described herein (e.g., . . . one or more second neural networks 404, . . . may refer to a computational model comprising interconnected nodes (neurons) configured to process input data, identify patterns, and generate outputs based on learned relationships between the data. In at least one embodiment, the one or more neural networks may comprise one or more parameters (e.g., one or more weights, one or more biases)”, where the operations comprise “biases” and weighted sum of values, “generate outputs based on . . . one or more parameters (e.g., one or more weights, one or more biases)”; see also Para. [0085], “ One or more second neural networks 404 may include convolutional neural networks such as, for example, LeNet, AlexNet, Visual Geometry Group, Inception, ResNet, U-Net, DenseNet, MobileNet, EfficientNet, Capsule Networks, YOLO, Fully Convolutional Network, Regions with Convolutional Neural Networks, V-Net, etc”, where the “second neural networks” may be one or more of architectures that comprise a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer, such as, for example, at least “LeNet” and “AlexNet”)
[the multi-stage method comprising providing an intermediate output to] a first input node of [the second neural network] . . . (Fig. 4, wherein the output of a first neural network, “FIRST NEURAL NETWORKS 402”, is provided, as indicated by the connecting arrow, to a second neural network, “SECOND NEURAL NETWORKS 404”, such that each input node, including a first input node, is provided an intermediate output, see Para. [0171] – [0173], “identify, using a first neural network, an identifier associated with the object . . . generate, using a second neural network, a first prediction of whether the object entered the tote” ; Para. [0111] – [0112], “At block 702, the one or more entities may identify, a structured identifier corresponding to an item (e.g., one or more first neural networks 402 illustrated in FIG. 4). After performing block 702, process 700 may move to block 702 and block 704 simultaneously. At block 704, the one or more entities may use one or more second neural networks (e.g., one or more second neural networks 404 illustrated in FIG. 4) to generate a first prediction on whether the item is properly stored within the tote”; and Para. [0047], “neural network inferencing may include . . . classification”)
[wherein the first neural network is a ] transformer or a recurrent [neural network] . . . (Para. [0081, “One or more first neural networks 402 may include feed forward neural networks, recurrent neural networks, long short-term memory networks, autoencoders, generative adversarial networks, transformers, etc.”)
. . . [the multi-stage method comprising] providing a confidence for the intermediate classification to a second input node of the neural network classifier . . . (Fig. 4, wherein the output of a first neural network, “FIRST NEURAL NETWORKS 402”, is provided, as indicated by the connecting arrow, to a second neural network, “SECOND NEURAL NETWORKS 404”, such that each input node, including a second input node, is provided an intermediate output, see Para. [0171] – [0173], “identify, using a first neural network, an identifier associated with the object . . . generate, using a second neural network, a first prediction of whether the object entered the tote”; Para. [0111] – [0112], “At block 702, the one or more entities may identify, a structured identifier corresponding to an item (e.g., one or more first neural networks 402 illustrated in FIG. 4). After performing block 702, process 700 may move to block 702 and block 704 simultaneously. At block 704, the one or more entities may use one or more second neural networks (e.g., one or more second neural networks 404 illustrated in FIG. 4) to generate a first prediction on whether the item is properly stored within the tote”; and Para. [0047], “neural network inferencing may include . . . classification”, which includes a confidence, “confidence scores”, for the intermediate classification, see Para. [0081], “one or more first neural networks 402 can generate bounding boxes and/or confidence scores, where the bounding boxes may indicate regions within first set of images 412 that include structured identifier for the one or more objects of interest”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the classifying of an event using a classifier by performing acts comprising: providing an intermediate classification for the event to the classifier; extracting one or more sentiments from a first record related to the event, using a neural network other than the classifier, wherein the neural network used to extract the one or more sentiments from the first record related to the event is a neural network; providing a first set of one or more unstructured data items comprising the one or more sentiments to the classifier; extracting a second set of one or more unstructured data items from a second record related to the event; and providing the second set of one or more unstructured data items to the classifier of Eledath with the multi-stage neural network classification method, wherein the output of a first neural network is provided to a second neural network, the second neural network comprising an input layer having a plurality of input nodes and a set of dense layers each of which has nodes with values provided by applying an activation function to a bias and a weighted sum of values from a previous layer; the multi-stage method comprising providing an intermediate output to a first input node of the second neural network, wherein the first neural network is a transformer or a recurrent neural network, and the multi-stage method comprising providing a confidence for the intermediate classification to a second input node of the neural network classifier of Bocamazo in order to utilize a neural network classifier between the first and second explicit classifiers, such that a transformer or recurrent neural network is utilized to provide an intermediate output to the neural network classifier, in order to supplement classification using staged neural networks with varied input data (Bocamazo, Para. [0171] – [0173], “identify, using a first neural network, an identifier associated with the object . . . generate, using a second neural network, a first prediction of whether the object entered the tote”; Bocamazo, Para. [0111] – [0112], “At block 702, the one or more entities may identify, a structured identifier corresponding to an item (e.g., one or more first neural networks 402 illustrated in FIG. 4). After performing block 702, process 700 may move to block 702 and block 704 simultaneously. At block 704, the one or more entities may use one or more second neural networks (e.g., one or more second neural networks 404 illustrated in FIG. 4) to generate a first prediction on whether the item is properly stored within the tote”; and Bocamazo, Para. [0047], “neural network inferencing may include . . . classification”) and to allow for and early termination of classification processes based on an intermediate neural network output (Bocamazo, Fig. 6, where the method may terminate, “NO”, at step “606”, which occurs between the use of the neural network other than the neural network classifier, “Using Neural Networks . . . 604”, and use of the neural network classifier, “Using Neural Networks . . . 610”; see also Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes”), which will result in optimal data use, increased efficiency, and enhanced accuracy (Bocamazo, Para. [0024], “one skilled in the art will appreciate in light of this disclosure including two or more neural networks to track one or more items or any other objects included in a set of images, certain embodiments may be capable of achieving certain advantages, including some or all of the following: (1) optimal use of sensor data (e.g., images), (2) reduced memory requirement caused by efficient inventory management, (3) real-time feedback reducing latency in information processing, (4) enhanced accuracy of the systems performing computer vision technologies, (5) intuitive and effective user interfaces (e.g., within stations), (6) advancing interoperability between devices (e.g., sensors), etc.”).
Additionally, before the effective filing date of the invention, the above-discussed combination would have also been rendered obvious because the use of multi-stage classifiers with a neural network in any position was both well-established in the field (Sathyanarayana et al., hereinafter Sathyanarayana, Pat. Pub. No. US 2006/0222221 A1, Para. [0026], “By dividing the classification process into a multiple stage procedure involving more than one classifier, the requirements placed on each component classifier 206 and 208 are reduced” and Sathyanarayana, Para. [0034] – [0035], “each component classifier 502-508 could be implemented using a different classification methodology such as Bayesian, k-nearest neighbor, neural network and the like . . . component classifiers 502-508 are configured with . . . different classification methodologies . . . One of skill in the art will readily recognize that a limitless number of configuration of component classifiers exist and, accordingly, the systems and methods described herein are not limited to any one configuration”) and known to increase speed and configurability by dividing data processing among multiple stages of classifiers (Sathyanarayana, Para. [0026], “By dividing the classification process into a multiple stage procedure involving more than one classifier, the requirements placed on each component classifier 206 and 208 are reduced. This allows the implementation of component classifiers that are less complex, which can translate into increased processing speed” and Sathyanarayana, Para. [0035], “component classifiers 502-508 are configured with different sensitivity and specificity level as well as different classification methodologies . . . Because the complexity of a component classifier generally increases along with the level of sensitivity/specificity, the use of multiple component classifiers 502-508 having a combination of various sensitivity/specificity levels and/or classification methodologies can result in the consumption of lower computation times and design times . . . One of skill in the art will readily recognize that a limitless number of configuration of component classifiers exist and, accordingly, the systems and methods described herein are not limited to any one configuration”).
Furthermore, Nizamani discloses . . . [classifying an event, wherein the event is] a request (Abstract, Pg. 347, “an approach that can automatically predict whether a new enhancement report will be approved is beneficial . . . With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method comprising classifying an event, wherein the event is data of Eledath in view of Bocamazo with the classifying an event, wherein the event is a request of Nizamani in order to utilize the advantages of the staged classification method disclosed by Eledath in view of Bocamazo, such as optimal data use, increased efficiency, and enhanced accuracy (Bocamazo, Para. [0024], “one skilled in the art will appreciate in light of this disclosure including two or more neural networks to track one or more items or any other objects included in a set of images, certain embodiments may be capable of achieving certain advantages, including some or all of the following: (1) optimal use of sensor data (e.g., images), (2) reduced memory requirement caused by efficient inventory management, (3) real-time feedback reducing latency in information processing, (4) enhanced accuracy of the systems performing computer vision technologies, (5) intuitive and effective user interfaces (e.g., within stations), (6) advancing interoperability between devices (e.g., sensors), etc.”), to classify requests, which will benefit all parties associated with the request (Nizamani, Pg. 347, Abstract, “Software applications often receive a large number of enhancement requests that suggest developers to fulfill additional functions. Such requests are usually checked manually by the developers, which is time consuming and tedious. Consequently, an approach that can automatically predict whether a new enhancement report will be approved is beneficial for both the developers and enhancement suggesters . . . The approach can help developers respond to the useful requests more quickly”; see also Nizamani, Pg. 348, Para. 1, “Software has to fulfill user needs, and these requirements change over time. Hence new feature enhancements become necessary for success of the software due to evolving user requirements and changing technologies (Rajlich 2014). Development of newer versions of a an application also requires new or improved feature enhancements. A non-trivial software therefore receives a number of suggestions about adding new feature enhancements and improving the existing ones”), in a manner that improves request classification accuracy (compare Nizamani, Pg. 378, Para. 5, “We therefore conclude that the deep learning algorithms may outperform the Bayes based approach when the dataset is sufficiently large” with Bocamazo, Para. [0092], “training framework 504 can be . . . Deeplearning4j, or other training framework”).
Regarding Claim 4, Eledath in view of Bocamazo and Nizamani teach the method of claim 1, wherein: the first record relating to the event comprises a factual summary created in connection with the intermediate classification for the event (Eledath, Pg. 16, Col. 11, Ln. 57-63, “the hypothesis-generation component 120 makes this determination using the interaction data 308 and additional data 310, such as a current state of a shelf, aisle, or lane corresponding to the interaction data, identit(ies) of any user(s) at or near the location corresponding to the interaction data, a state of a virtual cart of any proximate users, and/or the like”, where the “additional data 310”, which as discussed above is a comprising component of the first record of the event, comprises a factual summary, “current state of a shelf, aisle, or lane corresponding to the interaction data, identit(ies) of any user(s) . . . a state of a virtual cart of any proximate users, and/or the like”, created in connection with the intermediate classification for the event, “at or near the location corresponding to the interaction data”; see also Eledath, Fig. 3, where the “ADDITIONAL DATA 310” comprises “SHELF STATE”, “USER IDENTITY”, and “CART STATE”, which is within the broadest reasonable interpretation of a factual summary).
Regarding Claim 6, Eledath in view of Bocamazo and Nizamani teach the method of claim 1, wherein the second record related to the event is used as a basis for the intermediate classification for the event (Eledath, Pg. 16, Col. 11, Ln. 47-50, “The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308”, where, as discussed above, the “feature data 306” is generated based on the second record related to the event, which in turn is used as a basis to obtain the intermediate classification for the event, “interaction data”)
and for the first record related to the event (Eledath, Fig. 3, where the data from the “CAMERA[S] 102” are each within the broadest reasonable interpretation of the first record related to the event, such that the data from any of “CAMERA 102(1)” through “CAMERA 102(N)” can be considered a second record, which is the basis for the collective record, data from the “CAMERA[S] 102” and “ADDITIONAL DATA 310).
Regarding Claim 7, Eledath in view of Bocamazo and Nizamani teach the method of claim 1, wherein processing the event based on the output classification for the event comprises (Eledath, Pg. 16, Col. 12, Ln. 10-12, “The virtual-cart component 122 may receive the event data 312 and may update the state of one or more virtual carts corresponding to the event data 312”, where “update[ing] the state of one or more virtual carts” is within the broadest reasonable interpretation of processing the event, which is based on the output classification for the event, “corresponding to” the output of the second classifier used to generate “the event data 312”; see also Eledath, Fig. 3):
obtaining a final classification for the event based on the output classification for the event (Eledath, Pg. 25, Col. 30, Ln. 9-23, “determining, using the second classifier and based at least in part on inputting the interaction data to the second classifier, that the image data represents the predefined activity in the environment, the predefined activity comprising a performance of an action in the environment with respect to a first item at the at least one inventory location of interest . . . generating, using the third classifier and based at least in part on inputting the interaction data and the virtual-cart data into the third classifier, event data”, where the “event” can be further classified by a “third classifier” to generate a final classification, “event data”, which is based on the output classification of the event from “the second classifier”; see also Eledath, Fig. 3); and
processing the event based on the final classification for the event (Eledath, Pg. 16, Col. 12, Ln. 10-12, “The virtual-cart component 122 may receive the event data 312 and may update the state of one or more virtual carts corresponding to the event data 312”, where “update[ing] the state of one or more virtual carts” is within the broadest reasonable interpretation of processing the event, which is based on the final classification for the event, “the event data 312”; see also Eledath, Fig. 3).
Regarding Claim 8, Eledath in view of Bocamazo and Nizamani teach a system comprising: . . . a processor; and a non-transitory computer readable medium storing instructions to perform a method . . . wherein the non-transitory computer readable medium stores instructions for . . . (Eledath, Pg. 25, Col. 29, Ln. 43-51, “A system comprising: . . . one or more processors; and one or more computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising”; see also Eledath, Pg. 23, Col. 25, Ln. 37-42, “Embodiments may be provided as a software program or computer program product including a non-transitory computer-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described herein”)
terminating the method prior to classifying the event using the neural network classifier based on the initial classification for the event (Eledath, Pg. 17, Col. 13, Ln. 11-18, “At an operation 508, the event-determination component 114 may determine whether the interaction data represents an event . . . If not, then the process 500 returns the operation 502 for continuing to analyze received image data”, where, depending on “whether the interaction data represents an event”, “the process 500 returns the operation 502”, which is within the broadest reasonable interpretation of terminating the method because it ends the classification process for the current “interaction data”, resetting for a new classification method, “returns the operation 502 for continuing to analyze received image data”, which is based on the initial classification for the event, “feature data 306”, because it is used as a basis to obtain the intermediate classification for the event, “interaction data”, see Eledath, Pg. 16, Col. 11, Ln. 47-50, “The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308”, and which, in view of Bocamazo, the terminating occurs prior to classifying the event using the neural network classifier, see Bocamazo, Fig. 6, where the method may terminate, “NO”, at step “606”, which occurs between the use of the neural network other than the neural network classifier, “Using Neural Networks . . . 604”, and use of the neural network classifier, “Using Neural Networks . . . 610”; see also Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes”).
The reasons for obviousness were discussed in regard to the rejection of Claim 1 above and remain applicable here. Additionally, the remaining limitations are substantially the same as the limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 11, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 13, Eledath in view of Bocamazo and Nizamani teach the system of claim 8, wherein the instructions are operable to, when executed, make the second record related to the event available as a basis for the intermediate classification for the event (Eledath, Pg. 16, Col. 11, Ln. 47-50, “The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308”, where, as discussed above, the “feature data 306” is generated based on the second record related to the event, which in turn is used as a basis to obtain the intermediate classification for the event, “interaction data”)
and for the first record related to the event (Eledath, Fig. 3, where the data from the “CAMERA[S] 102” are each within the broadest reasonable interpretation of the first record related to the event, such that the data from any of “CAMERA 102(1)” through “CAMERA 102(N)” can be considered a second record, which is the basis for the collective record, data from the “CAMERA[S] 102” and “ADDITIONAL DATA 310)
from which the first set of unstructured data items are extracted (Eledath, Fig. 3, where the data from one of the “CAMERA[S] 102”, in combination with the “ADDITIONAL DATA 310”, is within the broadest reasonable interpretation of the record related to the event, and where, as discussed above, the first unstructured data items, “FEATURE DATA 306”, are extracted from the record using the “FEATURE-EXTRACTION COMPONENT 116”).
Regarding Claim 14, the additional elements of the dependent claim are substantially the same as the limitations of Claim 7, therefore it is rejected under the same rationale.
Regarding Claim 15, Eledath in view of Bocamazo and Nizamani teach a non-transitory computer readable medium having stored thereon instructions to perform a method . . . (Eledath, Pg. 23, Col. 25, Ln. 37-42, “Embodiments may be provided as a software program or computer program product including a non-transitory computer-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described herein”).
The remaining limitations are substantially the same as the limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 18, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 20, the additional elements of the dependent claim are substantially the same as the limitations of Claim 13, therefore it is rejected under the same rationale.
Regarding Claim 21, Eledath in view of Bocamazo and Nizamani teach the method of claim 1, wherein: the first explicit classifier is configured to provide the initial classification from the event from a set of potential initial classifications (Eledath, Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, from the set of all possible classifier outputs, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”; see also Eledath, Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306” that is used to generate the “INTERACTION DATA 306”);
the neural network classifier is configured to provide the neural network classification for the event from a set of potential neural network classifications (Eledath, Pg. 16, Col. 11, Ln. 54-67, “The interaction data 308 may be provided to the hypothesis-generation component 120 for determining whether the interaction data 308 represents one or more events . . . the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more event”, where the “first classifier” classifies the event, from the set of all possible outputs, using the interaction data, “whether the interaction data 308 represents one or more event”, which, as discussed above, is the intermediate classification that is obtained based on the initial classification of the event, “FEATURE DATA 306”, see Eledath, Fig. 3, which, in view of Bocamazo, is a neural network classifier, see Bocamazo, Abstract, “The system generates, using two or more neural networks, a plurality of predictions”; Bocamazo, Fig. 4; and Bocamazo, Para. [0047], “neural network inferencing may include . . . classification”);
the second explicit classifier is configured to provide the output classification for the event from a set of potential output classifications (Eledath, Pg. 16, Col. 11-12, Ln. 63-5, “the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more events and, if so, a second classifier to parse out the different events and localize the different events. As illustrated, after determining that the interaction data 308 indicates the existence of one or more events, the hypothesis-generation component 120 may generate and output event data 312”, where the “second classifier”, which is within the broadest reasonable interpretation of a second explicit classifier, provides an output classification, from the set of all possible output classifications, in order to generate the “output event data 312”; see also Eledath, Fig. 3; see also Eledath, Pg. 24, Col. 28, Ln. 21-27, “generating, using the first classifier and based at least in part on inputting the feature data to the first classifier, interaction data indicative of a first interaction between the user and the item . . . inputting the interaction data into a second classifier”, where the output of the “second classifier” is based on the output of the “first classifier”); and
the set of potential initial classifications, the set of potential neural network classifications, and the set of potential output classifications each comprise: a classification for approving the request; and a classification for denying the request (Eledath, Abstract, “This disclosure describes systems and techniques for identifying events . . . analyzed by one or more classifiers” and Eledath, Fig. 3, where, as discussed in detail above, the set of potential initial classifications, the set of potential neural network classifications, and the set of potential output classifications are used to classify data, which, in view of Nizamani, each comprises classifications for approving the request and a classification for denying the request, see Nizamani, Abstract, Pg. 347, “an approach that can automatically predict whether a new enhancement report will be approved is beneficial . . . With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected”; see also Eledath, Pg. 17, Col. 13, Ln. 11-18, “At an operation 508, the event-determination component 114 may determine whether the interaction data represents an event . . . If not, then the process 500 returns the operation 502 for continuing to analyze received image data” and Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes”, where denial is through termination of processing or an ultimate unfavorable ranking output, whereas approval is through continued processing or an ultimate favorable ranking).
Regarding Claim 22, Eledath in view of Bocamazo and Nizamani teach the method of claim 21, wherein, in the set of potential initial classifications (Eledath, Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where a first explicit classifier, “one or more classifiers 304”, classifies an event, from the set of all possible classifier outputs, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”; see also Eledath, Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306” that is used to generate the “INTERACTION DATA 306”):
the classification for approving the request is a classification for approving the request after further processing by the neural network classifier and the second explicit classifier; and the classification for denying the request is a classification for denying the request and terminating processing prior to processing by the neural network classifier (Eledath, Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where the first explicit classifier, “one or more classifiers 304”, classifies an event, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”; see also Eledath, Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306” that is used to generate the “INTERACTION DATA 306”, which, in view of Bocamazo and Nizamani, is feature data to determine approval of a request, see Nizamani, Abstract, Pg. 347, “an approach that can automatically predict whether a new enhancement report will be approved is beneficial . . . With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected”, such that an initial classification that provides insufficient features to continue the approval “process” is within the broadest reasonable interpretation of for denying the request and terminating processing prior to processing by the neural network classifier and an initial classification that provides sufficient features to continue the approval “process” is within the broadest reasonable interpretation of a classification for approving the request after further processing by the neural network classifier and the second explicit classifier, see Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes . . . If the tote is identified at block 606, process may move to block 608”; see also Eledath, Fig. 3, where the first explicit classifier is upstream of the other classifiers).
The reasons for obviousness were discussed in regard to the rejection of Claim 1 above and remain applicable here.
Regarding Claim 23, Eledath in view of Bocamazo and Nizamani teach the method of claim 1, wherein the first explicit classifier is an exclusionary rule based classifier (Eledath, Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where the first explicit classifier, “one or more classifiers 304”, classifies an event, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”, which is an exclusionary rule based classifier because it can be a “decision tree”, which uses rules to exclude branched outcome possibilities, see Eledath, Pg. 15, Col. 9, Ln. 12-16, “the feature data may be generated from the image data using one or more trained classifiers, such as artificial neural networks, support vector machines, decision trees, random forests, or the like”; see also Eledath, Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306”),
and wherein the second explicit classifier is a balancing classifier (Eledath, Pg. 16, Col. 11-12, Ln. 63-5, “the hypothesis-generation component 120 may utilize multiple classifiers in some instances, such as a first classifier for determining whether the interaction data 308 represents one or more events and, if so, a second classifier to parse out the different events and localize the different events. As illustrated, after determining that the interaction data 308 indicates the existence of one or more events, the hypothesis-generation component 120 may generate and output event data 312”, where the “second classifier”, is the second explicit classifier, provides an output classification in order to generate the “output event data 312”, which is a balancing classifier because it balances “interaction data 308 indicates the existence of one or more events” and can be a “CNN”, which is trained to balance the influences of multiple data features to output a classification result, see Eledath, Pg. 14, Col. 7, Ln. 50-53, “the hypothesis-generation component 120 may also utilize one or more trained classifiers, such as CNNs, SVMs, or the like, to determine whether the interaction data represents one or more events”; see also Eledath, Fig. 3).
Regarding Claim 24, Eledath in view of Bocamazo and Nizamani teach the system of claim 8, wherein the instructions for terminating the method prior to classifying the event using the neural network classifier based on the initial classification for the event are instructions for terminating the method for prior to classifying the event using the neural network classifier (Eledath, Pg. 17, Col. 13, Ln. 11-18, “At an operation 508, the event-determination component 114 may determine whether the interaction data represents an event . . . If not, then the process 500 returns the operation 502 for continuing to analyze received image data”, where, depending on “whether the interaction data represents an event”, “the process 500 returns the operation 502”, which is within the broadest reasonable interpretation of terminating the method because it ends the classification process for the current “interaction data”, resetting for a new classification method, “returns the operation 502 for continuing to analyze received image data”, which is based on the initial classification for the event, “feature data 306”, because it is used as a basis to obtain the intermediate classification for the event, “interaction data”, see Eledath, Pg. 16, Col. 11, Ln. 47-50, “The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308”, and which, in view of Bocamazo, the terminating occurs prior to classifying the event using the neural network classifier, see Bocamazo, Fig. 6, where the method may terminate, “NO”, at step “606”, which occurs between the use of the neural network other than the neural network classifier, “Using Neural Networks . . . 604”, and use of the neural network classifier, “Using Neural Networks . . . 610”; see also Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes”; see generally Eledath, Pg. 25, Col. 29, Ln. 43-51, “A system comprising: . . . one or more processors; and one or more computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform acts comprising” and Eledath, Pg. 23, Col. 25, Ln. 37-42, “Embodiments may be provided as a software program or computer program product including a non-transitory computer-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described herein”)
when the initial classification for the event is a classification denying the request (Eledath, Pg. 16, Col. 11, Ln. 26-52, “FIG. 3 illustrates an example architecture 300 and flow of data into and within components of the event-determination component 114 . . . the feature-extraction component 114 resides on the event-determination component 114 . . . the feature-extraction component 116 . . . may perform the feature extraction on the cameras 102 themselves . . . the feature-extraction component 116 may include one or more classifiers 304(1), 304(2), . . . , 304(N) for generating feature data 306 using the image data . . . The interaction-detection component 118 may receive the feature data 306 from the one or more classifiers 304(1)-(N) and may use this data to generate interaction data 308. Again, the interaction data 308 may indicate a time range and location of an interaction in the environment between an user and an item”, where the first explicit classifier, “one or more classifiers 304”, classifies an event, data of “an interaction in the environment between an user and an item”, by providing an initial classification for an event, “generating feature data 306”; see also Eledath, Fig. 3, where the first explicit classifier, “CLASSIFIER 304”, classifies event data from the “CAMERA 102” to provide an initial classification of the event, “FEATURE DATA 306” that is used to generate the “INTERACTION DATA 306”, which, in view of Bocamazo and Nizamani, is feature data to determine approval of a request, see Nizamani, Abstract, Pg. 347, “an approach that can automatically predict whether a new enhancement report will be approved is beneficial . . . With the approach, according to their available time, the developers can rank the reports and thus limit the number of reports to evaluate from large collection of low quality enhancement requests that are unlikely to be approved. To this end, we propose a multinomial naive Bayes based approach to automatically predict whether a new enhancement report is likely to be approved or rejected”, such that an initial classification that provides insufficient features to continue the approval “process”, see Bocamazo, Para. [0101], “Specifically, the one or more entities may identify whether tote slots described in conjunction with FIG. 1 include the tote. If no tote is identified at block 606, process 600 may move to block 602 to identify one or more totes, is within the broadest reasonable interpretation of a classification denying the request).
The reasons for obviousness were discussed in regard to the rejection of Claim 1 above and remain applicable here.
Regarding Claim 25, the additional elements of the dependent claim are substantially the same as the limitations of Claim 21, therefore it is rejected under the same rationale.
Regarding Claim 26, the additional elements of the dependent claim are substantially the same as the limitations of Claim 22, therefore it is rejected under the same rationale.
Regarding Claim 27, the additional elements of the dependent claim are substantially the same as the limitations of Claim 23, therefore it is rejected under the same rationale.
Response to Arguments
Applicant's arguments filed on June 16th, 2026 have been fully considered. Each argument is
addressed in detail below.
I. Applicant argues the objections to the claims should be withdrawn (Applicant’s Remarks, 06/16/2026, Pg. 10-11, Section “The Objections Should be Withdrawn”).
Applicant’s amendments to the claims have corrected all issues identified as grounds for objection in the March 16th, 2026 Office Action. As a result, the objections to the claims, as communicated in the March 16th, 2026 Office Action, have been withdrawn.
II. Applicant argues the rejections of the claims under 35 USC § 112 should be withdrawn (Applicant’s Remarks, 06/16/2026, Pg. 11, Section “The Rejections Under 35 U.S.C. § 112 Should be Withdrawn”).
Applicant’s amendments to the claims have corrected all issues identified as grounds for rejection under 35 USC § 112 in the March 16th, 2026 Office Action. As a result, the rejections to the claims under 35 USC § 112, as communicated in the March 16th, 2026 Office Action, have been withdrawn.
III. Applicant argues the rejections of the claims under 35 USC § 101 should be withdrawn (Applicant’s Remarks, 06/16/2026, Pg. 11-14, Section “The Rejections Under 35 U.S.C. § 101 Should be Withdrawn”).
Specifically, Applicant asserts the claims are subject matter eligible because “the neural network and explicit classifiers recited in the independent claims provide improvements to technology sufficient to establish eligibility” and “the independent claims [were amended] to include technical details which were identified during the Interview as tending to indicate subject matter eligibility” (Applicant’s Remarks, Pg. 11-12, Para. 3-2). In support of this assertion, Applicant makes several arguments.
1) First, Applicant argues independent Claim 1 recites eligible subject matter because its recitations of explicit classifiers and a neural network reflect the technological improvement disclosed in the specification of “more robust and efficient” classifications and “provides an improvement to technology which is sufficient to establish that it is directed to a practical application” (Applicant’s Remarks, Pg. 12-13, Para. 2-1).
However, while the specification sets forth the alleged improvement of more robust and efficient classifications, these improvements are asserted in a conclusory manner and without the detail necessary to be apparent to a person of ordinary skill in the art as to how the disclosure results in the asserted improvements (see MPEP 2106.04(d)(1)). Additionally, the claims recite limitations, such as staged use of classifiers and neural network classification, that have broad applicability across many fields of endeavor (see MPEP 2106.05(f)), such as to fail to reflect the alleged improvements (see MPEP 2106.04(d)(1)).
As a result, the argument is not persuasive.
2) Second, Applicant argues independent Claim 1 recites eligible subject matter because “claim 1 has been amended to recite that classifying the event using the neural network classifier comprises obtaining an intermediate classification and providing it, along with sentiments and confidence values to input nodes of the neural network classifier. Claim 1 has also been amended to recite details of the neural network classifier such as its use of dense layers along with node values provided by application of an activation function as described in paragraphs 15-16 of the application as filed. The applicant submits that these amendments should be seen as sufficient to establish the eligibility of claim 1, based both on the discussion during the Interview, as well as on the fact that those amendments specify particular machines e.g., the recurrent or transformer neural network which is recited as being used for sentiment extraction, as well as the neural network classifier having the characteristics which have been added to claim 1) which would be used in performing the method of claim 1” (Applicant’s Remarks, Pg. 13, Para. 2). However, similar to the elements discussed in regard to 1), the amendments to claim 1 fail to integrate the abstract ideas into a practical application because the amendments amount to recitations of generic computer components that have broad applicability across many fields of endeavor (see MPEP 2106.05(f)).
As a result, the argument is not persuasive.
3) Third, Applicant argues independent Claim 8 and independent Claim 15 recite eligible subject matter for substantially the same reasons as discussed in regard to the subject matter eligibility of independent Claim 1 (Applicant’s Remarks, Pg. 13, Para. 3). However, as discussed above, the arguments in favor of the subject matter eligibility for Claim 1 were not persuasive.
As a result, the argument is not persuasive.
4) Fourth, Applicant argues the dependent claims recite eligible subject matter because each depends on one of the independent claims, which Applicant argues are subject matter eligible (Applicant’s Remarks, Pg. 14, Para. 1). However, as discussed above, the arguments in favor of the subject matter eligibility for the independent claims were not persuasive.
As a result, the argument is not persuasive.
IV. Applicant argues the rejections of the claims under 35 USC § 103 should be withdrawn (Applicant’s Remarks, 06/16/2026, Pg. 14-17, Section “The Rejections Under 35 U.S.C. § 103 Should be Withdrawn”).
In response to Applicant’s amendments, the previously communicated rejections under 35 U.S.C. § 103, have been withdrawn. However, Applicants arguments are not persuasive in light of the new grounds for rejection, under 35 U.S.C. § 103, discussed in detail above. The new grounds of rejection rely on new combinations of the existing prior art of record and new prior art of record to teach the new combinations of elements in the amended claims, which were not presented in these arrangements in any of the previously presented claims. As a result, Applicant arguments against the previously communicated rejections under 35 U.S.C. § 103 are rendered moot.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW BRYCE GOLAN whose telephone number is (571)272-5159. The examiner can normally be reached Monday through Friday, 8:00 AM to 5:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW BRYCE GOLAN/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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