DETAILED ACTION
This Final Office Action is in response to the arguments and amendments filed July 16, 2026.
Claims 1, 4, 6, 10, 11, 13, 15, 19, and 20 have been amended.
Claims 7 and 16 have been cancelled.
Claims 21 and 22 are newly added.
Claims 1-6, 8-15, and 17-22 are currently pending and have been considered below.
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 1-6, 8-15, and 17-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards non-eligible subject matter.
In terms of Step 1, claims 1-6, 8-15, and 17-22 are directed towards one of the four categories of statutory subject matter.
In terms of Step 2(a)(1), independent claims 1, 10, and 19 (represented by claim 1) are directed towards, “A method for data processing, comprising: receiving first user input comprising a request to generate a model for predicting future asset service actions for one or more asset types; receiving, at a user interface, one or more second user inputs that connect asset data of one or more data stores to a unified data model associated with asset service action prediction, wherein the asset data indicates a history of previous service actions of the one or more asset types; ingesting, by an asset management model, the asset data from the one or more data stores based at least in part on the one or more second user inputs that connect the asset data to the unified data model”. The claims are describing a maintenance modeling system that is based on asset service data to predict service actions. The claims are providing a model based on history of previous actions that falls into the subcategory of business relation/commercial activity. As such, the independent claims are directed towards an abstract idea under the certain method of organizing human activity grouping.
Further, the claims are describing a collection of data, high level analyzing, and displaying the results (ingesting asset data). A person would be able to provide and request a model to determine asset modeling and providing asset prediction based on the collected data. This is further considered based on the limitations directed towards a user input request for the model and user input in terms of the connect data to a unified model. As such, the claims are further describing an additional abstract idea under the mental process grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of claims 1, 10, and 19 are, “An apparatus for data processing, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to (claim 10); A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to (claim 19); wherein the one or more data stores include at least a first data store comprising a first data organization model and a second data store comprising a second data organization model different from the first data organization model; wherein ingesting the asset data from the one or more data stores comprises performing a batch transformation that transforms the asset data from the first data organization model and the second data organization model into one or more asset service action data objects in accordance with the unified data model; generating, by the asset management model, an asset service action training data set based at least in part on the ingested asset data one or more asset service action data objects; and training the model to predict future asset service actions based at least in part on the asset service action training data set”. The additional elements are described in the originally filed specification [14, 29, and 67-72]. The computer elements are merely described as generic technology to implement the abstract idea. There is no technical improvement with respect to the computer, processor, and other technical aspects provided. In terms of the training, the originally filed specification describes the model and model training in paragraphs [32-38 and 68-71]. The training elements are described as tools to implement the abstract idea above. There is no specific modeling or training technique or improvement to the technology of training described. As such, the modeling and training steps are merely implementing the abstract idea using generic technology. Therefore, the additional elements are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract idea. The additional elements of claims 1, 10, and 19 are, “An apparatus for data processing, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to (claim 10); A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to (claim 19); wherein the one or more data stores include at least a first data store comprising a first data organization model and a second data store comprising a second data organization model different from the first data organization model; wherein ingesting the asset data from the one or more data stores comprises performing a batch transformation that transforms the asset data from the first data organization model and the second data organization model into one or more asset service action data objects in accordance with the unified data model; generating, by the asset management model, an asset service action training data set based at least in part on the ingested asset data one or more asset service action data objects; and training the model to predict future asset service actions based at least in part on the asset service action training data set”. The additional elements are described in the originally filed specification [14, 29, and 67-72]. The computer elements are merely described as generic technology to implement the abstract idea. There is no technical improvement with respect to the computer, processor, and other technical aspects provided. In terms of the training, the originally filed specification describes the model and model training in paragraphs [32-38 and 68-71]. The training elements are described as tools to implement the abstract idea above. There is no specific modeling or training technique or improvement to the technology of training described. As such, the modeling and training steps are merely implementing the abstract idea using generic technology. Therefore, the additional elements are not significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2-6, 8, 11-15, 17, and 20 are directed towards further aspects of the identified abstract idea and not describing additional elements beyond those identified above. The claims are directed towards, “further comprising: receiving information associated with an asset service action for an asset type of the one or more asset types; and providing, via the model for predicting future asset service actions, one or more recommendations for one or more future asset service actions for the asset type based at least in part on training the model and the information”, “wherein providing one or more recommendations for one or more future asset service actions comprises: assigning a relative likelihood score to each of the one or more future asset service actions based at least in part on training the model and the information”, “wherein the one or more asset service action data objects comprise one or more asset service action instances for each of the one or more asset types based at least in part on the unified data model, wherein each asset service action instance corresponds to an asset service action of the asset data, and wherein each asset service action instance comprises an asset type field, a service type field, and a service date field.”, “wherein each asset service action instance further comprises a parent asset field, one or more product identifier fields, a start time field associated with the asset service action, an end time field associated with the asset service action, a date of asset installation field, a geographic location field, an asset service status field, one or more asset usage fields, or any combination thereof”, and “wherein generating the asset service action training data set comprises: transforming the one or more asset service action data objects into a plurality of entries, wherein each entry of the plurality of entries comprises an asset type field, an asset service action type field, a next asset service action type field, and a binary prediction field, wherein the model is trained based at least in part on the plurality of entries”. The claims are further describing the mental process aspect in terms of the high level analysis providing elements of labeling or describing the data within the modeling, providing the service action that a person would accomplish based on the output, and describing the analysis in terms of the recommendation/prediction. These further describe the commercial/business relation in terms of the maintenance service provided and analyzed. The claims are not describing additional elements that are transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 8-9, and 17-18 are further describing additional elements beyond those identified above. The claims are directed towards, “wherein receiving the one or more second user inputs comprises: receiving an indication that a data object of the one or more data stores is to be ingested as one or more predefined fields of the unified data model” and “further comprising: providing, in response to training the model, a second user interface that comprises one or more indications of one or more metrics associated with training the model”. The claims are further describing aspects of a data store and user interface aspects. The interface and data store are described in the originally filed specification [22-26 and 110-114]. The additional elements are merely described as generic technology to implement the abstract idea. The data store and interface elements are not directed towards a technical improvement. As such, the claims are not describing additional elements that are transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 21 and 22 are directed towards further additional elements beyond those identified above. The claims are directed towards, “wherein the batch transformation includes at least one of normalizing the asset data from the one or more data stores, de-normalizing the asset data from the one or more data stores, or cleansing the asset data from the one or more data stores to generate the one or more asset service action data objects”. The claims are describing transformation techniques in terms of cleansing, normalization, and de-normalization. The specification describes the techniques in paragraph [42]. The additional elements are merely utilizing generic technology as data transformation techniques to implement the abstract idea. The transformations are merely tools and not describing improvements to the technology of the techniques themselves. As such, the claims are not directed towards additional elements that are significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
The claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-6, 8-15, and 17-22 are rejected under 35 USC 101 rejection as being directed towards non-eligible subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6-12, and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al [2024/0144052], hereafter Zhou, in view of Siebel et al [2017/0006135], hereafter Siebel.
Regarding claim 1, Zhou teaches a method for data processing, comprising: receiving first user input comprising a request to generate a model for predicting future asset service actions for one or more asset types; receiving, at a user interface, one or more second user inputs that connect asset data of one or more data stores to a unified data model associated with asset service action prediction, wherein the asset data indicates a history of previous service actions of the one or more asset types (Fig 4B, 11, and paragraphs [85-89]; Zhou teaches a machine learning system that is based on user request for a model (interpreted as first input), user selection of an objective (interpreted as second input), to provide device service prediction elements (failure, slow down, etc).);
generating, by the asset management model, an asset service action training data set based at least in part on the one or more asset service action data objects (Fig 4B, 11, and paragraphs [86-93 and 106-110]; Zhou teaches ingesting data in to the ML model based on the selected objective and then providing a service action in terms of scheduling, maintenance, and other service actions based on the data set output from the machine learning model.); and
training the model to predict future asset service actions based at least in part on the asset service action training data set (Fig 11, 13, and paragraphs [92-95 and 102-103]; Zhou discloses training the model and updating the model based on the event feedback.).
Zhou teaches the above-enclosed limitations in terms of asset service modeling that includes service actions and training, however, Zhou does not specifically teach data organization models and batch transformations;
Siebel teaches wherein the one or more data stores include at least a first data store comprising a first data organization model and a second data store comprising a second data organization model different from the first data organization model; ingesting, by an asset management model, the asset data from the one or more data stores based at least in part on the one or more second user inputs that connect the asset data to the unified data model, wherein ingesting the asset data from the one or more data stores comprises performing a batch transformation that transforms the asset data from the first data organization model and the second data organization model into one or more asset service action data objects in accordance with the unified data model (Fig 7 and paragraphs [211-216 and 229-233]; Siebel teaches a similar modeling system that specifically provides elements of data organization models including batch transformations and different data models to provide predictive and other analytical tools within a platform. Within the combination, Zhou provides the prediction analysis models and Siebel provides the batch transformation and unified model structural elements for data organization.).
Zhou provides an analysis platform based on different data and modeling elements, however, Zhou does not specifically teach the unified model and batch transformation for data organization.
Siebel teaches a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the analysis platform based on different data and modeling elements of Zhou the ability to include a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation as taught by Siebel since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 2, Zhou further teaches the method of claim 1, further comprising: receiving information associated with an asset service action for an asset type of the one or more asset types; and providing, via the model for predicting future asset service actions, one or more recommendations for one or more future asset service actions for the asset type based at least in part on training the model and the information (Figs 11, 13, and paragraphs [56-63 and 101-106]; Zhou teaches the device maintenance output recommendation based on the trained model utilizing the user input objective and other data.).
Regarding claim 3, Zhou further teaches the method of claim 2, wherein providing one or more recommendations for one or more future asset service actions comprises: assigning a relative likelihood score to each of the one or more future asset service actions based at least in part on training the model and the information (Paragraphs [58-65 and 102-107]; Zhou teaches a risk score based on the trained ML model.).
Regarding claim 6, Zhou further teaches the method of claim 1, wherein generating the asset service action training data set comprises: transforming the one or more asset service action data objects into a plurality of entries, wherein each entry of the plurality of entries comprises an asset type field, an asset service action type field, a next asset service action type field, and a binary prediction field, wherein the model is trained based at least in part on the plurality of entries (Paragraphs [62-66 and 87-95]; Zhou teaches the ingested asset data that includes type, action, prediction/risk threshold, and other asset fields that are utilized in training the machine learning model.).
Regarding claim 8, Zhou further teaches the method of claim 1, wherein receiving the one or more second user inputs comprises: receiving an indication that a data object of the one or more data stores is to be ingested as one or more predefined fields of the unified data model (Fig 2, 3, 4A, and paragraphs [67-75]; Zhou discloses several optimization modeling elements that are in a data store that are provided to the user for selection in the analysis.).
Regarding claim 9, Zhou further teaches the method of claim 1, further comprising: providing, in response to training the model, a second user interface that comprises one or more indications of one or more metrics associated with training the model (Fig 1 and paragraphs [95-96]; Zhou discloses providing a user interface to provide feedback for training the model.).
Regarding claim 10, Zhou discloses an apparatus for data processing, comprising: one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to (Fig 1 and paragraphs [35-39]; Zhou teaches the system elements to operate the prediction modeling.):
receive first user input comprising a request to generate a model for predicting future asset service actions for one or more asset types; receive, at a user interface, one or more second user inputs that connect asset data of one or more data stores to a unified data model associated with asset service action prediction, wherein the asset data indicates a history of previous service actions of the one or more asset types (Fig 4B, 11, and paragraphs [85-89]; Zhou teaches a machine learning system that is based on user request for a model (interpreted as first input), user selection of an objective (interpreted as second input), to provide device service prediction elements (failure, slow down, etc).);
generate, by the asset management model, an asset service action training data set based at least in part on the one or more asset service action data objects (Fig 4B, 11, and paragraphs [86-93 and 106-110]; Zhou teaches ingesting data in to the ML model based on the selected objective and then providing a service action in terms of scheduling, maintenance, and other service actions based on the data set output from the machine learning model.); and
train the model to predict future asset service actions based at least in part on the asset service action training data set (Fig 11, 13, and paragraphs [92-95 and 102-103]; Zhou discloses training the model and updating the model based on the event feedback.).
Zhou teaches the above-enclosed limitations in terms of asset service modeling that includes service actions and training, however, Zhou does not specifically teach data organization models and batch transformations;
Siebel teaches wherein the one or more data stores include at least a first data store comprising a first data organization model and a second data store comprising a second data organization model different from the first data organization model; ingest, by an asset management model, the asset data from the one or more data stores based at least in part on the one or more second user inputs that connect the asset data to the unified data model, wherein ingesting the asset data from the one or more data stores comprises performing a batch transformation that transforms the asset data from the first data organization model and the second data organization model into one or more asset service action data objects in accordance with the unified data model (Fig 7 and paragraphs [211-216 and 229-233]; Siebel teaches a similar modeling system that specifically provides elements of data organization models including batch transformations and different data models to provide predictive and other analytical tools within a platform. Within the combination, Zhou provides the prediction analysis models and Siebel provides the batch transformation and unified model structural elements for data organization.).
Zhou provides an analysis platform based on different data and modeling elements, however, Zhou does not specifically teach the unified model and batch transformation for data organization.
Siebel teaches a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the analysis platform based on different data and modeling elements of Zhou the ability to include a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation as taught by Siebel since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 11, Zhou further teaches the apparatus of claim 10, wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to: receive information associated with an asset service action for an asset type of the one or more asset types; and provide, via the model for predicting future asset service actions, one or more recommendations for one or more future asset service actions for the asset type based at least in part on training the model and the information (Figs 11, 13, and paragraphs [56-63 and 101-106]; Zhou teaches the device maintenance output recommendation based on the trained model utilizing the user input objective and other data.).
Regarding claim 12, Zhou further teaches the apparatus of claim 11, wherein, to provide one or more recommendations for one or more future asset service actions, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: assign a relative likelihood score to each of the one or more future asset service actions based at least in part on training the model and the information (Paragraphs [58-65 and 102-107]; Zhou teaches a risk score based on the trained ML model.).
Regarding claim 15, Zhou further teaches the apparatus of claim 10, wherein, to generate the asset service action training data set, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: transform the one or more asset service action data objects into a plurality of entries, wherein each entry of the plurality of entries comprises an asset type field, an asset service action type field, a next asset service action type field, and a binary prediction field, wherein the model is trained based at least in part on the plurality of entries (Paragraphs [62-66 and 87-95]; Zhou teaches the ingested asset data that includes type, action, prediction/risk threshold, and other asset fields that are utilized in training the machine learning model.).
Regarding claim 17, Zhou further teaches the apparatus of claim 10, wherein, to receive the one or more second user inputs, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: receive an indication that a data object of the one or more data stores is to be ingested as one or more predefined fields of the unified data model (Fig 2, 3, 4A, and paragraphs [67-75]; Zhou discloses several optimization modeling elements that are in a data store that are provided to the user for selection in the analysis.).
Regarding claim 18, Zhou further discloses the apparatus of claim 10, wherein the one or more processors are individually or collectively further operable to execute the code to cause the apparatus to: provide, in response to training the model, a second user interface that comprises one or more indications of one or more metrics associated with training the model (Fig 1 and paragraphs [95-96]; Zhou discloses providing a user interface to provide feedback for training the model.).
Regarding claim 19, Zhou discloses a non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to (Fig 1 and paragraphs [35-39]; Zhou teaches the system elements to operate the prediction modeling.):
receive first user input comprising a request to generate a model for predicting future asset service actions for one or more asset types; receive, at a user interface, one or more second user inputs that connect asset data of one or more data stores to a unified data model associated with asset service action prediction, wherein the asset data indicates a history of previous service actions of the one or more asset types (Fig 4B, 11, and paragraphs [85-89]; Zhou teaches a machine learning system that is based on user request for a model (interpreted as first input), user selection of an objective (interpreted as second input), to provide device service prediction elements (failure, slow down, etc).);
generate, by the asset management model, an asset service action training data set based at least in part on the one or more asset service action data objects (Fig 4B, 11, and paragraphs [86-93 and 106-110]; Zhou teaches ingesting data in to the ML model based on the selected objective and then providing a service action in terms of scheduling, maintenance, and other service actions based on the data set output from the machine learning model.); and
train the model to predict future asset service actions based at least in part on the asset service action training data set (Fig 11, 13, and paragraphs [92-95 and 102-103]; Zhou discloses training the model and updating the model based on the event feedback.).
Siebel teaches wherein the one or more data stores include at least a first data store comprising a first data organization model and a second data store comprising a second data organization model different from the first data organization model; ingest, by an asset management model, the asset data from the one or more data stores based at least in part on the one or more second user inputs that connect the asset data to the unified data model, wherein ingesting the asset data from the one or more data stores comprises performing a batch transformation that transforms the asset data from the first data organization model and the second data organization model into one or more asset service action data objects in accordance with the unified data model (Fig 7 and paragraphs [211-216 and 229-233]; Siebel teaches a similar modeling system that specifically provides elements of data organization models including batch transformations and different data models to provide predictive and other analytical tools within a platform. Within the combination, Zhou provides the prediction analysis models and Siebel provides the batch transformation and unified model structural elements for data organization.).
Zhou provides an analysis platform based on different data and modeling elements, however, Zhou does not specifically teach the unified model and batch transformation for data organization.
Siebel teaches a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the analysis platform based on different data and modeling elements of Zhou the ability to include a similar modeling analysis platform that specifically provides the structure and architecture to provide data organization modeling with a unified model and batch transformation as taught by Siebel since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 20, Zhou further teaches the non-transitory computer-readable medium of claim 19, wherein the instructions are further executable by the one or more processors to: receive information associated with an asset service action for an asset type of the one or more asset types; and provide, via the model for predicting future asset service actions, one or more recommendations for one or more future asset service actions for the asset type based at least in part on training the model and the information (Figs 11, 13, and paragraphs [56-63 and 101-106]; Zhou teaches the device maintenance output recommendation based on the trained model utilizing the user input objective and other data.).
Regarding claim 21, the combination teaches the above-enclosed limitations of the method of claim 1,
Zhou further teaches wherein the batch transformation includes at least one of normalizing the asset data from the one or more data stores, de-normalizing the asset data from the one or more data stores, or cleansing the asset data from the one or more data stores to generate the one or more asset service action data objects (Paragraph [62]; Zhou teaches a pre-processing technique of cleansing the data before providing the analysis. Within the combination, Siebel teaches the batch transformation and Zhou teaches the specific technique of cleansing data before providing to an ML analysis. Further, Siebel teaches normalization for the large data sets in the datastore [229-230 and 387-389].).
Regarding claim 22, the combination teaches the above-enclosed limitations of the apparatus of claim 10,
Zhou further discloses wherein the batch transformation includes at least one of normalizing the asset data from the one or more data stores, de- normalizing the asset data from the one or more data stores, or cleansing the asset data from the one or more data stores to generate the one or more asset service action data objects (Paragraph [62]; Zhou teaches a pre-processing technique of cleansing the data before providing the analysis. Within the combination, Siebel teaches the batch transformation and Zhou teaches the specific technique of cleansing data before providing to an ML analysis. Further, Siebel teaches normalization for the large data sets in the datastore [229-230 and 387-389].).
Claim(s) 4, 5, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al [2024/0144052], hereafter Zhou, in view of Siebel et al [2017/0006135], hereafter Siebel, further in view of Sopko, III et al [2008/0306799], hereafter Sopko.
Regarding claim 4, Zhou further teaches the method of claim 1, wherein the one or more asset service action data objects comprise one or more asset service action instances for each of the one or more asset types based at least in part on the unified data model, wherein each asset service action instance corresponds to an asset service action of the asset data (Paragraphs [58-63 and 102-106]; Zhou discloses service actions based on the trained model. The service action is described as inspection, maintenance, and other elements for the asset. The combination with respect to Siebel teaches the unified model and Zhou provides the service actions based on a trained model for the asset data.),
Zhou discloses the above-enclosed limitations of the service action prediction generation based on the model, however, Zhou does not specifically teach the field instances.
Sopko teaches wherein each asset service action instance comprises an asset type field, a service type field, and a service date field (Figs 6, 8, and paragraphs [50-55 and 62-66]; Sopko teaches a similar prediction maintenance system that provides action instances that includes asset type, service type, dates, timeliness, and other aspects.).
Zhou discloses a maintenance prediction modeling system that provides asset service actions based on the output, however, Zhou does not specifically teach type fields.
Sopko teaches a similar maintenance prediction service action system that specifically teaches the instance data field elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the maintenance prediction modeling system that provides asset service actions based on the output of Zhou to include a similar maintenance prediction service action system that specifically teaches the instance data field elements as taught by Sopko since the claimed invention is merely a combination of prior art elements and in the combination each element would have been performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 5, the combination teaches the above-enclosed limitations of the method of claim 4,
Sopko further teaches wherein each asset service action instance further comprises a parent asset field, one or more product identifier fields, a start time field associated with the asset service action, an end time field associated with the asset service action, a date of asset installation field, a geographic location field, an asset service status field, one or more asset usage fields, or any combination thereof (Figs 6, 8, and paragraphs [50-55 and 62-66]; Sopko teaches an asset service action instance that provides action status, usage, type, progress, scope, and other data fields.).
Zhou discloses a maintenance prediction modeling system that provides asset service actions based on the output, however, Zhou does not specifically teach type fields.
Sopko teaches a similar maintenance prediction service action system that specifically teaches the instance data field elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the maintenance prediction modeling system that provides asset service actions based on the output of Zhou to include a similar maintenance prediction service action system that specifically teaches the instance data field elements as taught by Sopko since the claimed invention is merely a combination of prior art elements and in the combination each element would have been performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 13, Zhou further discloses the apparatus of claim 10, wherein the one or more asset service action data objects comprise one or more asset service action instances for each of the one or more asset types based at least in part on the unified data model, wherein each asset service action instance corresponds to an asset service action of the asset data (Paragraphs [58-63 and 102-106]; Zhou discloses service actions based on the trained model. The service action is described as inspection, maintenance, and other elements for the asset. The combination with respect to Siebel teaches the unified model and Zhou provides the service actions based on a trained model for the asset data.),
Zhou discloses the above-enclosed limitations of the service action prediction generation based on the model, however, Zhou does not specifically teach the field instances.
Sopko teaches wherein each asset service action instance comprises an asset type field, a service type field, and a service date field (Figs 6, 8, and paragraphs [50-55 and 62-66]; Sopko teaches a similar prediction maintenance system that provides action instances that includes asset type, service type, dates, timeliness, and other aspects.).
Zhou discloses a maintenance prediction modeling system that provides asset service actions based on the output, however, Zhou does not specifically teach type fields.
Sopko teaches a similar maintenance prediction service action system that specifically teaches the instance data field elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the maintenance prediction modeling system that provides asset service actions based on the output of Zhou to include a similar maintenance prediction service action system that specifically teaches the instance data field elements as taught by Sopko since the claimed invention is merely a combination of prior art elements and in the combination each element would have been performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 14, The combination teaches the above-enclosed limitations of the apparatus of claim 13,
Sopko teaches wherein each asset service action instance further comprises a parent asset field, one or more product identifier fields, a start time field associated with the asset service action, an end time field associated with the asset service action, a date of asset installation field, a geographic location field, an asset service status field, one or more asset usage fields, or any combination thereof (Figs 6, 8, and paragraphs [50-55 and 62-66]; Sopko teaches an asset service action instance that provides action status, usage, type, progress, scope, and other data fields.).
Zhou discloses a maintenance prediction modeling system that provides asset service actions based on the output, however, Zhou does not specifically teach type fields.
Sopko teaches a similar maintenance prediction service action system that specifically teaches the instance data field elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the maintenance prediction modeling system that provides asset service actions based on the output of Zhou to include a similar maintenance prediction service action system that specifically teaches the instance data field elements as taught by Sopko since the claimed invention is merely a combination of prior art elements and in the combination each element would have been performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Response to Arguments
In response to the arguments filed July 16, 2026 on pages 9-12 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments allege that the claims, specifically the amended claim language regarding the data transformations and model training, provide eligible subject matter in terms of being transformative into a practical application. The arguments specifically discuss and point to the specification with respect to the type of user that provides a user-friendly and no-code user experience for ingesting data. The claims are not providing that level of detail and further describing a type of user is not indicative of providing a technical improvement. There is no relationship or description within the claims in terms of the level or competence of the user providing the data ingestion. The claims, specifically towards the amended claim limitations, describe ingesting data and providing data organization models that are utilized in training the unified model. The model and training steps were considered as additional elements and, based on the specification, determined to be generic technology. The ingesting and batch transformation techniques alongside the modeling and training are described as tools to implement the asset prediction modeling abstract idea. The additional elements are not describing technical improvements to the technology and as such the claims are not directed towards additional elements that are transformative into a practical application or significantly more. As such, claims 1, 10, and 19 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
Lacking any further arguments, claims 1-6, 8-15, and 17-22 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
In response to the arguments filed July 16, 2026 on pages 12-16 regarding the prior art rejections, specifically that the prior art does not teach the claimed invention.
Examiner respectfully disagrees.
The arguments allege that the previously cited prior art, specifically Zhou, does not teach the amended claim language regarding the ingesting and unified data model, alongside the batch transformation elements. Based on further search and consideration, newly added prior art Siebel is utilized to teach in combination with Zhou the elements of the unified model, batch transformation, and ingesting data organization elements. Within the combination, Zhou teaches the specific analysis for providing asset prediction and Siebel teaches the architecture and modeling elements for specific data organization modeling with batch transformation and ingesting steps for a unified model. As such, claims 1, 10, and 19 have been further considered and maintaining the prior art rejection under 35 USC 103, as per the amended claim language above.
Lacking any further arguments, claims 1-6, 8-15, and 17-22 are maintaining the 35 USC 103 rejection, as considered above in light of the amended and newly added claim limitations.
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 ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
