DETAILED ACTION
This Non-Final Office Action is in response to the originally filed specification and claims [June 28, 2024].
Claims 1-20 are currently pending and have been considered above.
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 1-20 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 1-20 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 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); generating an asset service action training data set based at least in part on the ingested asset data; 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 training in paragraphs [32-35 and 68-71]. The training elements are described as tools to implement the abstract idea above. There is no specific modeling training technique or improvement to the technology of training described. As such, the training step is 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); generating an asset service action training data set based at least in part on the ingested asset data; 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 training in paragraphs [32-35 and 68-71]. The training elements are described as tools to implement the abstract idea above. There is no specific modeling training technique or improvement to the technology of training described. As such, the training step is 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 ingesting the asset data according to the unified data model comprises: generating 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 ingested asset data 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 7-9, and 16-18 are further describing additional elements beyond those identified above. The claims are directed towards, “wherein the one or more data stores include 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 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).
The claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-20 are rejected under 35 USC 101 rejection as being directed towards non-eligible subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-12, and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou et al [2024/0144052], hereafter Zhou.
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).);
ingesting 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; generating an asset service action training data set based at least in part on the ingested asset data (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.).
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 ingested asset data 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 7, Zhou further teaches the method of claim 1, wherein the one or more data stores include 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 (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 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).);
ingest 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; generate an asset service action training data set based at least in part on the ingested asset data (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.).
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 predict 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 ingested asset data 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 16, Zhou further teaches the apparatus of claim 10, wherein the one or more data stores include 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 (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 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).);
ingest 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; generate an asset service action training data set based at least in part on the ingested asset data (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.).
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 predict 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.).
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) 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 Sopko, III et al [2008/0306799], hereafter Sopko.
Regarding claim 4, Zhou further teaches the method of claim 1, wherein ingesting the asset data according to the unified data model comprises: generating 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.),
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, to ingest the asset data according to the unified data model, the one or more processors are individually or collectively operable to execute the code to cause the apparatus to: generate 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.),
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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kanso et al [2022/0156631] (product end-of-life prediction modeling);
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
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