DETAILED ACTION
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 .
Status of Claims
This action is responsive to the application filed on September 1, 2024. Claims 1-5 were presented, and are pending examination.
Drawings
The drawings filed on September 1, 2024 are accepted.
Specification
The abstract of the disclosure is objected to because it exceeds the recommend length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1 and 5 are objected to because of the following informalities:
“AI”, recited in line 1 of claim 1, should be replaced with “Artificial Intelligence (AI)”.
“IoT”, recited in line 3 of claim 1, should be replaced with “Internet-of-Things (IoT)”.
“ML”, recited in line 21 of claim 1, should be replaced with “machine leaning (ML)”.
“ai”, recited in line 1 of claim 5, should be replaced with “Artificial Intelligence (AI)”.
“IoT”, recited in line 3 of claim 5, should be replaced with “Internet-of-Things (IoT)”.
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-5 are provisionally rejected on the ground of nonstatutory anticipatory type double patenting as being unpatentable over claims 1-5 of copending Application No. 18/822206.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Differences between the claims are indicated in italics, and subject matter which is worded differently, but recites the same or similar concepts are shown in bold. It should be noted that when differences are shown in italics, the instant claims have been recited in a broader manner, leaving out some details of the steps. Sometimes two of the instant claims together are the same subject matter of one of the patent claims, or the opposite is true. Finally, the underlining indicates that the limitation is found in the instant application and the patented application.
Instant Application No. 18/822,239
Copending Application No. 18/822,206
Claim 1:
A system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation; receiving a user query; initiating a just-in-time process to find an answer to the user query; communicating the question to an AI agent in the server computing device; with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps, and executing the multiple steps until the goal is reached.
Claim 1:
A system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation; receiving a user query; initiating a just-in-time process to find an answer to the user query; communicating the question to an AI agent in the server computing device; with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps, and executing the multiple steps until the goal is reached.
Claim 2:
wherein the AI agent uses a plurality of internal and external services to answer the question including a specific just-in-time training of the raw tracking data with a specified ML training methodology.
Claim 2:
wherein the AI agent uses a plurality of internal and external services to answer the question including a specific just-in-time training of the raw tracking data with a specified ML training methodology.
Claim 3:
wherein specific ML training methodology and ML algorithm are selected just-in-time for the AI agent to answer the user query with a higher precision.
Claim 3:
wherein specific ML training methodology and ML algorithm are selected just-in-time for the AI agent to answer the user query with a higher precision.
Claim 4:
wherein the AI agent runs the raw tracking data through multiple pre-trained models and selects an answer that best fits the user query.
Claim 4:
wherein the AI agent runs the raw tracking data through multiple pre-trained models and selects an answer that best fits the user query.
Claim 5:
A system for auto-tracking with just-in-time training and goal seeking ai agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: asset tracker provides data to a cloud-based server at preset intervals; storing the data for future use; based on a user query, with a tracking system of the server computing device accessing the data and applying the data to a machine learning mode; and creating and training a machine-learning (ML) model on the fly based on the query to provide an insight to the user related to the user query.
Claim 5:
A system for auto-tracking with just-in-time training and goal seeking ai agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: asset tracker provides data to a cloud-based server at preset intervals; storing the data for future use; based on a user query, with a tracking system of the server computing device accessing the data and applying the data to a machine learning mode; and creating and training a machine-learning (ML) model on the fly based on the query to provide an insight to the user related to the user query.
Examiner’s Note about the Format of 35 U.S.C. 102/103 Rejections
Generally, limitations of a claim are reproduced identically and followed by examiner’s explanation with citation from prior art in Italic enclosed by a parenthesis, (), for each limitation. In examiner’s explanation, the mapping of the key elements of a limitation to the disclosed elements of prior art is shown by stating the disclosed element immediately followed by the claimed element inside a parenthesis. Specific quotation from prior art is delineated with quotation mark, ““. If primary art fails to teach a limitation or part of the limitation, the limitation or the part of the limitation is placed inside double square brackets, [[ ]], for better understandability, and appropriate secondary art(s) is/are applied later addressing the deficiency of the primary art.
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.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nagaraju et al. (US PGPUB No. US 20180032908 A1), hereinafter, Nagaraju.
Regarding claim 1:
Nagaraju teaches:
A system for auto-tracking with just-in-time training and goal seeking AI agents comprising (Fig. 1 shows a system 10. Paragraph 0040 discloses providing real-time answer using artificial intelligence):
a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data (Fig. 3 shows plurality of forwarder 30 (asset trackers) associated with data sources 32. Paragraph 0059 discloses a data source 32 is an edge device 12 as stated “An example of the data source 32 includes the data generated by sensors of the edge devices 12.”. Paragraph 0026 discloses the edge device 12 is IoT device as stated “Examples of an edge device include a mobile device and an IoT device.”);
one or more communications hubs; a base station; one or more communication networks (Fig. 1 shows network 16. Paragraph 0053 discloses the network 16 is cellular network. Cellular network includes base station and other communication nodes.);
wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs (Fig. 1 shows the forwarder 30 communicates with other nodes using the network 16 which is cellular network comprising base station);
wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network (Fig. 1 shows other client device 26 communicates with other nodes via the network 16. Paragraph 0088 discloses the client device 26 is smartphone);
wherein the base station is configured to be in communication with the plurality of asset trackers (Fig. 1 shows the forwarder 30 communication with other nodes using the network 16 which is cellular network comprising base station);
a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic (Fig. 1 shows server computer system 14 (server computing device) in communication with the network 16):
wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device computed data, storing the raw data of the one or more asset trackers in the server computing device without analytical computation (paragraph 0058 discloses the forwarder 30 located in the edge devices 12 and indexer 34 is located in the server computing system 14 as stated “For example, the forwarders 30 could be located at the edge devices 12, and the indexers could be located at the server computer system 14.”. Paragraph 0060 discloses the forwarder 30 forwards raw data to the indexer 34 as stated “During operation, the forwarders 30 can identify which indexers 34 should receive data collected from the data sources 32 and forward the data to the appropriate indexers 34.”. Paragraph 0076 discloses storing the data in the data store as stated “In step 418, the indexer stores the events with an associated timestamp in a data store.”. As shown Fig. 4, the forwarder 30 , located in the edge device 12, does not perform any analytic function on the raw data);
receiving a user query (paragraph 0079 discloses receiving a query from a user device as stated “In step 502, a search head receives a search query from another device”);
initiating a just-in-time process to find an answer to the user query (Fig. 5, step 504, shows initiating the steps answer the query);
communicating the question to an AI agent in the server computing device (Fig. 5, steps 504, discloses search head (AI agent) analyzes the query. Paragraph 0129 discloses the server computing system 14 includes machine learning model );
with the AI agent: seeking a goal of answering the question, by breaking the question down into multiple steps (Fig. 5, step 504, shows breaking the query in various portions); and
executing the multiple steps until the goal is reached (Fig. 5, steps 506-510, shows executing multiple steps to produce final result).
As to claim 2, the rejection of claim 1 is incorporated. Nagaraju teaches all the limitations of claim 1 as shown above.
Nagaraju further teaches wherein the AI agent uses a plurality of internal and external services to answer the question including a specific just-in-time training of the raw tracking data with a specified ML training methodology (Fig. 5, steps 506, discloses the plurality of indexer 34 are used to answer the query).
As to claim 3, the rejection of claim 1 is incorporated. Nagaraju teaches all the limitations of claim 1 as shown above.
Nagaraju further teaches wherein specific ML training methodology and ML algorithm are selected just-in-time for the AI agent to answer the user query with a higher precision (paragraph 0033 discloses changing the machine learning algorithm on the fly for better prediction).
As to claim 4, the rejection of claim 1 is incorporated. Nagaraju teaches all the limitations of claim 1 as shown above.
Nagaraju further teaches wherein the AI agent runs the raw tracking data through multiple pre-trained models and selects an answer that best fits the user query (paragraph 0129 discloses training the model).
Regarding claim 5:
Nagaraju teaches:
A system for auto-tracking with just-in-time training and goal seeking ai agents comprising (Fig. 1 shows a system 10. Paragraph 0040 discloses providing real-time answer using artificial intelligence):
a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data (Fig. 3 shows plurality of forwarder 30 (asset trackers) associated with data sources 32. Paragraph 0059 discloses a data source 32 is an edge device 12 as stated “An example of the data source 32 includes the data generated by sensors of the edge devices 12.”. Paragraph 0026 discloses the edge device 12 is IoT device as stated “Examples of an edge device include a mobile device and an IoT device.”);
one or more communications hubs; a base station; one or more communication networks (Fig. 1 shows network 16. Paragraph 0053 discloses the network 16 is cellular network. Cellular network includes base station and other communication nodes.);
wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs (Fig. 1 shows the forwarder 30 communicates with other nodes using the network 16 which is cellular network comprising base station);
wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network (Fig. 1 shows other client device 26 communicates with other nodes via the network 16. Paragraph 0088 discloses the client device 26 is smartphone);
wherein the base station is configured to be in communication with the plurality of asset trackers (Fig. 1 shows the forwarder 30 communication with other nodes using the network 16 which is cellular network comprising base station);
a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic (Fig. 1 shows server computer system 14 (server computing device) in communication with the network 16):
asset tracker provides data to a cloud-based server at preset intervals paragraph 0058 discloses the forwarder 30 located in the edge devices 12 and indexer 34 is located in the server computing system 14 as stated “For example, the forwarders 30 could be located at the edge devices 12, and the indexers could be located at the server computer system 14.”. Paragraph 0060 discloses the forwarder 30 forwards raw data to the indexer 34 as stated “During operation, the forwarders 30 can identify which indexers 34 should receive data collected from the data sources 32 and forward the data to the appropriate indexers 34.”.;
storing the data for future use (paragraph 0076 discloses storing the data in the data store as stated “In step 418, the indexer stores the events with an associated timestamp in a data store.”);
based on a user query, with a tracking system of the server computing device accessing the data and applying the data to a machine learning mode (paragraph 0079 discloses receiving a query from a user device as stated “In step 502, a search head receives a search query from another device”. Paragraph 0129 discloses the server computing system 14 includes machine learning model ); and
creating and training a machine-learning (ML) model on the fly based on the query to provide an insight to the user related to the user query (Fig. 5, steps 506-510, shows executing multiple steps to produce final result. Paragraph 0033 discloses changing the machine learning algorithm on the fly for better prediction. Paragraph 0129 discloses training the model).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAMAL M HOSSAIN whose telephone number is (571)270-3070. The examiner can normally be reached 9:30-5:30 M-F.
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January 8, 2026
/KAMAL M HOSSAIN/ Primary Examiner, Art Unit 2444