DETAILED ACTION
This final rejection is responsive to communication filed December 26, 2025. Claim 1 is currently amended. Claims 6, 7, 9, 13, 18 and 19 have been canceled. Claims 21-22 have been added. Claims 1-5, 8, 10-12, 14-17 and 20-22 are pending in this application.
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 1-5, 8, 10-12, 14-17 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites for each received sensor dataset, adding the received sensor dataset to a matching dataset list as a matching dataset when it is determined that the received sensor dataset matches the search criteria; determining whether the matching dataset list is sufficient, the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or greater than a first matching dataset threshold and equal to or less than a second matching threshold; and determines if the received sensor dataset satisfies the user-defined search criteria based on said tagging. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, adding matching datasets to a list encompasses a user thinking about or manually adding matching data to a list after observing/evaluating to determine the data matches. Further, the “determining” encompasses mental observations or evaluations that are practically performed in the human mind, and the “determines” encompasses using evaluation to determine if a sensor dataset satisfies a condition based on tagging.
This judicial exception is not integrated into a practical application. The claim recites the additional elements of receiving, by a data management system, user-defined search criteria comprising one or more parameters of a driving scenario, wherein the user defines how much matching sensor data to be collected; receiving, by the data management system, one or more sensor datasets from one or more data gathering vehicles; and tagging, by the data management system, the received sensor dataset. These limitations are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require receiving or data gathering, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering. See MPEP 2106.05.
Claim 1 also has the additional elements performing steps “by a data management system” and “testing a vehicle function with the matching dataset list.” There is no description of how/where the testing is performed, and thus the broadest reasonable interpretation of the “testing” limitation represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Similarly, performing receiving, adding, and determining steps “by a data management system” amounts to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A, Prong Two above, the recitations of “receiving search criteria” and “receiving one or more sensor datasets” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The limitation of “tagging, by the data management system, the received sensor dataset” is recited at a high level of generality. This element amounts to storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Further, the “by a data management system” and “testing” limitations are recited at a high level of generality and thus represent adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Even when considered in combination, these additional elements represent insignificant extra-solution activity or mere instructions to apply on a computer, which does not provide an inventive concept.
Claim 2 recites an additional element that further describes receiving sensor data step. As explained above, this represents mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. Therefore, the additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Claim 2 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of “wherein the sensor data are received from the one or more data gathering vehicles subsequent to receiving the search criteria” is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 3 recites specifying, subsequent to receiving the search criteria, the one or more parameters of the search criteria to the one or more data gathering vehicles. This limitation falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. For example, a user can specify parameters by thinking about or manually writing parameters. This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 4 recites and for each existing sensor dataset, adding the existing sensor dataset to the matching dataset list as a matching dataset when it is determined that the existing sensor dataset matches the search criteria. This limitation falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. For example, adding matching datasets to a list encompasses a user thinking about or manually adding matching data to a list after observing/evaluating to determine the data matches.
Claim 4 recites the additional element of “searching, subsequent to receiving the search criteria, a sensor database for one or more sensor datasets already existing in the sensor database.” This searching limitation represents adding insignificant extra-solution activity and thus the additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Claim 4 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The “searching” limitation is recited at a high level of generality. This element amounts to storing and retrieving information in memory and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 5 recites the additional element “notifying, when it is determined that the matching dataset list is sufficient, the one or more data gathering vehicles that a sufficient amount of sensor data associated with the search criteria has been acquired”. This notifying limitation represents adding insignificant extra-solution activity and thus the additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Claim 5 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The “notifying” limitation is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 8 recites the additional elements “wherein the vehicle function is an emergency braking algorithm.” There is no description of how/where the testing is performed, and thus the broadest reasonable interpretation of this limitation represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Therefore, the additional element does not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception. Claim 8 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 10 recites a limitation that further describe the received search criteria. As such, this limitation is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. Therefore, the additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception. Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation describing the received search criteria is recited at a high level of generality. This element amount to receiving or transmitting data over a network and is well-understood, routine, conventional activity. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claims 11 and 12 recites wherein the received sensor dataset is added to the matching dataset list when it is determined that the received sensor dataset matches all or some of the plurality of parameters of the search criteria. This limitation falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. For example, adding matching datasets to a list encompasses a user thinking about or manually adding matching data to a list after observing/evaluating to determine the data matches. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claims 14-17 recite limitations that describe data defined by the user. As such, these limitations are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. Therefore, the additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception. Claims 14-17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitations of the user defining criteria is recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim 20 recites notifying the one or more data gathering vehicles when the received sensor dataset reaches a specified amount. This limitation is recited at a high level of generality, and thus is insignificant extra-solution activity. Further, the limitation represents applying abstract idea. Therefore, the additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Claim 20 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of notifying is recited at a high level of generality. This element amounts to receiving and transmitting data over a network and is well-understood, routine, conventional activity. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claims 21-22 recite the additional element of performing post processing when it is determined that the matching dataset list is sufficient, wherein the post processing comprises training an artificial intelligence network with the matching dataset list. This limitation includes a high level recitation of training an artificial intelligence network with previously determined data, and thus represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract – see MPEP 2106.05(f). Therefore, the additional element does not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Claims 21-22 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8, 10-12, 14-17, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Lange et al. (US 2021/0142592 A1) (‘Lange’) in view of Michalakis et al. (US 11,386,055 B2) (‘Michalakis’), and further in view of Soda et al. (US 11,189,109 B2) (‘Soda’).
With respect to claim 1, Lange teaches a method, comprising:
receiving, by a data management system, search criteria comprising one or more parameters of a driving scenario (scenario pattern and/or data collection parameters) (paragraphs 34, 36, and 79-80);
receiving, by the data management system, one or more sensor datasets from one or more data gathering vehicles (paragraph 78);
for each received sensor dataset, the data management system, adding the received sensor dataset to a matching dataset list as a matching dataset when it is determined that the received sensor dataset matches the search criteria (paragraphs 68, 80);
determining, by the data management system, whether the matching dataset list is sufficient, the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or greater than a first matching dataset threshold (number of data points) (paragraphs 79-80); and
testing a vehicle function with the matching dataset list (the results of the AI modeling, training, prediction, etc. can be fed back into one or more of the vehicle system) (Lange, paragraph 71).
Lange does not explicitly teach the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or less than a second matching dataset threshold.
Michalakis teaches the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or greater than a first matching dataset threshold and equal to or less than a second matching dataset threshold (requests can include a number range of incidents to collect, such as from 1000 to 3000 incidents)(col. 3 lines 13-16 and lines 40-54; claim 1).
It would have been obvious to a person having ordinary skill in the art to have modified Lange to determine sufficient matching data based on a two thresholds (or a range of data points) instead of one threshold (a number of data points) as taught by Michalakis because the goal of Lange is to send only a necessary number of data points, even if vehicle sensors are constantly or continuously collecting data (paragraph 79). Lange teaches adjust its instructions regarding the number of data points that are needed, and thus supports having two thresholds or a range data points. Further, the modification would only entail swapping the matching dataset list criteria of Lange with those of Michalakis to enable a set amount of data or a range of data to be collected, thereby providing more flexibility and achieving predictable results of collect a requisite amount of matching data.
Further regarding claim 1, Lange does not explicitly teach receiving user-defined search criteria comprising one or more parameters of a driving scenario, wherein the user defines how much matching sensor data to be collected; tagging, by the data management system, the received sensor dataset, wherein the data management system determines if the received sensor dataset satisfies the user-defined search criteria based on said tagging.
Soda teaches receiving user-defined search criteria comprising one or more parameters of a driving scenario, wherein the user defines how much matching sensor data to be collected (Fig. 3A, col. 3 lines 42-48; col. 9 lines 11-21);
tagging, by the data management system, the received sensor dataset (Soda, col. 3 line 49 – col. 4 line 4),
wherein the data management system determines if the received sensor dataset satisfies the user-defined search criteria based on said tagging (Soda, Fig. 3A; col. 3 line 49 – col. 4 line 17; col. 4 lines 29-39, col. 9 lines 35-40).
It would have been obvious to a person having ordinary skill in the art to have modified the search criteria of Lange to be user-defined and determined based on tagging as taught by Soda to enable a data collection method that allows a user to set parameters for information collection easily while recognizing a communication amount, thereby enabling more personalized and efficient data collection criteria (Soda, abstract, col. 1 lines 43-62).
With respect to claim 2, Lange in view of Michalakis and Soda teaches the method of claim 1, wherein the sensor data are received from the one or more data gathering vehicles subsequent to receiving the search criteria (Lange, paragraph 80).
With respect to claim 3, Lange in view of Michalakis and Soda teaches the method of claim 1, further comprising: specifying, subsequent to receiving the search criteria, the one or more parameters of the search criteria to the one or more data gathering vehicles (Lange, paragraph 80).
With respect to claim 4, Lange in view of Michalakis and Soda teaches the method of claim 1, further comprising: searching, subsequent to receiving the search criteria, a sensor database for one or more sensor datasets already existing in the sensor database (paragraphs 69 and 80); and for each existing sensor dataset, adding the existing sensor dataset to the matching dataset list as a matching dataset when it is determined that the existing sensor dataset matches the search criteria (Lange, paragraphs 69 and 80).
With respect to claim 5, Lange in view of Michalakis and Soda teaches notifying, when it is determined that the matching dataset list is sufficient, the one or more data gathering vehicles that a sufficient amount of sensor data associated with the search criteria has been acquired (Michalakis, col. 3 lines 56-61).
With respect to claim 8, Lange in view of Michalakis and Soda teaches the method of claim 1, wherein the vehicle function is an emergency braking algorithm (one type of vehicle function is hard braking) (Lange, paragraphs 31, 37, and 74).
With respect to claim 10, Lange in view of Michalakis and Soda teaches the method of claim 1, wherein the search criteria includes a plurality of parameters (scenario pattern and/or data collection parameters) (Lange, paragraphs 34, 36, and 79-80).
With respect to claim 11, Lange in view of Michalakis and Soda teaches the method of claim 10, wherein the received sensor dataset is added to the matching dataset list as the matching dataset when it is determined that the received sensor dataset matches all of the plurality of parameters of the search criteria (Lange, paragraphs 68 and 80; Michalakis, col. 3 lines 38-46; col. 8 lines 20-26).
With respect to claim 12, Lange in view of Michalakis and Soda teaches the method of claim 10, wherein the received sensor dataset is added to the matching dataset list as the matching dataset when it is determined that the received sensor dataset matches some of the plurality of parameters of the search criteria (Lange, paragraphs 68 and 80; Michalakis, col. 3 lines 38-46; col. 8 lines 20-26).
With respect to claim 14, Lange in view of Michalakis and Soda teaches the method of claim 1, wherein the user defines a total amount of matching data to be collected (Lange, paragraphs 79-80; Soda, Fig. 3A, col. 3 lines 42-48; col. 9 lines 11-21).
With respect to claim 15, Lange in view of Michalakis and Soda teaches the method of method of claim 1, wherein the user defines the matching dataset list by defining a set number of occurrences (Lange, paragraphs 79-80; Soda, col. 6 line 57 – col. 7 line 3).
With respect to claim 16, Lange in view of Michalakis and Soda teaches the method of method of claim 1, wherein the user defines a total amount of time for collecting data (Lange, paragraphs 79-80; Soda, col. 3 lines 20-24, col. 6 lines 57 – 67, col. 13 lines 1-4).
With respect to claim 17, Lange in view of Michalakis and Soda teaches the method of claim 1, wherein the user defines search terms for collecting data (Soda, col. 3 line 49 – col. 4 line 17; col. 4 lines 36-45).
With respect to claim 20, Lange in view of Michalakis and Soda teaches the method of claim 1, further comprising notifying the one or more data gathering vehicles when the received sensor dataset reaches a specified amount (Michalakis, col. 3 lines 56-61).
With respect to claim 21, Lange in view of Michalakis and Soda teaches the method of claim 1, further comprising performing post processing when it is determined that the matching dataset list is sufficient (Lange, paragraphs 71 and 81).
With respect to claim 22, Lange in view of Michalakis and Soda teaches the method of claim 21, wherein the post processing comprises training an artificial intelligence network with the matching dataset list (Lange, paragraphs 38, 42, and 70).
Claims 1-3, 5, 10-12, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over KURUSSITHODI et al. (US 2024/0059303 A1) (‘Kurussithodi’) in view of Michalakis et al. (US 11,386,055 B2) (‘Michalakis’), and further in view of Soda et al. (US 11,189,109 B2) (‘Soda’).
With respect to claim 1, Kurussithodi teaches a method, comprising:
receiving, by a data management system, search criteria comprising one or more parameters of a driving scenario (paragraphs 61 and 94);
receiving, by the data management system, one or more sensor datasets from one or more data gathering vehicles (paragraphs 21, 42, 97-98);
for each received sensor dataset, the data management system adding the received sensor dataset to a matching dataset list as a matching dataset when it is determined that the received sensor dataset matches the search criteria (paragraphs 42, 63, and 98-99);
determining, data management system, whether the matching dataset list is sufficient, the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or greater than a first matching dataset threshold (paragraphs 43 and 65) ; and
performing post processing when it is determined that the matching dataset list is sufficient (paragraph 66-67 and 100),
testing a vehicle function with the matching dataset list (stored rule can subsequently be used by the automation system 100 to execute one or more vehicle automation routines during operation of the vehicle by evaluating the stored rule, and performing the actions specified by the stored rule if the conditions specified by the stored rule are met) (paragraph 103).
Kurussithodi does not explicitly teach the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or less than a second matching dataset threshold.
Michalakis teaches the matching dataset list being deemed sufficient when a number of matching datasets in the matching dataset list is equal to or greater than a first matching dataset threshold and equal to or less than a second matching dataset threshold (requests can include a number range of incidents to collect, such as from 1000 to 3000 incidents)(col. 3 lines 13-16 and lines 40-54; claim 1).
It would have been obvious to a person having ordinary skill in the art to have modified Kurussithodi to determine sufficient matching data based on a two thresholds (or a range of data sets) instead of one threshold (a number of data sets) as taught by Michalakis because the goal of Kurussithodi is to determine when sufficient data has been collected and to stop collecting when sufficient data has been collected (paragraphs 43 and 65). Therefore, Kurussithodi would benefit from having two thresholds (i.e. a range of data to collect) to ensure the proper amount of data is collected. Further, the modification would only entail swapping the matching dataset list criteria of Kurussithodi with those of Michalakis to enable a set amount of data sets or a range of data sets to be collected, thereby providing more flexibility and achieving predictable results of collect a requisite amount of matching data.
Further regarding claim 1, Kurussithodi does not explicitly teach receiving user-defined search criteria comprising one or more parameters of a driving scenario, wherein the user defines how much matching sensor data to be collected; tagging, by the data management system, the received sensor dataset, wherein the data management system determines if the received sensor dataset satisfies the user-defined search criteria based on said tagging.
Soda teaches receiving user-defined search criteria comprising one or more parameters of a driving scenario, wherein the user defines how much matching sensor data to be collected (Fig. 3A, col. 3 lines 42-48; col. 9 lines 11-21);
tagging, by the data management system, the received sensor dataset (Soda, col. 3 line 49 – col. 4 line 4),
wherein the data management system determines if the received sensor dataset satisfies the user-defined search criteria based on said tagging (Soda, Fig. 3A; col. 3 line 49 – col. 4 line 17; col. 4 lines 29-39, col. 9 lines 35-40).
It would have been obvious to a person having ordinary skill in the art to have modified the search criteria of Kurussithodi to be user-defined and based on tagging as taught by Soda to enable a data collection method that allows a user to set parameters for information collection easily while recognizing a communication amount, thereby enabling more personalized and efficient data collection criteria (Soda, abstract, col. 1 lines 43-62).
With respect to claim 2, Kurussithodi in view of Michalakis and Soda teaches the method of claim 1, wherein the sensor data are received from the one or more data gathering vehicles subsequent to receiving the search criteria (Kurussithodi, paragraph 98).
With respect to claim 3, Kurussithodi in view of Michalakis and Soda teaches the method of claim 1, further comprising: specifying, subsequent to receiving the search criteria, the one or more parameters of the search criteria to the one or more data gathering vehicles (Kurussithodi, paragraph 97).
With respect to claim 5, Kurussithodi in view of Michalakis and Soda teaches the method of claim 1, further comprising: notifying, when it is determined that the matching dataset list is sufficient, the one or more data gathering vehicles that a sufficient amount of sensor data associated with the search criteria has been acquired (data manager determines that sufficient data has been collected and determines when to stop collecting data) (Kurussithodi, paragraph 65; Michalakis, col. 3 lines 56-61).
With respect to claim 10, Kurussithodi in view of Michalakis and Soda teaches the method of claim 1, wherein the search criteria includes a plurality of parameters (Kurussithodi, paragraphs 61 and 94).
With respect to claim 11, Kurussithodi in view of Michalakis and Soda teaches the method of claim 10, wherein the received sensor dataset is added to the matching dataset list as the matching dataset when it is determined that the received sensor dataset matches all of the plurality of parameters of the search criteria (Kurussithodi, paragraphs 42, 63, and 98-99; Michalakis, col. 3 lines 38-46; col. 8 lines 20-26).
With respect to claim 12, Kurussithodi in view of Michalakis and Soda teaches the method of claim 10, wherein the received sensor dataset is added to the matching dataset list as the matching dataset when it is determined that the received sensor dataset matches some of the plurality of parameters of the search criteria (Kurussithodi, paragraphs 42, 63, and 98-99; Michalakis, col. 3 lines 38-46; col. 8 lines 20-26).
With respect to claim 21, Kurussithodi in view of Michalakis and Soda teaches the method of claim 1, further comprising performing post processing when it is determined that the matching dataset list is sufficient (Kurussithodi, paragraphs 66-67 and 100).
With respect to claim 22, Kurussithodi in view of Michalakis and Soda a teaches the method of claim 21, wherein the post processing comprises training an artificial intelligence network with the matching dataset list (Kurussithodi, paragraph 100).
Response to Arguments
Applicant's arguments filed December 26, 2025 have been fully considered but they are not persuasive. Applicant argues that the claimed invention provides a clear advantage over existing systems/methods, and thus integrates the judicial exception into a practical application. The examiner disagrees. The claimed limitations do not appear to reflect any improvement in the functioning of a computer or to another technology or technical field. The steps of adding data to a list, determining if a matching dataset list is sufficient, and determining if user-defined criteria is satisfied are mental steps. The steps regarding receiving criteria and datasets are mere data gathering steps required to implement the abstract idea and thus represent insignificant extra-solution activity. The testing step is applying the abstract idea with a computer, and the tagging step is also adding insignificant extra-solution activity. Further, defining data collection criteria, collecting data based on the defined criteria, and post processing the collected data does not appear to represent an improvement in the functioning of a computer or to another technology or technical field.
Applicant further argues that Soda fails to teach determining if the received sensor dataset satisfies the user-defined search criteria based on said tagging because the determination is done prior to the generation of the data tag. The examiner disagrees. Soda teaches that “When the data user connects the user terminal 200 to the data collection apparatus 10 to check a data collection status or collect real data R, meta information that is based on tag data T collected by the data collection apparatus 10 is displayed on the user terminal 200. At the same time, UI picture for allowing manipulations for collecting real data R corresponding to each piece of tag data T.” Soda further teaches that “the data user specifies tag data T corresponding to real data R to be collected through the user terminal” and “by collecting, stepwise, such tag data T and real data R selected on the basis of the tag data T” (see Figs. 1D, 1E and column 4). Soda further teaches that “ in the collection condition setting picture, it is possible to specify a more detailed condition for collection of data. For example, “ordinary” and “meta information” between which selection can be made using, for example, a radio button of a GUI widget correspond to real data R and data T (described above), respectively.” Therefore, it is clear that real data R is collected or uploaded based on the tag data T.
Conclusion
THIS ACTION IS MADE FINAL. 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 ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156
March 25, 2026
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