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 .
Response to Amendment
This Office action is responsive to Applicant’s AMENDMENT A, filed March 6, 2026. Claims 1-20 are pending.
Response to Arguments
In response to the rejection of claims 10-18 under 35 U.S.C. 101 as set forth in the prior Office action dated December 8, 2025, pages 2-3, Applicant has amended independent claim 10 to recite a “non-transitory” computer readable storage medium. The amendment is deemed sufficient to overcome the rejection under 35 U.S.C. 101, and thus the rejection is withdrawn.
In response to the rejection of claims 1-5, 7-14 and 16-20 under 35 U.S.C. 101 as set forth in the prior Office action, pages 3-6, Applicant has amended independent claims 1, 10 and 19 to recite “wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample.” The amendments are deemed sufficient to overcome the rejection under 35 U.S.C. 101, and thus the rejection is withdrawn.
In response to the rejection of claims 1-4, 7, 10-13, 16, 19 and 20 under 35 U.S.C. 103 as set forth in the prior Office action, pages 6-15, Applicant has independent claims 1, 10 and 19 to recite “wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample.” Applicant has further amended claim 19 to recite “wherein causing the inspection collected data set to be uploaded to the cloud site includes outputting an instruction to the second edge device to upload the inspection collected data set to the cloud site.” The amendment to claim 19 fully incorporates the allowable subject matter of claim 6, and is thus deemed sufficient to overcome the rejection under 35 U.S.C. 103.
The amendments to claims 1 and 10, however, only partially incorporate the allowable subject matter of claim 6. The first part of claim 6 incorporated into claims 1 and 10, namely “outputting an instruction to a second edge device to capture the first data sample,” is taught by one of the cited prior art references (WO 2022/094476 A1 (Bricker)). Bricker discloses a local controller 108 (which may be implemented as a tablet or other mobile or portable computing device (paragraph [0065])) that includes a processor and memory having instructions to execute modules 110 and 112 of pressure elastography device 104 and ultrasound device 106, respectively (paragraph [0064]). Providing instructions to a second edge device, such as the pressure elastography device and the ultrasound device, to capture data samples would have been obvious to one of ordinary skill in the art, at the time of Applicant’s invention, for it allows for an edge device to receive data from other edge devices to be used for analysis via machine learning or deep learning operations, thereby increasing flexibility. It is only when recited in combination with the second part of claim 6 that the limitation, as a whole, is deemed to be allowable subject matter. Therefore, Applicant’s amendments to claims 1 and 10 are not deemed sufficient to overcome the rejection under 35 U.S.C. 103.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 2, 7, 10, 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication US 2025/0343739 A1 (hereinafter “Abad”) in view of Internation Application Publication WO 2022/094476 A1 (hereinafter “Bricker”).
Regarding claim 1, Abad discloses a computer-implemented method (network node 110, 125, and wireless device 150 each include memory 930 coupled to processing circuitry 910, wherein the memory comprises machine readable computer program instructions that are executed by the processing circuitry (paragraph [0116], [0121])), comprising:
- obtaining, on a first edge device, a plurality of artificial intelligence (AI) rules (wireless device obtains a test specification 220 defining a test to be performed on data set 230, the test specification describing one or more operations which are to be performed on a data set to determine whether the data set is likely to be relevant to an AI operation (paragraph [0054], [0057]));
- applying, on the first edge device, the AI rules to a plurality of evaluated data samples for determining whether to include the data samples in an inspection collected data set (wireless device performs a test of a candidate data set according to the test specification in order to determine the relevance of the data set for possible use in the AI operation (paragraph [0058])); and
- in response to a determination that a first of the data samples satisfies each of the Al rules, causing the first data sample to be included in the inspection collected data set (network node receives a test outcome 240 sent from the wireless device and verifies if the data set of the wireless device meets an acceptance criterion for use in the AI operation, based on the received test outcome (paragraph [0059]); a data set fulfilling all of the conditions can be associated with a positive test outcome (paragraph [0054]));
Abad does not expressly disclose the steps of:
- wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample; and
- causing the inspection collected data set to be uploaded to a cloud site.
Bricker discloses a method and system for a local cloud infrastructure for a pressure elastography-based measurement system. Local controller 108 (which may be implemented as a tablet or other mobile or portable computing device (paragraph [0065])) is provided, the controller including a processor and memory having instructions to execute modules 110 and 112 of pressure elastography device 104 and ultrasound device 106, respectively (paragraph [0064]). Also, first and second data sets received at a data collection appliance and gateway are transmitted to a local or remote cloud server, wherein the first and second data sets are subsequently analyzed via machine learning or deep learning operations (e.g., in the local/remote cloud server) (paragraph [0009]).
Providing instructions to a second edge device, such as the pressure elastography device and the ultrasound device, to capture data samples would have been obvious to one of ordinary skill in the art, at the time of Applicant’s invention, for it allows for an edge device to receive data from other edge devices to be used for analysis via machine learning or deep learning operations, thereby increasing flexibility. Also, in view of Bricker, one of ordinary skill in the art would have recognized that data sets may be uploaded to a cloud site for performing AI operations, and this would provide added flexibility to the teaching of Abad, for the AI operations may be performed without exacting an additional processing load on Abad's wireless device. Therefore, it would have been obvious for one of ordinary skill in the art to have modified the teaching of Abad by providing output of an instruction to a second edge device to capture the first data sample, and provide inspection collected data to be uploaded to a cloud site for AI processing, such as taught by Bricker.
Regarding claim 2, Abad comprises: in response to a determination that a second of the data samples does not satisfy at least one of the AI rules, causing the second data sample to be excluded from the inspection collected data set (method allows for optimization of the input data by selecting only the most relevant data sets available for use in the AI operation (paragraph [0062]), which implies that data sets not judged to be relevant are excluded).
Regarding claim 7, Abad discloses wherein causing the first data sample to be included in the inspection collected data set includes capturing, by the first edge device, the first data sample, wherein the inspection collected data set is uploaded to the cloud site by the first edge device (wireless device obtains a candidate data set for possible use in the AI operation (paragraph [0058]); the proposed methods may be implemented on a remote server comprised in a cloud-based computing platform (paragraph [0109])).
Regarding claim 10, Abad discloses a computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith (network node 110, 125, and wireless device 150 each include memory 930 coupled to processing circuitry 910, wherein the memory comprises machine readable computer program instructions that are executed by the processing circuitry (paragraph [0116], [0121])), the program instructions readable and/or executable by a first edge device to cause the first edge device to:
- obtain, on the first edge device, a plurality of artificial intelligence (AI) rules (wireless device obtains a test specification 220 defining a test to be performed on data set 230, the test specification describing one or more operations which are to be performed on a data set to determine whether the data set is likely to be relevant to an AI operation (paragraph [0054], [0057]));
- apply, on the first edge device, the AI rules to a plurality of evaluated data samples for determining whether to include the data samples in an inspection collected data set (wireless device performs a test of a candidate data set according to the test specification in order to determine the relevance of the data set for possible use in the AI operation (paragraph [0058])); and
- in response to a determination that a first of the data samples satisfies each of the Al rules, cause the first data sample to be included in the inspection collected data set (network node receives a test outcome 240 sent from the wireless device and verifies if the data set of the wireless device meets an acceptance criterion for use in the AI operation, based on the received test outcome (paragraph [0059]); a data set fulfilling all of the conditions can be associated with a positive test outcome (paragraph [0054])).
Abad does not expressly disclose the steps of:
- wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample; and
- causing the inspection collected data set to be uploaded to a cloud site.
As set forth above regarding claim 1, Bricker discloses a method and system for a local cloud infrastructure for a pressure elastography-based measurement system. Local controller 108 (which may be implemented as a tablet or other mobile or portable computing device (paragraph [0065])) is provided, the controller including a processor and memory having instructions to execute modules 110 and 112 of pressure elastography device 104 and ultrasound device 106, respectively (paragraph [0064]). Also, first and second data sets received at a data collection appliance and gateway are transmitted to a local or remote cloud server, wherein the first and second data sets are subsequently analyzed via machine learning or deep learning operations (e.g., in the local/remote cloud server) (paragraph [0009]).
Providing instructions to a second edge device, such as the pressure elastography device and the ultrasound device, to capture data samples would have been obvious to one of ordinary skill in the art, at the time of Applicant’s invention, for it allows for an edge device to receive data from other edge devices to be used for analysis via machine learning or deep learning operations, thereby increasing flexibility. Also, in view of Bricker, one of ordinary skill in the art would have recognized that data sets may be uploaded to a cloud site for performing AI operations, and this would provide added flexibility to the teaching of Abad, for the AI operations may be performed without exacting an additional processing load on Abad's wireless device. Therefore, it would have been obvious for one of ordinary skill in the art to have modified the teaching of Abad by providing output of an instruction to a second edge device to capture the first data sample, and provide inspection collected data to be uploaded to a cloud site for AI processing, such as taught by Bricker.
Regarding claim 11, Abad disclose the program instructions readable and/or executable by the first edge device to cause the first edge device to: in response to a determination that a second of the data samples does not satisfy at least one of the AI rules, cause the second data sample to be excluded from the inspection collected data set (method allows for optimization of the input data by selecting only the most relevant data sets available for use in the Al operation (paragraph [0062]), which implies that data sets not judged to be relevant are excluded).
Regarding claim 16, Abad discloses wherein causing the first data sample to be included in the inspection collected data set includes capturing, by the first edge device, the first data sample, wherein the inspection collected data set is uploaded to the cloud site by the first edge device (wireless device obtains a candidate data set for possible use in the AI operation (paragraph [0058]); the proposed methods may be implemented on a remote server comprised in a cloud-based computing platform (paragraph [0109])).
Allowable Subject Matter
Claims 19 and 20 are allowed.
Claims 3-6, 8, 9, 12-15, 17 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 3 and 12, the cited prior art fails to disclose or suggest Applicant's computer-implemented method of claim 2 or computer program product of claim 11, comprising: in response to the determination that the second of the data samples does not satisfy a first of the AI rules: performing an adjustment to cause the second data sample to satisfy the first AI rule, and reapplying the AI rules in response to a determination that the adjustment has been performed; in response to the determination that the second of the data samples does not satisfy the first of the AI rules, outputting a first suggested adjustment to a user device display; and in response to a determination that an attempt has been made to perform the first suggested adjustment, reapplying the AI rules.
Regarding claims 4 and 13, the cited prior art fails to disclose or suggest Applicant's computer-implemented method of claim 1 or computer program product of claim 10, wherein the evaluated data samples are images that are actively being evaluated through a camera of the second edge device but have not yet been captured by the camera.
Claims 5 and 14 depend from claim 4 and 13, respectively.
Regarding claims 6 and 15, the cited prior art fails to disclose or suggest Applicant's computer-implemented method of claim 1 or computer program product of claim 10, wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample, wherein causing the inspection collected data set to be uploaded to the cloud site includes outputting an instruction to the second edge device to upload the inspection collected data set to the cloud site.
Regarding claims 8 and 17, the cited prior art fails to disclose or suggest Applicant's computer-implemented method of claim 1 or computer program product of claim 10, wherein the AI rules are sequentially applied to the first data sample, wherein the AI rules are applied in parallel to a second of the data samples.
Regarding claims 9 and 18, the cited prior art fails to disclose or suggest Applicant's computer-implemented method of claim 1 or computer program product of claim 10, wherein a first of the evaluated data samples is an image, wherein a second of the evaluated data samples is selected from a group consisting of a thermal reading and an audio clip.
Regarding claim 19, the cited prior art fails to disclose or suggest Applicant’s system, including the step: wherein causing the first data sample to be included in the inspection collected data set includes outputting an instruction to a second edge device to capture the first data sample, wherein causing the inspection collected data set to be uploaded to the cloud site includes outputting an instruction to the second edge device to upload the inspection collected data set to the cloud site.
Claim 20 depends from claim 19.
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.
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/THOMAS D LEE/Primary Examiner, Art Unit 2683