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 .
Outstanding USC 101 rejection remained.
Claims 1-13 remained pending.
Please refer to the action below.
Examiner Notes
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. However, the claimed subject matter, not the specification, is the measure of the invention.
Response to Remarks/Arguments
Applicants’ arguments of 02/27/2026, corresponding to pages 6-11 pertaining to the outstanding USC 101 rejection and the 103 rejection have been considered, however, they are not persuasive.
Applicant argues on pages 6-7 that “the Office alleged that the claims are directed to an abstract idea that is not integrated into a practical application because "nothing in the claim . .. precludes the steps from practically being performed in the mind and/or purely by software." Applicant respectfully traverses the rejections. First, under Step 2A, Prong One, the claims do not recite a judicial exception. As detailed in MPEP § 2106, at Step 2A, "Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon."2 MPEP § 2106 and a recent USPTO § 101 Memo3 emphasize that "Examiners should accordingly be careful to distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis)."As noted above, in rejecting all claims under 35 U.S.C. § 101, the Office alleged that "nothing in the claim . .. precludes the steps from practically being performed in the mind."5 As an initial matter, all of Applicant's claims explicitly preclude the claimed limitations from being performed in the human mind. Indeed, independent Claim 1 recites a "method ... performed by a computer system" (emphasis added), independent Claim 7 is a system claim comprising "instructions ... executed by [a] processor" (emphasis added), and independent Claim 13 is a 1 Office Action at pp. 4-5. 2 MPEP § 2106.04(II)(A)(1). 3 USPTO Memo, Subject "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101," dated August 4, 2025; hereinafter "USPTO § 101 Memo." 4 MPEP § 2106.04(II)(A)(1) (emphasis in original); see also USPTO § 101 Memo, page 3. s Office Action at p. 5.
computer readable medium claim comprising "computer program code." Moreover, under MPEP § 2106.04(a)(2), "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations."6 Applicant's claims do not recite mental processes because they involve technical processes that cannot practically be performed in the human mind. In particular, each of the independent claims requires obtaining a person-depicting sensor feed, executing on- device anonymization of that feed (e.g., face blurring or replacement), and generating a processed data feed, none of which are practically performed in the human mind. As also noted above, in rejecting all claims under 35 U.S.C. § 101, the Office alleged that "nothing in the claim . . .precludes the steps from practically being performed . . .purely by software."7 However, the Office has provided no basis for alleging ineligibility based solely on whether the claimed steps can be performed by software. Indeed, for example, a computer readable medium claim is a well-established and patent-eligible claim format that generally involves steps performed purely by software (e.g., computer program code)”.
The Examiner respectfully disagrees with the above assertions. The 101 rejection remained for the following reasons: 101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG), the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) certain methods of organizing human activity, and/or b) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 35 U.S.C. 101 rejection. Claim 1 recites:
A method for enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person,
the method being performed by a computer system comprising a training data provider,
the method comprising:
obtaining a data feed capable of depicting the person;
selecting a level of anonymisation, from a plurality of levels of anonymisation;
anonymising the data feed according to the selected level of anonymisation, resulting in a processed data feed;
transmitting the processed data feed as training data for training a central ML model in a central node; and
receiving an indication to increase or reduce the level of anonymisation from the central node;
wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation.
The examiner submits that the foregoing bolded limitation(s) constitute a “certain methods of organizing and grouping human activity,” or a “mental process”. Specifically, the “select”, “anonymising”, “receiving”, and “repeated” steps encompass the methods of organizing and grouping human activity and/or the mental steps found in the obtaining, receiving, anonymizing and repeating of the next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation. Furthermore, the “anonymizing” and the “repeated iteration” step, encompasses a person, observing the obtained or received feeds and determining whether to apply a more stringent more anonymizing method based on predetermined set systems. Accordingly, claim 1 recites an abstract idea.
Applicant further argues on page 7 that “Second, Applicant asserts that the claims are directed to eligible subject matter under Step 2A, Prong Two at least because the claims integrate any judicial exception into a practical application.8 Under MPEP § 2106.04(d)(I), "[l]imitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include ... an improvement in the functioning of a computer, or an improvement to other technology or technical field." Applicant's claims integrate any judicial exception into a practical application via source-side anonymization and a feedback loop that modulates anonymization before transmission, improving privacy and data minimization”.
The Examiner further respectfully disagrees with the above assertions. Regarding the analysis of 101 Analysis – Step 2A, Prong II.
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person,
the method being performed by a computer system comprising a training data provider,
the method comprising:
obtaining a data feed capable of depicting the person;
selecting a level of anonymisation, from a plurality of levels of anonymisation;
anonymising the data feed according to the selected level of anonymisation, resulting in a processed data feed;
transmitting the processed data feed as training data for training a central ML model in a central node; and
receiving an indication to increase or reduce the level of anonymisation from the central node;
wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “the method being performed by a computer system comprising a training data provider …” and “transmitting the processed data feed as training data for training a central ML model in a central node …” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “the method being performed by a computer system comprising a training data provider …” and “transmitting the processed data feed as training data for training a central ML model in a central node …” limitations are recited at a high level of generality and merely automates the “select”, “anonymising”, “receiving”, and “repeated” steps, respectively, therefore acting as a generic computer to perform the abstract idea. The central node, data provider, and the computer system are claimed generically and do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. These additional limitation(s) is/are no more than mere instructions to apply the exception using a computer (the data provider and the central node). Furthermore, regarding the additional limitation of “the method being performed by a computer system comprising a training data provider …” and “transmitting the processed data feed as training data for training a central ML model in a central node …”, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. Specifically, they are insignificant extra-solution activities, which are mere data gathering, monitoring, and anonymizing/outputting.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an order combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Applicant further argues on pages 7-8 that “Third, under Step 2B, Applicant asserts that the claims recite significantly more than any judicial exception. Under MPEP § 2106.05(d), a "consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well- understood, routine, conventional activities previously known to the industry." "If the additional 6 MPEP § 2106.04(a)(2)(III)(A). 7 Office Action at p. 5. 8 See MPEP § 2106.04(II)(A)(2). element (or combination of elements) is a specific limitation other than what is well-understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility."9 Applicant's Claim 1, for example, calls for a specific architecture and ordering: a sensor-side training-data provider that anonymizes a person-depicting feed before any network transmission, with a central-node feedback signal that modulates subsequent anonymization at the source. This source-side gating improves computer functionality in a concrete way, and the Office has not demonstrated Applicant's feedback-controlled edge anonymization to be well-understood, routine, and conventional. For at least the foregoing reasons, Applicant's claims recite patent-eligible subject matter under 35 U.S.C. § 101. Reconsideration and withdrawal of the rejections are respectfully requested”.
The Examiner further respectfully disagrees with the above assertions: as Per 101 Analysis – Step 2B
Regarding Step 2B of the PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the “receiving” limitations and the “repeated” limitation amount to nothing more than mere instructions to apply the exception using a generic computer component (the processor). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “the method being performed by a computer system comprising a training data provider …” and “transmitting the processed data feed as training data for training a central ML model in a central node …” are well-understood, routine gathering, grouping that can be performed mentally by a person or a software. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “generate a training set…” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere outputting of data is a well understood, routine, and conventional function. Hence, claim 1 is not subject matter eligible.
Claims 7 and 13 recite similar limitations to those discussed above with regards to claim 1 and therefore discussion is omitted for brevity. Hence, independent claims 1, 7, and 13 are not subject matter eligible.
Applicant further argues on pages 9-10 that “Notwithstanding other deficiencies in Fridental, the Office acknowledged that Fridental does not teach or suggest "receiving an indication to increase or reduce the level of anonymisation from the central node; wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation."10 Instead, the Office relied on Kuta for teaching these limitations, particularly citing paragraphs [0048] and [0086]-[0087]."While Kuta appears to describe that "multiple transformations may be sequentially applied to iteratively move between a sequence of identities,"12 the Office's reliance on Kuta is misplaced because none of paragraphs [0048] and [0086]-[0087] teach or suggest that any such iterations are based on any sort of indication to increase or reduce the level of anonymization, and certainly not based on any such indication to increase or reduce the level of anonymization received from a central ML node, as explicitly called for in Applicant's Claim 1. The iterations in Kuta appear to be intra-module processing steps that do not appear to involve any sort of feedback loop from a central ML node governing future anonymization choices in the manner recited by Applicant. Thus, Claim 1 is patentable over the cited references. Claims 2-6 are also patentable over the cited references for the same reasons as Claim 1, by virtue of their dependency thereon, and for the additional limitations recited therein. Reconsideration and withdrawal of the rejections are respectfully requested”.
The Examiner further respectfully disagrees with the above assertions of Kuta et al. Kuta clearly in at least para. 0023-0029 teaches the trained anonymizer machine learning models configured to de-identify and anonymize receive feeds data of a user. Kuta further teaches in at least para. 0048 and 0086-0087, that “ in some embodiments, multiple transformations may be sequentially applied to iteratively move between a sequence of identities” clearly further indicating in the art that the method may be repeated, wherein a next iteration of the selecting is based obviously on the indication to either increase or reduce the level of anonymisation.
Applicant further argues on pages 9-10 that “Claims 7 is directed to a training data provider for enabling training of a machine learning model, for monitoring a person based on a data feed capable of depicting a person, and Claim 13 is directed to a non-transitory computer readable medium storing a computer program for enabling training of a machine learning model, for monitoring a person based on a data feed capable of depicting a person. Claims 7 and 13 include limitations similar to some of those discussed above with respect to Claim 1. Thus, Claims 7 and 13 are patentable over the cited references for at least reasons similar to those provided above with respect to Claim 1. Claims 8-12 are also patentable
10 Office Action at pp. 9-10”.
The Examiner further respectfully disagrees with the above assertions of Kuta et al. As previously stated, Kuta clearly in at least para. 0023-0029 teaches the trained anonymizer machine learning models configured to de-identify and anonymize receive feeds data of a user. Kuta further teaches in at least para. 0048 and 0086-0087, that “ in some embodiments, multiple transformations may be sequentially applied to iteratively move between a sequence of identities” clearly further indicating in the art that the method may be repeated, wherein a next iteration of the selecting is based obviously on the indication to either increase or reduce the level of anonymisation.
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 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.
Claims 1-4, 7-10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable and obvious over Fridental et al. (US 2017/0289504, cited in IDS), in view of Kuta et al. (US 20220012362, A1).
Regarding claim 1, Fridental teaches a method for enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person (para. 0045-0047 further supported by para. 0019-0021 teaches machine learning and computer vision algorithms system comprising methods and systems for enabling further in at least para. 0047 implied training of the machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person to detect and identify a label and state of the monitored person),
the method being performed by a computer system comprising a training data provider (the machine learning algorithms or the computer vision of further para. 0047 further comprises said system and a training data module or provider),
the method comprising:
obtaining a data feed capable of depicting the person (captured data feed capable of depicting the person of further para. 0019-0021 and 0045-0047);
selecting a level of anonymisation, from a plurality of levels of anonymisation (the system further in at least para. 0045-0046 may determine to protect the privacy of the monitored person by selecting one or more plurality of types or levels of anonymisation such as face blurring, face removal and the like as one of said selection level of anonymisation);
anonymizing the data feed according to the selected level of anonymisation, resulting in a processed data feed (para. 0019-0021 and 0045-0047);
transmitting the processed data feed as training data for training a central ML model in a central node (cited sanitized data feed transmission of further para. 0046-0049 further supported by para. 0084-0086 as processed or anonymized data feed to the computer vision algorithms comprising the machine learning algorithms as understoodly in a case training data for training a central ML model in a central node 150 to identify in a case a state of the person or a medical emergency condition of the monitored person); and
Fridental is silent regarding the above lined-out items such as said receiving an indication to increase or reduce the level of anonymisation from the central node; wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation.
Kuta in at least para. 0016 and 0029 trained anonymizer machine learning models and training models which may locally or remotely located in a node configured to anonymize or de-identify collected person data feed of further para. 0036 including at least face data content, the models further in at least para. 0018, and 0023-0026 may receive an instruction to use at least a naïve anonymization approach or a face-swapping transformation anonymization based on a determined level of anonymization to at least as cited “remove, reduce, or obscure some or all personal identifiable information of a source identity”, the models may further receive in at least para. 0048 and 0086-0087 an indication/instruction to perform a number of iterations processes to increase or reduce the level of anonymisation where the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein said transmitting the processed data feed, and receiving an indication to increase or reduce the level of anonymisation from the central node; wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta’s currently repeated processes of anonymization iterations complements the method for enabling training of the anonymizing machine learning, ML, model of Fridental in the sense that the architecture of Kuta when combined with the architecture of the training ML model of Fridental provides a method and system based on the instructed set of next iterations with a further selection level of face-swapping anonymisation to in a case increasing or reduce the level of anonymisation of the monitored person whereby identity integrity of said person is preserved and is not revealed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 2 (according to claim 1), Fridental further teaches the levels of anonymisation include in, in order of increasing anonymisation: blurring of face,
However, Fridental is silent regarding the above lined-out items such as wherein the levels of anonymisation include in, in order of increasing anonymisation: replacing face with a computer-generated face image, replacing face with a picture of someone else's face.
Kuta further teaches in at least para. 0003, 0029-0030, and 0086-0087 the levels of applied anonymisation including image perturbations which may obviously comprise in the art in order of increasing anonymisation blurring of the face, or blurring of entire body, Kuta further teaches at least in para. 0086-0087 face swapping anonymization comprising at least replacing face with a computer-generated face image, replacing face with a picture of someone else's face. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein levels of anonymisation include in, in order of increasing anonymisation: blurring of face, replacing face with a computer-generated face image, replacing face with a picture of someone else's face, blurring of entire body, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta further complements Fridental with said levels of anonymisation to uphold in a case the privacy of the monitored person where identification or reidentification of said person is suppressed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 3 (according to claim 1), Fridental is silent regarding wherein the anonymising comprises selecting another face of a same gender as the person in the data feed.
Kuta further teaches in at least para. 0048, and 0086-0087 plurality of levels of anonymisation including face swapping comprising at least selecting in a case another face of a same gender as the person in the data feed. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein said selecting another face of a same gender as the person in the data feed, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta further complements Fridental with face swapping anonymisation method to uphold in a case the privacy of the monitored person where identification or reidentification of said person is suppressed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 4 (according to claim 1), Fridental further teaches wherein further comprising: determining a label associated with the data feed (determined at least in para. 0018-0021 emergency events and/or areas of naked skin of a monitored person indicative of said label further indicating something that occurs relatively rarely that can be anonymised in the training data);
and including the label in association with the processed data feed (identified events of further para. 0018-0021 including said labels are further associated with the sanitized or processed data feed).
Regarding claim 7, Fridental teaches a training data provider for enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person (para. 0045-0047 further supported by para. 0019-0021 and Fig. 3 teaches methods and systems comprising processing module 140 and/or cloud 150 or system 300 comprising machine learning and computer vision algorithms further including the training data provider for enabling further in at least para. 0047 implied training of the machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person to detect and identify a label and state of the monitored person),
the training data provider comprising:
a processor (Fig. 3, module 320 and/or processing circuitry of the Abstract); and
a memory (the at least one receiving means of further para. 0042 and 0058 comprising said memory), storing instructions that, when executed by the processor, cause the training data provider to:
obtain a data feed capable of depicting the person (captured data feed capable of depicting the person of further para. 0019-0021 and 0045-0047);
select a level of anonymisation, from a plurality of levels of anonymisation (the system further in at least para. 0045-0046 may determine to protect the privacy of the monitored person by selecting one or more plurality of types or levels of anonymisation such as face Blurring, face removal and the like as one of said selection level of anonymisation);
anonymise the data feed according to the selected level of anonymisation, resulting in a processed data feed (sanitized data feeds of further para. 0019-0021 and 0045-0047);
transmit the processed data feed as training data for training a central ML model in a central node (cited sanitized data feed transmission of further para. 0046-0049 further supported by para. 0084-0086 as processed or anonymized data feed to the computer vision algorithms comprising the machine learning algorithms as understoodly in a case training data for training a central ML model in a central node 150 to identify in a case a state of the person or a medical emergency condition of the monitored person);
Fridental is silent regarding the above lined-out items such as receive an indication to increase or reduce the level of anonymisation from the central node; and repeat said instructions, wherein a next iteration of the instructions to select is based on the indication to increase or reduce the level of anonymisation.
Kuta in at least para. 0016 and 0029 trained anonymizer machine learning models and training models which may locally or remotely located in a node configured to anonymize or de-identify collected person data feed of further para. 0036 including at least face data content, the models further in at least para. 0018, and 0023-0026 may receive an instruction to use at least a naïve anonymization approach or a face-swapping transformation anonymization based on a determined level of anonymization to at least as cited “remove, reduce, or obscure some or all personal identifiable information of a source identity”, the models may further receive in at least para. 0048 and 0086-0087 an indication/instruction to perform a number of iterations processes to increase or reduce the level of anonymisation where the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein said transmitting the processed data feed, and receiving an indication to increase or reduce the level of anonymisation from the central node; wherein the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta’s currently repeated processes of anonymization iterations complements the method for enabling training of the anonymizing machine learning, ML, model of Fridental in the sense that the architecture of Kuta when combined with the architecture of the training ML model of Fridental provides a method and system based on the instructed set of next iterations with a further selection level of face-swapping anonymisation to in a case increasing or reduce the level of anonymisation of the monitored person whereby identity integrity of said person is preserved and is not revealed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 8 (according to claim 7), Fridental further teaches the levels of anonymisation include in, in order of increasing anonymisation: blurring of face,
However, Fridental is silent regarding the above lined-out items such as wherein the levels of anonymisation include in, in order of increasing anonymisation: replacing face with a computer-generated face image, replacing face with a picture of someone else's face.
Kuta further teaches in at least para. 0003, 0029-0030, and 0086-0087 the levels of applied anonymisation including image perturbations which may obviously comprise in the art in order of increasing anonymisation blurring of the face, or blurring of entire body, Kuta further teaches at least in para. 0086-0087 face swapping anonymization comprising at least replacing face with a computer-generated face image, replacing face with a picture of someone else's face. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein levels of anonymisation include in, in order of increasing anonymisation: blurring of face, replacing face with a computer-generated face image, replacing face with a picture of someone else's face, blurring of entire body, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta further complements Fridental with said levels of anonymisation to uphold in a case the privacy of the monitored person where identification or reidentification of said person is suppressed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 9 (according to claim 7), Fridental is silent regarding wherein the instructions to anonymise comprise instructions that, when executed by the processor, cause the training data provider to select another face of a same gender as the person in the data feed.
Kuta further teaches in at least para. 0048, and 0086-0087 plurality of levels of anonymisation including face swapping comprising at least selecting in a case another face of a same gender as the person in the data feed. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein said cause the training data provider to select another face of a same gender as the person in the data feed, Kuta further complements Fridental with face swapping anonymisation method to uphold in a case the privacy of the monitored person where identification or reidentification of said person is suppressed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 10 (according to claim 7), Fridental further teaches wherein further comprising instructions that, when executed by the processor, cause the training data provider to:
determine a label associated with the data feed (determined at least in para. 0018-0021 emergency events and/or areas of naked skin of a monitored person indicative of said label further indicating something that occurs relatively rarely that can be anonymised in the training data);
and include the label in association with the processed data feed (identified events of further para. 0018-0021 including said labels are further associated with the sanitized or processed data feed).
Regarding claim 13, Fridental teaches a non-transitory computer readable medium storing a computer program for enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person (computer vision systems of at least para. 0008-0009 and Figs. 1-3 comprises computer executable codes stored in an understood computer readable medium for enabling at least in para. 0047 training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person),
the computer program comprising computer program code which, when executed on a training data provider causes the training data provider (the system as illustrated in at least Figs. 1-3 may be executed on processing module 140 and/or cloud 150 or system 300 further comprising said training data provider) to:
obtain a data feed capable of depicting the person (captured data feed capable of depicting the person of further para. 0019-0021 and 0045-0047);
select a level of anonymisation, from a plurality of levels of anonymisation (the system further in at least para. 0045-0046 may determine to protect the privacy of the monitored person by selecting one or more plurality of types or levels of anonymisation such as face Blurring, face removal and the like as one of said selection level of anonymisation);
anonymise the data feed according to the selected level of anonymisation, resulting in a processed data feed (sanitized data feeds of further para. 0019-0021 and 0045-0047);
transmit the processed data feed as training data for training a central ML model in a central node (cited sanitized data feed transmission of further para. 0046-0049 further supported by para. 0084-0086 as processed or anonymized data feed to the computer vision algorithms comprising the machine learning algorithms as understoodly in a case training data for training a central ML model in a central node 150 to identify in a case a state of the person or a medical emergency condition of the monitored person);
Fridental is silent regarding the above lined-out items such as receive an indication to increase or reduce the level of anonymisation from the central node; and repeat said computer program code, wherein a next iteration of the computer program code to select is based on the indication to increase or reduce the level of anonymisation.
Kuta in at least para. 0016 and 0029 trained anonymizer machine learning models and training models which may locally or remotely located in a node configured to anonymize or de-identify collected person data feed of further para. 0036 including at least face data content, the models further in at least para. 0018, and 0023-0026 may receive an instruction to use at least a naïve anonymization approach or a face-swapping transformation anonymization based on a determined level of anonymization to at least as cited “remove, reduce, or obscure some or all personal identifiable information of a source identity”, the models may further receive in at least para. 0048 and 0086-0087 an indication/instruction to perform a number of iterations processes to increase or reduce the level of anonymisation where the method is repeated, wherein a next iteration of the selecting is based on the indication to increase or reduce the level of anonymisation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta to include wherein said receive indication to increase or reduce the level of anonymisation from the central node; and repeat said computer program code, wherein a next iteration of the computer program code to select is based on the indication to increase or reduce the level of anonymisation, as discussed above, as both prior arts are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, anonymizing the data feed according to a selected level of anonymisation, and transmitting the processed data feed as training data for training a ML model to detect a state of the monitored person, Kuta’s currently repeated processes of anonymization iterations complements the method for enabling training of the anonymizing machine learning, ML, model of Fridental in the sense that the architecture of Kuta when combined with the architecture of the training ML model of Fridental provides a method and system based on the instructed set of next iterations with a further selection level of face-swapping anonymisation to in a case increasing or reduce the level of anonymisation of the monitored person whereby identity integrity of said person is preserved and is not revealed according to known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claims 5, and 11 are rejected under 35 U.S.C. 103 as being unpatentable and obvious over Fridental in view of Kuta, and further in view of Orellano et al. (2018/0333083, A1).
Regarding claim 5 (according to claim 4), Fridental in view of Kuta are silent regarding wherein the label indicates a near-fall event of the person.
Orellano teaches in at least para. 0051-0054 and 0025-0028 a machine learning model application configured to monitor a person based on a data feed capable of depicting a person, the model is trained to detect a label indicates a near-fall event of the person. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta, and further in view of Orellano to include wherein the label indicates a near-fall event of the person, as the prior arts of Fridental in view of Kuta, and further in view of Orellano are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, and to initiate or coordinate corrective actions when a fall or near-fall is detected, Orellano further complements Fridental in view of Kuta with a specific machine learning application trained to detect near-fall events which when added to the anonymisation models of Fridental in view of Kuta further facilitates to detect at least falling events and other medical conditions included in the transmitted data feeds to alert and/or obtain medical assistance for said monitored person according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 11 (according to claim 10), Fridental in view of Kuta are silent regarding wherein the label indicates a near-fall event of the person.
Orellano teaches in at least para. 0051-0054 and 0025-0028 a machine learning model application configured to monitor a person based on a data feed capable of depicting a person, the model is trained to detect a label indicates a near-fall event of the person. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Fridental in view of Kuta, and further in view of Orellano to include wherein the label indicates a near-fall event of the person, as the prior arts of Fridental in view of Kuta, and further in view of Orellano are in the same field of endeavor of enabling training of a machine learning, ML, model, for monitoring a person based on a data feed capable of depicting a person, and to initiate or coordinate corrective actions when a fall or near-fall is detected, Orellano further complements Fridental in view of Kuta with a specific machine learning application trained to detect near-fall events which when added to the anonymisation models of Fridental in view of Kuta further facilitates to detect at least falling events and other medical conditions included in the transmitted data feeds to alert and/or obtain medical assistance for said monitored person according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claims Standings
Claims 6 and 12 remained objected over the prior arts of record 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, and if all outstanding rejections are overcome.
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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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 06/02/2026
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