Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
Claim 10:
“the application configured for: training a generative Artificial Intelligence (AI) system”
“the application configured for: … capturing data using one or more sensor of the device including capturing biometric data or behavioral data”
“the application configured for: … determining a trust score using one or more on-device heuristic models;”
Claim 14:
“wherein the application is further configured for generating periodic samples of the data, compressing the periodic samples and sharing the periodic samples with a server device.”
Claim 15:
Dependent from 10 “the application configured for: … determining a trust score using one or more on-device heuristic models;”
“wherein determining the trust score includes predicting a next element in a sequence based on a context of previously generated elements, and when sampled points generated by the device based on the captured data do not align with predicted points from a model, the trust score is reduced.”
Claim 16:
“wherein the application is further configured for blocking one or more components or one or more applications on the device when the trust score is below a threshold.”
Claim 19:
“ a server device configured;”
“and a user device configured for: training a generative Artificial Intelligence (AI) system, wherein the generative AI system utilizes autoregression and transformers to learn and train;”
“a user device configured for … capturing data using one or more sensor of the device including capturing biometric data or behavioral data;”
“a user device configured for … generating periodic samples of the data, compressing the periodic samples and sharing the periodic samples with the server device;”
“a user device configured for … and determining a trust score using one or more on-device heuristic models.”
Claim 23:
Dependent from claim 19 “a user device configured for … and determining a trust score using one or more on-device heuristic models.”
“wherein determining the trust score includes predicting a next element in a sequence based on a context of previously generated elements, and when sampled points generated by the device based on the captured data do not align with predicted points from a model, the trust score is reduced.”
Claim 24:
“wherein the user device is further configured for blocking one or more components or one or more applications on the device when the trust score is below a threshold.”
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-9, and 17-26 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 8, 17, 25, the claims recite “and/or device handoff.” The claim cannot be interpreted as both “and” as well as “or” and therefore is indefinite, applicant is respectfully requested to use “and” or “or.”
Regarding claims 9, 18, and 26, the claims recite “and facial/voice data.” Examiner respectfully notes that “facial/voice” is indefinite and should recite “facial, or voice data” or “facial and voice data.”
Regarding claims 19-26, claim 19 recites “a server configured.” Examiner notes the language is indefinite as it is unclear what it is configured for. Should recite “a server”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 9-10, 12, 18-19, 21, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR.
Regarding claim 1, Kounavis teaches a method programmed in a non-transitory memory of a device comprising: (0152; a method performed by a device (see Fig 1-2 device); 0177; a computer program product which may include one or more transitory or non-transitory machine-readable storage media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may result in the one or more machines carrying out operations in accordance with embodiments)
training a generative Artificial Intelligence (AI) system, (0057; training a neural network model; [0152] In one embodiment, as illustrated in method 1000 of FIG. 10, the implementation asymmetry between the training and classification/scoring stages is shown as building a functional artificial intelligence (AI) system using the novel MP architecture.)
wherein the generative AI system utilizes transformers to learn and train (0065-0066 and 0131; where the neurons are transformer neurons);
capturing data using one or more sensor of the device (0049; computing device includes sensors) including capturing biometric data or behavioral data; (0157-0158; capturing by sensors of the computing device biometric readings and well as head-tracking and other movement (behavioral data); 0180 and -185-0186; hand and eye movements)
and determining a trust score (scoring for a specific task) using one or more on-device heuristic models (scoring and classification logic). (0069; 0074; 0151-0152; 0217; the algorithms compete and are scored using scoring and classification with respect to a task until a clear winner emerges; [0149] As discussed above, in one embodiment, the novel MP architecture stages a competition between algorithms, where algorithms are allowed to mutate in order to improve their scores. [0151] Once the training process has determined a learned algorithm, the classification/scoring process, as facilitated by classification and scoring logic 207, may not need to use the same layered architecture for performing classification. Since the training process returns an interpretable, explainable, and implementable algorithm, the classification/scoring process may just implement the learned algorithm and not the entire layered architecture. [0152] In one embodiment, as illustrated in method 1000 of FIG. 10, the implementation asymmetry between the training and classification/scoring stages is shown as building a functional artificial intelligence (AI) system using the novel MP architecture. )
Kounavis does not explicitly teach the underlined wherein the generative AI system utilizes autoregression and transformers to learn and train
In an analogous art NR teaches wherein the generative AI system utilizes autoregression and transformers to learn and train (0073; a generative neural model that uses training/learning methods that include but are not limited to autoregression and transformer methods)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis] to include [ a generative model that uses autoregression and transformer methods for training and learning ] as is taught by [NR].
The suggestion/motivation for doing so is to [improve autonomous control [0002-0003]].
Regarding claim 3, Kounavis in view of NR teach the method of claim 1 and is disclosed above, Kounavis teaches wherein the data includes movement information, video information (0155-0158; captured video) or audio information (0155-0158; captured audio and sound). (0157-0158; capturing by sensors of the computing device biometric readings and well as head-tracking and other movement (behavioral data))
Regarding claim 9, Kounavis in view of NR teach the method of claim 1 and is disclosed above, Kounavis further teaches wherein the biometric data comprises fingerprint data and facial/voice data (0160; captured biometric data including fingerprint and facial data and voice data)
Regarding claim 10, the claim inherits the same rejection as claim 1 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 12, the claim inherits the same rejection as claim 3 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 18, the claim inherits the same rejection as claim 9 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 19, the claim inherits the same rejection as claim 1 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Regarding claim 21, the claim inherits the same rejection as claim 3 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Regarding claim 26, the claim inherits the same rejection as claim 9 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Claim(s) 2, 4, 11, 13, 20, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR, and further in view of Soni et al. (US 20200342362 A1).
Regarding claim 2, Kounavis in view of NR teach the method of claim 1 and is disclosed above, Kounavis in view of NR do not explicitly teach wherein the generative AI system is trained using synthetic data.
In an analogous art Soni teaches wherein the generative AI system is trained using synthetic data. (Fig 8; [0091] In the example apparatus 900, the 1D data generator 930 includes one or more AI models, such as a t-GAN, other GAN, etc., (e.g., the generative model 270, 275, GAN 300, 800 etc.), to process one-dimensional time series data to generate synthetic (e.g., artificial) data to impute missing data from a waveform (e.g., as shown in the example of FIG. 4, etc.), generate additional synthetic data for a training and/or testing data set … The correlated time series data/annotation output is provided by the output processor 940 to the communication interface 910 to be transmitted for storage and/or use, for example, in training an AI model, testing an AI model, imputing and/or interpolating missing data in an incomplete waveform, etc.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [training an AI model using synthetic data] as is taught by [Soni].
The suggestion/motivation for doing so is to [improve data correlation [0003-0005]].
Regarding claim 4, Kounavis in view of NR teach the method of claim 1, and is disclosed above, Kounavis in view of NR do not explicitly teach wherein the data captured comprises a time series.
In an analogous art Soni teaches wherein the data captured comprises a time series (0076;In some examples, k events can be encoded as time series data over k channels to learn, using one or more GANs and/or other generative models, a distribution of (n+k) variables/channels, where n is a number of channels for real time-series data. In such examples, real, measured data can be obtained from one or more sensors, monitors, etc., for n variables (e.g., real, multi-channel, time-series data, etc.) and a corresponding k events (e.g., annotations, etc.))
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [include capturing time-series data] as is taught by [Soni].
The suggestion/motivation for doing so is to [improve data correlation [0003-0005]].
Regarding claim 11, the claim inherits the same rejection as claim 2 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 13, the claim inherits the same rejection as claim 4 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 20, the claim inherits the same rejection as claim 2 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Regarding claim 22, the claim inherits the same rejection as claim 4 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR, further in view of Hashimoto (US 20230097749 A1)
Regarding claim 5, Kounavis in view of NR teach the method of claim 1, and is disclosed above, Kouvanis in view of NR do not explicitly teach generating periodic samples of the data, compressing the periodic samples and sharing the periodic samples with a server device
In an analogous art Hashimoto teaches generating periodic samples of the data, compressing the periodic samples and sharing the periodic samples with a server device. (0051; In some embodiments, the sensor data includes a time period before and after the data used to generate the invariant feature map was captured. In some embodiments, the sensor data is associated with location and time data. The sensor data, and any optional additional information, is then transmitted to the server, e.g., server 18 (FIG. 1). In some embodiments, the sensor data is transmitted wirelessly. In some embodiments, the sensor data is transmitted via a wired connection. In some embodiments, the sensor data is post-processed, such as data compression, resize, crop, and etc., before uploaded. [0052] In operation 426, the sensor data is received by the server.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [generating periodic data and compressing the data and sending to the server] as is taught by [Hashimoto].
The suggestion/motivation for doing so is to [improve data collection [0001-0002]].
Regarding claim 14, the claim inherits the same rejection as claim 5 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Claim(s) 6, 15, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR, further in view of Iyer et al. (US 20220075605 A1)
Regarding claim 6, Kounavis in view of NR teach the method of claim 1, and is disclosed above, Kouvanis in view of NR do not explicitly teach wherein determining the trust score includes predicting a next element in a sequence based on a context of previously generated elements, and when sampled points generated by the device based on the captured data do not align with predicted points from a model, the trust score is reduced.
Iyer wherein determining the trust score (confidence score) includes predicting a next element in a sequence (predicting potential next sequences) based on a context of previously generated elements (previous data input and training_, (0030 The ML model may then consume this input and predict one or more potential next sequences of activities for autocompletion, along with a confidence score. If the confidence score(s) exceed a suggestion threshold (e.g., 75%, 90%, etc.), the next sequence(s) may be displayed to the user or completed automatically by the RPA designer application; 0035; The ML model may then be run again when the next activity is added until the suggestion confidence threshold is met. Thus, the confidence score(s) for predicted next sequence(s) and the suggestion confidence threshold may be used to determine whether to suggest a given sequence from the ML model.)
and when sampled points (previous user activity/sequences) generated by the device based on the captured data (0035; Once the user adds an activity to the workflow, the last N activities including this newly added activity, or potentially all previous activities, may be considered by the ML model to check whether a next logical sequence of activities can be predicted and autocompleted.) do not align with predicted points from a model (predicted next sequence), the trust score is reduced. (confidence score is lower) (When the actual v. predicted sequences differ the confidence score will be lower during the next suggest based on feedback. 0030; If the confidence score(s) exceed a suggestion threshold (e.g., 75%, 90%, etc.), the next sequence(s) may be displayed to the user or completed automatically by the RPA designer application. Autocompletion may have its own, higher threshold in some embodiments (e.g., 95%, 99%, etc.). [0039] In some embodiments, the ML model may be trained via attended feedback, unattended feedback, or both. Attended feedback includes where the developer is actively involved in producing the training data. For instance, the RPA developer may be prompted for reasons why he or she did not want to use the predicted next sequence of activities and provide this to the server side for training. Unattended feedback includes information gleaned without the user's active participation (or potentially, knowledge). For instance, the mere fact that a user has rejected the sequence of activities may provide information that the ML model may not be working as intended for that given user. [0040] The attended feedback, unattended feedback, or both, provide input for training the local and global ML models. The global ML model is a generalized model for all RPA developers or a subset of RPA developers, and the local ML model is personalized and user-specific. If the local ML model does not exist or does not find a sequence that meets or exceeds the suggestion confidence threshold, the global ML model may be consulted to attempt to find a suggestion meets or exceeds the suggestion confidence threshold for prediction. )
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [using sequence prediction and feedback to impact confidence scoring] as is taught by [Iyer].
The suggestion/motivation for doing so is to [improve AI/ML prediction and suggestions [0001-0007]].
Regarding claim 15, the claim inherits the same rejection as claim 6 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 23, the claim inherits the same rejection as claim 6 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Claim(s) 7, 16, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR, further in view of Dashevskiy et al. (US 11985152 B1)
Regarding claim 7, Kounavis in view of NR teach the method of claim 1, and is disclosed above, Kouvanis in view of NR do not explicitly teach further comprising blocking one or more components or one or more applications on the device when the trust score is below a threshold.
In an analogous art Dashevskiy teaches blocking one or more components or one or more applications on the device when the trust score is below a threshold. ((Col 6 Lines 14-23) In some examples, if a trust score falls below or meets a certain threshold or value, the application and its traffic on the network may be affected. For example, if the trust score is below a certain threshold, the application may be monitored on cellular network 108 at a heightened level. In other examples, if the trust score is below a certain threshold, the application traffic may be throttled or blocked. In other examples, if the trust score is below a certain threshold, the application traffic may be placed on a lower priority.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [blocking applications when a trust score falls below a threshold] as is taught by [Dashevskiy].
The suggestion/motivation for doing so is to [improve data collection [0001-0002]].
Regarding claim 16, the claim inherits the same rejection as claim 7 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 24, the claim inherits the same rejection as claim 7 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Claim(s) 8, 17, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kounavis et al. (US 20190108447 A1) nor Nandakumar Raghav (US 20240334236 A1) hereinafter NR, further in view of Stewart et al. (US 20230254161 A1)
Regarding claim 8, Kounavis in view of NR teach the method of claim 1, and is disclosed above, Kouvanis in view of NR do not explicitly teach wherein the behavioral data comprises a shaking motion, a gait motion, micro-tremors, device pickup, and/or device handoff.
In an analogous art Stewart teaches wherein the behavioral data comprises a shaking motion, a gait motion, micro-tremors, device pickup, and/or device handoff (0054; biometric authentication of a user and calculating a confidence level to a user device as a function of the biometric authentication of the user. Confidence level in biometric authentication may be computed, for instance, using one or more biometric authentication measures to suggest if a user device is being used by its owner. For instance, a variety of biometric authentication measures to confirm behavior biometrics of a user may be tested, for example speech, voice, signature, keystroke, and/or gait may be measured and analyzed to determine if a user device 108 is being used by its owner. Biometric authentication measures may also employ the use of biometric sensors and scanners that may detect and acquire data necessary for biometric recognition and verification. This may include for example, sensors that may scan and analyze a user face, palm, vein, fingerprint, iris, retina, hand geometry, finger geometry, tooth shape, radiographic dental image, ear shape, olfactory, speech, voice, signature, keystroke dynamics recorder, and/or devices to perform movement signature recognition and/or gait energy images.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of [Kounavis in view of NR] to include [blocking applications when a trust score falls below a threshold] as is taught by [Dashevskiy].
The suggestion/motivation for doing so is to [improve data tracking [0001-0002]].
Regarding claim 17, the claim inherits the same rejection as claim 8 for reciting similar limitations in the form of a device claim, Kounavis teaches a device (Figs 1-2 computing device)
Regarding claim 25, the claim inherits the same rejection as claim 8 above for reciting similar limitations in the form of a system claim Kounavis teaches a (0164; system)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDERRAHMEN H CHOUAT whose telephone number is (571)431-0695. The examiner can normally be reached on Mon-Fri from 9AM to 5PM PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Parry, can be reached at telephone number 571-272-8328. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Abderrahmen Chouat
Examiner
Art Unit 2451
/Chris Parry/Supervisory Patent Examiner, Art Unit 2451
Prosecution Insights