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 .
Claims 1-20 are pending for examination. Claims 1, 7, and 14 are independent.
Specification
The disclosure is objected to because of the following informalities:
[0021] “In this case, the record processing system 102 may user a combination of attributes…” Examiner believes “user” is meant to be “use” instead.
Appropriate correction is required.
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.
Claim 11 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.
Claim 11 recites the limitation “a combination of the record and the one or more other records”. It is unclear to the Examiner what “the record” specifically refers to; for the purposes of examination, “the record” will be understood as “the candidate record” introduced in independent Claim 7.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-12, 14-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim is a process, machine, manufacture, or composition of matter.
In the instant application, Claims 1-6 are directed to a machine, Claims 7-13 are directed to a machine, and Claims 14-20 are directed to a process. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
With respect to Claim 1:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
generate, (This step of generating a determination of the sentiment value for the candidate record is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
determine whether the sentiment value satisfies a threshold (This step of determining whether the sentiment value satisfies a threshold is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
selectively perform a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold (This step of performing an action based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
A system for machine learning based processing, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: (The memory and at least one processor are understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).)
obtain a training dataset including information associated with a set of processed records; (Obtaining information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing; (Training a machine learning model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
using the machine learning model (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
receive the candidate record for processing; (Receiving information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra-solution activitiesmodel and computer component as a tool to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
obtain a training dataset including information associated with a set of processed records; (Obtaining information is understood as a well-understood, routine, and conventional function, being exemplary of receiving or gathering data — see MPEP 2106.05(d)(II)(i).)
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing; (Training a machine learning model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
using the machine learning model (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
receive the candidate record for processing; (Receiving information is understood as a well-understood, routine, and conventional function, being exemplary of receiving or gathering data — see MPEP 2106.05(d)(II)(i).)
The additional elements as disclosed above alone or in combination do not recite significantly more than a judicial exception as they are well-understood, routine, conventional activities previously known to .
With respect to Claim 7:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
generate, ; (This step of generating a determination of the sentiment value for the candidate record is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
determine whether the sentiment value satisfies a threshold; (This step of determining whether the sentiment value satisfies a threshold is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
selectively perform a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold; (This step of performing an action based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
monitor a client device to determine a result of selectively performing the first processing action or the second processing action; and (This step of monitoring a device to make a determination is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. observation).)
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: (The non-transitory computer-readable medium is understood as mere instructions to apply the exception using a generic computer component — see MPEP 2106.05(f).).
obtain a training dataset including information associated with a set of processed records; (Obtaining information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing; (Training a machine learning model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
using the machine learning model (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
receive the candidate record for processing; (Receiving information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
update the machine learning model based on the result of selectively performing the first processing action or the second processing action. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra-solution activities in combination with generic implementation of a model and computer component as a tool to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
obtain a training dataset including information associated with a set of processed records; (Obtaining information is understood as a well-understood, routine, and conventional function, being exemplary of receiving or gathering data — see MPEP 2106.05(d)(II)(i).)
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing; (Training a machine learning model is understood as mere instructions to apply the exception on a computer — see MPEP 2106.05(f).)
using the machine learning model (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
receive the candidate record for processing; (Receiving information is understood as a well-understood, routine, and conventional function, being exemplary of receiving or gathering data — see MPEP 2106.05(d)(II)(i).)
update the machine learning model based on the result of selectively performing the first processing action or the second processing action. (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. judgement) — see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not recite significantly more than a judicial exception as they are well-understood, routine, conventional activities previously known to .
With respect to Claim 14:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
generat; (This step of generating a determination of the sentiment value for the candidate record is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
determin; (This step of determining whether the sentiment value satisfies a threshold is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
selecting, by the device, a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold; and (This step of performing an action based on a result is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. judgement).)
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
A method, comprising: (The method is understood as the idea of a solution and a generality of the application of the judicial exception — see MPEP 2106.05(f).)
receiving, by a device, a candidate record for processing; (Receiving information is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
using a machine learning model associated with determining a sentiment value (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
transmitting, by the device and based on selecting the first processing action or the second processing action, one or more messages associated with causing the first processing action or the second processing action to be performed. (Transmitting information based on a result is understood as an insignificant extra-solution activity — see MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra-solution activities in combination with generic implementation of a model and computer component as a tool to perform the abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
A method, comprising: (The method is understood as the idea of a solution and a generality of the application of the judicial exception — see MPEP 2106.05(f).)
receiving, by a device, a candidate record for processing; (Receiving information is understood as a well-understood, routine, and conventional function, being exemplary of receiving or gathering data — see MPEP 2106.05(d)(II)(i).)
using a machine learning model associated with determining a sentiment value (This step is reciting a judicial exception with the words “apply it” (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
transmitting, by the device and based on selecting the first processing action or the second processing action, one or more messages associated with causing the first processing action or the second processing action to be performed. (Transmitting information is understood as a well-understood, routine, and conventional function, being exemplary receiving and transmitting information over the internet — see MPEP 2106.05(d).)
The additional elements as disclosed above alone or in combination do not recite significantly more than a judicial exception as they are well-understood, routine, conventional activities previously known to the industry in combination with generic implementation of a model and computer component as a tool.
With respect to Claims 2 and 15:
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
Additional Elements:
wherein the training dataset includes at least one of: a biometric dataset, a biomarker dataset, an image dataset, a processed record dataset, a demographic dataset, or a user history dataset. (The specification of the training dataset is understood to be a field of use limitation — see MPEP 2106.05(h).)
With respect to Claims 3 and 16:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the one or more processors, when configured to generate the determination of the sentiment value, are further configured to:
determine one or more attributes of the candidate record, the one or more attributes corresponding to one or more features of the machine learning model; (This step for determining one or more attributes of the candidate record that correspond to one or more features of the machine learning model is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
generating the determination of the sentiment value based on the one or more attributes of the candidate record. (This step of generating a determination of the sentiment value for the candidate record is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
With respect to Claim 4:
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
Additional Elements:
wherein the one or more attributes include at least one of: an attribute relating to a user associated with the candidate record for processing, or an attribute relating to an entity associated with the candidate record for processing. (The specification of the one or more attributes is understood to be a field of use limitation. The limitation further specifies the one or more attributes of the candidate record — see MPEP 2106.05(h).)
With respect to Claim 5:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2 & 2B:
Additional Elements:
wherein the first processing action is associated with successfully processing the candidate record; (The specification of the first processing action is understood to be a field of use limitation — see MPEP 2106.05(h).)
wherein the second processing action is associated with at least one of: delaying processing of the candidate record, or rejecting processing of the candidate record. (The specification of the second processing action is understood to be a field of use limitation — see MPEP 2106.05(h).)
With respect to Claim 8:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the determination of the sentiment value is associated with a determination of an emotional state of a user requesting processing of the candidate record, (This step for determining the sentiment value associated with determination of an emotional state of a user is practically performable in the human mind and is understood to be a recitation of a mental process with the aid of pen and paper (i.e. evaluation).)
2A Prong 2 & 2B:
Additional Elements:
the machine learning model being a sentiment analysis model for determining the emotional state of the user. (This step is reciting a judicial exception with the words "apply it" (or an equivalent), or merely invoking computers or machinery as a tool to perform the abstract idea (i.e. execution) — see MPEP 2106.05(f).)
With respect to Claim 9:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to:
identify one or more other records processed within a threshold time period of generation of the candidate record, the one or more records being of a pre-selected type; (This step for identifying one or more records within a time period is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. evaluation).)
generate the determination of the sentiment value based on the one or more other records. (This step for generating a determination of the sentiment value is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. judgement).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
With respect to Claim 10:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to:
generate the determination of the sentiment value based on the candidate record being of the other pre-selected type relating to the pre-selected type of the one or more other records. (This step for generating a determination of the sentiment value is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. evaluation).)
2A Prong 2 & 2B:
Additional Elements:
wherein the candidate record is of another pre-selected type relating to the pre-selected type of the one or more other records (The specification of the candidate record is understood to be a field of use limitation — see MPEP 2106.05(h).)
With respect to Claim 11:
2A Prong 1: The claim recites an abstract idea, law of nature, or natural phenomenon.
wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to:
identify one or more other records, the one or more other records being previously processed records or other candidate records for processing, a combination of the record and the one or more other records being a pre-selected type of combination; (This step for identifying one or more records is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. evaluation).)
generate the determination of the sentiment value based on the one or more other records. (This step for generating a determination of the sentiment value is practically performable in the human mind and is understood to be a recitation of a mental process (i.e. evaluation).)
2A Prong 2 & 2B: The judicial exception is not integrated into a practical application.
With respect to Claims 12 and 17:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
wherein the one or more instructions further cause the device to:
transmit one or more alerts to one or more third party entities based on the sentiment value. (Transmitting information based on a result is understood as an insignificant extra-solution activity, — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the one or more instructions further cause the device to:
transmit one or more alerts to one or more third party entities based on the sentiment value. (Transmitting information is understood as a well-understood, routine, and conventional function, exemplary of receiving and transmitting information over the internet — see MPEP 2106.05(d).)
With respect to Claim 19:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
transmitting an alert to a pre-selected device based on the sentiment value. (Transmitting information based on a result is understood as an insignificant extra-solution activity, being exemplary of mere data outputting — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting an alert to a pre-selected device based on the sentiment value. (Transmitting information is understood as a well-understood, routine, and conventional function, exemplary of transmitting data — see MPEP 2106.05(d)(II)(i).)
With respect to Claim 20:
2A Prong 1: The claim does not recite an Abstract idea.
2A Prong 2: The judicial exception is not integrated into a practical application.
Additional Elements:
transmitting an alert to a browser extension associated with a client device requesting processing of the candidate record, based on the sentiment value, to trigger a function of the browser extension. (Transmitting information based on a result is understood as an insignificant extra-solution activity, being exemplary of mere data outputting — see MPEP 2106.05(g).)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
transmitting an alert to a browser extension associated with a client device requesting processing of the candidate record, based on the sentiment value, to trigger a function of the browser extension. (Transmitting information is understood as a well-understood, routine, and conventional function, exemplary of transmitting data — see MPEP 2106.05(d)(II)(i).)
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-12, 14-17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cheng (US 2021/0390445 A1).
With respect to Claim 1:
Cheng teaches: A system for machine learning based processing, the system comprising:
one or more memories ([Fig. 1, 0035, 0074] disclose “The server may include a processor, a memory.”); and
one or more processors, communicatively coupled to the one or more memories, configured to: ([0036] discloses “The processor may be coupled to the memory.”)
obtain a training dataset including information associated with a set of processed records ([0095, 0097] discloses how the plurality of data obtained by the server “may be preprocessed in the plurality of databases.” [0008] discloses “training, using the plurality data, a sentiment analysis machine learning model.” [0021] discloses “the machine learning models are trained by multi-channel data inputs.” and [0062] discloses “those multi-channel data inputs are processed and analyzed by the sentiment analysis ML model”);
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing ([0096] discloses “The decision engine of the server receives user-configured metadata inputs from a user device… the sentiment analysis model of the server performs a sentiment analysis on the plurality data inputs… the decision engine of the server determines an action based on a result from the sentiment analysis. [0097] discloses “The sentiment analysis ML model may be trained using labeled or unlabeled data collected from the plurality of data devices.” [0052] discloses “The plurality of data devices may be third party devices and store the various data separately, without aggregating the data for a long period of time,” indicating that the candidate record may be stored and not yet processed by the model.);
generate, using the machine learning model, a determination of the sentiment value for the candidate record ([0099 - 0104] discloses “the sentiment analysis ML model performs a sentiment analysis on the data to produce a result of sentiment analysis.” and describes the plurality of data that the sentiment analysis ML model may receive and analyze.);
receive the candidate record for processing ([0117] discloses “the decision engine receive(s) from the user device user-configured data of the user that comprises the user-configured criteria.”);
determine whether the sentiment value satisfies a threshold ([0118] discloses “the decision engine compares each component of the sentiment analysis result to one corresponding user-configured metadata of the user. For example, the decision engine compares the heart rate level in the emotional state with the heart rate leve(l) threshold in the user-configured criteria.”); and
selectively perform a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold. ([0021] discloses “the machine learning models may be configured to identify users’ emotional/mental/behavioral states based on the multi-channel data inputs to make corresponding decisions, such as, triggering a recommendation, to prompt, prevent, or alert the users in response to user-configured criteria and commands.” [0023] discloses “The decisions (e.g., recommendation/prompt/alert/action) are determined not only based on the behavioral state and/or the emotional state evaluated by the ML models, but also based on the user-configured criteria.”)
With respect to Claim 7:
Cheng teaches: A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: ([0130] discloses “The computer-accessible medium can contain executable instructions thereon.”.)
obtain a training dataset including information associated with a set of processed records ([0095, 0097] discloses how the plurality of data obtained by the server “may be preprocessed in the plurality of databases.” [0008] discloses “training, using the plurality data, a sentiment analysis machine learning model.” [0021] discloses “the machine learning models are trained by multi-channel data inputs.” and [0062] discloses “those multi-channel data inputs are processed and analyzed by the sentiment analysis ML model”);
train, using the training dataset, a machine learning model to determine a sentiment value associated with a candidate record that is queued for processing;
receive the candidate record for processing ([0117] discloses “the decision engine receive(s) from the user device user-configured data of the user that comprises the user-configured criteria.”);
generate, using the machine learning model, a determination of the sentiment value for the candidate record ([0099 - 0104] discloses “the sentiment analysis ML model performs a sentiment analysis on the data to produce a result of sentiment analysis.” and describes the plurality of data that the sentiment analysis ML model may receive and analyze.);
determine whether the sentiment value satisfies a threshold ([0118] discloses “the decision engine compares each component of the sentiment analysis result to one corresponding user-configured metadata of the user. For example, the decision engine compares the heart rate level in the emotional state with the heart rate leve(l) threshold in the user-configured criteria.”);
selectively perform a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold ([0021] discloses “the machine learning models may be configured to identify users’ emotional/mental/behavioral states based on the multi-channel data inputs to make corresponding decisions, such as, triggering a recommendation, to prompt, prevent, or alert the users in response to user-configured criteria and commands.” [0023] discloses “The decisions (e.g., recommendation/prompt/alert/action) are determined not only based on the behavioral state and/or the emotional state evaluated by the ML models, but also based on the user-configured criteria.”)
monitor a client device to determine a result of selectively performing the first processing action or the second processing action; and ([0123] discloses “the server transmits information of the action to the user device.” [0124] discloses “the server receives feedback on the information of the action from the user device. In response to receiving the information of the action, the user may use the user device to provide their feedback regarding the information of the action.”)
update the machine learning model based on the result of selectively performing the first processing action or the second processing action. ([0125] discloses that “the server determines whether to perform the action based on the feedback. The server may modify the action based on the feedback… The ML models may be retrained using the feedback… the ML models can be tuned based on the feedback and/or added inputs.”)
With respect to Claim 14:
Cheng teaches: A method, comprising: ([Claim 20] discloses “A method, comprising:”)
receiving, by a device, a candidate record for processing ([0117] discloses “the decision engine receive(s) from the user device user-configured data of the user that comprises the user-configured criteria.”);
generating, using a machine learning model associated with determining a sentiment value, a determination of the sentiment value for the candidate record ([0099 - 0104] discloses “the sentiment analysis ML model performs a sentiment analysis on the data to produce a result of sentiment analysis.” and describes the plurality of data that the sentiment analysis ML model may receive and analyze.);
determining, by the device, whether the sentiment value satisfies a threshold ([0118] discloses “the decision engine compares each component of the sentiment analysis result to one corresponding user-configured metadata of the user. For example, the decision engine compares the heart rate level in the emotional state with the heart rate leve(l) threshold in the user-configured criteria.”);
selecting, by the device, a first processing action associated with the candidate record or a second processing action associated with the candidate record based on whether the sentiment value satisfies the threshold ([0021] discloses “the machine learning models may be configured to identify users’ emotional/mental/behavioral states based on the multi-channel data inputs to make corresponding decisions, such as, triggering a recommendation, to prompt, prevent, or alert the users in response to user-configured criteria and commands.” [0023] discloses “The decisions (e.g., recommendation/prompt/alert/action) are determined not only based on the behavioral state and/or the emotional state evaluated by the ML models, but also based on the user-configured criteria.”) and
transmitting, by the device and based on selecting the first processing action or the second processing action, one or more messages associated with causing the first processing action or the second processing action to be performed. ([0123] discloses “The server transmits information of the action to the user device… the information of the action can be presented to the user in various fashions, such as, text messages, a web portal displayed on the user device, and so on.”)
With respect to Claims 2 and 15:
Cheng teaches: The system of Claim 1, wherein the training dataset includes at least one of: a biometric dataset, a biomarker dataset, an image dataset, a processed record dataset, a demographic dataset, or a user history dataset. ([0008] discloses “The method comprises: generating, by a plurality of devices, a plurality of data comprising image data, voice data, text data, geolocation data, biometric data, transactional data, and user metadata; training, using the plurality [of] data, a sentiment analysis machine learning model,”)
With respect to Claims 3 and 16:
Cheng teaches: The system of Claim 1 and the method of Claim 14 respectively, wherein the one or more processors, when configured to generate the determination of the sentiment value, are further configured to:
determine one or more attributes of the candidate record, the one or more attributes corresponding to one or more features of the machine learning model; ([0055, 0061, 0068] discloses “the decision engine analyzes and evaluates one or more components of the emotional state produced from the sentiment analysis ML model and the behavioral state produced from the behavior analysis ML model again(st) the corresponding user-configured metadata inputs to make a decision and determine a responsive action based on the decision) and
generate the determination of the sentiment value based on the one or more attributes of the candidate record. ([0056] discloses “the emotional state is determined based on a combination of data inputs, for example, including words that the user says, words that the user types, a type of way that the body temperature is taken, non-verbal cues, and so forth.”)
With respect to Claim 4:
Cheng teaches: The system of Claim 3, wherein the one or more attributes include at least one of:
an attribute relating to a user associated with the candidate record for processing, ([0051] discloses that the various data may include “biometric data (e.g. a heart rate of the user).”) or
an attribute relating to an entity associated with the candidate record for processing. ([0103] discloses that the sentiment analysis model “receives history transactional data of the user. The history transactional data may include, but not limited to, transaction charges, transaction date and time, transaction locations, merchants, warranty information, and the like.”)
With respect to Claim 5:
Cheng teaches: The system of Claim 1, wherein the first processing action is associated with successfully processing the candidate record; ([0021] discloses “the recommendation/prompt/alert/action can include, for example, launching a ridesharing application on a device of a user when the user is evaluated to have an angry face and irregular voice at a time after 11:00 pm local time.”) and
wherein the second processing action is associated with at least one of: delaying processing of the candidate record, ([0021] discloses “freezing a credit card of the user when the user is evaluated to act abnormally while shopping,”) or
rejecting processing of the candidate record. ([0025] discloses “If a charge to the credit card is outside of the pre-specified time frame, then the charge is considered as invalid and would be declined.”)
With respect to Claim 8:
Cheng teaches: The non-transitory computer-readable medium of Claim 7, wherein the determination of the sentiment value is associated with a determination of an emotional state of a user requesting processing of the candidate record, the machine learning model being a sentiment analysis model for determining the emotional state of the user. ([0104] discloses “the sentiment analysis ML model performs a sentiment analysis on the data to produce a result of sentiment analysis… The result comprises an emotional state of the user.”)
With respect to Claim 9:
Cheng teaches: The non-transitory computer-readable medium of Claim 7, wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to: identify one or more other records processed within a threshold time period of generation of the candidate record, the one or more records being of a pre-selected type; and generate the determination of the sentiment value based on the one or more other records. ([0056] discloses “the sentimental analysis ML model not only captures the actual syntax of the words, but also how the words are spoken (e.g., how the user’s body is reacting during that time). This can be an indicator of a stress level component of the emotional state.”)
With respect to Claim 10:
Cheng teaches: The non-transitory computer-readable medium of Claim 9, wherein the candidate record is of another pre-selected type relating to the pre-selected type of the one or more other records; ([0062] discloses “one or more components of the emotional state and/or the behavioral state may meet the corresponding data thresholds of the user-configured criteria.”) and
wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to: generate the determination of the sentiment value based on the candidate record being of the other pre-selected type relating to the pre-selected type of the one or more other records. ([0062] discloses “the ML model and the decision engine may produce an action to freeze a credit card of the user if the user’s heart rate produced from the ML model is greater than the heart rate threshold of the user-configured criteria.”)
With respect to Claim 11:
Cheng teaches: The non-transitory computer-readable medium of Claim 7, wherein the one or more instructions, that cause the device to generate the determination of the sentiment value, cause the device to: identify one or more other records, the one or more other records being previously processed records or other candidate records for processing, a combination of the record and the one or more other records being a pre-selected type of combination; and generate the determination of the sentiment value based on the one or more other records. ([0103] discloses “the sentiment analysis ML model receives history transactional data of the user.” [0104] discloses “the sentiment analysis ML model performs a sentiment analysis on the image data, voice data, geolocation data, biometric data, and the history transactional data to produce a result of sentiment analysis.”)
With respect to Claims 12 and 17:
Cheng teaches: The non-transitory computer-readable medium of Claim 7 and the method of Claim 14 respectively, wherein the one or more instructions further cause the device to: transmit one or more alerts to one or more third party entities based on the sentiment value. ([0064] discloses “when the user is determined to have unusual behaviors such as walking in an unbalanced manner and/or an elevated heart rate, the action may be to automatically call a doctor or an ambulance for the user because the user may be in a stroke. The action may further include notifying a law enforcement officer, calling a taxi, or donating money.”)
With respect to Claim 19:
Cheng teaches: The method of claim 14, further comprising: transmitting an alert to a pre-selected device based on the sentiment value. ([0123] discloses “the server transmits information of the action to the user device. The information of the action may comprise an emotional state of the user determined by the sentiment analysis model.”)
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.
Claims 6, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng (US 2021/0390445 A1) in view of Bikumala et al. (US 2021/0012371 A1), hereinafter “Bikumala”.
With respect to Claims 6, 13, and 18:
Cheng teaches: The system of Claim 1, the non-transitory computer-readable medium of Claim 7, and the method of Claim 14 respectively.
Cheng does not teach: wherein the one or more processors are further configured to: adjust a layout or order of one or more elements of a webpage based on the sentiment value.
However, Bikumala teaches in the same field of endeavor: wherein the one or more processors are further configured to: adjust a layout or order of one or more elements of a webpage based on the sentiment value. ([0016] discloses “the computing device (or the server) may modify portions of the site substantially in real time based on the user’s micro-expression (or sentiment). For example, if the micro-expression indicates that the user is squinting, the computing device (or server) may instruct the browser to increase a size of the portion of the site that the user is viewing.”)
Cheng and Bikumala are both analogous art to the present invention because both are from the same field of endeavor directed towards using machine learning to perform sentiment analysis of a user.
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Cheng’s teachings by adjusting a layout of a webpage or interface base on the sentiment value as taught by Bikumala. One would have been motivated to make this modification in order to improve personalization for users/customers and better target or direct specific feature(s) towards users whose sentiment is determined to be more relevant to those features.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng (US 2021/0390445 A1) in view of Walters et al. (US 2022/0084033 A1), hereinafter “Walters”.
With respect to Claim 20:
Cheng teaches: The method of claim 14, further comprising: transmitting an alert to a browser([0021] discloses “The recommendation/prompt/alert/action can include, for example, launching a ridesharing application on a device of a user when the user is evaluated to have an angry face.” [0123] discloses “the server transmits information of the action to the user device. The information of the action may comprise an emotional state of the user determined by the sentiment analysis model.”)
Cheng does not teach: further comprising: transmitting an alert to a browser extension associated with a client device requesting processing of the candidate record, based on the sentiment value, to trigger a function of the browser extension.
However, Walters teaches in the same field of endeavor: further comprising: transmitting an alert to a browser extension associated with a client device requesting processing of the candidate record, based on the sentiment value, to trigger a function of the browser extension. ([0081] discloses “The browser extension server can use the data provided by the browser extension application to make a determination as to whether the user is about to engage into a risky transaction (e.g., signing up or purchasing a product on a website that is likely going to exploit the user's financial and personal information). If the browser extension server determines that the user is about to engage in a risky transaction, the browser extension server can transmit a signal to the browser extension application to warn the user and/or take remedial action.”)
Cheng and Walters are both analogous art to the present invention because both are from the same field of endeavor directed towards monitoring user activity and using that activity data to provide information/action recommendations.
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify Cheng’s teachings by utilizing a browser extension on the client device to take action based on analysis results as taught by Walters. One would have been motivated to make this modification in order to improve personalization and/or safety for users/customers.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al. (US 2021/0374671 A1) discloses emotion-based action recommendations using machine learning models to perform sentiment analysis and using the analysis output to provide recommendations to the user.
Warner et al. (US 2023/0162230 A1) discloses using machine learning models to perform sentiment analysis towards the goal of targeting specific content to the user.
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/BRIAN D. BUI/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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