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.
Response to Amendment
This office action is responsive to the amendments filed on 09/10/2026. As directed by the amendments, Claims 1, 6-7, 11, 13-14, 16, and 18 are amended.
Response to Arguments
The following is the Examiner’s response to the Applicant’s arguments and remarks filed on 09/10/2026.
Specification
Examiner’s response: Applicant’s arguments and amendments, see page 11 of applicant response filed on 09/10/2026, with respect to “Objection to the Specification” have been fully considered and are persuasive. The Objection to the Specification has been withdrawn.
35 U.S.C. §112(b)
Examiner’s response: Applicant’s arguments and amendments, see page 12 of applicant response filed on 09/10/2026, with respect to “Rejection under 35 U.S.C. §112(b)” have been fully considered and are persuasive. The rejection under 35 U.S.C. §112(b) has been withdrawn.
35 U.S.C. §101
Applicant argues: “independent claims 1, 7, and 14, as amended, and the claims that depend thereon, are patent-eligible under 35 U.S.C. §101.”
Examiner’s response: Applicant’s arguments in light of the amended claims have been fully considered and are persuasive. The rejection under 35 U.S.C. §101 has been withdrawn.
35 U.S.C. §102
Applicant argues: Regarding “Rejection under 35 U.S.C. §102 based on CHENG” and “Rejection of Dependent Claims” on pages 12-13 of applicant response, applicant states
“the cited sections of the applied reference, whether taken alone or in any reasonable combination, do not disclose at least "adjust an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items," as recited in claim 1, as amended. Independent claims 7 and 14, as amended, recite similar features” and
“these claims [6, 13, 18, 20] are patentable for at least the reasons set forth above with respect to their parent claims and for their additional distinguishing features recited therein.”
Examiner’s response: Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See rejection below under 35 U.S.C. §103.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-12 and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (US 2021/0390445 A1), hereinafter "Cheng", in view of Martineau et al. (US 2019/0392330 A1), hereinafter "Martineau".
With respect to Claim 1:
Cheng teaches: A system for machine learning based processing, the system comprising:
one or more memories; and ([Fig. 1, 0035, 0074] Cheng discloses “The server may include a processor, a memory.”)
one or more processors, coupled to the one or more memories, configured to: ([0036] Cheng discloses “The processor may be coupled to the memory.”)
obtain a training dataset including information associated with a set of processed records; ([0095, 0097] Cheng discloses how the plurality of data obtained by the server “may be preprocessed in the plurality of databases.” [0008] Cheng discloses “training, using the plurality data, a sentiment analysis machine learning model.” [0021] Cheng discloses “the machine learning models are trained by multi-channel data inputs.” [0062] Cheng 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] Cheng 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] Cheng discloses “The sentiment analysis ML model may be trained using labeled or unlabeled data collected from the plurality of data devices.” [0052] Cheng 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] Cheng 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] Cheng 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] Cheng 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; and ([0021] Cheng 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] Cheng 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.”)
Cheng does not explicitly teach: adjust an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items.
However, Martineau teaches in the same field of endeavor: adjust an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items. ([0088] Martineau discloses selecting a suggested item to place in the user’s recommendation list of “items having features that satisfy the recommendation target” The suggested item is placed “in a specified position or ranking with respect to other items already in the user’s recommendation list, such as in order of decreasing score.” [0089] Martineau states that the recommendation target or item “could represent a combination of aspects that the user likes or otherwise has a positive sentiment about. [0090] Martineau clarifies that the list of recommended items is generated on a graphical user interface or webpage as can be seen in [Figs. 12-14B].)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Cheng’s teachings by using the determined sentiment value to prioritize certain items over others in a presentation to the user as taught by Martineau. One would have been motivated to make this modification in order to personalize the user’s experience on the webpage by highlighting or bringing to attention items determined to be of greater interest to him/her.
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] Cheng 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] Cheng discloses how the plurality of data obtained by the server “may be preprocessed in the plurality of databases.” [0008] Cheng discloses “training, using the plurality data, a sentiment analysis machine learning model.” [0021] Cheng discloses “the machine learning models are trained by multi-channel data inputs.” [0062] Cheng 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] Cheng 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] Cheng discloses “The sentiment analysis ML model may be trained using labeled or unlabeled data collected from the plurality of data devices.” [0052] Cheng 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.)
receive the candidate record for processing; ([0117] Cheng 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] Cheng 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] Cheng 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] Cheng 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] Cheng 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] Cheng discloses “the server transmits information of the action to the user device.” [0124] Cheng 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] Cheng 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.”)
Cheng does not explicitly teach: adjust an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items.
However, Martineau teaches in the same field of endeavor: adjust an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items. ([0088] Martineau discloses selecting a suggested item to place in the user’s recommendation list “in a specified position or ranking with respect to other items already in the user’s recommendation list, such as in order of decreasing score.”)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Cheng’s teachings by using the determined sentiment value to prioritize certain items over others in a presentation to the user as taught by Martineau. One would have been motivated to make this modification in order to personalize the user’s experience on the webpage by highlighting or bringing to attention items determined to be of greater interest to him/her.
With respect to Claim 14:
Cheng teaches: A method, comprising: ([Claim 20] Cheng discloses “A method, comprising:”)
receiving, by a device, a candidate record for processing ([0117] Cheng 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] Cheng 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] Cheng 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] Cheng 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] Cheng 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] Cheng 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.”)
Cheng does not explicitly teach: adjusting, by the device, an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items.
However, Martineau teaches in the same field of endeavor: adjusting, by the device, an order of a plurality of items displayed on a webpage based on the sentiment value, wherein the adjusting prioritizes at least one item of the plurality of items relative to at least one other item of the plurality of items. ([0088] Martineau discloses selecting a suggested item to place in the user’s recommendation list “in a specified position or ranking with respect to other items already in the user’s recommendation list, such as in order of decreasing score.”)
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Cheng’s teachings by using the determined sentiment value to prioritize certain items over others in a presentation to the user as taught by Martineau. One would have been motivated to make this modification in order to personalize the user’s experience on the webpage by highlighting or bringing to attention items determined to be of greater interest to him/her.
With respect to Claims 2 and 15:
Cheng in view of Martineau 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] Cheng 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 in view of Martineau 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] Cheng 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] Cheng 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 in view of Martineau 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] Cheng 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] Cheng 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 in view of Martineau teaches: The system of Claim 1, wherein the first processing action is associated with successfully processing the candidate record; and ([0021] Cheng 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.”)
wherein the second processing action is associated with at least one of: delaying processing of the candidate record, or ([0021] Cheng discloses “freezing a credit card of the user when the user is evaluated to act abnormally while shopping,”)
rejecting processing of the candidate record. ([0025] Cheng 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 6:
Cheng in view of Martineau teaches: The system of claim 1, wherein the one or more processors, when adjusting the order of the plurality of items, are configured to: cause the at least one item to be displayed at a top of the webpage. ([Fig. 16; 0152] Martineau describes a graphical user interface wherein “The items in the list could be ranked, such as by significance of the searched aspect(s) to those in the list and the user’s sentiment towards the searched aspect(s).”)
With respect to Claim 8:
Cheng in view of Martineau 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] Cheng 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 in view of Martineau 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] Cheng 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 in view of Martineau 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; and ([0062] Cheng 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.”)
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] Cheng 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 in view of Martineau 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 candidate 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] Cheng discloses “the sentiment analysis ML model receives history transactional data of the user.” [0104] Cheng 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 in view of Martineau 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] Cheng 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 in view of Martineau teaches: The method of claim 14, further comprising: transmitting an alert to a pre-selected device based on the sentiment value. ([0123] Cheng 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 13 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Martineau and in further view of Prendki (US 2018/0218431 A1).
With respect to Claim 13:
Cheng in view of Martineau teaches: The non-transitory computer-readable medium of claim 7, wherein the one or more instructions, that cause the device to adjust the order of the plurality of items, cause the device to: determine whether the sentiment value corresponds to a first sentiment value level or a second sentiment value level; ([0144] Martineau recites in regard to the list (i.e. plurality) of items, “[t]he items 1204 in each group can be ordered [i.e., adjusting the order], such as based on the significance of the associated aspect to those items 1204 and the user’s sentiment towards those aspects.” The multiple aspects corresponding to the user’s sentiment are understood as first and second sentiment value levels.)
cause, based on the sentiment value corresponding to the first sentiment value level, a first item associated with a first cost to be displayed at a top of the webpage; and (Fig. 16; 0152] Martineau discusses a user interface displaying a list of recommended items corresponding to the user-provided aspect (i.e. sentiment value level), wherein the list may be ranked by the aspect/sentiment relationship. The ranking implies a top-to-bottom list ranking wherein the first item is displayed at the top. [Fig. 19A; 0158] Martineau describes “node 1912” that represents a product associated with a price, and [0162] may be recommended to the user.)
Cheng in view of Martineau does not explicitly teach: cause, based on the sentiment value corresponding to the second sentiment value level, a second item associated with a second cost lower than the first cost to be displayed at the top of the webpage.
However, Prendki teaches in the same field of endeavor: cause, based on the sentiment value corresponding to the second sentiment value level, a second item associated with a second cost lower than the first cost to be displayed at the top of the webpage. ([Figs. 20-21, 0146] Prendki provides illustrations of catalog search/browse results wherein between Figs. 20 and 21, the input weight (i.e. sentiment value level) of “material” (input element 2012/2112) has been adjusted corresponding to the user sentiment of the material aspect. In the post-adjustment Fig. 21, the item at the top of the list or webpage is associated with a cost (2133) lower than the cost of the item at the bottom of the webpage, and has higher correspondence to the user-input sentiment values (2140, 2110). )
It would have been obvious for one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Cheng in view of Martineau’s teachings by presenting the webpage results in an order that prioritizes sentiment and user preference over item cost as taught by Prendki. One would have been motivated to make this modification in order to personalize results and better highlight items that are appealing to certain user(s) [Prendki 0118].
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Martineau and in further view of Kataria et al. (US 2019/0164170 A1), hereinafter “Kataria”.
With respect to Claim 18:
Cheng in view of Martineau teaches: The method of claim 14,
Cheng in view of Martineau does not explicitly teach: wherein selecting the second processing action comprises: selecting, by the device, a delay period based on the sentiment value; and
delaying, by the device, processing of the candidate record for the delay period.
However, Kataria teaches in the same field of endeavor: wherein selecting the second processing action comprises: selecting, by the device, a delay period based on the sentiment value; and ([0041] Kataria recites “delaying the communication until the individual has a more positive intent or for a predetermined length of time… If the system determines that the individual has a negative sentiment, the system may increase the length of delay for sending the communication.” [0039] Kataria clarifies that preventing (e.g., delaying) communications is an additional (i.e. second) action that the system may take.)
delaying, by the device, processing of the candidate record for the delay period. ([0041] Kataria recites “if the system determines that the user has a negative sentiment, the system may delay sending a communication from the individual,” wherein the “sending a communication from the individual” is understood as a processing of the candidate record.)
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 in view of Martineau’s teachings by delaying the candidate record processing by a selected delay period based on the sentiment value as taught by Kataria. One would have been motivated to make this modification in order to “prevent possible rash, harsh, or otherwise ill-advised communications” (Kataria [0041]) or actions by the user (i.e. candidate, individual).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Martineau and in further view of Walters et al. (US 2022/0084033 A1), hereinafter “Walters”.
With respect to Claim 20:
Cheng in view of Martineau teaches: The method of claim 14, further comprising: transmitting an alert to a browserrecord, based on the sentiment value, to trigger a function([0021] Cheng 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] Cheng 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 in view of Martineau 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] Walters 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.”)
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 in view of Martineau’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.
Finkelstein et al. (US 10,410,273 B1) discloses a recommendation system that uses identified negative sentiment cues from users to filter or modify recommendations to a user or group of users.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/BRIAN D. BUI/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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