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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/829,867 filed on September 10, 2024. Claims 1 to 20 are currently pending with the application.
Priority
The instant application is a Continuation of PCT/CN2023/083692, filed on March 24, 2023, and claims foreign priority to applications CHINA 202210307260.8 and CHINA 202210306451.2, filed on March 25, 2022. Applicant’s claim for the benefit of the prior-filed application under 35 U.S.C. 119(e), 120, 121, or 365(c), or 386(c), and claim for foreign priority under 35 U.S.C. 119 (a)-(d) or (f), 365(a) or (b), or 386(a), is acknowledged. Receipt of certified copies of papers as required by 37 CFR 1.55 is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/10/2024, 06/05/2025, and 09/25/2025, were filed before the mailing date of the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Examiner’s Note
Examiner respectfully points out that claim 6 recites the contingent limitations “in response to the final score of the target multimedia item being less than or equal to a first score threshold, reducing…” and “in response to the final score of the target multimedia item being greater than or equal to a second score threshold”, in lines 4 and 7, respectively, and that claim 7 recites the contingent limitations “in response to the final score of the target multimedia item being greater than a first score threshold and less than a second score threshold, keeping…” in line 1 at page 3. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the conditions precedent are not met (See MPEP 2111.04(II)). Therefore, since claims 6 and 7 are method claims, the contingent limitations recited above are not required.
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 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 20 recite determining a target multimedia item, and recommending the item.
The limitation of determining a target multimedia item, which specifically recites “determining a target multimedia item from a plurality of candidate multimedia items based on the first user data, the second user data, and the scenario data”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by at least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by at least one processor” language, “determining”, in the context of this claim encompasses the user mentally, with the aid of pen and paper, identifying an item of multimedia from a list of items, based on data associated with a user and current usage data, which could be identifying a song from a list that match user and usage specified conditions.
Continuing with the analysis, the limitation of recommending the item, which specifically recites “recommending the target multimedia item to the user”, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “by the least one processor”, nothing in the claim element precludes the steps from practically being performed in a human mind. For example, but for the “by the least one processor” language, “recommending”, in the context of this claim encompasses the user mentally and with the aid of pen and paper, writing down, or saying out loud, the previously identified multimedia item. If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements – “obtaining first user data of a user wearing a wearable device, wherein the first user data of the user is captured by the wearable device”, “obtaining second user data of the user, wherein the second user data comprises at least one of attribute data of the user or multimedia preference data of the user”, “obtaining scenario data corresponding to a current multimedia usage scenario”, at least one processor, and a memory (claim 20). The “obtaining” limitations amount to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The at least one processor and memory in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activity identified above, which include the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claims are not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “after recommending the target multimedia item to the user: obtaining real-time physiological data of the user collected by the wearable device during playback of the target multimedia item; and updating, based at least in part on the real-time physiological data of the user, a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items; wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario”. The updating limitation can be performed in the human mind with the aid of pen and paper, and therefore is further elaborating on the abstract idea, as well as the limitation “wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario”. The obtaining limitation amounts to data gathering steps, which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)), and recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d) (II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claim does not amount to significantly more than the abstract idea. Same rationale applies to claims 3, to 5, and 10 to 18.
Claim 6 is dependent on claim 4 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitations of “in response to the final score of the target multimedia item being less than or equal to a first score threshold, reducing at least one of the recommending weight or the recommending frequency of the at least one first multimedia item; or in response to the final score of the target multimedia item being greater than or equal to a second score threshold, increasing at least one of a recommending weight or a recommending frequency of the at least one first multimedia item”. As previously indicated in the Examiner’s note above, since claim 6 is a method claim, the limitations are not required. Nonetheless, and in the interest of compact prosecution, all the limitations have been considered as if the conditions precedent are positively recited. The limitations, however, can be performed in the human mind with the aid of pen and paper, by modifying a weight or frequency corresponding to the item in a sheet of paper, and therefore, the claim is further elaborating on the abstract idea. The claim does not amount to significantly more. Same rationale applies to claim 7.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitations of “the first user data comprises physiological data obtained from at least one physiological measurement of the user by the wearable device”, which is tying the abstract idea to a field of use by further specifying the target data, and which is simply an attempt to limit the application of the abstract idea to a particular technological environment; merely indicating a field of use or technological environment in which to apply the judicial exception does not meaningfully limit the claim (See MPEP 2106.05(h)). Same rationale applies to claims 9 and 19.
Additionally, the claims do not include a requirement of anything other than conventional, generic computer technology for executing the abstract idea, and therefore, do not amount to significantly more than the abstract idea.
Claims 1 to 20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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, 3, 8 to 16, and 18 to 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cruz Huertas et al. (U.S. Publication No. 2019/0318008) hereinafter Cruz.
As to claim 1:
Cruz discloses:
A method for multimedia recommendation, comprising:
obtaining, by at least one processor, first user data of a user wearing a wearable device, wherein the first user data of the user is captured by the wearable device [Paragraph 0027 teaches identifying the user’s mood, and cognitive status (from wearable sensor readings); Paragraph 0039 teaches receiving user data, including cognitive status, from wearable biometric sensors; Paragraph 0057 teaches receiving current user data from wearable biometric sensors];
obtaining, by the at least one processor, second user data of the user, wherein the second user data comprises at least one of attribute data of the user or multimedia preference data of the user [Paragraph 0003 teaches receiving a plurality of user data; Paragraph 0030 teaches determining the user media preferences by location, activity, mood, etc., and user listening behavior; Paragraph 0069 teaches querying records of learned user preferences and behaviors];
obtaining scenario data corresponding to a current multimedia usage scenario [Paragraph 0027 identifying current environmental conditions, and current user’s activity; Paragraph 0057 teaches receiving current user data, including current user’s activity];
determining, by the at least one processor, a target multimedia item from a plurality of candidate multimedia items based on the first user data, the second user data, and the scenario data [Paragraph 0024 teaches selecting musical media based on identified current location, user activity data, user’s mood and cognitive status, environmental conditions, previously learned media preferences of the user, etc.; Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences; Paragraph 0028 teaches searching personal media databases and services for songs matching characteristics and retrieve the resulting media, where the personal media databases and services include the candidate multimedia items; Paragraph 0069 teaches creating a dataset of the current user situation (e.g., including current user’s cognitive status, environmental conditions, activity data) and querying records of learned user preferences and behaviors, to be sent to a customized playlist selection module, to select media corresponding to the user’s current activity, mood, cognitive status, context, etc.; Paragraph 0072 teaches identifying and returning media titles suitable for the user’s current situation]; and
recommending the target multimedia item to the user [Paragraph 0054 teaches recommending the determined particular songs to the user; Paragraph 0072 teaches delivering the retrieved resulting media playlist to the user; Paragraph 0073 teaches providing the music playlist to the user].
Same rationale applies to claim 20, since it recites the same limitations as claim 1.
As to claim 3:
Cruz discloses:
after recommending the target multimedia item to the user: obtaining user interaction data corresponding to the target multimedia item [Paragraph 0075 teaches receiving user feedback of the retrieved media playlist, where the user can click on a “Reject” button]; and
updating a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items based on the user interaction data [Paragraph 0075 teaches based on the received user’s feedback, continue to search through the records for a better suited media playlist for the user’s current situation, in other words, updating the records so the previously selected items are not provided to the user, but to search for new items].
As to claim 8:
Cruz discloses:
the first user data comprises physiological data obtained from at least one physiological measurement of the user by the wearable device [Paragraph 0039 teaches user data includes user’s cognitive status, utilizing wearable biometric sensors associated with physiological recognition and behavioral recognition].
As to claim 9:
Cruz discloses:
the scenario data comprises at least one of environmental data of current environment, time data, or intention data [Paragraph 0028 teaches data includes context, i.e., data and time, environmental conditions, current activity, etc.].
As to claim 10:
Cruz discloses:
determining at least one second multimedia item from the plurality of candidate multimedia items based on the first user data, the second user data, and the scenario data [Paragraph 0079 teaches media selection based on the current raw data associated with the user and the current external conditions and listening behavior of the user; Paragraph 0076 teaches detecting change of the user status, i.e., user’s heartbeat rate or pace of breathing exceeds a certain threshold]; and
determining the target multimedia item from the at least one second multimedia item based on a recommendation parameter of the at least one second multimedia item [Paragraph 0076 teaches media customization program will automatically play a corresponding music based on the detected physiological status of the user, where the correlation was previously saved in the database, therefore, determining a second item based on a recommendation parameter of the second item].
As to claim 11:
Cruz discloses:
obtaining target user data of the user based on the first user data, the second user data and the scenario data [Paragraph 0028 teaches obtaining the user’s mood, cognitive status, context, environmental conditions, location, and user’s activity];
obtaining multimedia characteristic data of the plurality of candidate multimedia items [Paragraph 0025 teaches assessing the characteristics of one or more songs; Paragraph 0028 teaches obtaining characteristics and types of media, to find songs matching the characteristics]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the target user data and the multimedia characteristic data of the plurality of candidate multimedia items [Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences, based on the characteristics of the songs; Paragraph 0028 teaches searching and retrieving songs matching the characteristics, and related to the current location, activity, mood, cognitive status, context and environmental conditions].
As to claim 12:
Cruz discloses:
determining recommendation probabilities of the plurality of candidate multimedia items based on the target user data and the multimedia characteristic data of the plurality of candidate multimedia items [Paragraph 0023 teaches provide media files for those songs that are more probable to be played based on the context, where the context includes location, user activity, weather, date/time, cognitive state, and the songs characteristics, in other words, where probabilities are determined in order to provide the songs that are most probable to be played]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the recommendation probabilities of the plurality of candidate multimedia items [Paragraph 0023 teaches provide media files for those songs that are more probable to be played based on the context].
As to claim 13:
Cruz discloses:
obtaining target user data of the user based on the first user data and the attribute data of the user [Paragraph 0067 teaches determining current user data associated with user’s anatomical functions or systems, including elevated high blood pressure, respiratory breathing pace, heartbeat rate, etc., and based on biometric sensors, and thresholds that are determined based on the user’s personal data (e.g., age, gender)]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the target user data and the scenario data [Paragraph 0069 teaches determining media corresponding to the current user’s context data, and data associated with the location, user’s activity, mood, context, and environmental conditions].
As to claim 14:
Cruz discloses:
wherein the second user data comprises multimedia preference data of the user [Paragraph 0030 teaches determining user media preferences]; and
wherein determining the target multimedia item from the plurality of candidate multimedia items based on the first user data, the second user data, and the scenario data comprises: correcting the multimedia preference data of the user based on the first user data and the scenario data to obtain multimedia preference correction data of the user [Paragraph 0030 teaches updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.; Paragraph 0053 teaches updating and storing the learned user listening behavior]; and
determining the target multimedia item from the plurality of candidate multimedia items based on the multimedia preference correction data [Paragraph 0025 teaches determine playlist for current location, activity, mood, cognitive status, context or environmental conditions, according to the user preferences, hence the updated user preferences].
As to claim 15:
Cruz discloses:
obtaining multimedia playback history data of the user [Paragraph 0053 teaches determining user queried songs, and additional user data and data on external conditions; Paragraph 0054 teaches determining music queried by the user, hence, playback history data]; and
obtaining the multimedia preference data of the user based on the multimedia playback history data [Paragraph 0053 teaches correlate the raw data to determine the media preferences of the user based on location, activity, mood, cognitive status, context and environmental conditions (i.e., the user listening behavior), where the correlated raw data, which associates the queried songs with corresponding user data and data on external conditions, may be categorized and sent to the records of learned user preferences and behaviors database to update and store the learned user listening behavior].
As to claim 16:
Cruz discloses:
obtaining a multimedia preference correction parameter based on the first user data and the scenario data [Paragraph 0030 teaches determining user media preferences, and updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.]; and obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter [Paragraph 0030 teaches determining user media preferences, and updating the training into the records of learned user preferences and behaviors database, to grow the correlation, by determining location, activity (e.g., exercise, reading, picnic), mood, etc.; Paragraph 0053 teaches updating and storing the learned user listening behavior].
As to claim 18:
Cruz discloses:
selecting the target multimedia item from the plurality of candidate multimedia items based on the multimedia preference correction data and the multimedia playback history data of the user [Paragraph 0030 teaches Behavior Capture may then update the training into the records of learned user preferences and behaviors database, where the behavior capture determines the media that the user was listening in a location, during an activity, gestures, and corresponding cognitive status; Paragraph 0054 teaches music queried and the raw data on the user and external conditions are stored in the records of learned user preferences and behaviors database and utilized to update the user listening behavior, which will be used in a next time, to recommend the particular songs based on the updated preference data and the listening behavior or playback history].
As to claim 19:
Cruz discloses:
the plurality of candidate multimedia items comprise a plurality of audio candidates [Paragraph 0026 teaches selecting songs that are more in line with the user’s taste and preferences; Paragraph 0028 teaches searching personal media databases and services for songs matching characteristics and retrieve the resulting media, therefore, items comprise audio candidates or songs], and the current multimedia usage scenario comprises at least one of stress relief and relaxation, sleep aid, exercise or concentration [Paragraph 0030 teaches user’s activity includes exercise, reading, picnic, etc.].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 are rejected under 35 U.S.C. 103 as being unpatentable over Cruz Huertas et al. (U.S. Publication No. 2019/0318008) hereinafter Cruz, and further in view of Lyon et al. (U.S. Publication No. 2023/0111477) hereinafter Lyon.
As to claim 2:
Cruz discloses:
after recommending the target multimedia item to the user: obtaining real-time physiological data of the user collected by the wearable device during playback of the target multimedia item [Paragraph 0003 teaches receiving a plurality of user data including user reaction data to a plurality of media selections corresponding to the user; Paragraph 0076 teaches biometric sensors may be utilized to monitor a change in user status when a specific music genre or specific media selection is played]; and
updating, based at least in part on the real-time physiological data of the user, a recommendation parameter of at least one first multimedia item in the plurality of candidate multimedia items [Paragraph 0055 teaches raw data associated with the user reaction (e.g., relaxed, calm, increased anxiety, decreased frustration) to certain media selections may be correlated, and the correlated raw data associated with the user reactions and the corresponding media selections may be categorized and sent to the records of learned user preferences and behaviors database to update and store the learned user listening behavior; Paragraph 0076 teaches such user status (or user reaction) changes and corresponding media selections may be saved in the records of learned user preferences and behaviors database, for future recommendation, therefore, updating recommendation information associated with the corresponding media based on the user’s physiological data].
Cruz does not appear to expressly disclose wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario.
Lyon discloses:
wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario [Paragraph 0013 teaches receiving data associated with a user while the user is viewing a plurality of media content segments, and generating a score for each of the plurality of media content segments based at least in part on the received data, the score being indicative of a change in a sleepiness level of the user, determining a current sleepiness level of the user, and recommending, based at least in part on the current sleepiness level of the user and the generated scores, one or of more of the plurality of media content segments to the user to aid in changing the current sleepiness level of the user; Paragraph 0066 teaches received user data includes physiological data].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Cruz, by incorporating wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario, as taught by Lyon [Paragraph 0013, 0066], because both applications are directed to generating recommendations to users based on detected data, including detected physiological data associated with the user; calculating a recommendation parameter for the media item, enables the assessment of the effectiveness of the item in modifying or achieving a target state for the user, thereby improving the user’s experience (See Lyon Para [0005]).
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cruz Huertas et al. (U.S. Publication No. 2019/0318008) hereinafter Cruz, and further in view of Kimmerling (U.S. Publication No. 2018/0158084).
As to claim 17:
Cruz discloses all the limitations as set forth in the rejections of claim 16 above, but does not appear to expressly disclose the multimedia preference data comprises a multimedia preference vector of length M, the multimedia preference correction parameter comprises a multimedia preference correction vector of length M; and wherein obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter comprises: multiplying M elements of the multimedia preference vector with M elements of the multimedia preference correction vector to obtain the multimedia preference correction data.
Kimmerling discloses:
the multimedia preference data comprises a multimedia preference vector of length M, the multimedia preference correction parameter comprises a multimedia preference correction vector of length M; and wherein obtaining the multimedia preference correction data of the user based on the multimedia preference data and the multimedia preference correction parameter comprises: multiplying M elements of the multimedia preference vector with M elements of the multimedia preference correction vector to obtain the multimedia preference correction data [Paragraph 0050 teaches product of the individual's preference vector and the item's vector is indicative of how much the individual should like the item, with higher numbers indicating an item that should be more preferred; Paragraph 0134 teaches recalculate the user preference vector based on the additional data and then recalculate the scores and update the recommendation, where a multivariate regression may be run against the drivers expressed by the movies using the idiosyncratic adjustment to determine the new user preference vector].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Cruz, by incorporating wherein the recommendation parameter comprises at least one of a recommending weight or a recommending frequency, and the updated recommendation parameter is used for subsequently selecting a multimedia item to be recommended to the user in the current multimedia usage scenario, as taught by Kimmerling [Paragraph 0050, 0134], because both applications are directed to generating recommendations to users based on preference data; employing the use of vectors in the calculations of user preferences improves the selection of candidates, improving thereby, the user’s experience (See Kimmerling Para [0121]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL PEREZ-ARROYO whose telephone number is (571)272-8969. The examiner can normally be reached Monday - Friday, 8:00am - 5:30pm, Alt Friday, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL PEREZ-ARROYO/Primary Examiner, Art Unit 2169