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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR10-2024-0151614, filed on 10/30/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/30/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is/are being considered by the examiner.
Allowable Subject Matter
Claims 6-9 and 15-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding dependent claim 6,
The most relevant references have been included in the attached form {TP-892 Notice of References Cited:
YENDIGERI et al. (US PGPUB No. 2023/0141408; Pub. Date: May 11, 2023),
YENDIGERI is directed to a system for generating recommendations via a plurality of machine learning and natural language generation models of a solution system. The system may generate dynamic client solutions via models optimized for particular data types including models for processing text data and models for processing image data that can execute independently. For example, a recommendation model for image data is independent from a recommendation model for text data (See [0013]).
Choi et al. (US PGPUB No. 2024/0312219; Pub. Date: Sep. 19, 2024),
Choi is directed to a system for using machine learning models to fuse feature maps associated with different sensors and different instances in time to generate one or more outputs. The system comprises training engine 608 configured to use one or more loss functions that measure loss/error in outputs 610, which are representative of an output of one or more machine learning models 106, when compared to ground truth data 604. Any type of loss function may be used such that different outputs 610 may have different loss functions. (See [0064]).
Hart et al. (US PGPUB No. 2024/0265281; Pub. Date: Aug. 8, 2024),
Hart is directed to a system for providing natural language understanding of a development process. The system may perform a translation service loaded with a translation model that aways requests for translation with a given "from_language" and a specified "to_language" that returns translated text to the service that requested it, i.e. a plurality of translation data items (See [0039]).
Fischer (US PGPUB No. 2023/0281996; Pub. Date: Sep. 7, 2023)
Fischer is directed to a system for providing a screenshare to one or more devices as a live share. Communication system 100 may include a machine learning (ML) platform configured to build and train an ML model to analyze sensor data from one or more sensors including audio sensors, tactile sensors and olfactory sensors (See [0051]-[0052]).
Claim 6 recites the following limitations:
6. The server of claim 5, wherein the processor is configured to: generate a first quality loss function, a first cost loss function, a first efficiency loss function, and a first total loss function for the text data by inputting the text data into the predictive model based on the following [Equation 1];
generate a second quality loss function, a second cost loss function, a second efficiency loss function, and a second total loss function for the image data by inputting the image data into the predictive model based on the following [Equation 1];
and generate a third quality loss function, a third cost loss function, a third efficiency loss function, and a third total loss function for the supplementary data by inputting the supplementary data into the predictive model based on the following [Equation 1],
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where L denotes the first total loss function, the second total loss function, and the third total loss function, Lquality denotes the first quality loss function, the second quality loss function, and the third quality loss function, Lcost denotes the first cost loss function, the second cost loss function, and the third cost loss function, Lefficiency denotes the first efficiency loss function, the second efficiency loss function, and the third efficiency loss function, w1, w2, w3 denote respective weights for the quality loss function, the cost loss function, and the efficiency loss function, Laccuracy denotes a value obtained by dividing a correct score for a domain, Lcompleteness denotes a completeness score value for a domain, B1, B2 denote weight values for calculating the quality loss function, Ltoken denotes a calculated value for tokens relative to price, Loverhead denotes a cost total value excluding price, o1, o2 denote weight values for calculating the cost loss function, Ltime denotes a ratio value for actual processing time, Lcomputation denotes a ratio value for the number of tokens used, and y1, y2 denote weight values for calculating the efficiency loss function.
The references, neither alone nor in combination, disclose the limitations of claim 6 indicated above.
Therefore, claim 6 contains allowable subject matter.
Regarding dependent claims 7-9,
Claims 7-9 are dependent upon claim 6 either directly or via intervening claims, therefore claims 7-9 are allowable for at least the same reasons indicated above with regard to dependent claim 6.
Regarding dependent claim 15,
Claim 15 is analogous to the subject matter of dependent claim 6. Therefore, claim 15 contains the same allowable subject matter indicated above.
Regarding dependent claims 16-18,
Claims 16-18 are dependent upon claim 15 either directly or via intervening claims, therefore claims 16-18 are allowable for at least the same reasons indicated above with regard to dependent claim 15.
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.
Claim(s) 1-2 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over YENDIGERI et al. (US PGPUB No. 2023/0141408; Pub. Date: May 11, 2023) in view of Choi et al. (US PGPUB No. 20240312219 ); Pub. Date: Sep. 19, 2024).
Regarding independent claim 1,
YENDIGERI discloses a server for recommending a customized model based on a predictive model, the server comprising: a communication interface;
and a processor, wherein the processor is configured to: receive at least one user data item from an external electronic device of the user through the communication interface; See Paragraph [0013], (Disclosing a system for generating recommendations via a plurality of machine learning and natural language generation models of a solution system. The system may generate dynamic client solutions via models optimized for particular data types including models for processing text data and models for processing image data that can execute independently. For example, a recommendation model for image data is independent from a recommendation model for text data. Note FIG. 3 wherein solution system 301 is coupled with a user device 340 and database system 330 via network 320, i.e. a server for recommending a customized model based on a predictive model, the server comprising: a communication interface (e.g. network 320);)
determine text data for the user by inputting the at least one user data item into a first sub-deep learning model; See Paragraph [0013], (The system may generate dynamic client solutions via models optimized for particular data types including models for processing text data and models for processing image data that can execute independently. For example, a recommendation model for image data is independent from a recommendation model for text data, i.e. determine text data for the user by inputting the at least one user data item into a first sub-deep learning model (e.g. via a recommendation model for text data);)
determine image data for the user by inputting the at least one user data item into a second sub-deep learning model; See Paragraph [0013], (The system may generate dynamic client solutions via models optimized for particular data types including models for processing text data and models for processing image data that can execute independently. For example, a recommendation model for image data is independent from a recommendation model for text data, i.e. determine image data for the user by inputting the at least one user data item into a second sub-deep learning model (e.g. via a recommendation model for text data);)
determine a customized model for the user by inputting at least one or more of the text data and/or the image data into the predictive model; See FIG. 2 & Paragraph [0043], (FIG. 2 illustrates a set of observations corresponding to the historical project data, referred to as a feature set 210, may be associated with a target variable 215 representing a value that a machine learning model is trained to predict, i.e. determine a customized model for the user by inputting at least one or more of the text data and/or the image data into the predictive model (e.g. the predictive model receives the feature set as an input to predict an output value for a target variable);)
and transmit the customized model to the external electronic device of the user through the communication interface, See FIG. 1E, (The solution system may provide the digitized dynamic client solution to one or more user devices Note FIG. 3 wherein solution system 301 communicates with user device 340 via network 320, i.e. transmit the customized model to the external electronic device of the user through the communication interface.)
wherein the predictive model is trained on the basis of a plurality of text data items and a plurality of image data items, a plurality of customized models,
first result data in which the plurality of customized models is determined to be accurate, See FIG. 2 & Paragraph [0038], (FIG. 2 illustrates an example 200 of training and utilizing a machine learning model using feature set 210 comprising historical project data, financial analysis data, operational KPIs, etc. Note [0016] wherein solution system 301 may extract client data from data sources including database systems, websites, images, documents, etc., i.e. wherein the predictive model is trained on the basis of a plurality of text data items and a plurality of image data items (e.g. training data comprises text and image data), a plurality of customized models (e.g. model outputs may be used to retrain the ML models),)
first result data in which the plurality of customized models is determined to be accurate, See Paragraph [0035], (The machine learning models may be retrained using generated final digitized dynamic client solutions as additional training data, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model, i.e. first result data in which the plurality of customized models is determined to be accurate (e.g. model outputs that satisfy or represent a solution to a user problem are accurate data that is then fed back into the model during retraining).)
YENDIGERI does not disclose the step wherein the predictive model is trained on the basis of second result data in which some of the plurality of customized models is determined to have errors, and third result data in which the plurality of customized models is determined to have errors.
Choi discloses the step wherein the predictive model is trained on the basis of second result data in which some of the plurality of customized models is determined to have errors, and third result data in which the plurality of customized models is determined to have errors. See Paragraph [0064], (Disclosing a system for using machine learning models to fuse feature maps associated with different sensors and different instances in time to generate one or more outputs. The system comprises training engine 608 configured to use one or more loss functions that measure loss/error in outputs 610, which are representative of an output of one or more machine learning models 106, when compared to ground truth data 604. Any type of loss function may be used such that different outputs 610 may have different loss functions, i.e. second result data in which some of the plurality of customized models is determined to have errors, and third result data in which the plurality of customized models is determined to have errors (e.g. a plurality of loss functions may be used to determine an error metric associated with a plurality of outputs of a plurality of ML models).)
YENDIGERI and Choi are analogous art because they are in the same field of endeavor, machine learning model solutions. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of YENDIGERI to include the method of calculating error metrics for ML model outputs as disclosed by Choi. Paragraph [0003] of Choi discloses that the system may pre-process feature maps associated with previous instances in time in parallel in order to reduce latency using one or more layers of the ML models. The ML models may then use the final feature map to generate one or more outputs.
Regarding dependent claim 2,
As discussed above with claim 1, YENDIGERI-Choi discloses all of the limitations.
YENDIGERI further discloses the step wherein the at least one user data item includes sentence data created by the user, translation data created by the user, summary data created by the user, conversational data created by the user, voice data sensed or produced by the user, tactile data sensed or produced by the user, olfactory data sensed or produced by the user, input image data visually confirmed by the user or input by the user, analysis data of the user, and mathematical operation data of the user. See Paragraph [0039], (The machine learning system may receive a set of observations as input from the solution system to generate ML model outputs. The set of observations are obtained from historical data identifying experiences and/or work product from previous projects, i.e. wherein the at least one user data item includes sentence data created by the user, summary data created by the user (e.g. historical project data includes text identifying experiences, work product data, etc.).)
Regarding independent claim 10,
The claim is analogous to the subject matter of independent claim 1 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 11,
The claim is analogous to the subject matter of dependent claim 2 directed to a method or process and is rejected under similar rationale.
Claim(s) 3-4 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over YENDIGERI in view of Choi as applied to claim 2 above, and further in view of Hart et al. (US PGPUB No. 2024/0265281; Pub. Date: Aug. 8, 2024).
Regarding dependent claim 3,
As discussed above with claim 2, YENDIGERI-Choi discloses all of the limitations.
YENDIGERI further discloses the step wherein the processor is configured to: determine the text data by inputting at least one of the sentence data, the translation data, the summary data, and the conversational data into the first sub-deep learning model; See Paragraph [0039], (The machine learning system may receive a set of observations as input from the solution system to generate ML model outputs. The set of observations are obtained from historical data identifying experiences and/or work product from previous projects, i.e. wherein the processor is configured to: determine the text data by inputting at least one of the sentence data, the translation data, the summary data, and the conversational data into the first sub-deep learning model (historical project data is provided the ML system. Note [0013] wherein the system may independently execute recommendation models for received data, including executing a recommendation model for text data;)
classify the text data as first text result data when it is determined that the text data does not exceed a preset text threshold; See Paragraph [0048], (Observations may be classified into any of a plurality of clusters when a threshold degree of similarity is met. A new observation may be classified into a first cluster and provide a fist recommendation or may classify the observation into a second cluster that provides a second recommendation, i.e. classify the text data as first text result data when it is determined that the text data does not exceed a preset text threshold (e.g. a similarity threshold may not be met for a first cluster but may be met for a second cluster).)
and classify the text data as second text result data when it is determined that the text data exceeds the text threshold, See Paragraph [0048] & [0050], (Trained machine model 225 may classify observations into clusters wherein observations within a cluster may have a threshold degree of similarity, i.e. classify the text data as second text result data when it is determined that the text data exceeds the text threshold (e.g. an observation is classified as belonging to a particular cluster if a similarity threshold is met),)
wherein the first sub-deep learning model is trained on the basis of a plurality of user data items, a plurality of sentence data items, See FIG. 2 & Paragraph [0038], (FIG. 2 illustrates an example 200 of training and utilizing a machine learning model using feature set 210 comprising historical project data, financial analysis data, operational KPIs, etc. Note [0016] wherein solution system 301 may extract client data from data sources including database systems, websites, images, documents, etc., i.e. wherein the first sub-deep learning model is trained on the basis of a plurality of user data items, a plurality of sentence data items, (e.g. the ML models are trained on historical project data including text).)
a plurality of summary data items, See Paragraph [0015], (Historical project data may identify experiences and work products from previous projects associated with other clients and/or a target client. The data may also be based on technologies of clients, geographies of clients, industries of clients, whether deals were won for client solutions, web metrics of client solutions shared with clients for identifying the most popular client solutions, and/or the like, i.e. a plurality of summary data items (e.g. the experience and/or work product information ).)
and a plurality of text data items, See Paragraph [0015], (Historical project data may identify experiences and work products from previous projects associated with other clients and/or a target client. The data may also be based on technologies of clients, geographies of clients, industries of clients, whether deals were won for client solutions, web metrics of client solutions shared with clients for identifying the most popular client solutions, and/or the like, i.e. a plurality of text data items (e.g. the experience and/or work product information ).)
a plurality of first text result data items, and a plurality of second text result data items. See Paragraph [0035], (The machine learning models may be retrained using generated final digitized dynamic client solutions as additional training data, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model, i.e. a plurality of first text result data items, and a plurality of second text result data items (e.g. the ML models are retrained using solution outputs which include text. Client solutions may be provided to users for feedback/edits. Therefore, client solutions and edited client solutions may be used to train the ML and NLG models).)
Hart discloses the step wherein the first sub-deep learning model is trained on the basis of a plurality of translation data items, See Paragraph [0039], (Disclosing a system for providing natural language understanding of a development process. The system may perform a translation service loaded with a translation model that aways requests for translation with a given "from_language" and a specified "to_language" that returns translated text to the service that requested it, i.e. a plurality of translation data items (e.g. the translation model is trained to translate data).)
and a plurality of conversational data items, See Paragraph [0042], (Finetuning/training service 102 may fine-tune a large language model based on generic inputs 130 and specific inputs 132 which may include conversation history corpora ingested from communications such as chat, email, etc., i.e. and a plurality of conversational data items (e.g. an LLM may be tuned using conversation history data).)
YENDIGERI, Choi and Hart are analogous art because they are in the same field of endeavor, machine learning model solutions. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of YENDIGERI-Choi to include the method of fine-tuning an LLM using historical data and performing translation as disclosed by Hart. Paragraph [0036] of Hart discloses that the system may employ a custom enhancement model for a client that may enhance the results of a search by converting the results of a match on the NLP search engine to a natural language response to a user's query by adding context or nuance to the query or results based on the intent of the query determined upon request, suggesting further changes to the query that may provide a better match, etc.
Regarding dependent claim 4,
As discussed above with claim 3, YENDIGERI-Choi-Hart discloses all of the limitations.
YENDIGERI further discloses the step wherein the processor is configured to: determine the image data by inputting at least one of the input image data, the analysis data, and the at least one user data item into the second sub-deep learning model; See Paragraph [0039], (The machine learning system may receive a set of observations as input from the solution system to generate ML model outputs. The set of observations are obtained from historical data identifying experiences and/or work product from previous project.) See Paragraph [0013], (Image data may be processed by a recommendation model configured specifically for managing image data, i.e. determine the image data by inputting at least one of the input image data, the analysis data, and the at least one user data item into the second sub-deep learning mode (e.g. historical project data may include text, images, etc. associated with previous products/projects);)
classify the image data as first image result data when it is determined that the image data does not exceed a preset image threshold; See Paragraph [0048], (Observations may be classified into any of a plurality of clusters when a threshold degree of similarity is met. A new observation may be classified into a first cluster and provide a fist recommendation or may classify the observation into a second cluster that provides a second recommendation, i.e. classify the image data (e.g. observation data may include image data as described in [0013] wherein image data may be processed by a recommendation model) as first text result data when it is determined that the image data does not exceed a preset image threshold (e.g. a similarity threshold may not be met for a first cluster but may be met for a second cluster).)
and classify the image data as second image result data when it is determined that the image data exceeds the image threshold, See Paragraph [0048] & [0050], (Trained machine model 225 may classify observations into clusters wherein observations within a cluster may have a threshold degree of similarity, i.e. classify the image data as second image result data when it is determined that the image data exceeds the image threshold (e.g. an observation is classified as belonging to a particular cluster if a similarity threshold is met),)
wherein the second sub-deep learning model is trained on the basis of a plurality of user data items, a plurality of analysis data items, See FIG. 2 & Paragraph [0038], (FIG. 2 illustrates an example 200 of training and utilizing a machine learning model using feature set 210 comprising historical project data, financial analysis data, operational KPIs, etc. Note [0016] wherein solution system 301 may extract client data from data sources including database systems, websites, images, documents, etc., i.e. wherein the first sub-deep learning model is trained on the basis of a plurality of user data items, a plurality of analysis data items, (e.g. the ML models are trained on historical project data including text, images, descriptions, experiences and work products).)
a plurality of first image result data items, and a plurality of second image result data items. See Paragraph [0035], (The machine learning models may be retrained using generated final digitized dynamic client solutions as additional training data, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model, i.e. a plurality of first image result data items, and a plurality of second image result data items (e.g. the ML models are retrained using solution outputs which include images. Client solutions may be provided to users for feedback/edits. Therefore, client solutions and edited client solutions may be used to train the ML and NLG models).)
Additionally, Choi further discloses the step wherein the second sub-deep learning model is trained on the basis of a plurality of input image data items, and a plurality of image data items, See Paragraph [0022], (The system may process image data using a machine learning model such as a perception model trained to output data representing information associated with the surrounding environment. Note [0062] wherein sensor data used for training may include images, i.e. and a plurality of input image data items (e.g. images captured by the sensor and fed to the ML model), and a plurality of image data items (e.g. images previously used to train the ML model.))
Regarding independent claim 12,
The claim is analogous to the subject matter of dependent claim 3 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 4 directed to a method or process and is rejected under similar rationale.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over YENDIGERI in view of Choi and Hart as applied to claim 5 above, and further in view of Hart et al. (US PGPUB No. 2024/0265281; Pub. Date: Aug. 8, 2024).
Regarding dependent claim 5,
As discussed above with claim 4, YENDIGERI-Choi-Hart discloses all of the limitations.
YENDIGERI further discloses the step wherein the system may classify the supplementary data as first supplementary result data when it is determined that the supplementary data does not exceed a preset supplementary threshold; See Paragraph [0048], (Observations may be classified into any of a plurality of clusters when a threshold degree of similarity is met. A new observation may be classified into a first cluster and provide a fist recommendation or may classify the observation into a second cluster that provides a second recommendation, i.e. classify the text data as first text result data when it is determined that the text data does not exceed a preset text threshold (e.g. a similarity threshold may not be met for a first cluster but may be met for a second cluster).)
and classify the supplementary data as second supplementary result data when it is determined that the supplementary data exceeds the supplementary threshold, See Paragraph [0048] & [0050], (Trained machine model 225 may classify observations into clusters wherein observations within a cluster may have a threshold degree of similarity, i.e. classify the text data as second text result data when it is determined that the text data exceeds the text threshold (e.g. an observation is classified as belonging to a particular cluster if a similarity threshold is met),)
wherein the third sub-deep learning model is trained on the basis of a plurality of user data items, See FIG. 2 & Paragraph [0038], (FIG. 2 illustrates an example 200 of training and utilizing a machine learning model using feature set 210 comprising historical project data, financial analysis data, operational KPIs, etc. Note [0016] wherein solution system 301 may extract client data from data sources including database systems, websites, images, documents, etc., i.e. wherein the third sub-deep learning model is trained on the basis of a plurality of user data items, (e.g. the ML models are trained on historical project data including text).)
a plurality of supplementary data items, See FIG. 2 & Paragraph [0038], (FIG. 2 illustrates an example 200 of training and utilizing a machine learning model using feature set 210 comprising historical project data, financial analysis data, operational KPIs, etc. a plurality of supplementary data items (e.g. operational KPI data is used to train the ML models).)
a plurality of first supplementary result data items, and a plurality of second supplementary result data items. See Paragraph [0035], (The machine learning models may be retrained using generated final digitized dynamic client solutions as additional training data, thereby increasing the quantity of training data available for training the one or more machine learning models and/or the natural language generation model, i.e. a plurality of first supplementary result data items, and a plurality of second supplementary result data items. (e.g. the ML models are retrained using solution outputs which include feature set data processed by the ML models which includes operational KPIs. Client solutions may be provided to users for feedback/edits. Therefore, client solutions and edited client solutions may be used to train the ML and NLG models).)
YENDIGERI-Choi-Hart does not disclose the step wherein the processor is configured to: determine supplementary data for the user by inputting at least one of the at least one user data item, the voice data, the tactile data, and the olfactory data into a third sub-deep learning model;
wherein the third sub-deep learning model is trained on the basis of a plurality of voice data items, a plurality of tactile data items, and a plurality of olfactory data items,
Fischer discloses the step wherein the processor is configured to: determine supplementary data for the user by inputting at least one of the at least one user data item, the voice data, the tactile data, and the olfactory data into a third sub-deep learning model; See Paragraphs [0051]-[0052], (Disclosing a system for providing a screenshare to one or more devices as a live share. Communication system 100 may include a machine learning (ML) platform configured to build and train an ML model to analyze sensor data from one or more sensors including audio sensors, tactile sensors and olfactory sensors, i.e. determine supplementary data for the user by inputting at least one of the at least one user data item, the voice data, the tactile data, and the olfactory data into a third sub-deep learning model (e.g. the trained ML model may process sensor data including voice, tactile and olfactory).)
wherein the third sub-deep learning model is trained on the basis of a plurality of voice data items, a plurality of tactile data items, and a plurality of olfactory data items, See Paragraphs [0051]-[0052], (Communication system 100 may include a machine learning (ML) platform configured to build and train an ML model to analyze sensor data from one or more sensors including audio sensors, tactile sensors and olfactory sensors, i.e. a plurality of voice data items, a plurality of tactile data items, and a plurality of olfactory data items.)
YENDIGERI, Choi, Hart and Fischer are analogous art because they are in the same field of endeavor, machine learning model solutions. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of YENDIGERI-Choi-Hart to include the method of training an ML model to process sensor data as disclosed by Fischer. Paragraph [0053] of Fischer discloses that the system may use the trained ML model to process participant behavior and level of engagement data in real-time during a live share session. The determined participant behavior data may be used to assess a level of interest in live-share content.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 5 directed to a method or process and is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159 /ALBERT M PHILLIPS, III/Primary Examiner, Art Unit 2159
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