DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 05/07/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites
“1. A computer-implemented method of refining generative artificial intelligence (AI) model outputs, the method comprising:
receiving, from a first device, a component list for an item that is to be included in a menu of items, the component list including a plurality of components;
generating, using a first generative AI model, a text natural language response that includes a description for the item based on the component list;
obtaining visual media data of the item that depicts one or more components of the plurality of components of the component list;
providing the text natural language response and the visual media data of the item to a second generative AI model;
modifying, using the second generative AI model, the description for the item of the text natural language response based on detection in the visual media data of at least one component in the component list by the second generative AI model; and
providing the modified description to the first device for inclusion in the menu of items.”
Claims 1 and 15 recite substantially the same concept but do so in the context of a method and a system.
The limitations recited in the independent claims as drafted covers a mental process. More specifically, the underlying abstract idea revolved around what happen once a merchant modifies the description of the product. For example, the merchant receives a list of component, write down on the paper a description of the product based on the list of components, looks at the photo the product, and modifies the description based on the photo and provides the modified description to the customer.
Claim 9 recites
“9. A computer-implemented method of refining generative artificial intelligence (AI) model outputs, the method comprising:
receiving, from a first device, a component list for an item that is to be included in a menu of items, the component list including a plurality of components;
obtaining context data associated with the item;
determining a prompt for a first generative AI model that includes or is based on the component list and is based on the context data;
providing the prompt to the first generative AI model;
generating, using the first generative AI model, a text natural language response that includes a description for the item based on the prompt;
obtaining visual media data of the item that depicts one or more components of the plurality of components of the component list; providing the text natural language response and the visual media data of the item to a second generative AI model;
modifying, using the second generative AI model, the description for the item of the text natural language response based on detection in the visual media data of at least one component in the component list by the second generative AI model; and
providing the modified description to the first device for inclusion in the menu of items.”
The limitations recited in the independent claims as drafted covers a mental process. More specifically, the underlying abstract idea revolved around what happen once a merchant modifies the description of the product. For example, the merchant receives a list of components and example description, write down on the paper a description of the product based on the list of components and the example description , looks at the photo the product, and modifies the description based on the photo and provides the modified description to the customer.
The judicial exception is not integrated into a practical application. In particular, claims recite the additional limitations of “one or more processor”, “one or more memories having computer-readable instructions stored thereon…”, “first device”. The additional element(s) or combination of elements such as processor, memory and/or device in the claim(s) other than the abstract idea per se amount(s) to no more than (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device other than modifying the description for the item. The mere recitation of processor, memory, device and/or the like is akin of adding the word “apply it” and/or “use it” with a computer in conjunction with the abstract idea. The paragraph [0403-0404] of the present specification discloses “[0403] In at least one example, each processor 1308 can itself comprise one or more processors or processing cores. For example, the processor(s) 1308 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. In some examples, the processor(s) 1308 can be one or more hardware processors and/or logic circuits of any suitable type specifically programmed or configured to execute the algorithms and processes described herein. The processor(s) 1308 can be configured to fetch and execute computer-readable processor-executable instructions stored in the computer-readable media 1310, [0404] Depending on the configuration of the user device 1302, the computer-readable media 1310 can be an example of tangible non-transitory computer storage media and can include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of information such as computer-readable processor-executable instructions, data structures, program components or other data. The computer-readable media 1310 can include, but is not limited to, RAM, ROM, EEPROM, flash memory, solid-state storage, magnetic disk storage, optical storage, and/or other computer-readable media technology. Further, in some examples, the user device 1302 can access external storage, such as RAID storage systems, storage arrays, network attached storage, storage area networks, cloud storage, or any other medium that can be used to store information and that can be accessed by the processor(s) 1308 directly or through another computing device or network. Accordingly, the computer-readable media 1310 can be computer storage media able to store instructions, components or components that can be executed by the processor(s) 1308. Further, when mentioned, non-transitory computer-readable media exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.”
As filed in the specification, the computer is listed as a general-purpose computer and are mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims recites “using a first generative AI model”, “using the second generative AI model” at a high level of generality. The first generative AI model and the second generative AI model is/are used to generally apply the abstract idea without placing any limits on how the AI models functions. Rather, these limitations only recite the outcome of “generating a text natural language response that includes a description for the item based on the component list;” “modifying the description for the item of the text natural language response based on detection in the visual media data of at least one component in the component list;” “generating a text natural language response that includes a description for the item based on the prompt;” “determining that one or more components detected in the visual media data differ from components in the component list;” “adding a component to the modified description based on detection of the component in the visual media data by the second generative AI model”, “determining that one or more components detected in the visual media data mismatch components in the component list;” “generating the component list using the first generative AI model based on the identification.” “modifying the description based on the description tone.” “determining a category of the item”, “modifying the description based on the category.” “modifying a prompt based on the user feedback data and providing the prompt to the first generative AI model or the second generative AI model to generate the second description of the item.” and do not include any details about how the “generating”, “modifying”, “adding”, “determining” are accomplished. See MPEP 2106.05(f). Claim 6 recites “the second generative AI model is trained to detect features including objects in visual media data.” There is no technical detail(s) on how the second generative AI model is trained to detect features including objects in visual media data.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The dependent claims further do not remedy the issues noted above. More specifically, Claim 2 defines the visual media data. There are no additional limitations presented. Claim 3 defines the component list for the food item. There are no additional limitations presented. Claim 4 recites a mental process of determining a prompt and providing the determined prompt to the AI model. There are no additional limitations presented. Claim 5 recites a mental process of adding and/or removing a component to modify the description. There are no additional limitations presented. Claim 6 recites a mental process of detecting the components in the component list. There are no additional limitations presented. Claim 7 recites a mental process of detecting a plurality of objects in the picture/the video and ignoring a particular category object. There are no additional limitations presented. Claim 8 recites a mental process of identifying a potential hazard of the item and notifying of the potential hazard. There are no additional limitations presented. Claim 10 recites a mental process of using one or more example descriptions associated with the item to generate the description for the item. There are no additional limitations presented. Claim 11 defines the context data. There are no additional limitations presented. Claim 12 recites a mental process of determining that one or more components in the photo/the picture differ from components in the component list and providing the determined result to a user. There are no additional limitations presented. Claim 13 recites a mental process of mismatching between the component in the photo/the picture and the component in the component list and generating new photo/picture. There are no additional limitations presented. Claim 14 recites a mental process of modifying the photo/the picture. There are no additional limitations presented. Claim 16 recites identifying the item and generating the component lists. There are no additional limitations presented. Claim 17 recites a mental process of determining a description tone (e.g., funny, bubbly, serious, lighthearted, exaggerated, etc.), and modifying the description. There are no additional limitations presented. Claim 18 recites a mental process of identifying a category of the item and modifying the description based on the determined category. There are no additional limitations presented. Claim 19 recites a mental process of modifying the one or more characteristics of the description. There are no additional limitations presented. Claim 20 recites a mental process of receiving a feedback of the user and modifying the description based on the user’s feedback. There are no additional limitations presented.
For at least the supra provided reasons, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
5. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
6. Claims 1-2, 4-6, 9-11, 15-16, 18-19 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Wright (US 2023/0259692 A1.)
With respect to Claim 1, Wright discloses
A computer-implemented method of refining generative artificial intelligence (AI) model outputs, the method comprising:
receiving, from a first device, a component list for an item that is to be included in a menu of items, the component list including a plurality of components (Wright [0080] describes receiving a component list from a merchant (e.g., “ribbed crop tank top”, “linen halter top”). See paragraph [0080]);
generating, using a first generative AI model, a text natural language response that includes a description for the item based on the component list (Wright [0119] describes creating a product description based on the component list);
obtaining visual media data of the item that depicts one or more components of the plurality of components of the component list (Wright [0133] describes obtaining a photo of the product with which the product title associated is uploaded, [0135] describes performing image processing on the photo);
providing the text natural language response and the visual media data of the item to a second generative AI model (Wright [0010] describes modifying the description based on the attribute related to the product as depicted in the image, [0136-0137] describes the product description generator receives a modifiable product description and one or more words from the image processor);
modifying, using the second generative AI model, the description for the item of the text natural language response based on detection in the visual media data of at least one component in the component list by the second generative AI model (Wright [0010] the method may further include a step of processing an image depicting the product to obtain an attribute related to the product as depicted in the image. In some embodiments, the method may further include a step of including, in the content, a particular word or phrase associated with the attribute. In some embodiments, the method may further include modifying the product description to include the particular word or phrase associated with the attribute. In some embodiments, the alternative word or phrase that may be substituted in place of the candidate for modification may be the particular word or phrase associated with the attribute, [0137-0138] describes the product description generator identifies the one or more words of the modifiable product description as an inaccuracy and modifies the inaccuracy by replacing and/or adding one or more words); and
providing the modified description to the first device for inclusion in the menu of items (Wright [0141] describes the modified product description is sent to the merchant at the merchant device. See Fig. 4 element 410 Product Description Generator, 420 Merchant Device.)
With respect to Claim 2, Wright discloses
wherein the visual media data includes at least one of: one or more images or one or more videos (Wright [0133] describes a photo of the product with which the product title associated is uploaded, Fig. 7.)
With respect to Claim 4, Wright discloses
further comprising:
determining a prompt for the first generative AI model that is based on the component list and is based on one or more example descriptions associated with the item, wherein the example descriptions are associated with one or more other items that are different than the item and are retrieved from a database of descriptions (Wright [0166] describes using the product title (e.g., ribbed crop tank top) and/or example product description as a prompt for generative language model. See paragraph [0009]); and
providing the prompt to the first generative AI model (Wright [0166] describes providing the prompt to the generative language model.)
With respect to Claim 5, Wright disclose
wherein modifying the description includes at least one of:
adding a component to the modified description based on detection of the component in the visual media data by the second generative AI model (Wright Fig. 11 element 1106, [0138] [0138] For example, FIG. 11 illustrates an embodiment wherein the modifiable product description 1004 has been modified following image processing of the photo 1010, to result in a modified product description 1004′. The modified product description 1004′ includes a word “short-sleeve” 1106, and it is evident that the word “short-sleeve” 1106 has replaced the word “long-sleeve” 1006 of the modifiable product description 1004); or
removing a component in the component list from the modified description based on lack of detection of the component in the visual media data by the second generative AI model (Wright Fig. 11 and [0138] the word “short-sleeve” has replaced the word “long-sleeve”. The word “long-sleeve” is removed and the word “short-sleeve” is added.)
With respect to Claim 6, Wright disclose
wherein modifying the description includes detecting in the visual media data, by the second AI model, the components in the component list, wherein the second generative AI model is trained to detect features including objects in visual media data (Wright [0007] describes the image analysis model trained to infer distinct visual features of product, [0135] the image processor is a trained machine learning model. The image processor image is capable of extracting relevant features of the product depicted in the photo. Relevant features may include type of product, shape, length, color, or pattern. For example, for a clothing product, the image processor may be able to determine the type (e.g., top, pants, shoes, socks, etc.), shape (e.g. V-neck, shawl collared, wide-legged, etc.), length (e.g., short-sleeve, sleeveless, mid-thigh, etc.), color (e.g., red and green, tone-on-tone, etc.), or pattern (e.g., zig-zag, striped, polka dotted, etc.) of the product. For the women's striped top depicted in the photo 1010, the image processor may be able to identify that it is a striped short-sleeve top with a round neck.)
With respect to Claim 9, Wright discloses
A computer-implemented method of refining generative artificial intelligence (AI) model outputs, the method comprising:
receiving, from a first device, a component list for an item that is to be included in a menu of items, the component list including a plurality of components (Wright [0080] describes receiving a component list from a merchant (e.g., “ribbed crop tank top”, “linen halter top”). See paragraph [0080]);
obtaining context data associated with the item (Wright [0082] describes receiving example product title and product description pairs as part of input for the generative language model);
determining a prompt for a first generative AI model that includes or is based on the component list and is based on the context data (Wright Fig. 5 input 502, generative language model 510 and output 512, [0080-0082] using the component list and example product title and production description as a prompt for the generative language model);
providing the prompt to the first generative AI model (Wright Fig. 5 input 502, generative language model 510 and output 512, [0080-0082] using the component list and example product title and production description as a prompt for the generative language model);
generating, using the first generative AI model, a text natural language response that includes a description for the item based on the prompt (Wright Fig. 5 output 512 output the description of the product based on the component list and the example product title and production description);
obtaining visual media data of the item that depicts one or more components of the plurality of components of the component list (Wright [0133] describes obtaining a photo of the product with which the product title associated is uploaded, [0135] describes performing image processing on the photo);
providing the text natural language response and the visual media data of the item to a second generative AI model (Wright [0010] describes modifying the description based on the attribute related to the product as depicted in the image, [0136-0137] describes the product description generator receives a modifiable product description and one or more words from the image processor);
modifying, using the second generative AI model, the description for the item of the text natural language response based on detection in the visual media data of at least one component in the component list by the second generative AI model (Wright [0010] the method may further include a step of processing an image depicting the product to obtain an attribute related to the product as depicted in the image. In some embodiments, the method may further include a step of including, in the content, a particular word or phrase associated with the attribute. In some embodiments, the method may further include modifying the product description to include the particular word or phrase associated with the attribute. In some embodiments, the alternative word or phrase that may be substituted in place of the candidate for modification may be the particular word or phrase associated with the attribute, [0137-0138] describes the product description generator identifies the one or more words of the modifiable product description as an inaccuracy and modifies the inaccuracy by replacing and/or adding one or more words); and
providing the modified description to the first device for inclusion in the menu of items (Wright [0141] describes the modified product description is sent to the merchant at the merchant device. See Fig. 4 element 410 Product Description Generator, 420 Merchant Device.)
With respect to Claim 10, Wright discloses
wherein the context data includes one or more example descriptions associated with the item, wherein at least one of the example descriptions is associated with one or more other items that are different than the item and include one or more characteristics of the item (Wright [0080] describes example product title and product description pairs.)
With respect to Claim 11, Wright disclose
wherein the context data includes at least one of:
user information indicating one or more characteristics of a user requesting the description for the item (Wright [0150] describes generating the description based on the context data, wherein the context data is location/setting context), or entity information indicating one or more characteristics of an entity associated with the user.
With respect to Claim 15, claim 15 recites the similar features as Claim 1, thus Claim 15 is rejected as the same ground as Claim 1.
With respect to Claim 16, Wright discloses
wherein the operations further comprise:
obtaining an identification of the item (Wright [0136] describes receiving the product title “Women’s Striped Top”); and
generating the component list using the first generative AI model based on the identification (Wright [0136] characteristics of striped, short-sleeve, and round neck of the women’s striped top are identified.)
With respect to Claim 18, Wright discloses
wherein the operations further comprise:
determining a category of the item by the first generative AI model (Wright [0135] describes determining a type of the product); and
modifying the description, by at least one of the first or second AI model, based on the category (Wright [0136] modifying the description of the product based on the type of product).
With respect to Claim 19, Wright discloses
wherein the operation of modifying the description based on the category includes modifying one or more characteristics of the description, wherein the one or more characteristics includes at least one of a length of the description and a tone of the description (Wright [0082] describes modifying the description with respect to tone, length or any other characteristic possessed by the example product description.)
Claim Rejections - 35 USC § 103
7. 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 of this title, 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.
8. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Wright (US 2023/0259692 A1) in view of Neumann (US 2024/0071598 A1.)
With respect to Claim 3, Wright discloses all the limitations of Claim 1 upon which Claim 3 depends. Wright fail to explicitly teach
wherein the item is a food item, the plurality of components are a plurality of ingredients of the food item, and the component list is an ingredients list that lists the plurality of ingredients of the food item.
However, Neumann et al. teach
wherein the item is a food item, the plurality of components are a plurality of ingredients of the food item, and the component list is an ingredients list that lists the plurality of ingredients of the food item (Neumann et al. [0078] describes GAN receives ingredients as input and generates corresponding description for food.)
Wright and Neumann et al. are analogous art because they are from a similar field of endeavor in the Signal processing algorithm and applications. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of generating modifying the description of the item based on the visual data as taught by Wright, using teaching of food ingredients as taught by Neumann et al. for the benefit of generating corresponding description for food (Neumann et al. [0078] describes GAN receives ingredients as input and generates corresponding description for food.)
9. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wright (US 2023/0259692 A1) in view of Mankowski et al. (US 2023/0098779 A1.)
With respect to Claim 7, Wright et al. disclose all the limitations of Claim 6 upon which Claim 7 depends. Wright et al. fail to explicitly teach
wherein detecting the components in the visual media data includes segmenting the visual media data, detecting a plurality of objects in the visual media data, and ignoring one or more of the objects, wherein the one or more ignored objects:
are of a particular category; or
are below a threshold relevance score associated with the item.
However, Mankowski et al. teach
wherein detecting the components in the visual media data includes segmenting the visual media data, detecting a plurality of objects in the visual media data, and ignoring one or more of the objects, wherein the one or more ignored objects (Mankowski et al. [0100] describes storing portions of images from images acquired by the cameras, [0142] describes determining a type of an external object, and ignore images of some types of object):
are of a particular category (Mankowski et al. [0100] describes storing portions of images from images acquired by the cameras, [0142] describes determining a type of an external object, and ignore images of some types of object); or
are below a threshold relevance score associated with the item.
Wright and Mankowski et al. are analogous art because they are from a similar field of endeavor in the Signal processing algorithm and applications. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of modifying the description of the item based on the image of the item as taught by Wright, using teaching of determining a type of object in the portions of the image as taught by Mankowski et al. for the benefit of ignoring a particular type of object (Mankowski et al. [0100] describes storing portions of images from images acquired by the cameras, [0142] describes determining a type of an external object, and ignore images of some types of object)
10. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wright (US 2023/0259692 A1) in view of Wong et al. (US 2026/0162432 A1.)
Wright et al. disclose all the limitations of Claim 1 upon which Claim 8 depends. Wright et al. fail to explicitly teach
wherein modifying the description includes identifying one or more components of the item in the visual media data that present a potential hazard to a user of the item; and
modifying the description to include an indication of the potential hazard.
However, Wong et al. teach
wherein modifying the description includes identifying one or more components of the item in the visual media data that present a potential hazard to a user of the item (Wong et al. [0010] describes analyzing objects in the detecting image and identifying safety risk factor); and
modifying the description to include an indication of the potential hazard (Wong et al. [0010-0011] describes sending warning notification to the user).
Wright and Wong et al. are analogous art because they are from a similar field of endeavor in the Signal processing algorithm and applications. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of modifying the description of the item based on the image of the item as taught by Wright, using teaching of detecting safety risk factors of the object in the detected image as taught by Wong et al. for the benefit of sending the warning notification to the user (Wong et al. [0010-0011] describes sending warning notification to the user in response to detecting safety risk factor).
Allowable Subject Matter
11. Claims 12-14, 17 and 20 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. However, claims 1-20 stand rejected under 101 Abstract idea, and for the application to pass to allowance the rejection need to be overcome. Any amendments to overcome the rejection that results in any change in scope require further search and/or consideration in order to determine it allowability.
The following is a statement of reasons for the indication of allowable subject matter: the prior art(s) taken alone or in combination fail(s) to teach the following element(s) in combination with the other recited elements in the claim(s).
“determining, by the second AI model, that one or more components detected in the visual media data differ from components in the component list; and
providing an indication to the first device of the one or more components that differ.” as recited in Claim 12.
“determining, by the second AI model, that one or more components detected in the visual media data mismatch components in the component list; and
generating new visual media data based on the visual media data and based on the components in the component list, if a threshold number of mismatches are detected between the components in the component list and the one or more detected components in the visual media data.” as recited in Claim 13.
“generating modified visual media data based on the visual media data, wherein the modified visual media data includes components from the component list that are not detected in the visual media data.” as recited in Claim 14.
“determining a description tone based on at least the visual media data and context data associated with the item; and
modifying the description, by at least one of the first or second AI model, based on the description tone.” as recited in Claim 17.
“obtaining user feedback data based on one or more actions of a user, wherein the user feedback data is based on the modified description, wherein the user feedback data includes at least one of:
an indication that the user changed the modified description and indications of the changes made to the modified description by the user; or
an indication that the user used the modified description in the menu;
receiving a request to generate a second description of the item; and
modifying a prompt based on the user feedback data and providing the prompt to the first generative AI model or the second generative AI model to generate the second description of the item.” as recited in Claim 20.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See PTO-892.
a. Tammineni et al. (US 2026/0162171 A1.) In this reference, Tammineni et al. disclose a method and a system for modifying a description of an item based on image of the item.
b. Ahas (US 12,555,154 B2.) In this reference, Ahas disclose a method and a system for modifying subset of the product description.
c. Sinesio et al. (US 11,755,276 B2.) In this reference, Sinesio et al. disclose a method and a system for reducing the length of the identified description.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THUYKHANH LE whose telephone number is (571)272-6429. The examiner can normally be reached Mon-Fri: 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew C. Flanders can be reached on 571-272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUYKHANH LE/Primary Examiner, Art Unit 2655
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