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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding to claim 2, 9, and 16
The limitation “generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content items” It is unclear that the prioritized slots are added to the first or the second set of relevancy-ranked content items.
Dependent claims are failed to cure deficiencies. Dependent claims are rejected.
Claim Rejections - 35 USC § 101
Claims 2-21 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.
Regarding to claims 2-8
Step 1, This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A – Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step “generating a search criterion based on a search request received from a client device” Given broadest interpretation, this step is simply evaluations of user’s request for relevance terms in user’s request as search criterion. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
Step “responsive to the search criterion, generating a first set of relevancy-ranked content items” Ranking items simply gives one item has higher rank than another item. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
Step “generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content items” Ranking or re-ranking items based on rules which add prioritized slots among relevancy-ranked content items can be interpreted as adding prioritized slots randomly among relevancy-ranked content items. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
Step “assigning the second set of relevancy-ranked content items to an ordered display slot position” This step is interpreted as putting content items into an order. This step is nothing more than observations, evaluations, judgments that can be performed in human mind (i.e., a mental process [Wingdings font/0xF3] abstract idea).
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the mentioned steps fall within the mental process grouping of abstract ideas. They are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements/limitations: “search criterion”, “a client device”, “a first set of relevancy-ranked content items”, “a second set of relevancy-ranked content item”, “multidimensional sorting rules”, “prioritized slots”, “an ordered display slot position”, “user interface”, and “providing, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions”
a) MPEP § 2106.05(a) "Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field."
The limitations “search criterion”, “a client device”, “a first set of relevancy-ranked content items”, “a second set of relevancy-ranked content item”, “multidimensional sorting rules”, “prioritized slots”, “an ordered display slot position”, “user interface”, and “providing, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions” does not make any improvements to the functionalities of a computer, database technology, or any other technologies.
b) MPEP § 2106.05(b) Particular Machine. The judicial exception does not apply to any particular machine.
The claim is silent regarding specific limitations directed to an improved computer system, processor, memory, network, database, or Internet, nor do applicant direct examiner’s attention to such specific limitations. "[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 573 U.S. at 223; see also Bascom Glob. Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1348 (Fed. Cir. 2016) ("An abstract idea on 'an Internet computer network' or on a generic computer is still an abstract idea."). Applying this reasoning here, the claim is not directed to a particular machine, but rather merely implement an abstract idea using generic computer components such as “search criterion”, “a client device”, “a first set of relevancy-ranked content items”, “a second set of relevancy-ranked content item”, “multidimensional sorting rules”, “prioritized slots”, “an ordered display slot position”, “user interface.” Thus, the claims fail to satisfy the "tied to a particular machine" prong of the Bilski machine-or-transformation test.
c) MPEP § 2106.05(c) Particular Transformation.
The claim operates to gathering data/user request, organizing query results, adding slots, and displaying ordered query results. The steps are not a "transformation or reduction of an article into a different state or thing constituting patent-eligible subject matter[.]" See In re Bilski, 545 F.3d 943, 962 (Fed. Cir. 2008) (en bane), aff'd sub nom, Bilski v. Kappas, 561 U.S. 593 (2010); see also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) ("The mere manipulation or reorganization of data ... does not satisfy the transformation prong."). Applying this guidance here, the claims fail to satisfy the transformation prong of the Bilski machine-or-transformation test.
d) MPEP § 2106.05(e) Other Meaningful Limitations.
This section of the MPEP guides: Diamond v. Diehr provides an example of a claim that recited meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. 450 U.S. 175, ... (1981). In Diehr, the claim was directed to the use of the Arrhenius equation (an abstract idea or law of nature) in an automated process for operating a rubber-molding press. 450 U.S. at 177-78 .... The Court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found them to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products. 450 U.S. at 184... In contrast, the claims in Alice Corp. v. CLS Bank International did not meaningfully limit the abstract idea of mitigating settlement risk. 573 U.S._ .... In particular, the Court concluded that the additional elements such as the data processing system and communications controllers recited in the system claims did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers") or were well-understood, routine, conventional activity. MPEP § 2106.05(e). The limitation displaying ordered query results is not meaningful limitations because it is a post-solution activity The limitation is not a meaningful limitation.
e) MPEP § 2106.05(g) Insignificant Extra-Solution Activity.
The limitation displaying ordered query results is not meaningful limitations because it is a post-solution activity.
f) MPEP § 2106.05(h) Field of Use and Technological Environment.
[T]he Supreme Court has stated that, even if a claim does not wholly pre-empt an abstract idea, it still will not be limited meaningfully if it contains only insignificant or token pre- or post-solution activity-such as identifying a relevant audience, a category of use, field of use, or technological environment. Ultramercial, Inc. v. Hulu, LLC, 722 F.3d 1335, 1346 (Fed. Cir. 2013). “database”, “transaction database”, “machine learning model”, “computer processor”, “artificial intelligence program”, “text embedded vector analysis” limitations are simply a field of use that attempts to limit the abstract idea to a particular technological environment.
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim does not recite any non-convention or non-generic arrangement because displaying ordered query results is a post-solution activity. Taking these limitations as an ordered combination adds nothing that is not already present when the elements are taken individually. Therefore, the claim does not amount to significantly more than the recited abstract idea. The claim is not patent eligible.
Claim 3 recites “wherein generating the first set of relevancy-ranked content items comprises: processing the search criterion with a generative Al search and retrieval system comprising one more machine learning models.” This limitation simply uses a computer system to implement an abstract idea (e.g., identify relevant terms/search criterion in user request can be performed in human mind). Further, “generating, by the generative Al search and retrieval system, a relevancy-ranked output listing of content items responsive to the search criterion” is also using a computer system to implement an abstract idea (e.g., ranking items can be performed in human mind). The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 4 recites “wherein the content items of the relevancy-ranked output listing each comprise a content identifier and a content description” Limitations “a content identifier” and “a content description” are generic computer components. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 5 recites “generating an initial set of content items; and processing the initial set of content items by an inferencing machine learning model trained to determine content item score values, thereby generating item score values for the initial set of content items” Giving scores to content items can be performed by human. The claim simply uses “inferencing machine learning model” as a tool to implement the abstract idea. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 6 recites “modifying an item score value to adjust a content item position placement in the initial set of content items” Modifying a score can be performed by human and position of items are changed in accordance with the modified score. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 7 recites “generating annotations for one or more of the content items in the first set of relevancy-ranked content items.” Generating annotations can be performed by human. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 8 recites “wherein the annotations comprise a content item type for a respective content item.” By observing a type of a content item, a human can give annotations according to the type of the item. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 9 is similar to claim 1. The claim is rejected based on the same reason.
Claim 10 recites “assign the second set of relevancy-ranked content items to an ordered display slot position by generating a carousel display structure definition for the second set of relevancy-ranked content items.” This step is interpreted as putting content items into an order for display. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 11 recites “wherein the one or more prioritized slots comprise a novelty slot or a promoted slot.” Novelty slot or promoted slot is considered as positions in the query results. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 12 recites “providing the first set of relevancy-ranked content items and instructions to apply the one or more multi-dimensional sorting rules a language learning model (LLM) or generative artificial intelligence (AI) subsystem; and receiving, from the LLM or generative AI subsystem, the second set of relevancy-ranked content items.” Sending data and rules as input to a system (e.g., LLM or Ai subsystem) and obtain output (e.g., second set of relevancy-ranked content item). A person can easily perform this step. The claim simply uses computer system as a tool to implement the abstract idea. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 13 is similar to claim 3. The claim is rejected based on the same reason.
Claim 14 is similar to claim 5. The claim is rejected based on the same reason.
Claim 15 is similar to claim 6. The claim is rejected based on the same reason.
Claim 16 is similar to claim 1. The claim is rejected based on the same reason.
Claim 17 is similar to claim 11. The claim is rejected based on the same reason.
Claim 18 is similar to claim 7. The claim is rejected based on the same reason.
Claim 19 is similar to claim 8. The claim is rejected based on the same reason.
Claim 20 is similar to claim 10. The claim is rejected based on the same reason.
Claim 21 recites “to generate the first set of relevancy-ranked content items or the second set of relevancy-ranked content items by utilizing a machine learning model.” Ranking items can be performed in human mind. The claim simply uses machine learning model as a tool to implement the abstract idea. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 4-9, 11-13, 15, and 18 of U.S. Patent No. 12292896. Although the claims at issue are not identical, they are not patentably distinct from each other because claim(s) 1, 4-9, 11-13, 15, and 18 of patent # 12292896 contain(s) every element of claim(s) 2-21 of the instant application and as such anticipate(s) claim(s) 2-21 of the instant application.
Application: 19189769
Patent:12292896
Claim 2
A computer-implemented method comprising:
generating a search criterion based on a search request received from a client device;
responsive to the search criterion, generating a first set of relevancy-ranked content items;
generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content items;
assigning the second set of relevancy-ranked content items to an ordered display slot position; and
providing, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions.
Claim 1
A computer-implemented method performed by one or more processors, comprising the operations of:
receiving a request from a client device to return a set of content items;
generating, by the one or more processors, a search criterion to search for content items responsive to the request;
generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;
executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;
generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and
rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.
Claim 3
The computer-implemented method of claim 2, wherein generating the first set of relevancy-ranked content items comprises:
processing the search criterion with a generative Al search and retrieval system comprising one more machine learning models; and
generating, by the generative Al search and retrieval system, a relevancy-ranked output listing of content items responsive to the search criterion.
Claim 4
The computer-implemented method of claim 1, wherein generating the first set of relevancy-ranked output comprises:
causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and
generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.
Claim 4
The computer-implemented method of claim 3, wherein the content items of the relevancy-ranked output listing each comprise a content identifier and a content description.
Claim 4
wherein the content items of the output listing each have a content identifier and a content description.
Claim 5
The computer-implemented method of claim 2, further comprising:
generating an initial set of content items; and
processing the initial set of content items by an inferencing machine learning model trained to determine content item score values, thereby generating item score values for the initial set of content items.
Claim 5
The computer-implemented method of claim 1, wherein causing the generated search criterion to be processed, via a search system, comprises the operations of:
performing, by the one or more processors, a first stage scoring process, to generate an initial set of content items; and
performing, by the one or more processors, a second stage scoring process, by processing the initial set of content items by an inferencing machine learning model trained to determine content item score values; and
generating, by the inferencing machine learning model, item score values for the initial set of content items.
Claim 6
The computer-implemented method of claim 5, further comprising
modifying an item score value to adjust a content item position placement in the initial set of content items.
Claim 6
The computer-implemented method of claim 5, further comprising the operations of:
performing, by the one or more processors, a third stage scoring process that modifies an item score value to adjust a content item position placement in the first set of relevancy-ranked output listing.
Claim 7
The computer-implemented method of claim 2, further comprising generating annotations for one or more of the content items in the first set of relevancy-ranked content items.
Claim 7
The computer-implemented method of claim 5, further comprising the operations of:
performing, by the one or more processors, a blender process that generates annotations for one or more of the content items in the relevancy-ranked output listing, wherein the generated annotations comprise a type of content item for a respective content item in the relevancy-ranked output listing.
Claim 8
The computer-implemented method of claim 7, wherein the annotations comprise a content item type for a respective content item.
Claim 7
wherein the generated annotations comprise a type of content item for a respective content item in the relevancy-ranked output listing.
Claim 9
A system comprising:
one or more processors; and
a memory coupled to the one or more processors, wherein the memory includes instructions executable by the one or more processors to:
responsive to a search criterion based on a search request received from a client device, generate a first set of relevancy-ranked content items;
reorder the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy- ranked content items, thereby generating a second set of relevancy-ranked content items;
assign ordered display slot positions to content items of the second set of relevancy- ranked content items; and
provide, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions.
Claim 8
A system comprising one or more processors configured to perform the operations of:
receiving a request from a client device to return a set of content items; generating, by the one or more processors, a search criterion to search for content items responsive to the request;
generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;
executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;
generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and
rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.
Claim 10
The system of claim 9, wherein the memory further includes instructions executable by the one or more processors to assign the second set of relevancy-ranked content items to an ordered display slot position by generating a carousel display structure definition for the second set of relevancy-ranked content items.
Claim 9
The system of claim 8, wherein generating the carousel display structure definition of the second set of relevancy-ranked content items comprises: assigning the second set relevancy-ranked content items into a predetermined number of display groupings, wherein the predetermined number of display groupings includes 2 or more groupings, and wherein each display grouping includes a predetermined number of display slots, wherein a display slot is an ordered position in a display grouping.
Claim 11
The system of claim 9, wherein the one or more prioritized slots comprise a novelty slot or a promoted slot.
Claim 12
The system of claim 9, wherein the memory further includes instructions executable by the one or more processors to generate the second set of relevancy-ranked content items by:
providing the first set of relevancy-ranked content items and instructions to apply the one or more multi-dimensional sorting rules a language learning model (LLM) or generative artificial intelligence (AI) subsystem; and
receiving, from the LLM or generative AI subsystem, the second set of relevancy-ranked content items.
Claim 13
The system of claim 9, wherein the memory further includes instructions executable by the one or more processors to generate the first set of relevancy-ranked content items by:
providing the search criterion to a generative AI search and retrieval system comprising one more machine learning models; and
generating, by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the search criterion.
Claim 11
The system of claim 8, wherein generating the first set of relevancy-ranked output comprises:
causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and
generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.
Claim 14
The system of claim 9, wherein the memory further includes instructions executable by the one or more processors to:
generate an initial set of content items; and
process the initial set of content items by an inferencing machine learning model trained to determine content item score values, thereby generating item score values for the initial set of content items.
Claim 12
The system of claim 8, wherein causing the generated search criterion to be processed, via a search system, comprises the operations of:
performing, by the one or more processors, a first stage scoring process, to generate an initial set of content items; and
performing, by the one or more processors, a second stage scoring process, by processing the initial set of content items by an inferencing machine learning model trained to determine content item score values; and
generating, by the inferencing machine learning model, item score values for the initial set of content items.
Claim 15
The system of claim 14, wherein the memory further includes instructions executable by the one or more processors to modify an item score value to adjust a content item position placement in the initial set of content items.
Claim 13
The system of claim 12, further comprising the operations of: performing, by the one or more processors, a third stage scoring process that modifies an item score value to adjust a content item position placement in the first set of relevancy-ranked output listing.
Claim 16
A non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor to:
generate a first set of relevancy-ranked content items based on a search criterion generated from a search request received from a client device;
apply one or more multidimensional sorting rules to reorder the first set of relevancy- ranked content items, wherein the one or more multidimensional sorting rules add one or more prioritized slots among relevancy-ranked content items;
generate a second set of relevancy-ranked content items having ordered positions; and
provide, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered positions.
Claim 15
A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations of:
receiving a request from a client device to return a set of content items;
generating, by the one or more processors, a search criterion to search for content items responsive to the request;
generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system;
executing, by the one or more processors, one or more multidimensional sorting rules to the first set of relevancy-ranked content items, and
generating a second set of relevancy-ranked content items having a different sorted order than the first set of relevancy-ranked content items, wherein one of the multidimensional sorting rules includes a promoted slot selector that modifies a sort order of a promoted content item, wherein one or more of the content items of the first relevancy ranked content items have a display slot position that has been changed to a different display slot position for the same content items of the second set of relevancy-ranked content items;
generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position; and
rendering, via a user interface of the client device, at least a portion of the second set of relevancy-ranked content items in the respective multiple display groupings and the display slot positions according to the display structure definition.
Claim 17
The non-transitory computer readable medium of claim 16, wherein the one or more prioritized slots comprise a novelty slot or a promoted slot.
Claim 18
The non-transitory computer readable medium of claim 16, further storing instructions which, when executed by at least one processor, cause the at least one processor to generate annotations for one or more of the content items in the first set of relevancy-ranked content items.
Claim 18
The non-transitory computer readable medium of claim 15, wherein generating the first set of relevancy-ranked output comprises:
causing the generated search criterion to be processed, via the generative AI search and retrieval system, comprising one more machine learning models; and
generating by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the generated search criterion, wherein the content items of the output listing each have a content identifier and a content description.
Claim 19
The non-transitory computer readable medium of claim 18, wherein the annotations comprise a content item type for a respective content item.
Claim 20
The non-transitory computer readable medium of claim 16, further storing instructions which, when executed by at least one processor, cause the at least one processor to generate a carousel display structure definition for the second set of relevancy-ranked content items.
Claim 15
generating, by the one or more processors, a carousel display structure definition of the second set of relevancy-ranked content items, wherein the carousel display structure definition identifies multiple display groupings and an order of each display grouping for the relevancy-ranked content items, and wherein the second set of relevancy-ranked content items have an associated display grouping value and a display slot position
Claim 21
The non-transitory computer readable medium of claim 16, further storing instructions which, when executed by at least one processor, cause the at least one processor to generate the first set of relevancy-ranked content items or the second set of relevancy-ranked content items by utilizing a machine learning model.
Claim 15
generating, by the one or more processors, a first set of relevancy-ranked content items, via a generative Artificial Intelligence (AI) search and retrieval system
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-7, 9, 11-18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chao (U.S. Pub 2024/0403373 A1), in view of Shen (U.S. Pub 2015/0278341 A1)
Claim 2
Chao discloses a computer-implemented method comprising:
generating a search criterion based on a search request received from a client device ([0096], line 1-2, “… [0096] The search engine 406 may receive the search query 502 consisting of search terms…” [0074], line 2-5, “… The search engine 304 may implement a webpage interface that is accessible by a user on user devices 101-104 in order to invoke searches for search strings…” ([0097], line 3-5, “… the search query 502 may be parsed and processed (including normalization…” [0119], “… additional parsing may be performed on the search query 502. For example, in some cases, the search query 502 may include the names of a provider of a data listing 423. In some cases, absent additional processing, the LLM 510 may have difficulty determining context from a proper name, as some company names are in a foreign language, or a string of characters that have no meaning in any language. However, a provider name in the search query 502 may be extremely useful in providing relevant search results. Additional processing of the search query 502 may identify data of this type and augment the search process…” <examiner note: parsed/normalized query [Wingdings font/0xF3] search criterion>);
responsive to the search criterion, generating a first set of relevancy-ranked content items ([0097], line 3-5, “…. the search query 502 may be parsed and processed (including normalization in some embodiments) and passed into the embedding engine 512…” [0142], line 1-5, “… the user interface 1300 may include a plurality of data listings 423 (423B, 423C, 423D are illustrated) that may be the relevant results in response to the search query 502. In some embodiments, the plurality of data listings 423 may be listed in order of relevance…”);
generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules (multidimensional sorting rules [Wingdings font/0xF3] data listing signals such as characteristics of data listings, data, structure and using data listing signals to promote or demote data listings) ([0120], line 16-18, “… the output of the LLM 510 may be the ordinal ranking of the data listing embeddings 710…” [0121], line 16-20, “… the output scores of the LLM 510 may not always be sufficient for getting a high-quality ranking. As a result, the nearest-neighbor results from the LLM 510 from block 604 may be combined with data listing signals…” [0123], line 1-2, “… Based on the data listing signals, the ranking order of the results returned by the LLM 510 may be adjusted. For example, some data listings 423 that are listed as highly relevant by the LLM 510 may be adjusted downwards based on the data listing signals. As another example, some data listings 423 that are listed as less relevant by the LLM 510 may be adjusted upwards based on the data listing signals…” <examiner note: the adjusted ranking order of the results [Wingdings font/0xF3] second set of relevancy-ranked content items>);
assigning the second set of relevancy-ranked content items to an ordered display slot position (fig. 13); and
providing, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions (fig. 13 shows the results includes data listing 1, 2, 3…; data listing descriptions and each data listing 423 associates with ranking value [0142], line 4-9, “… the plurality of data listings 423 may be listed in order of relevance. For example, the most relevant data listing 423, as determined based on the LLM 510 as described herein, may be listed first, the second-most relevant data listing 423 may be listed second, and so on…”)
However, Chao does not explicitly disclose generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content item
Shen discloses generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content item ([0044], “… the search engine uses one or more models … to calculate a similarity degree between the text description of the product and the inquiry term used by the user… Assuming that matched products A to I are obtained, the products A to I are ranked to obtain a sequence ABCDEFGHI… a ranking sequence of C, E, F is adjusted to E, C, F according to their bidding records. The products whose sequences are adjusted are returned and output as EABCDF. Similarly, in the interval corresponding to scores less than 20, with respect to advertised products with the preset label and normally displayed, e.g., G, H, I, are re-ranked to IGH according to their bidding records…” <examiner note: the second set of relevancy-ranked content items are generated based on multidimensional sorting rules a) relevancy of the product and the query and b) bidding records. One or more prioritized slots are added such as E is added to the top, C is added the fourth position, I is added to the top of the second interval>)
assigning the second set of relevancy-ranked content items to an ordered display slot position ([0044], “… The products whose sequences are adjusted are returned and output as EABCDF… IGH according to their bidding records <examiner note: an ordered display slot position EABCDF… IGH); and
providing, for display on a user interface of the client device, at least a portion of the second set of relevancy-ranked content items according to the ordered display slot positions ([0072], “… the ranked search result or the search result in which the adjustment of rankings are completed, renders the page displayed at the browser, and returns the search result to the browser. The search objects in the search result are displayed according to their rankings at the browser…” EABCDF… IGH)
Chao discloses generating a second set of relevancy-ranked content items by reordering the first set of relevancy-ranked content items based on one or more multidimensional sorting rules, however, Chao does not disclose one or more multidimensional sorting rules which add one or more prioritized slots among relevancy-ranked content item. Shen discloses a second ranking model that is used to adjust the ranking of search objects with the preset labels so that the search objects with preset labels are boosted/prioritized in the second set of relevancy-ranked content items. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to as disclosed by Shen into Chao because “… combine two rankings within one search process, ensure correlations of the search result, and improve consistency and continuity of returned search result. In addition, the present techniques reduce complicated mixing and redundancy removing algorithms to lower data processing complexity, improve data processing efficiency, and increase data processing system performance…”
Claim 3
Claim 2 is included, Chao discloses wherein generating the first set of relevancy-ranked content items comprises:
processing the search criterion with a generative Al search and retrieval system comprising one more machine learning models ([0097], line 3-5, “… the search query 502 may be parsed and processed (including normalization…” [0119], “… additional parsing may be performed on the search query 502. For example, in some cases, the search query 502 may include the names of a provider of a data listing 423. In some cases, absent additional processing, the LLM 510 may have difficulty determining context from a proper name, as some company names are in a foreign language, or a string of characters that have no meaning in any language. However, a provider name in the search query 502 may be extremely useful in providing relevant search results. Additional processing of the search query 502 may identify data of this type and augment the search process…”; and
generating, by the generative Al search and retrieval system, a relevancy-ranked output listing of content items responsive to the search criterion (fig. 5, exchange manager 124 includes LLM 510, [0089], line 9-10, “… The LLM 510 may contain a learned embedding component illustrated as embedding engine 512…” [0093], line 1-2, “… The LLM 510 may also include a generative engine 514… [0094], line 1-4, “… the generative engine 514 may employ a transformer architecture that enables it to capture complex language patterns and generate highly realistic and human-like text…” [0097], line 3-5, “…. the search query 502 may be parsed and processed (including normalization in some embodiments) and passed into the embedding engine 512…” [0142], line 1-5, “… the user interface 1300 may include a plurality of data listings 423 (423B, 423C, 423D are illustrated) that may be the relevant results in response to the search query 502. In some embodiments, the plurality of data listings 423 may be listed in order of relevance…” <examiner note: LLM 510, embedding engine 512, generative engine 514 are considered as machine learning models>)
Claim 4
Claim 3 is included, Chao discloses wherein the content items of the relevancy-ranked output listing each comprise a content identifier and a content description (fig. 13, data listing 1 423B, data listing 2 423C are considered as identifiers of the data listings; data listing description 1305B, data listing description 1305C are descriptions of listings>)
Claim 5
Claim 2 is included, Chao discloses further comprising: generating an initial set of content items ([0097], line 7-12, “… The embedding corresponding to the search query 502 may then be used to search for nearest neighbors to the embedding in the embedding store 516 from among the retrieved data listings 423, which may contain embeddings for each of the data listings 423 of the data exchange…” <examiner note: 1st stage output a subset of data listings 423 using nearest neighbor search>); and processing the initial set of content items by an inferencing machine learning model trained to determine content item score values, thereby generating item score values for the initial set of content items ([0097], line 12-18, “… the data listings 423 corresponding to the nearest-neighbor embeddings may be passed to a next phase where, for each retrieved listing, information from the corresponding embedding is combined with other signals to compute the final aggregated sum score for each data listing 423 of the retrieved data listings 423…” [0099], line 1-3, “… The use of the LLM 510 to process and/or rank search results from a search query 502 may provide a number of benefits…”)
Claim 6
Claim 5 is included, Chao discloses modifying an item score value to adjust a content item position placement in the initial set of content items ([0097], line 12-18, “… the data listings 423 corresponding to the nearest-neighbor embeddings may be passed to a next phase where, for each retrieved listing, information from the corresponding embedding is combined with other signals to compute the final aggregated sum score for each data listing 423 of the retrieved data listings 423…” [0099], line 1-3, “… The use of the LLM 510 to process and/or rank search results from a search query 502 may provide a number of benefits…”)
Claim 7
Claim 2 is included, Chao discloses further comprising generating annotations for one or more of the content items in the first set of relevancy-ranked content items ([0131], line 1-5, “… FIG. 9, the top data listings 423 of results may be examined and, for each one, the LLM 510 may be prompted to generate the listing explanation 916 explaining why the listing is relevant to the user's search query 502… The use of the listing explanation 916 may enable the user to make better decisions in adopting or discarding the data listings 423 of the results…” <examiner note: listing explanations explain how relevant (i.e., type) of the data listing to the user query>)
Claim 9 is similar to claim 2. The claim is rejected based on the same reason.
Claim 11
Claim 9 is included, Shen discloses wherein the one or more prioritized slots comprise a novelty slot or a promoted slot (<examiner note: the slots for products C, E, F are promoted or novelty slot>)
Claim 12
Claim 9 is included, Shen disclose to generate the second set of relevancy-ranked content items by: providing the first set of relevancy-ranked content items and instructions to apply the one or more multi-dimensional sorting rules a language learning model (LLM) or generative artificial intelligence (AI) subsystem; and receiving, from the LLM or generative AI subsystem, the second set of relevancy-ranked content items ([0120], line 16-18, “… the output of the LLM 510 may be the ordinal ranking of the data listing embeddings 710…” [0121], line 16-20, “… the output scores of the LLM 510 may not always be sufficient for getting a high-quality ranking. As a result, the nearest-neighbor results from the LLM 510 from block 604 may be combined with data listing signals…” [0123], line 1-2, “… Based on the data listing signals, the ranking order of the results returned by the LLM 510 may be adjusted. For example, some data listings 423 that are listed as highly relevant by the LLM 510 may be adjusted downwards based on the data listing signals. As another example, some data listings 423 that are listed as less relevant by the LLM 510 may be adjusted upwards based on the data listing signals…” <examiner note: the adjusted ranking order of the results [Wingdings font/0xF3] second set of relevancy-ranked content items. The data listing signal [Wingdings font/0xF3] multi-dimensional sorting rules and the ranking order of the results are input into the LLM 510 and the ranking is adjusted>)
Claim 13
Claim 9 is included, Chan discloses wherein the memory further includes instructions executable by the one or more processors to generate the first set of relevancy-ranked content items by:
providing the search criterion to a generative AI search and retrieval system comprising one more machine learning models ([0097], line 3-5, “… the search query 502 may be parsed and processed (including normalization…” [0119], “… additional parsing may be performed on the search query 502. For example, in some cases, the search query 502 may include the names of a provider of a data listing 423. In some cases, absent additional processing, the LLM 510 may have difficulty determining context from a proper name, as some company names are in a foreign language, or a string of characters that have no meaning in any language. However, a provider name in the search query 502 may be extremely useful in providing relevant search results. Additional processing of the search query 502 may identify data of this type and augment the search process…”;; and
generating, by the generative AI search and retrieval system, a relevancy-ranked output listing of content items responsive to the search criterion (fig. 5, exchange manager 124 includes LLM 510, [0089], line 9-10, “… The LLM 510 may contain a learned embedding component illustrated as embedding engine 512…” [0093], line 1-2, “… The LLM 510 may also include a generative engine 514… [0094], line 1-4, “… the generative engine 514 may employ a transformer architecture that enables it to capture complex language patterns and generate highly realistic and human-like text…” [0097], line 3-5, “…. the search query 502 may be parsed and processed (including normalization in some embodiments) and passed into the embedding engine 512…” [0142], line 1-5, “… the user interface 1300 may include a plurality of data listings 423 (423B, 423C, 423D are illustrated) that may be the relevant results in response to the search query 502. In some embodiments, the plurality of data listings 423 may be listed in order of relevance…” <examiner note: LLM 510, embedding engine 512, generative engine 514 are considered as machine learning models>)
Claim 14
Claim 9 is included, Chan discloses wherein the memory further includes instructions executable by the one or more processors to: generate an initial set of content items ([0097], line 7-12, “… The embedding corresponding to the search query 502 may then be used to search for nearest neighbors to the embedding in the embedding store 516 from among the retrieved data listings 423, which may contain embeddings for each of the data listings 423 of the data exchange…” <examiner note: 1st stage output a subset of data listings 423 using nearest neighbor search>); and process the initial set of content items by an inferencing machine learning model trained to determine content item score values, thereby generating item score values for the initial set of content items ([0097], line 12-18, “… the data listings 423 corresponding to the nearest-neighbor embeddings may be passed to a next phase where, for each retrieved listing, information from the corresponding embedding is combined with other signals to compute the final aggregated sum score for each data listing 423 of the retrieved data listings 423…” [0099], line 1-3, “… The use of the LLM 510 to process and/or rank search results from a search query 502 may provide a number of benefits…”)
Claim 15
Claim 14 is included, Chan discloses wherein the memory further includes instructions executable by the one or more processors to modify an item score value to adjust a content item position placement in the initial set of content items ([0097], line 12-18, “… the data listings 423 corresponding to the nearest-neighbor embeddings may be passed to a next phase where, for each retrieved listing, information from the corresponding embedding is combined with other signals to compute the final aggregated sum score for each data listing 423 of the retrieved data listings 423…” [0099], line 1-3, “… The use of the LLM 510 to process and/or rank search results from a search query 502 may provide a number of benefits…”)
Claim 16 is similar to claim 1. The claim is rejected based on the same reason.
Claim 17
Claim 16 is included, Shen discloses wherein the one or more prioritized slots comprise a novelty slot or a promoted slot (<examiner note: the slots for products C, E, F are promoted or novelty slot>)
Claim 18
Claim 16 is included, Chao discloses further storing instructions which, when executed by at least one processor, cause the at least one processor to generate annotations for one or more of the content items in the first set of relevancy-ranked content items ([0131], line 1-5, “… FIG. 9, the top data listings 423 of results may be examined and, for each one, the LLM 510 may be prompted to generate the listing explanation 916 explaining why the listing is relevant to the user's search query 502… The use of the listing explanation 916 may enable the user to make better decisions in adopting or discarding the data listings 423 of the results…” <examiner note: listing explanations explain how relevant (i.e., type) of the data listing to the user query>)
Claim 21
Claim 16 is included, Chao discloses generate the first set of relevancy-ranked content items or the second set of relevancy-ranked content items by utilizing a machine learning model ([0120], line 16-18, “… the output of the LLM 510 may be the ordinal ranking of the data listing embeddings 710…” [0121], line 16-20, “… the output scores of the LLM 510 may not always be sufficient for getting a high-quality ranking. As a result, the nearest-neighbor results from the LLM 510 from block 604 may be combined with data listing signals…” [0123], line 1-2, “… Based on the data listing signals, the ranking order of the results returned by the LLM 510 may be adjusted. For example, some data listings 423 that are listed as highly relevant by the LLM 510 may be adjusted downwards based on the data listing signals. As another example, some data listings 423 that are listed as less relevant by the LLM 510 may be adjusted upwards based on the data listing signals…” <examiner note: the adjusted ranking order of the results [Wingdings font/0xF3] second set of relevancy-ranked content items>);
Claim(s) 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chao (U.S. Pub 2024/0403373 A1), in view of Shen (U.S. Pub 2015/0278341 A1), as applied to claim 7 and 18 respectively, and further in view of Chakraborty (U.S. Pub 2021/0035180 A1)
Claim 8
Claim 7 is included, however, Chan and Shen do not explicitly disclose wherein the annotations comprise a content item type for a respective content item.
Chakraborty discloses wherein the annotations comprise a content item type for a respective content item ([0037], “… Each active listing can be associated with configuration data (e.g., “Item is a promoted listing”, or “Item is not a promoted listing”), recommendation data (e.g., “show trending ad rate”, “show ad rate needed for page 1”, and “show ad rate needed to improve results for this item”)…”)
Chan discloses listing explanation/annotations to the listing; however, the listing annotations do not include content type for the respective content item. Chakraborty discloses configuration data that show the item is promoted listing or not promoted listing, and so on. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include annotations include content type of the item as disclosed by Chakraborty into Chan and Shen to provide as explanations to the types of the content items.
Claim 19 is similar to claim 8. The claim is rejected based on the same reason.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chao (U.S. Pub 2024/0403373 A1), in view of Shen (U.S. Pub 2015/0278341 A1), as applied to claim 9 and 16 respectively, and further in view of Clark (U.S. Pub 2021/0334886 A1)
Claim 10
Claim 9 is included, however, Chao does not disclose to assign the second set of relevancy-ranked content items to an ordered display slot position by generating a carousel display structure definition for the second set of relevancy-ranked content items.
Clark discloses assign the second set of relevancy-ranked content items to an ordered display slot position by generating a carousel display structure definition for the second set of relevancy-ranked content items ([0045], “… generates a user interface element 303 for each of the identified groupings. The user interface element 303… include the item listings 106 associated with the selected items. The item listings 106 are arranged in the user interface element 303 according to the determined order of presentation such that the user sees the items determined to be of greatest relevance and/or interest prior to the items determined to be of least relevant and/or interest. According to various embodiments, the user interface element 303 can comprise an aisle 109 or other type of content…”)
The rankings of search results are adjusted and displayed as disclosed by Chao and Shen. However, Chao and Shen do not explicitly disclose search results are grouped and items in the groups are in ordered. Clark discloses search results are grouped, items in each grouped are in ordered of relevance to provide unique experience for a user to interact with the search results. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the teaching of Clark into Chao and Shen so that user is provided an interactive experience that is uniquely tailored for a specific user account according to type and arrangement of the content that is presented to each user associated with the user account. The user can further rearrange and/or specify the positioning of the content via user interactions (e.g., pinning a user interface element to indicate a preferred position, dragging a user interface element to a different location on a user interface, etc.).
Claim 20 is similar to claim 10. The claim is rejected based on the same reason
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/Primary Examiner, Art Unit 2154