Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application is claiming priority as continuation of US Application 16956172 file 11/22/2024.
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.
Claim 2
Step 1, this part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a computer-implemented method that performs at least one step. Thus, the claim is to a method, 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.
Limitation: “generating a search criterion based on a search request received from a client device” This limitation recites a judicial exception because it encompasses mental processes. 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
Limitation: “responsive to the search criterion, generating a first set of relevancy-ranked content items” This limitation recites a judicial exception because it encompasses mental processes. Generating relevancy-ranked content implies evaluating and comparing content items to determine their relative order, which is an evaluative judgment process.
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 inject, among the first set of relevancy-ranked content items, one or more prioritized slots allocated according to a position-dependent sort rule that differs from a sort rule applied to other positions” This limitation recites a judicial exception because it encompasses mental processes. Reordering content based on sorting rules involves organizing information to establish a specific hierarchy or structure, which is an act of judgment regarding prioritization. A person can take a ranked list of items, and apply a regular sort rule to most items, but for some slots can be "Promoted Slot" or "Novelty Slot," Applying different sorting rules based on a row or line position is a manual, logical step that a human can easily do it on a sheet of paper, this position-dependent sorting rule does not escape the human mind.
Limitation: “assigning the second set of relevancy-ranked content items to an ordered display slot position” This limitation recites a judicial exception because it encompasses mental processes. Assigning items to positions requires a judgment regarding the order and location relative to others.
"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. Here, the mentioned steps fall within the mental processes grouping of abstract ideas and 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:
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
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
MPEP § 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field:
The additional element “providing, for display on a user interface of the client device…” does not integrate the abstract idea into a practical application because it merely displays results derived from the mental process steps. Other 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” do not make any improvements to the functionalities of a computer, database technology, or any other technologies.
MPEP § 2106.05(b) 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 “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” The additional element “providing, for display on a user interface of the client device...” does not integrate the abstract idea into a practical application because "client device" is a generic computer component that merely uses standard output functions.
MPEP § 2106.05(c) Particular Transformation:
The additional element “providing, for display on a user interface of the client device..." does not integrate the abstract idea into a practical application because it effects no physical transformation or reduction of an article; it only performs data output to a display. 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.
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 additional element “providing, for display on a user interface of the client device...” merely is extra-solution activity or field-of-use; displaying information to a user does not impose a meaningful limit on the mental process of ranking and assigning.
MPEP § 2106.05(g) Insignificant Extra-Solution Activity:
The additional element “providing, for display on a user interface of the client device...” simply provides information for display is generic computer output that does not add an inventive concept at Step 2A Prong Two.
MPEP § 2106.05(h) Field of Use and Technological Environment:
The additional element “providing, for display on a user interface of the client device...” merely links to the technological environment (a client device UI) rather than improving the technology itself or applying the exception in a meaningful way.
Accordingly, the additional limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B, examine the elements of the independent claim-both individually and as an ordered combination-to see if they provide an inventive concept that adds "significantly more" than the exception itself. The claim recites standard computer functions for ranking, sorting, and displaying information. There is no unconventional use or configuration beyond executing the abstract idea on a generic computer. The claim simply recites the abstract mental processes of searching and ranking combined with generic display output without explaining a technical improvement to data processing or network performance. The additional elements, both individually and in combination, do not provide an inventive concept that amounts to significantly more than the judicial exception.
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.
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chao (U.S. Pub 2024/0403373 A1), in view of Liang (U.S. Pub 2023/0095289 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 inject, among the first set of relevancy-ranked content items, one or more prioritized slots allocated according to a position-dependent sort rule that differs from a sort rule applied to other positions;
Liang discloses
inject, among the first set of relevancy-ranked content items ([0016], “… a second list of organic pieces of content to display are received. Each of these lists may be ordered in accordance with some earlier-applied ranking model.), one or more prioritized slots allocated according to a position-dependent sort rule that differs from a sort rule applied to other positions ([0019], “… the top 15 results in the ranking… Then the pattern would be 3 organic, 2 sponsored, 3 organic, 1 sponsored, 2 organic, 2 sponsored, 1 organic…” [0040], “… the sponsored content selector 202 … determines the ordered list of sponsored pieces of content…” [0042], “… the organic content selector 204… determines the ordered list of organic pieces of content…” <examiner note: the prioritized slots [Wingdings font/0xF3] sponsored slots. The sponsored slots has different sort rule than the sort rule of organic pieces of content>)
assigning the second set of relevancy-ranked content items to an ordered display slot position ([0054], “… the pattern is then passed to a content slot filler 220, which actually fills the assigned slots with content, in accordance with the pattern, but using a ranking that is different than the ranking determined by the pattern determination component. In an example embodiment, the content slot filler 220 uses the order in the ordered list of sponsored pieces of content from the sponsored content selector 202 to determine which pieces of content to fill the sponsored content slots in the pattern with, and uses the order in the ordered list of organic pieces of content from the organic content selector 204 to determine which pieces of content to fill the organic content slots in the pattern with…”); 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. 3, actual display slot 310)
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 injecting one or more prioritized slots among relevancy-ranked content item. Liang discloses dynamic slotting of content impressions, and specifically dynamically determining a pattern for the slotting using a blending model because fixed slotting is suboptimal. Different users have different reactions to the presentation of sponsored pieces of content as well as to where the sponsored pieces of content are presented. By incorporating the dynamic slotting of content impression as disclosed by Liang into Chang because it may be more beneficial to devote more slots to organic pieces of content that the user is likely to engage with rather than fill fixed slots with sponsored pieces of content that the user is unlikely to engage with.
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, Liang discloses wherein the one or more prioritized slots comprise a novelty slot or a promoted slot (fig. 3)
Claim 12
Claim 9 is included, Chao 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, Liang discloses wherein the one or more prioritized slots comprise a novelty slot or a promoted slot (<examiner note: fig. 3, item 310>)
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 Liang (U.S. Pub 2023/0095289 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 does 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 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 Liang (U.S. Pub 2023/0095289 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. However, Chao does 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 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.
Response to Arguments
Section Double Patenting – pg. 8
Examiner withdraws the rejection because a terminal disclaimer has been filed.
Section 35 U.S.C. 101 – pg. 9
Applicant argues “… Applicant respectfully submits that the currently amended independent claims are directed to patent-eligible subject matter and not directed to mental processes or any other abstract idea. As set forth in MPEP § 2106(III)(A), "a claim with limitation(s) that cannot practically be performed in the human mind does not recite a mental process." Applicant submits that the amended claim above does not recite a mental process because no human mind can "generat[e] a search criterion based on a search request received from a client device;" "responsive to the search criterion, generat[e] a first set of relevancy-ranked content items;" "generat[e] 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 inject, among the first set of relevancy-ranked content items, one or more prioritized slots allocated according to a position-dependent sort rule that differs from a sort rule applied to other positions;" "assign[] the second set of relevancy-ranked content items to an ordered display slot position;" and "provid[e], 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," as recited by currently amended independent claim 2 and similarly recited by currently amended independent claims 9 and 16…”
Applicant argues that a human mind cannot practically perform the steps in claims 2, 9, and 16. Examiner respectfully disagrees because a step is a mental process if a person can practically do it using just a pen and paper. A person can easily do every single step in this claim manually
Step 1: The person reads an incoming search request and writes down the search criteria.
Step 2: he looks through documents and creates a first list of documents that are ranked by relevance.
Step 3: he reads the rules and rearranges the list to have room/slot for a "promoted" or "new" item.
Step 4: he put these re-ordered items into final, numbered display rows.
Because a person can manually follow the above steps using paper and a pen, the core of the invention is a mental process.
Running these same steps on a computer does not change this fact. Therefore, the rejection under Step 2A, Prong One must be maintained.
Applicant argues that “… Applicant further submits that even assuming, arguendo, that the claims recite an abstract idea, the amendments to the claims integrate any such abstract idea into a practical application under Step 2A Prong Two…
…
Thus, rather than merely generating a single ranking score, the claims recite a specific technique for transforming a first relevancy-ranked content listing into a second content listing having injected prioritized slots governed by different allocation rules and for generating a corresponding display arrangement for presentation on a client device. Accordingly, the claims incorporate any alleged abstract idea into a practical application in satisfaction of Step 2A Prong Two of the eligibility analysis…” (pg. 9-11)
Applicant argues that even if the claims recite a mental process, they integrate that process into a "practical application."
Applicant states that conventional systems collapse multiple sorting rules into a single score, whereas the claimed invention uses a multi-dimensional sorting framework to refine a display structure for a client device.
Applicant’s argument has been considered, a claim integrates a mental process into a practical application if it applies the concept in a meaningful way such as improving the functions of a computer or another technology. However, an improvement to the underlying mental process itself, or simply using a generic computer as a tool does not qualify as a practical application.
The paragraphs cited by Applicant shows an improvement only to the sorting logic, not to computer technology:
[0088] describes an allocation position has a multi-dimensional “sort key” function
[0089] describes a rule processing engine that applies rules to sort a relevancy ranked listing of content items
[0090] describes injecting some slots into the content listing
[0116] states that these multi-dimensional sorting rules organize the list before displaying it.
These paragraph does not show an improvement to the physical computer screen or the device's processing power.
The claims do not recite any hardware modifications. The "client device" and "user interface" mentioned in the claims are generic tools used to display data.
Taking a mental sorting process and telling a generic computer screen to display the output does not create a practical application. It is merely a set of instructions to "apply" the sorting logic on a standard computer.
Because the additional computer elements do nothing more than serve as a generic tool to automate the sorting logic, the claim as a whole does not integrate the judicial exception into a practical application. Accordingly, the claims remain directed to an abstract idea, and the rejection under Step 2A, Prong Two is maintained.
Step 2B, the only additional hardware elements in the claims are a "client device" and a "user interface." The specification does not disclose any new or modified computer hardware. Instead, these generic components are simply used as tools to automate the mental sorting steps and display the final list. Because the claim elements do not add anything significantly more to the abstract idea, the claim fails Step 2B. Accordingly, the rejection of claims 2–21 under 35 U.S.C. § 101 is maintained.
Section 35 U.S.C 112
The rejections are withdrawn as necessitated by Amendment
Section 35 U.S.C 103
Applicant s arguments with respect to claims 2-21 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant s disclosure
U.S. Pub 2021/0382952 – Yates discloses an online system that identifies allocations of both organic and promoted content on a given page. The allocations of page content are compared against one another and configured to prioritize for overall utility based on objective factors that quantify a page “look and feel” as measured by machine learning models. The page allocations are operated on an automatic and continuous basis for each user viewing the page. In some embodiments, the page content allocations are based on individual viewing users stored characteristics.
U.S. Pub 2024/0354317 – Fayyaz discloses a technique uses an encoder system to produce an index of target item embeddings. Each target item embedding is input-agnostic and universal in the sense that different expressions of a target concept, produced using different combinations of input modes, map to the same target item embedding in the index. The encoder system throttles the amount of computations it performs based on the assessed capabilities of an execution platform. A retrieval system processes a multimodal input query by first generating a candidate set of target item embeddings in the index that match the input query, and then using a filtering operation to identify those target item embeddings that are most likely to match the input query. The encoder system and the retrieval system rely on language-based components having weights that are held constant during a training operation. Other weights of these systems are updated during the training operation.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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
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