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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Representative claim 1 recites:
“receiving, by one or more processors, a prefix text input associated with a search query that is provided as input via a user interface during a query session associated with a user profile;
identifying, by the one or more processors, a preceding text input associated with a previous search query that is input prior to the search query during the query session associated with the user profile, wherein the preceding text input is a first search string provided during the query session, and the prefix text input is a second search string provided during the query session;
identifying, by the one or more processors, a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain;
identifying, by the one or more processors and using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input,
wherein (i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node clusters,
(ii) the plurality of nodes corresponds to a plurality of code pairs of the cross-code dataset, and
(iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs;
identifying, by the one or more processors and using a machine learning classification model, one or more search labels for the search query from the plurality of search clusters identified using the cluster matching model,
wherein the machine learning classification model is trained based on one or more ground-truth classifications associated with the one or more search labels; and
initiating, by the one or more processors and during the query session, the performance of a query resolution operation for the search query based on the one or more search labels.”
Independent claims 11 and 20 recite similar subject matter.
The additional elements in the independent claims include “receiving … a prefix text input associated with a search query” and “a user interface.” Claim 11 includes a memory and processors coupled to the memory. Claim 20 includes “one or more non-transitory computer-readable storage medium.”
The claimed “cluster matching model” and “machine learning classification model” are both recited at high levels of abstraction. They appear to simply be generic machine learning models. No details are claimed regarding any learning process of the models. As such, for 35 USC 101 purposes, the models are not “additional elements.”
This judicial exception is not integrated into a practical application because none of the additional elements in the claims appear to embody a practical application for the mental process. None of the additional elements appears to improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological process.
Regarding the additional elements, it is noted that “Receiving … a prefix text input associated with a search query” is merely a pre-solution data gathering step and does not embody a practical application (see MPEP 2106.05(g)). Displaying results of a data analysis through a user interface also does not show an improvement to technology (see MPEP 2106.05(a)(II)). The “memory” and “processors” of claim 11 and the “non-transitory computer-readable storage medium” of claim 20 are all claimed as generic computing elements at a high level of abstraction. The recitation of generic hardware is little more than using a computer to perform an abstract idea, see MPEP 2106.05(f).
Because none of the additional elements of the claims appear to embody a practical application, the mental process isn’t integrated into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Receiving … a prefix text input associated with a search query” is merely pre-solution data gathering (see MPEP 2106.05(g)) and is not significantly more than the mental process. Displaying an output of a data analysis is insignificant extra-solution activity and is well known (see MPEP 2106.05(g)((3)). The “memory” and “processors” of claim 11 and the “non-transitory computer-readable storage medium” of claim 20 are all claimed as generic computing elements at a high level of abstraction. The recitation of generic hardware is little more than using a computer to perform an abstract idea (see MPEP 2106.05(f)(2)) and is not significantly more than the judicial exception.
As such, none of the additional elements of the claims, in part or in whole, appear to amount to significantly more than the judicial exception. None of the additional elements, in part or in whole, appear to improve the processing of a computer, require the use of a specific machine, or provide a technological solution to a technological problem.
Dependent claims 2, 4-10, 12, 14-19, and 21-22 appear to merely involve additional data definitions, data observation, and data analysis. The additional elements of the claims appear to be merely related to pre-solution and post-solution processing, and do not provide a practical application to the mental process nor, in part nor as a whole, amount to significantly more than the judicial exception.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim does not fall within at least one of the four categories of patent eligible subject matter. The claim is directed towards “a computer program product.” Paragraph [0019] of the originally filed specification defines “computer program product” as an element that “may include a non-transitory computer-readable storage medium storing applications, programs, program modules, scripts, source code, program code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like (also referred to herein as executable instructions, instructions for execution, computer program products, program code, and/or similar terms used herein interchangeably). Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non-volatile media).”
This is an open ended definition. This open ended definition may include “non-transitory computer-readable storage media,” but does not exclude transitory media.
Additionally, as noted previously, “non-transitory computer-readable storage media” includes “all computer-readable media such as the volatile memory 202 and/or the non-volatile memory 204” (see specification as filed, paragraph [0040]).
Wireless media and forms of energy that store instructions are commonly understood in the art to be “computer-readable media.”
Because Applicant has defined a “computer program product” in an open ended fashion that may include “non-transitory computer-readable storage media” and because Applicant has defined “non-transitory computer-readable storage media” as explicitly including “all computer-readable media,” and because “computer-readable media” is understood in the art to include signals and forms of energy, the “computer program product” of claim 20 is not clearly directed towards patent eligible subject matter.
Applicant has amended claim 20 to be directed towards a “computer program product.”
However, the specification appears to indicate that a “computer program product” is merely a software component. Notably, the specification as filed indicates that “embodiments of the present disclosure may also take the form of an entirely hardware embodiment, an entirely computer program product embodiment, and/or an embodiment that comprises a combination of computer program products and hardware performing certain steps or operations” (see specification as filed paragraph [0022]), “such computer program products may include one or more software components” (see specification as filed paragraph [0017]), and “Thus, it should be understood that each block of the block diagrams and flowchart illustrations may be implemented in the form of a computer program product, an entirely hardware embodiment, a combination of hardware and computer program products, and/or apparatuses, systems, computing devices, computing entities, and/or the like carrying out instructions, operations, steps, and similar words used interchangeably (e.g., the executable instructions, instructions for execution, program code, and/or the like) on a computer-readable storage medium for execution” (see specification as filed, paragraph [0023]).
In each definition, “a computer program product” is presented as an alternative to an embodiment containing hardware. Thus, claim 20, directed toward a “computer program product,” may be comprised entirely of software instructions.
A “computer program product” consisting solely of software instructions without hardware is neither a process, a machine, a manufacture, nor a composition of matter. As such, claim 20 is not directed towards a statutory class.
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.
Claims 1-2, 4, 7-8, 11-12, 14, 17-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040).
As to claim 1, Zhu teaches a computer-implemented method comprising:
receiving, by one or more processors, a prefix text input associated with a search query that is provided as input via a user interface during a query session associated with a user profile (see Zhu paragraph [0119]. Zhu gives an example of a prefix “h” being input);
identifying, by the one or more processors, a preceding text input associated with a previous search query that is input prior to the search query during the query session associated with the user profile, wherein the preceding text input is a first search string provided during the query session, and the prefix text input is a second search string provided during the query session (see Zhu paragraph [0119]. The prefix “h” will receive suggestions based on the user’s previous queries. If the user history includes “super bowl” followed by “half-time show,” the prefix “h” may be suggested to be “half-time show” based on a previous query of “superbowl.” It is noted that this data is gathered in a session and may be learned from a “same-person session”);
Identifying, by the one or more processors, a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain (see Zhu paragraph [0116]-[0119]. Zhu relies upon sending a request to a search-history query suggestion database. The search query suggestion database is associated with previous search queries and includes query pairs. These query pairs are mapped code pairs. This dataset includes high co-occurrence query pairs, and thus identifies code pairs based on a frequency distribution. This frequency distribution is associated with a plurality of stored query pairs that have been interacted with by a user for a particular search domain, notably, those with an initial query of “super bowl.” It is noted that paragraph [0074] of the specification as filed defines “cross-code dataset” as “a data entity that includes a plurality of mapped code pairs.” Thus, Zhu teaches a “cross-code dataset” as claimed);
…
identifying, by the one or more processors and using a machine learning classification model, one or more search labels for the search query … , wherein the machine learning classification model is trained based on one or more ground-truth classifications associated with the one or more search labels (see Zhu paragraph [0127]. Suggestions of queries to output to a user may be ranked and judged based off of a machine learning model. These query suggestions are output to a user and are thus “search labels” to the extent claimed); and
initiating, by the one or more processors and during the query session, the performance of a query resolution operation for the search query based on the one or more search labels (see Zhu paragraph [0119]. A list of query suggestions is provided to the user).
Zhu does not clearly teach:
identifying, by the one or more processors and using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, wherein
(i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node cluster,
(ii) the plurality of nodes corresponds to a plurality of code pairs of the cross-code dataset, and
(iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs;
identifying, by the one or more processors and using a machine learning classification model, one or more search labels for the search query from the plurality of search clusters identified using the cluster matching model, wherein the machine learning classification model is trained based on one or more ground-truth classifications associated with the one or more search labels; and
Gupta teaches:
identifying, by the one or more processors and using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input (see Gupta paragraph [0025]. Cluster groups have sub-topics that may also be displayed, including labels with each sub-topic. It is noted that sub-topics may be tree structures, paragraph [0050]. As noted in paragraph [0029], these clusters are shown in response to the entry of a prefix of a search query. It is noted that Zhu, above, teaches to identify query suggestions based on the prefix and previous search queries), wherein
(i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node cluster (see Gupta paragraphs [0027]-[0029] and Figures 2A-2C. It is noted that cluster groups are identified, as shown in Figures 2A-2C. Each of the cluster groups contains a plurality of “nodes” representing completed search terms. These “nodes” correspond to completed search terms (“search labels) in a particular search domain (the cluster group label). For example, “Top News Local” is a search label within the particular search domain “Top News”),
(ii) the plurality of nodes corresponds to a plurality of code pairs of the cross-code dataset (see Gupta paragraph [0046] in view of Zhu paragraph [0119]. Gupta paragraph [0046] shows that the plurality of nodes may correspond to completed terms that match a partial input, with the example of “to” matching “top news,” “tom brady,” “torrid.” Also see figures 2A-2C, which show how each subsequent node comprises the parent node paired with additional information, such as “Top News Local” and “Top News US” being child nodes of “Top News.” Zhu paragraph [0119], shows similar pairing in a query suggestion database, pairing “Super Bowl” with “funny ads,” “score,” and “half-time show.” Thus, the references combined render obvious the claimed limitation that the plurality of nodes may correspond to a plurality of code pairs), and
(iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs (see Gupta paragraphs [0046]-[0048]. The model is trained based on matching a relevance between a prefix and multiple candidate suggestions that match that prefix. Because the prefix matches each of the candidate suggestions, which are then clustered in a learning process, the model is “trained” “based on predetermined query-prefix pairs.” It is additionally noted that Zhu, cited above, analyzes query suggestions based on query prefix pairs, see [0119]);
identifying, by the one or more processors and using a machine learning classification model, one or more search labels for the search query from the plurality of search clusters identified using the cluster matching model, wherein the machine learning classification model is trained based on one or more ground-truth classifications associated with the one or more search labels (see Gupta paragraph [0065]. Labels, or names, for a search query cluster for each of the clusters may be determined based on a machine learning model. The machine learning model is trained based on natural language clustering, or “ground-truth classifications,” associated with names, or search labels).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Gupta, because Gupta provides the benefit of a visual display and user interface that will allow a user to more easily navigate between categories and sub-categories related to a prefix. This will enhance the ability of Zhu to convey search suggestions to a user.
As to claim 2, Zhu as modified by Gupta teaches the computer-implemented method of claim 1, wherein initiating the performance of the query resolution operation comprises:
providing, via the user interface, a presentation of one or more selectable labels reflective of the one or more search labels (see Gupta paragraphs [0028]-[0032]) and Figures 2A-2C);
receiving, via the user interface, a selection input identifying a selectable label of the one or more selectable labels that corresponds to a particular search label of the one or more search labels (see Gupta paragraphs [0028]-[0032]) and Figures 2A-2C); and
initiating the search query with the particular search label (see Gupta paragraphs [0028]-[0032]) and Figures 2A-2C).
As to claim 4, Zhu as modified by Gupta teaches the computer-implemented method of claim 1, wherein a node cluster of the one or more node clusters is generated using a k-means hierarchical clustering model based on an encoded data object corresponding to a historical query-prefix pair (see Gupta paragraph [0036]).
As to claim 7, Zhu as modified by Gupta teaches the computer-implemented method of claim 1, wherein the one or more search labels are identified from a plurality of search labels for the particular search domain (see Gupta paragraphs [0028]-[0032]) and Figures 2A-2C).
As to claims 11 and 20, see the rejection of claim 1.
As to claim 12, see the rejection of claim 2.
As to claim 14, see the rejection of claim 4.
As to claim 17, see the rejection of claim 7.
As to claim 21, Zhu as modified by Gupta teaches the computer-implemented method of claim 1, wherein initiating the performance of the query resolution operation comprises initiating the performance of the query resolution operation based on (i) the one or more search labels and (ii) the particular search domain (see Gupta paragraphs [0027]-[0029] and Figures 2A-2C).
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040), further in view of Ajmera et al. (US Pre-Grant Publication 2024/0386330), further in view of Riesa et al. (US Pre-Grant Publication 2019/0347323).
As to claim 5, Zhu as modified by Gupta teaches the computer-implemented method of claim 4.
Zhu as modified does not teach wherein the encoded data object comprises
(i) a Term Frequency-Inverse Document Frequency TF-IDF score for a historical preceding search query and a historical search prefix of a historical subsequent search query subsequent to the historical preceding search query and
(ii) a one-hot encoding of a ground truth label corresponding to the historical search prefix.
Ajmera teaches wherein the encoded data object comprises
(i) a Term Frequency-Inverse Document Frequency TF-IDF score for a historical preceding search query and a historical search prefix of a historical subsequent search query subsequent to the historical preceding search query (see paragraphs [0041]-[0042]. All portions of a data element, such as subfields / prefixes and the whole data element) may be used in IDF matching).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Ajmera, because both references are directed towards searching for data. Ajmera simply provides additional search techniques to Zhu, which will enhance the search of Zhu by making the search more accurate.
Riesa teaches:
(ii) a one-hot encoding of a ground truth label corresponding to the historical search prefix (see Riesa paragraph [0047]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Riesa, because both references are directed towards data analysis. Riesa simply provides additional tools to learn about tokens that are input into the system, which will enhance the search of Zhu by providing further analysis and additional context to searches.
As to claim 6, Zhu as modified by Gupta teaches the computer-implemented method of claim 5, wherein the historical search prefix comprises a combination of a first character, a second character, and a third character of the historical subsequent search query (see Zhu paragraph [0119]).
As to claim 15, see the rejection of claim 5.
As to claim 16, see the rejection of claim 6.
Claim 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040), further in view of Singh et al. (US Pre-Grant Publication 2022/0188664).
As to claim 8, Zhu as modified by Gupta teaches the computer-implemented method of claim 1.
Zhu does not teach wherein the mapped code pair of the cross-code dataset comprises an assessment code mapped to a respective intervention code based on a threshold cooccurrence value of the frequency distribution.
Singh teaches wherein the mapped code pair of the cross-code dataset comprises an assessment code mapped to a respective intervention code based on a threshold cooccurrence value of the frequency distribution (see Singh paragraph [0033] which considers code co-occurrence value thresholds in a frequency distribution. See paragraphs [0023]-[0024] for a discussion of primary and secondary event codes, which map to the “assessment code” and “intervention code”)
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Singh, because both references are directed towards identifying co-occurrence of pairs of data codes. Singh simply provides additional cooccurrence analysis techniques to Zhu, which will enhance the search of Zhu by making the search more accurate.
As to claim 18, see the rejection of claim 8.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040), in view of Singh et al. (US Pre-Grant Publication 2022/0188664), and further in view of Chen et al. (US Pre-Grant Publication 2011/0302031)
As to claim 9, Zhu as modified by Gupta teaches the computer-implemented method of claim 8.
Zhu does not teach wherein the plurality of interaction data objects is associated with a time interval based on a refresh rate.
Chen teaches wherein the plurality of interaction data objects is associated with a time interval based on a refresh rate (see Chen claim 8 and paragraph [0023]. Chen teaches to identify query selection models based on user sessions that are updated periodically, or at a time interval).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Chen, because both references are directed towards searching for data. Chen simply provides additional query and user analysis to Zhu, which will enhance the search of Zhu by making the search and results more reflective of user behavior.
As to claim 19, see the rejection of claim 9.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040), and further in view of Ramsey et al. (US Patent 11,366,966).
As to claim 10, Zhu as modified by Gupta teaches the computer-implemented method of claim 1.
Zhu does not teach wherein the cluster matching model is trained using a plurality of binary classification models.
Ramsey teaches wherein the cluster matching model is trained using a plurality of binary classification models (see 9:30-10:3. Multiple binary classification models may be used to train a topic identification system).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Ramsey, because both references are directed towards classifying data objects. Ramsey simply provides additional classification techniques to Zhu, which will enhance the accuracy of a classification identification system of Zhu by disambiguating topics.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US Pre-Grant publication 2017/0097939) in view of Gupta et al. (US Pre-Grant Publication 2023/0394040), in view of Leskovec et al. (US Pre-Grant Publication 2008/0313119).
As to claim 22, Zhu as modified teaches the computer-implemented method of claim 1.
Zhu as modified does not teach wherein the plurality of nodes of the clustered hierarchical tree is grouped as the one or more node clusters based on the one or more predetermined query-prefix pairs utilized during training of the cluster matching model.
Leskovec teaches:
wherein the plurality of nodes of the clustered hierarchical tree is grouped as the one or more node clusters based on the one or more predetermined query-prefix pairs for a training dataset utilized during training of the cluster matching model (see paragraph Leskovec paragraph [0139] for training transitions between queries based on pairs. This is a training dataset that is used to train a cluster matching model. It is noted that Gupta, cited above as part of the combination, teaches wherein sub-topics of a clustered hierarchical tree may be arranged in tree structures related to partial strings, paragraphs [0025] and [0050]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Zhu by the teachings of Leskovec, because Leskovec provides the benefit of a exploring transitions between submitted queries in a training model. This will help the system to learn associations between queries. This will enhance the ability of Zhu to convey search suggestions to a user.
Response to Arguments
Applicant's arguments filed 25 June 2026 have been fully considered but they are not persuasive.
Response to Rejections under 35 USC 103
Applicant argues that “The Office Action cites Zhu as allegedly disclosing a preceding text input associated with a previous search query that is input prior to a search query during a query session associated with a user profile. However, Zhu describes that "search history-based query suggestion database 1735 includes high co-occurrence query pairs, which have been mined from person search query logs specific to the person 102." See Zhu, paragraph [0119]. In other words, Zhu describes co- occurrence query pairs that are statistical correlations mined from search query logs for a person. As discussed during the Interview, Zhu does not describe "identifying ... a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain" or "identifying, ... using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, wherein (i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node clusters, (ii) the plurality of nodes corresponds to a plurality of code pairs of the cross- code dataset, and (iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs," as recited by independent claims 1, 11, and 20.”
In response to this argument, it is noted that Zhu does show "identifying ... a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain." Zhu relies upon sending a request to a search-history query suggestion database. The search query suggestion database is associated with previous search queries and includes query pairs. This dataset includes high co-occurrence query pairs, and thus identifies code pairs based on a frequency distribution. This frequency distribution is associated with a plurality of stored query pairs that have been interacted with by a user for a particular search domain, notably, those with an initial query of “super bowl.” Thus, Zhu teaches these details as claimed (see Zhu paragraphs [0116]-[0119]).
It is noted that applicant defines “cross-code dataset” as an element “that includes a plurality of mapped code pairs” (see the specification as filed, paragraph [0074]). It is noted that Applicant provides no explicit definition for a “code.”
Zhu shows a dataset storing such “codes” because Zhu shows pairs of queries that have been submitted in association with one another. Each of these queries are “codes” under a broadest reasonable interpretation.
Applicant is reminded that any defining feature of a “code” or “cross-code” dataset from the specification receives no patentable weight until claimed.
Applicant continues, arguing that “Gupta fails to cure the deficiencies of Zhu. Gupta describes that "[a]fter candidate suggestions are generated, the cluster engine 336 clusters the candidate suggestions into groups based on similarity between the candidate suggestions." See Gupta, paragraph [0048]. Gupta further describes that "suggestions may be received by user device 102 and rendered on visual display 110" and "the suggestions may be displayed in one or more cluster groups, such as cluster groups 116 to 122, within the visual display 110." See Gupta, paragraph [0025]. In other words, Gupta describes clustering candidate suggestions based on a partial query string and displaying cluster groups with sub-topics. However, as discussed during the Interview, Gupta does not describe "identifying ... a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain" or "identifying, ... using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, wherein (i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node clusters, (ii) the plurality of nodes corresponds to a plurality of code pairs of the cross-code dataset, and (iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs," as recited by independent claims 1, 11, and 20.”
It is noted that the step of “"identifying, ... using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, wherein (i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node clusters, (ii) the plurality of nodes corresponds to a plurality of code pairs of the cross- code dataset, and (iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs," is taught by a combination of Zhu in view of Gupta for the reasons provided in the rejection above.
Response to Rejections under 35 USC 101
Applicant argues that “As amended, the claims recite machine learning techniques for improving query resolutions by "intelligently perform[ing] relevant search clustering in a search domain to improve traditional search query resolutions", which is not an abstract idea and - even if it is found to be an abstract idea - encompasses an improvement in search engine capabilities such that the alleged judicial exception is integrated into a practical application where "the performance of traditional query engines" is improved.” Applicant then cites paragraph [0092] of the specification.
Applicant concludes that “As such, the claimed techniques enable autocomplete functionality for a search query to be "intelligently searched in a way not traditionally available to existing search engines" such that the search engine "may leverage the cross-code dataset to generate more relevant connections between a search query ..., which may reduce information gaps and improve retrieval options and the accuracy of query resolutions for search queries." See Specification as filed, paragraph [0097].”
In response to this argument, it appears as if the improvement is directed not towards the functioning of the machine learning models, but rather the application of machine learning models to a particular context.
In Recentive Analytics, Inc. v. Fox Corp., the decision notes that “Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
Because Applicant’s claimed improvements appear to be directed towards the application of generic machine learning techniques to the field “search engines” to improve the response of “search engines” simply through the use of generic machine learning, in view of Recentive, Applicant’s argument is unpersuasive.
Applicant cites much of the language of claim 1, with emphasis, then adds “Each of the above steps outlines (1) steps related to a query session, (2) data preprocessing for a machine learning classification model by utilizing a cluster matching model to identify a plurality of search clusters from a clustered hierarchical tree, and (3) application of the machine learning classification model to identify search labels for a search query associated with the query session - none of which can be practically performed within the human mind. A human, for example, cannot receive input via a user interface or utilize a machine learning classification model to identify a search label for a search query.”
Applicant concludes “The amended independent claims are not directed to "mental processes" or any other abstract idea according to MPEP § 2106. Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101 at least because the claimed invention is not directed to a judicial exception under prong one of Step 2A.”
In response to this argument, it is noted that “identification” steps can be performed by a human being with pen and paper or a generic computer. Steps that merely identify data through the use of data analysis are mental process steps. Each of the claimed identification steps merely analyze data and produce a result from the analysis.
Each of the “cluster matching model” and “classification model” are claimed at high levels of abstractions. They appear to be generic machine learning models that accept particular input and produce particular output. No description of the analyses performed by each of these models is claimed. Because the machine learning models are claimed as “black boxes” that are merely used to produce a desired output of data, the claims do not show an “improvement to machine learning technology” through the use of the generic machine learning models.
Applicant argues that “Regarding the second prong of the Alice/Mayo Test, the Office Action alleges that "none of the additional elements in the claims appear to embody a practical application." Office Action, p. 4. However, the Office Action also notes that, while an improvement in search engines may be recited in the Specification, the cross-code datasets and mapping techniques upon which the search engine improvement relies do not appear in the claimed independent claims. Office Action, p. 21. The claims are amended herein to incorporate several features, including those identified in the Office Action, that improve search engine performance. Accordingly, even if claim 1, as amended, is directed to an abstract idea- which, Applicant submits, it is not - the claim recites a combination of additional elements that improves a technical field such that the claim as a whole integrates any alleged abstract idea into a practical application that is patent eligible under 35 U.S.C. § 101.”
In response to this argument, it is noted that the cross-code datasets and mapping techniques are claimed in the context of data analysis, or mental process steps. As noted above, a human being with access to the cross-code dataset is capable of mentally “identifying …. A cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain.” The identification steps are not “additional elements” in the context of 35 USC 101 analysis; the identification steps are data analysis, or mental process steps.
While the use of these identification steps may result in an improved data analysis, an improved data analysis is still a data analysis. An improved mental process is still a mental process, and thus patent ineligible.
Applicant argues that “Claim 1, as amended herein, recites a machine learning technique that leverages a cross- code dataset to improve query resolutions by "intelligently perform[ing] relevant search clustering in a search domain to improve traditional search query resolutions." See Specification, paragraph [0092].”
Applicant argues that “In this regard, claim 1, as amended, goes beyond the application of generic machine learning models and instead recites a specific machine learning pipeline that includes: (1) identifying a cross-code dataset associated with the previous search query; (2) using a cluster matching model to identify search clusters from a clustered hierarchical tree whose nodes correspond to code pairs of the cross-code dataset; and (3) using a machine learning classification model to identify search labels from those search clusters. This machine learning pipeline reflects the improvement described in the Specification where the machine learning pipeline "reduce[s] information gaps and improve[s] retrieval operations" while also improving the accuracy for identifying search labels for a search query using the machine learning classification model" by "identifying a cross-code dataset associated with the previous search query based on a mapped code pair identified from a frequency distribution associated with a plurality of interaction data objects for a particular search domain," "identifying, ... using a cluster matching model, a plurality of search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, wherein (i) a plurality of nodes of the clustered hierarchical tree is grouped as one or more node clusters, (ii) the plurality of nodes corresponds to a plurality of code pairs of the cross-code dataset, and (iii) the cluster matching model is trained based on one or more predetermined query-prefix pairs" and "identifying, ... using a machine learning classification model, one or more search labels for the search query from the plurality of search clusters identified using the cluster matching model, wherein the machine learning classification model is trained based on one or more ground- truth classifications associated with the one or more search labels." See Specification, paragraph [0100].”
Applicant concludes “Thus, Applicant asserts that the elements of claim 1 constitute an improvement in a technical field (e.g., machine learning) and computer functionality (e.g., search engine performance) such that the claim, as a whole, integrates any alleged abstract idea into a practical application.”
In response to this argument, it is noted that each machine learning model merely performs data analysis steps. As noted in Recentive Analytics, Inc. v. Fox Corp., “Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. Today, we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” The analysis performed by the machine learning steps, in the context of the data analysis step, are merely the application of a “cluster matching model” and “a machine learning classification model” to a new data environment.
Each of the identification steps is merely a data analysis step. The fact that the data analysis steps are performed by a generic machine learning model does not integrate any additional elements into a practical application nor provide significantly more than the abstract idea. It is simply using a generic machine learning model to input particular data, perform a data analysis, and output particular data. As noted in Recentive, such application of generic machine learning to a new data environment remains patent ineligible.
Applicant states that there is an “improvement to machine learning.” However, given that no details regarding the processes by which the machine learning models learn are claimed, this argument is unpersuasive.
Applicant argues that there is an “improvement to search engine performance.” An improvement to search engine performance is an improvement to a data analysis process. An improvement to a data analysis process is an improvement to a mental process. An improved mental process remains a mental process, albeit improved. As such, this improvement does not integrate the data analysis steps of the claims into a practical application.
Applicant argues that “More specifically, the MPEP states that "[l]imitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include an improvement in the functioning of a computer, or an improvement to other technology or technical field." MPEP § 2106.04(d). Claim 1 recites a combination of elements that are specifically designed to improve both machine learning technology and query engine technology.”
Applicant cites the identifying and initiating steps of the claim.
Applicant adds that “claim 1 recites an improvement to data preprocessing for a machine learning classification model by” performing the various identifying steps.
Applicant continues, “For instance, by [performing the identifying steps,” claim 1 “reduce[s] information gaps and improv[s] retrieval operations” while also improving the accuracy for identifying search labels for a search query using the machine learning classification model.”
As noted above, no actual improvement to the machine learning models themselves are supported by the claims. An improvement to data preprocessing is an improvement to data processing, or data analysis, and thus an improvement to a mental process. As noted above, an improved mental process is still a mental process and is patent ineligible.
Applicant’s improvement to query engine technology appears to be, at best, an improved method of data analysis. As noted above, improved mental processes (or data analyses) remain mental processes and are thus not patent eligible.
Applicant argues that “Second, claim 1 recites features that improve the functioning of a computing device (e.g., improve search engine performance) to enable "improved query resolutions and autocomplete functionality for search queries" within a query session. Id. For example, "[t]he enhanced query resolutions of the present disclosure may be leveraged to initiate the performance of various computing tasks that improve the performance of a computing system (e.g., a computer itself, etc.) with respect to various prediction-based actions ... such as for the resolution of search queries and/or the like." See Specification, paragraph [0169]. In this respect, claim 1 is similar to claim 3 of Example 47 of the Subject Matter Eligibility Examples, which provides an example of subject matter that integrates a judicial exception into a practical application by improving the functioning of a computer. For instance, in Example 47 of the Subject Matter Eligibility Examples, claim 3 was found eligible because it recited specific steps (detecting source addresses, dropping malicious packets, blocking future traffic) that reflected an improvement in network security technology described in the Specification. Similarly, claim 1 as amended recites specific technical steps (e.g., identifying a cross-code dataset of mapped code pairs, using a cluster matching model on a clustered hierarchical tree whose nodes correspond to code pairs of the cross-code dataset, and using a classification model to identify search labels from the resulting search clusters) that reflect the improvement (e.g., improved query resolutions and improved autocomplete functionality) in query resolution technology described in the Specification for the subject application.”
In response to this argument, it is noted that each of Applicant’s claimed “specific technical steps” is directed towards identifying data, or a data analysis. An improvement to data analysis is an improvement to a mental process. An improved mental process remains a mental process and is not patent eligible.
Applicant argues that “Second, claim 1 recites features that improve the functioning of a computing device (e.g., improve search engine performance) to enable "improved query resolutions and autocomplete functionality for search queries" within a query session. Id. For example, "[t]he enhanced query resolutions of the present disclosure may be leveraged to initiate the performance of various computing tasks that improve the performance of a computing system (e.g., a computer itself, etc.) with respect to various prediction-based actions ... such as for the resolution of search queries and/or the like." See Specification, paragraph [0169]. In this respect, claim 1 is similar to claim 3 of Example 47 of the Subject Matter Eligibility Examples, which provides an example of subject matter that integrates a judicial exception into a practical application by improving the functioning of a computer. For instance, in Example 47 of the Subject Matter Eligibility Examples, claim 3 was found eligible because it recited specific steps (detecting source addresses, dropping malicious packets, blocking future traffic) that reflected an improvement in network security technology described in the Specification. Similarly, claim 1 as amended recites specific technical steps (e.g., identifying a cross-code dataset of mapped code pairs, using a cluster matching model on a clustered hierarchical tree whose nodes correspond to code pairs of the cross-code dataset, and using a classification model to identify search labels from the resulting search clusters) that reflect the improvement (e.g., improved query resolutions and improved autocomplete functionality) in query resolution technology described in the Specification for the subject application.”
Applicant is requested to provide a citation to the specification as filed describing the specifics on how “improve[d] search engine performance” results in an improvement to the computing device or its hardware components as claimed.
Applicant argues that “Lastly, the present claims resemble those previously held eligible by the Federal Circuit. For example, in CosmoKey Solutions GBMH & Co. v. Duo Security LLC, No. 2020-2043 (Fed. Cir. Oct. 4, 2021) (hereinafter CosmoKey), the Federal Circuit distinguished between techniques that improve a process using generic steps the predate computers and those that recite specific steps that depart from earlier approaches to improve a specific computer problem. CosmoKey, p. 8-9. The court noted that the former is ineligible, while the latter provides a non-abstract computer- functionality improvement if done by a specific technique that departs from earlier approaches to solve a specific computer problem. Id. The claims in CosmoKey recite an activation/deactivation scheme of an authentication function that provides enhanced security (e.g., a computer functionality) and low complexity with minimal user input. Id., p. 10. The activation/deactivation scheme of CosmoKey allowed for user authentication with fewer resources, less user interaction, and simpler device. Id., p. 13. Like CosmoKey, claim 1 recites a new scheme, a machine learning scheme using a cluster matching model in combination with a machine learning classification model and cross-code dataset, that, as described herein and throughout the Specification, "reduce[s] information gaps and improve retrieval options and the accuracy of query resolutions for search queries - even if the query terms are outside of manually curated keywords. This, in turn, reduces manual interventions while intelligently searching the specialty mappings that are easily scalable and modifiable." See Specification, paragraph [0097]. Moreover, the improved machine learning scheme allows for improved search engine performance within a computer in a unique manner that differs from traditional search engines. Thus, like CosmoKey, claim 1 provides a non-abstract computer-functionality improvement that is done by a specific technique that departs from earlier approaches to solve a specific computer problem.”
In response to this argument, Examiner cannot find anything in the current claims directed to “activation/deactivation scheme of an authentication function that provides enhanced security (e.g., a computer functionality) and low complexity with minimal user input.” Additionally, the Examiner notes that the claimed “machine learning models” contain no details regarding how the machine learning is trained nor citations to the specification regarding how such a training is an improvement to machine learning techniques. While there may be “improved search engine performance” in the cited portions of the specification, it is noted that the claimed “identifying” steps are all merely directed towards data analysis, or mental process, steps. None of the identifying steps meaningfully integrates additional elements in the claims beyond the mental process into this improvement. An improvement to a data analysis is an improvement to a mental process. An improved mental process remains a mental process, albeit improved, and thus remains patent ineligible.
Applicant argues that “Step 2B of the Alice/Mayo test focuses on whether the additional limitations present in the claim and their combination are unconventional and provide an inventive concept. See MPEP j 2106.05.II. Applicant respectfully notes that the prior art rejections are overcome in light of the claim amendments herein. Accordingly, Applicant respectfully submits that the Office Action improperly rejects independent claim 1 (and the claims depending therefrom) as being directed to patent ineligible subject matter and requests withdrawal of the rejection.”
In response to this argument, as noted in the rejection above, rejections remain under 35 USC 103.
Conclusion
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 CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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/CHARLES D ADAMS/ Primary Examiner, Art Unit 2165
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