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 .
DETAILED ACTION
Response to Amendment
This communication is in response to the amendment filed on 12/22/2025 for the application No. 18/779,634, Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Patent No. 12,045,861.
Claims 1, 8, 15 and 2, 9,16, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8,15 and 2,9,17 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 2, 9,16, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2, 9,17 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 3, 10, 17, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3, 10, 17 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 4, 11, 18, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4, 11,18 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 5, 12, 19, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5, 12, 19 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 6, 13, 20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6, 13, 20 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 7, 14, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7,14 of Patent No. 12,045,861. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 1-20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Patent No. 11,205,196.
Claims 1, 8, 15, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8,15 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 2, 9,16, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 2, 9,17 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 3, 10, 17, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3, 10, 17 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 4, 11, 18, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4, 11,18 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 5, 12, 19, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5, 12, 19 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 6, 13, 20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6, 13, 20 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 7, 14, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7,14 of Patent No. 11,205,196. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are not compliant with 101, according with the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c). Examiner’s analysis is presented below for all the claims.
Claim 1: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a method.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim? Yes. The claim recites the limitations of
b) determining, … whether a bandwidth-latency between the first user device and the first server cluster has a lowest latency; d) parsing, … to determine an identified trip purpose; f) identifying one or more customized content items for the first user based on
the identified trip purpose. The “determining, parsing, identifying” limitations, as drafted, is a process and system that, under its broadest reasonable interpretation, covers performance of the limitations as certain methods of organizing human activity, advertising, marketing or sales activities or behaviors. The method for transmitting customized content items to one or more user devices. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application? No. The claim recites additional limitations, such as, “a) receiving, …first user data …c) transmitting, …, the first user data …. provides the first user data with the lowest latency; h) and transmitting, …, the one or more customized content items …”. These are limitations toward accessing or receiving data. It is merely gathering data.
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure:
“ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” The limitations comprise generic recited computer elements, software and data. They are not sufficient to integrate the abstract idea because it merely reflects the use of conventional technology and amounts to only generally linking the use of an abstract idea to a particular technological environment. MPEP 2106.05(h).
The combination of these additional elements can also be considered no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
Accordingly, even in combination, these additional elements 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 as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea).
Step 2B : claim provides an inventive concept? No.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim,
“ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” amount to no more than mere instructions to apply the exception. i.e., mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Here, the limitations:
“ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Other limitations in the claim, such as:
“a) receiving, …first user data …c) transmitting, …, the first user data …. provides the first user data with the lowest latency; h) and transmitting, …, the one or more customized content items …”. These are limitations toward accessing or receiving data. It is merely gathering data. Accessing or receiving data is very well understood, routine and conventional computer task activity; It represents insignificant extra solution activity. Mere data-gathering step[s] cannot make an otherwise nonstaturory claim statutory In re Grams,888 F.2d 835, 840 (Fed. Cir. 1989) (quoting In re Meyer, 688 F.2d 789, 794 (CCPA 1982)).
Further, the instant specification does not provide any indication that the elements
“ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” were are anything other than generic software and hardware, and the OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); and v. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; court decisions cited in MPEP 2106.05(d)(II) indicate that merely computer receives and sends information over a network and presenting or displaying information, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the “ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” limitations (pointed above) are well-understood, routine, conventional activity is supported under Berkheimer Option 2. See MPEP 2106.05 (d). The claim is ineligible.
Claim 15: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a computer-readable recording medium.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Claim 8: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a system.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Dependent claims 2-7, 9-14 and 16-20, the claims recite elements such as “
wherein parsing the one or more electronic communication includes parsing one of an email inbox and a text message inbox; and further includes parsing an entire mailbox corresponding to the email inbox or the text message inbox”, etc. These elements do not integrate the system of organizing human activity into a practical application. The claims are ineligible.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20200065857 (Lagi) in view of US PG. Pub. No. 20080222267 (Horn) and in view of US Patent No. 9976864 (Kahn).
As to claims 1, 8 and 15, Lagi discloses
A computer-implemented method for transmitting customized content items to one or more user devices (see Lagi abstract Fig. 1 and associated disclosure), the method comprising:
a) receiving, at a first server cluster of a plurality of server clusters, first user data corresponding to a first user device of a first user;
(“…[0039] According to some embodiments, providing the personalized message includes transmitting the personalized message to a client device of the user”, paragraph 39 and “…The information extraction system 204 may extract text and any other relevant data that represents information about a company, an individual, an event [Examiner interprets as receiving, by a server processor, first user data], or the like. Of particular interest to users of the directed content platform 200 disclosed herein, such as marketers and salespeople, are documents that contain information about events that indicate the direction or intent of a company and/or direction or intent of an individual…”, paragraph 124 and Fig. 10.
“[0113] FIG. 9 depicts an embodiment of a user interface in which activity resulting from the use of the platform is reported to a marketer or other user. Among other metrics that are described herein, the user interface can report on what customers, such as ones to be entered into or already tracked in the CRM system, have had a first session of engagement with content, paragraph 113
“the system 200 may serve a relevant resource, such as from a knowledge graph, which may be customized for the recipient with content that is relevant to the customer's history (such as from a CRM system) or that relates to events of the customer's organization [Examiner interprets as travel information based on the travel-related data] (such as extracted by the information extraction system)…”, paragraph 182);
d) parsing, (see “The content development platform 100 may include a parser 110 for parsing the stored content …”, paragraph 86 and Fig. 7) by the second server cluster, one or more electronic communication of the first user device to determine [event];
([0008] The system may include a range of machine learning features and systems that facilitate automated generation of directed content for communications with large numbers of recipients, while nevertheless providing personalized or customized communication content for the recipients. In embodiments, the system learns about potential recipients by extracting publicly and privately available information.…”, paragraph 8.
“…0011] According to some embodiments, the information extraction system includes an entity extraction system that extracts entity data from the documents and an event extraction system that extracts the event data from the documents…”,paragraph 11. “…012] According to some embodiments, the information extraction system updates the knowledge graph with information extracted from the documents…”, paragraph 12
“…The content development platform 100 may include a parser 110 for parsing the stored content from the crawling activity of the automated crawler 104 to generate a plurality of key phrases 112 and to generate a content corpus 114 from the primary online content object 102. ..”, paragraph 86 and Fig. 7.
“…The information extraction system 204 may extract text and any other relevant data that represents information about a company, an individual, an event [Examiner interprets as travel-related data], or the like. Of particular interest to users of the directed content platform 200 disclosed herein, such as marketers and salespeople, are documents that contain information about events that indicate the direction or intent of a company and/or direction or intent of an individual…”, paragraph 124.
“…, the information extraction system 204 may use the language patterns to extract various forms of information, including but not limited to entities, entity attributes, relationships between respective entities and/or respective events, events, event types, attributes of events from the obtained documents….”, paragraph 125);
e) identifying one or more customized content items for the first user based on the identified [information] …
(“…event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance”., abstract.
“[0078] Relevance … indicates how close a term or phrase is to other content put up on the user's site or domain. The lower the relevance, the further away the term or phrase is from what the core topic of the site or domain is. This can be automatically determined by a crawler that crawls the site or domain to determine its main or core topic of interest to consumers. If relevance is offered as a service by the present system and method a score can be presented through a user or machine interface indicating how relevant the new input text is to an existing content pool”, paragraph 78.
See also, “…The information extraction system 204 may extract text and any other relevant data that represents information about a company, an individual, an event, or the like. Of particular interest to users of the directed content platform 200 disclosed herein, such as marketers and salespeople, are documents that contain information about events that indicate the direction or intent of a company and/or direction or intent of an individual…”, paragraph 124.
“the system 200 may serve a relevant resource, such as from a knowledge graph, which may be customized for the recipient with content that is relevant to the customer's history (such as from a CRM system) or that relates to events of the customer's organization (such as extracted by the information extraction system)…”, paragraph 182);
determining, by one or more machine learning models [of the second server] cluster, a relevancy score for the one or more customized content items;
(“…a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.“, abstract, paragraph 10
“…The score can further be presented as a “relevance” metric…”, paragraph 71);
f) and transmitting, by the … server cluster, the one or more customized content items to the first user device for display.
(“…knowledge graphs 210 representing specific types of entities (e.g., businesses, people, places, products), relationships between entities, the types of those relationships, relevant events, and/or relationships between events and entities; a machine learning system 212 that learns/trains classification models that are used to extract events, entities, and/or relationships, scoring models that are used to identify intended recipients of directed content, and/or models that are used to generate directed models; a lead scoring system 214 that scores one or more organizations and/or individuals with respect to a content generation task, the lead scoring system referencing information in the knowledge graph; and a content generation system 216 that generates content of a communication to a recipient in response to a request from a client to generate directed content pertaining to a particular objective, wherein the recipient is an individual for which the leading scoring system has determined a threshold level of relevance to the objective of a client…”, paragraph 118).
Although, Lagi discloses extensively an event relevant to the recipient (abstract). Lagi does not expressly disclose
b) determining, by the first server cluster, whether a bandwidth-latency between the first user device and the first server cluster has a lowest latency;
c) transmitting, by the first server cluster, the first user data to a second server cluster based on determining the second server cluster provides the first user data with the lowest latency;
of the second server
However, Horn discloses
“[0001] The present invention relates generally to a cluster system for serving content of a domain to clients, and more particularly relates to balancing a load over multiple servers.”, paragraph 1 and abstract.
“[0107] Various methods for selecting an SCM for a GET request are based on the geographical locations of the various SCMs and the HTTP client. In some embodiments after FIG. 4, latency for delivering content from a subordinate cluster to an HTTP client 410 depends on the geographic distance of the HTTP client from a subordinate cluster. In one embodiment, the left subordinate cluster managed by SCM 440 is relatively close to client 410, and the right subordinate cluster managed by SCM 450 is relatively distant. In this embodiment, the left subordinate cluster manifests lower latency for sending content to client 410 as compared to the right subordinate cluster. In one aspect, the lower latency is because information travels a shorter distance. In another aspect, the lower latency is because the left subordinate cluster has relatively more resources available for processing the request. Based on the left cluster having lower latency, DM 430 selects SCM 440 for servicing a GET request from HTTP client 410. Accordingly, DM 430 sends an HTTP message to HTTP client 410 for redirecting the GET request to the URL 1.domain.com of left SCM 440”, paragraph 107).
“…selecting the … server [Examiner equates second server] is based on a latency method”, claim 5 of Horn and paragraph 11.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Horn’s teaching with the teaching of Lagi. One would have been motivated to provide functionality to check for the cluster with lower latency in order to support serving of data requests (see Horn abstract).
Although, Lagi discloses extensively an event [a trip is an event] relevant to the recipient. Lagi does not expressly discloses
to determine an identified trip purpose;
based on the identified trip purpose;
But, Kahn that is in the business of “providing a recommendation and/or a travel interface based upon a predicted travel intent.”, abstract.
Kahn discloses “evaluating a set of user signals associated with a user to identify a plurality of potential travel locations; clustering the potential travel locations to create a location cluster, the location cluster comprising two or more potential travel locations of the plurality of potential travel locations; identifying a travel destination based upon the location cluster; and providing a recommendation based upon the potential destination” (see at least claims 1, 9 and 15).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kahn ’s teaching with the teaching of Lagi. One would have been motivated to provide functionality to evaluating a set of user signals associated with a user to identify a plurality of potential travel location in order to provide “recommendations may be created based upon the predicted travel intent” (Kahn col 2:18-30).
As to claim 8, it comprises the same limitations that claim 1 above therefore is rejected in similar manner and further the claim comprises
a storage device that stores instructions for transmitting customized electronic content elements to one or more users (see at least “content clusters” 130 and 132 in Fig. 2 and associated disclosure); and
at least one processor that executes the instructions to perform a method (see Fig. 1 and associated disclosure).
As to claim 15, it comprises the same limitations that claim 1 above therefore is rejected in similar manner.
As to claims 2, 9, and 16, Lagi discloses
wherein parsing the one or more electronic communication includes parsing one of an email inbox and a text message inbox;
(“…Among other benefits, the content development platform 100 uses a range of automated processes to extract and analyze existing online content of an enterprise, parse and analyze the content…”, paragraph 65);
and further includes parsing an entire mailbox corresponding to the email inbox or the text message inbox.
(“…The content development platform 100 may include a parser 110 for parsing the stored content from the crawling activity of the automated crawler 104 [Examiner interprets as entire mailbox corresponding to the email inbox] to generate a plurality of key phrases 112 and to generate a content corpus 114 from the primary online content object 102. ..”, paragraph 86 and Fig. 7).
As to claims 3, 10, and 17, Lagi discloses further comprising: parsing the entire mailbox corresponding to the email inbox or the text message inbox by implementing entity recognition natural language processing techniques.
(“…The information extraction system 204 may utilize natural language processing and/or machine-learned classification models (e.g., neural networks) to identify entities, events, and relationships from one or more parsed documents. ..”, paragraph 125).
As to claims 4, 11 and 18, Lagi discloses further comprising: clustering, via an extraction module, the first user data into [event]
(“…among other benefits, the content development platform 100 uses a range of automated processes to extract and analyze existing online content of an enterprise, parse and analyze the content, and develop a cluster of additional content that is highly relevant to the enterprise, without reliance on conventional keyword-based techniques…”, paragraph 65);
identifying trip properties corresponding to clustered [event];
(“[0125] In embodiments, the information extraction system 204 (e.g., the entity extraction system 224 and the event extraction system 226) may discover and make use of patterns in language to extract and/or derive information relating to entities, events, and relationships [Examiner interprets as trip purpose, group composition, or timeframe]. These patterns may be defined in advance and/or may be learned by the system 200 (e.g., the information extraction system 204 and/or the machine-learning system 212). The information extraction system 204 may identify a list of words (and sequences of words) and/or values contained in each received document. In embodiments, the information extraction system 204 may use the language patterns to extract various forms of information, including but not limited to entities, entity attributes, relationships between respective entities and/or respective events, events, event types, attributes of events from the obtained documents. The information extraction system 204 may utilize natural language processing and/or machine-learned classification models (e.g., neural networks) to identify entities, events, and relationships from one or more parsed documents [Examiner interprets as trip information]. For example, a news article headline may include the text “Company A pleased to announce deal to acquire Company B.” …. Using the event classification of “company acquisition event” and the results of the natural language processing, the information extraction module 204 may infer that Company B now owns Company A. The information extraction system 204 may extract additional information from the news article, such as a date of the acquisition”, paragraph 125);
and associating the clustered [event]…with past, present, and future [event] arrangements.
(paragraph 125);
Although, Lagi discloses extensively an event relevant to the recipient. Lagi does not expressly discloses
trip and travel and travel-related data elements.
But, Kahn discloses “evaluating a set of user signals associated with a user to identify a plurality of potential travel locations; clustering the potential travel locations to create a location cluster, the location cluster comprising two or more potential travel locations of the plurality of potential travel locations; identifying a travel destination based upon the location cluster; and providing a recommendation based upon the potential destination” (see at least claims 1, 9 and 15).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kahn ’s teaching with the teaching of Lagi. One would have been motivated to provide functionality to evaluating a set of user signals associated with a user to identify a plurality of potential travel location in order to provide “recommendations may be created based upon the predicted travel intent” (Kahn col 2:18-30).
As to claims 5, 12 and 19, Lagi discloses further comprising: wherein trip properties further comprise at least: one or more of a trip purpose, group composition, or timeframe
(“[0125] In embodiments, the information extraction system 204 (e.g., the entity extraction system 224 and the event extraction system 226) may discover and make use of patterns in language to extract and/or derive information relating to entities, events, and relationships [Examiner interprets as trip purpose, group composition, or timeframe]. These patterns may be defined in advance and/or may be learned by the system 200 (e.g., the information extraction system 204 and/or the machine-learning system 212). The information extraction system 204 may identify a list of words (and sequences of words) and/or values contained in each received document. In embodiments, the information extraction system 204 may use the language patterns to extract various forms of information, including but not limited to entities, entity attributes, relationships between respective entities and/or respective events, events, event types, attributes of events from the obtained documents. The information extraction system 204 may utilize natural language processing and/or machine-learned classification models (e.g., neural networks) to identify entities, events, and relationships from one or more parsed documents [Examiner interprets as trip information]. For example, a news article headline may include the text “Company A pleased to announce deal to acquire Company B.” …. Using the event classification of “company acquisition event” and the results of the natural language processing, the information extraction module 204 may infer that Company B now owns Company A. The information extraction system 204 may extract additional information from the news article, such as a date of the acquisition”, paragraph 125);
Although, Lagi discloses extensively an event relevant to the recipient. Lagi does not expressly discloses
trip or travel and travel-related data elements.
But, Kahn discloses “evaluating a set of user signals associated with a user to identify a plurality of potential travel locations; clustering the potential travel locations to create a location cluster, the location cluster comprising two or more potential travel locations of the plurality of potential travel locations; identifying a travel destination based upon the location cluster; and providing a recommendation based upon the potential destination” (see at least claims 1, 9 and 15).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kahn ’s teaching with the teaching of Lagi. One would have been motivated to provide functionality to provide “recommendations may be created based upon the predicted travel intent” (Kahn col 2:18-30).
As to claims 6, 13 and 20, Lagi discloses further comprising:
wherein determining the relevant score by the
one or more machine learning models
(“[0141] In embodiments, the machine learning system 212 trains models that are configured to parse job postings of an entity to determine the nature of an organizations hiring as an indicator of need for particular types of goods and services. Among other things, job postings tend to be fairly truthful, as inaccurate information would tend to adversely impact the process of finding the right employees. In embodiments, the machine learning system 212 may include a classifier that learns, based on a training data set and/or under human supervision,…”, paragraphs 141 and 146).
further comprises identifying features from user data related to the features of a past, present, or future trip.
(“customers increasingly expect more personalized interactions with enterprises, such as via context-relevant chats that properly reflect the history of a customer's relationship with the enterprise”, paragraph108.
“…In some embodiments, the system may automatically infer or generate message templates from historical data provided by the user and/or other users of the system 200. Historical data may include, but is not limited to, historical communication data (e.g., previously sent messages) and/or historical and current customer relationship data, such as the dates, amounts, and attributes of business transactions. In some embodiments, the system 200 may further rely on the objective of a message to generate the template”, paragraph 150).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20200065857 (Lagi) in view of US PG. Pub. No. 20080222267 (Horn) in view of US Patent No. 9976864 (Kahn) and in view of US PG. Pub. No. 20080319649 (NATH).
As to claims 7 and 14, Lagi discloses further comprising:displaying, in an email or a text message graphical user interface having a dedicated travel tab, the customized content items having a highest relevancy scores
(“…The system further includes a machine learning system that trains one or more models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation. The system also includes a lead scoring system that scores the relevance of an item of retrieved information to an individual, the lead scoring system referencing information in the knowledge graph. The system further includes a content generation system that generates content of a personalized message to a recipient, wherein the recipient is an individual for which the lead scoring system has determined a threshold level of relevance and wherein the content generation system uses the understanding from the set of machine learning systems to generate the content….”, paragraph 10.
“…In some embodiments, the system determines a likelihood of a recipient being interested in an offering of the user. In some embodiments, the system provides an indicator of the likelihood to the user…”, paragraph 32.
“[0035] According to some embodiments, the content generation system generates a targeted message to each recipient that exceeds a threshold likelihood of interest in engaging in a conversation about an offering of the user…”, paragraph 35.
“…0078] Relevance as a metric generally indicates how close a term or phrase is to other content put up on the user's site or domain. The lower the relevance, the further away the term or phrase is from what the core topic of the site or domain is. This can be automatically determined by a crawler that crawls the site or domain to determine its main or core topic of interest to consumers. If relevance is offered as a service by the present system and method a score can be presented through a user or machine interface indicating how relevant the new input text is to an existing content pool…”, paragraph 78).
Lagi, does not expressly disclose but NATH discloses
message graphical user interface with a dedicated travel tab
(“[0028] As a result of the execution of step (222) the links for each data source are thus known and the corresponding raw documents can be downloaded. Downloads occur under the control of the underlying protocol used by the Web, i.e., HTTP which stands for `hypertext transfer protocol`. HTTP defines how messages are formatted and transmitted over the Internet and other public or private networks using the TCP/IP suite of protocols. This is achieved from TAB system through standard application program interfaces or API's so that travel information can be downloaded from each selected data source and raw documents, eventually stored in database tables on the basis of their corresponding country or travel destinations, become available for being further processed. Documents are in HTML, which stands for `hypertext markup language`, the authoring language used to create documents on the Web. HTML defines the structure and layout of a Web document by using a variety of tags and attributes”, paragraph 28, Fig. 2 and associated disclosure).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate NATH’s teaching with the teaching of Lagi. One would have been motivated to provide functionality of information in tabs in order to providing advices and warnings per travel destination to end-users. (see NATH abstract).
Response to Arguments
Applicant’s arguments of 12/22/2025 have been very carefully considered but are not persuasive.
Rejection of claims 1-20 under Double Patenting is maintained because no Terminal Disclaimer has been filed in this case.
Applicant argues (remarks 12-14)
IV. Section 103 Rejections
In the Office Action, claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C.
§103 as allegedly being unpatentable over US PG. Pub No. 20200065857 to Lagi
(hereinafter Lagi) in view of US PG. Pub. No. 20080222267 to Horn (hereinafter Horn)
and in view of US Patent No. 9976864 to Kahn (hereinafter Kahn). (Office Action at
page 11.) Claims 7 and 14 are rejected under 35 U.S.C. §103 as allegedly being
unpatentable over Lagi in view of Horn in view of Kahn and in view of US PG. Pub. No.
20080319649 to Nath (hereinafter Nath). (Office Action at page 24.)
To facilitate prosecution and without acquiescing to the rejection, Applicant has
amended the independent claims to overcome these rejections. As amended, the
outstanding rejections should be withdrawn because the cited art fails to teach or
suggest, and the rejection fails to otherwise consider, each and every element of the…
Lagi discloses a system that finds and retrieves documents from an information
network and stores event information in a knowledge graph. While Lagi includes a
machine learning system that trains models to score the relevance of information to an
individual, this does not teach or suggest the claimed feature of identifying customized
content items and "determining, by one or more machine learning models of the second
server cluster, a relevancy score for the one or more customized content items."
Horn and Kahn, secondary references, do not cure the deficiencies of Lagi as
both Horn and Kahn are also silent regarding the additional limitations presented. Both
Horn and Kahn fail to teach the implementation of a machine learning model, let alone
one that teaches or suggests…
The Examiner notes that for clarification purposes, the prior Office action of record did not have possession of the currently amended portion(s) of the claim(s) that the Applicant is currently arguing. Action on these newly amended limitations is contained herein.
The Examiner respectfully notes that applicant has not provided persuasive rebuttal evidence to overcome the prima facie case. Further, the elements of this instant Application are old and well known at the time of the invention. The combination set for the rejection produce results that are predictable.
Applicant argues (remarks 9-12)
111. Section 101 Rejections
In the Office Action, claims 1-20 are rejected under 35 U.S.C. §101 as allegedly
being directed to non-statutory subject matter. (Office Action at page 6.) Although
Applicant does not necessarily agree to the observations or characterizations of the
claims made in the Office Action, to facilitate prosecution, Applicant has amended the
independent claims to clarify the recited subject matter.
Applicant submits that the independent claims, as amended, are not directed to
an abstract idea at least because the present claims recite novel and technical features
that are more than a generalized application of an abstract idea. The present claims
involve a practical application of a particular technological solution. Applicant
respectfully requests reconsideration of the rejection based on the amendments above
and on the remarks that follow.
Specifically, claim 1, as amended, is directed to multiple technical improvements,
including e.g., "determining, by one or more machine learning models of the second
server cluster, a relevancy score for the one or more customized content items," as
disclosed in Fig. 5 and in at least paragraphs [019], [028], [034], and [037] - [111]. The
claimed invention, in concordance with the specification, outlines problems with the
existing technology and the improvements provided in the claims. For example,
paragraphs [003] - [002] explain:…The method includes the use of the technical elements to overcome the problems described above, e.g., a computer, first and second server clusters, a machine learning model, and a user display. Each of these elements assist to provide a technical solution to a technical problem.
In response the Examiner agrees that the instant application is in compliance with the utility requirement. The claimed invention has a readily apparent well-established utility (see MPEP 2107). But, per MPEP 2106 an invention also must have to comply with the Subject Matter Eligibility test under Alice framework (see MPEP 2106). The instant claims are directed to an abstract idea. None of the limitations considered as an ordered combination, provides eligibility, because taken as a whole, the claim simply instruct the practitioner to implement an abstract idea with routine, conventional technology.
Additionally, this case is not rejected under 101 only because the invention ability to run on a general purpose computer, but also because the detail facially sufficient analysis provided above, where the Examiner looked both the instant claims and the specification to elaborate Examiner's facially sufficient analysis. The additional elements in the instant claims “ at a first server cluster of a plurality of server clusters, … corresponding to a first user device of a first user; by the first server cluster, by the first server cluster, … to a second server cluster based on determining the second server cluster …; … by the second server cluster, one or more electronic communication of the first user device; to the first user device for display; determining, by one or more machine learning models of the second server cluster, a relevancy score for the one or more customized content items; ” do not provide significantly more to the abstract idea identified above, the method for transmitting customized content items to one or more user devices.
The additional elements do not: Improve another technology or technical field; Improve the functioning of a computer itself; Add a specific limitation other than what is well-understood, routine, and conventional in the field; Add meaningful limitations that amount to more than generally linking the use of the exception to a particular technological environment; Improve computer related technology by allowing computer performance of a function not previously performable by a computer. (see MPEP 2106.05). Accordingly, the claims are ineligible for patent protection (see complete and facially sufficient analysis of the rejection above).
In addition, the subject matter recited in the independent claims describes an
embodiment in which a relevancy score is determined by one or more machine learning
models. Such an integration is an additional technical improvement in the efficient
handling of data transmission. Utilizing a machine learning model may require additional
processing resources and energy. Thus, it becomes even more important to manage
bandwidth and latency issues so that computing resources are conserved and
processing efficiencies are promoted. Such features solve a technical problem, e.g.,
efficiently transmitting customized content items, and such features represent a
technical improvement to data handling and processing. Thus, the independent claims
are directed to eligible subject matter, and Applicant respectfully requests
reconsideration of the Section 101 rejections.
In response the Examiner asserts that in the step 2B of the 2019 PEG test, the claims do not provide an eligible inventive concept. All the claim limitations are well-understood, routine, conventional activity that is supported under Berkheimer Option 2. For these reasons the claims are ineligible.
Additionally, the Examiner notes that generic elements such as “by one or more machine learning models”, as claimed here, are well-understood, routine, conventional elements and activity, see for example the provided references of record. These elements are fully supported under Berkheimer Option 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“A Smart Email Client Prototype for Effective Reuse of Past Replies”. IEEE. 2018. “Email communication is widely used in managing customer queries such as complaints and inquiries. With an increasing number of customer emails being received every day, better tools are needed to answer emails efficiently, reuse past efforts and prevent overwhelming and cluttered email backlog. Finding similar past cases is difficult since customer requests might use non-standard terminology. In this paper, we develop a smart email client prototype which helps in replying to an email by providing a list of replies gleaned from the past replied emails. The suggested replies are ranked according to the level of similarity. We have evaluated real-world email data in order to ascertain that reuse is possible, but requires careful retrieval mechanisms. We implement and evaluate a case-based reasoning approach as a methodology to solve the problem by reusing previously written solutions from the past replies stored in the case base. We build a retrieval algorithm that finds similar cases beyond the exact matching, by using text processing and semantics analysis techniques. To optimize retrieval, we apply and cross-evaluate several text analysis techniques such as lexical analysis and synonym expansion, and our evaluation shows that synonym expansion could improve the chance of retrieving a more relevant match even at lower ranks. We evaluate our prototype based on the quality of retrieval results, index size, and processing time elapsed.”
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 extension fee 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 MARIA VICTORIA VANDERHORST whose telephone number is (571)270-3604. The examiner can normally be reached on business hours from Monday through Friday from 8:30 AM to 4:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashraf Waseem can be reached on 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIA V VANDERHORST/ Primary Examiner, Art Unit 3621 3/7/2026