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
This is a final Office Action for application 18/665,264, in response to applicant arguments filed on 12/08/25 and 11/06/2025. Claims 1, 19 and 20 are currently amended. Claims 1-20 are pending and examined below. Examiner by mistake in the previous office action cited paragraph numbers from the corresponding Freeman PGPUB instead of column and line numbers of the Freeman patent. In this office action, the column and line numbers of the patent reference of Freeman is added to correct the last office action.
Response to Arguments
Applicant's arguments filed 12/08/2025 and 09/11/2025 have been fully considered but they are not persuasive. Examiner’s position remains same as the last office action. Examiner further clarifies the position taken in the last office action in this office action. Examiner also in this office action provides (cites) corresponding column and line numbers from the Freeman patent and thus correcting the unintended mistake of the last office action. All the paragraph sections provided (and copied) in the last office action are also present in the patent verbatim. The Applicant with very little effort could have easily found the cited sections in the patent.
Relevant sections of instant specification:
[0020] New results relate to simulation of result ranking and impressions generated by natural language-based triggers of sponsored search. In this context, a trigger pattern may be a natural language expression, chosen by or on behalf of a search result sponsor, that specifies when a sponsored message associated with the search results should be presented to the user, by way of display, audio playback or other presentation formats.
[0021] The natural language expression that specifies a trigger pattern is translated by a NL processing module into a semantic data structure, one that represents its meaning, and is called the deep structure of the pattern. A natural language query can be similarly processed into its meaning—also a deep structure, a.k.a. semantic representation. Both deep structures (that of the pattern and that of the query) are tested for compatibility to decide if the query triggers the pattern. The compatibility or semantic match process that lies at the core of this approach will be discussed later.
[0022] Consider an example. A user submits the query, “find me a nice Chinese restaurant within 2 miles of Lynn Way”. Sponsors a...n have already submitted sponsored messages with the natural language triggers:
(1) “Asian food near Sunnyvale”;
(2) “nice restaurants in Silicon Valley”;
(3) “Chinese near Santa Clara”;
(4) “fine Chinese restaurants within 3 miles of Main and First Streets Sunnyvale”; and
(5) “expensive Chinese restaurants between San Jose and Palo Alto”.
[0023] Natural language understanding determines the deep structure of the user query by interpreting the type of food as Chinese, the quality of restaurant as nice, and the location as a latitude and longitude combined with a surrounding area, however that area is coded. (Alternative ways of coding areas are identified below.) Each of the sponsored trigger queries is understood and its deep structure is represented in a data structure compatible with the data structure that expresses the user query. The sponsored trigger queries may include further instructions on handling of implicit context and rules for fuzzy matching. The messages associated with the trigger queries carry a price point.
[0030] One new result is using past queries as a yardstick for determining the scope of competition between sponsors’ natural language-based triggers. A related new result is being able to predict, in the absence of keyword analysis, how combinations of natural language-based triggers and price points would have fared against past natural language trigger patterns or will fare in the future against pending trigger patterns.
[0081] The queries and price points database 127 records projected results of past queries replayed against active patterns and price points to predict future price distributions. The set of past queries that is processed against active patterns may be limited by time, criteria, frequency or other query property. The processing can involve multiple criteria. It can take into account effective dates, expiration and/or quota fulfillment of active bids.
Relevant sections of prior art of Freeman US Patent 7,818,176:
Summary:
(9)“According to various aspects of the invention, a system and method for selecting and presenting advertisements based on natural language processing of voice-based inputs is provided. A natural language voice-based input may be received by a voice user interface. The voice-based input may include a user utterance, and a request may be identified from the utterance. Appropriate action may be taken to service the request, while one or more advertisements may be selected and presented to the user. Advertisements may be selected based on various criteria, including content of the input (e.g., concepts, semantic indicators, etc.), an activity related to the input (e.g., a relation to a request, a requested application, etc.), user profiles (e.g., demographics, preferences, location, etc.), or in other ways. A user may subsequently interact with the advertisement (e.g., via a voice-based input), and action may be taken in response to the interaction. Furthermore, the interaction may be tracked to build statistical profiles of user behavior based on affinities or clusters among advertisements, user profiles, contexts, topics, semantic indicators, concepts, or other criteria.” (Col 1, line 65 – col 2, line 18)
Description:
(6) Referring to FIG. 1, an exemplary system 100 for implementing a voice user interface is illustrated according to various aspects of the invention. System 100 may enable users to perform various tasks on a voice-enabled device. For example, users may control navigation devices, media devices, personal computers, personal digital assistants, or any other device supporting voice-based inputs. System 100 may enable users to request voice-enabled devices to retrieve information or perform various tasks, among other things, using natural language voice-based inputs. For example, system 100 may interpret natural language voice-based inputs and generate responses using, among other things, techniques described in U.S. patent application Ser. No. 10/452,147, entitled "Systems and Methods for Responding to Natural Language Speech Utterance," filed Jun. 3, 2003, which issued as U.S. Pat. No. 7,398,209 on Jul. 8, 2008, and U.S. patent application Ser. No. 10/618,633, entitled "Mobile Systems and Methods for Responding to Natural Language Speech Utterance," filed Jun. 15, 2003, which issued as U.S. Patent No. 7,693,720 on Apr. 6, 2010, both of which are hereby incorporated by reference in their entirety. For example, as described in U.S. patent application Ser. No. 10/452,147, the system 100 may include a speech recognition engine (e.g., an Automatic Speech Recognizer 110) that may recognize words and phrases in an utterance using entries in one or more dictionary and phrase tables. In addition, as further described therein, fuzzy set possibilities or prior probabilities for the words in the dictionary and phrase tables may be dynamically updated to maximize the probability of correct recognition at each stage of the dialog (e.g., the probabilities or possibilities may be dynamically updated based on application domains, questions or commands, contexts, user profiles and preferences, user dialog histories, recognizer dictionary and phrase tables, word spellings, and/or other criteria). (Freeman Col 2, line 60 – col 3, line 27)
(10) “Conversational language processor 120 may build long-term and/or short-term shared knowledge in one or more knowledge source. For example, shared knowledge sources may include information about previous utterances, requests, and other user interactions to inform generating an appropriate response to a current utterance. The shared knowledge may include public/non-private (i.e., environmental) knowledge, as well as personal/private (i.e., historical) knowledge. For example, conversational language processor 120 may use context determination module 130 to establish a context for a current utterance by having domain agents 135 competitively generate a context-based interpretation of the utterance (e.g., by scoring possible interpretations and selecting a highest scoring interpretation). As such, agents 135 may model various domains (e.g., navigation, music, a specific user, global users, advertising, e-commerce, etc.), and conversational language processor 120 may interpret and/or respond to a voice-based input accordingly. For example, context-based interpretations and responses to a voice-based input may be generated using techniques described in U.S. patent application Ser. No. 11/197,504, entitled "Systems and Methods for Responding to Natural Language Speech Utterance," filed Aug. 5, 2005, which issued as U.S. Pat. No. 7,640,160 on Dec. 29, 2009, and U.S. patent application Ser. No. 11/212,693, entitled "Mobile Systems and Methods of Supporting Natural Language Human-Machine Interactions," filed Aug. 29, 2005, both of which are hereby incorporated by reference in their entirety.” (Col 4, lines 17 – 44)
(11) Furthermore, conversational language processor 120 may support adaptive misrecognition to reinterpret a current utterance and/or one or more previous utterances. For example, information contained in a current utterance may indicate that interpretations for one or more previous utterances were incorrect, and therefore, the previous utterances may be reinterpreted to improve subsequent interpretations. Accordingly, conversational language processor 120 may use the techniques described herein, along with various other techniques, to interpret and respond to conversational, natural language utterances. Conversational language processor 120 may use various other techniques as will be apparent, such as those described in U.S. patent application Ser. No. 11/200,164, entitled "System and Method of Supporting Adaptive Misrecognition in Conversational Speech," filed Aug. 10, 2005, which issued as U.S. Pat. No. 7,620,549 on Nov. 17, 2009, and U.S. patent application Ser. No. 11/580,926, entitled "System and Method for a Cooperative Conversational Voice User Interface," filed Oct. 16, 2006, both of which are hereby incorporated by reference in their entirety. For example, as described in U.S. patent application Ser. No. 11/200,164, an environmental model may be accessed to determine user location, user activity, track user actions, and/or other environmental information to invoke context, domain knowledge, preferences, and/or other cognitive qualities to enhance the interpretation of questions and/or commands. In addition, as further described therein, based on information received from a general cognitive model, the environmental model, and/or a personalized cognitive model, which provide statistical abstracts of user interaction patterns, the system 100 may enhance responses to commands and questions by including a prediction of user behavior. (Col 4, line 45 – col 5, line 9)
(12) Referring to FIG. 2, an exemplary advertising system 200 is illustrated according to various aspects of the invention. System 200 may include a server 230 for receiving one or more advertisements from an advertiser 220, wherein the advertisements may be stored in a data repository 260 associated with server 230. For example, advertisements may include sponsored messages or marketing communications, calls to action, purchase opportunities, trial downloads, coupons, or any other suitable marketing, advertising, campaign, or other information, as would be apparent to those skilled in the art. A voice-enabled device 210 may receive a voice-based input and establish communications with advertising server 230. Subsequently, advertising server 230 may select one or more advertisements from among the advertisements stored in data repository 260, and the selected advertisements may be provided to the voice-enabled device for presentation to a user. (Col 5, lines 10-26)
(18) Furthermore, the user's subsequent interaction with an advertisement may be tracked using tracking module 255. For example, tracking module 255 may determine whether a conversion or click-through occurs for each advertisement presented to users. Further, tracking module 255 may maintain accounting and/or billing information associated with advertisers 220. For example, advertisers 220 may specify a maximum insertion cost, a cost-per-click-through, an average insertion cost, or other criteria specifying a budget constraint for an advertisement. As such, tracking module 255 may track which advertisements are selected and/or presented, which advertisements result in a conversion or click-through, whether a click-through or conversion results in a transaction or sale, associations between advertisements and users, requests, concepts, semantic indicators, and/or other criteria. For example, tracking user interaction with advertisements may be used to build user-specific and/or global statistical profiles that map or cluster advertisements to topics, semantic indicators, contexts, concepts, etc. based on user behavior, demographics, targeting constraints, content of advertisements, content of requests, actions associated with requests, or other statistically relevant information. Accordingly, the tracking information may be used to bill or invoice advertisers 220, as well as to improve subsequent performance and relevance of advertisements selected using advertisement selection module 250. Other techniques and features of selecting and presenting advertisements based on voice-based inputs may suitably be employed, as would be apparent. (Col 6, lines 38 – 65)
(21) The requests may be part of a conversational interaction between a user and a system or device, whereby an interpretation of requests in a current utterance may be based upon previous utterances in a current conversation, utterances in previous conversations, context-based information, local and/or global user profiles, or other information. For example, a previous request may be reinterpreted based on information included in subsequent requests, a current request may be interpreted based on information included in previous requests, etc. Furthermore, the conversational interaction may take various forms, including query-based conversations, didactic conversations, exploratory conversations, or other types of conversations. For example, the conversational language processor may identify a type of conversation, and information may be extracted from the utterance accordingly to identify the one or more requests in operation 310. Moreover, the conversational language processor may determine whether any of the requests are incomplete or ambiguous, and .action may be taken accordingly (e.g., a system response may prompt a user to clarify an incomplete and/or ambiguous request). The conversational language processor may therefore use various techniques to identify a conversation type, interpret utterances, identify requests, or perform other tasks, such as those described in the aforementioned U.S. Patent Applications and U.S. Patents, which are hereby incorporated by reference in their entirety. (Col 7, line 32 – 57)
(25) Content of a voice-based input may be determined based on various criteria, including contextual or conceptual information (e.g., semantic indicators, qualifiers, or other information). For example, a given concept may include various semantically equivalent indicators having an identical meaning. Thus, for instance, a voice-based input may be "Play some tunes!" or "Play some music!" or other variants thereof, each of which may be interpreted as relating to a specific idea (or concept) of "Music." Thus, concept or content information in a request may be used to select an advertisement. For example, a user may request to calculate a route in Seattle, Wash. (e.g., "How do I get to the Space Needle?"). Based on a context of the requested task (e.g., "Navigation," "Seattle," etc.), a voice search engine may retrieve an address of the Space Needle and a navigation application may calculate the route. Furthermore, user profile information may indicate that the user is visiting Seattle from out-of-town (e.g., the profile may indicate that the user's home is Sacramento), and therefore, an advertisement for popular points-of-interest in Seattle may be selected. In another example, the user may request information about a sporting event (e.g., "Get me the kickoff time for the Eagles game on Sunday"). Based on a context of the requested information (e.g., "Search," "Sports," "Philadelphia," etc.), the requested information may be retrieved, while an advertisement for Eagles apparel or memorabilia may be selected. (Col 8, line 44 – col 9, line 2)
(30) The output presented to the user in operation 325 may be provided to the user in various ways. For example, in various implementations, the output may include a voice-based or otherwise audible response. In another example, when an associated device includes a display mechanism, the output may be displayed on the display device. It will be apparent that many combinations or variants thereof may be used, such as augmenting a voice-based response with information on a display device. For example, a user may request information about restaurants, and an advertisement may be selected based on a user preference indicating a favorite type of restaurant (e.g., a Chinese restaurant may be selected based on a user profile indicating a preference for Chinese). Therefore, in one example, the output presented in operation 325 may display information about various restaurants matching the requested information, while a voice-based advertisement for the Chinese restaurant may be played to the user (e.g., via a speaker or other suitable mechanism for playing voice back to the user). Many other variations will be apparent (e.g., a graphical advertisement may be displayed on a display device, while a corresponding or different voice-based advertisement may be played audibly). (Col 10, lines 30 – 51)
(32) User advertisement interaction may be tracked in an operation 345. For example, operation 345 may track historical data about users, conversations, topics, contexts, or other criteria to associate information with the selected advertisement. The tracking information may therefore be used to build statistical profiles defining affinities, click-through or conversion rates, or other information about various advertisements, topics, or other criteria on a user-specific and/or a global-user level. Thus, clusters or mappings may be created between advertisements, topics, concepts, demographics, or other criteria based on user behavior with the advertisements (e.g., whether a user interacts with the advertisement in operation 330). (Col 11, lines 11 – 23)
Dictionary: "subsequent" means happening later in time or order
Examiner response to argument 1:
The applicant argues that Freeman does not disclose “predicting future ad price distributions.”
The Applicant specification[0081] indicates “The queries and price points database 127 records projected results of past queries replayed against active patterns and price points to predict future price distributions”. That means the past results are replayed against active/current patterns and price points to predict future advertisement price.
Prior art of Freeman (col 4, line 45 – col 5, line 9) and (col 6, lines 38- 65) disclose that various interaction attributes, user interaction or behavior with the advertisement, content of advertisement, topics are tracked and this information is used to bill or invoice advertisers. Advertiser is not billed for the product but the advertisement contrary to what the applicant tried to argue. These are the factors that decide the price of the advertisement. Also, the system can predict user behavior based on the tracked information (col 4, line 45 – col 5, line 9). Freeman (col 7, lines 32 – 57) states that the interaction of user and the system is in the form of a query and the current query is interpreted based on the previous query.
Accordingly, the tracking information may be used to bill or invoice advertisers 220, as well as to improve subsequent performance and relevance of advertisements selected using advertisement selection module 250.
Freeman discloses that based on information received from a general cognitive model, the environmental model, and/or a personalized cognitive model, which provide statistical abstracts of user interaction patterns, the system 100 may enhance responses to commands and questions by including a prediction of user behavior. (Col 4, line 45 – col 5, line 9)
it is clear from various excerpts of Freeman that the user’s tracked behavior i.e., user’s interaction (e.g., past and present query) with the advertisement dictates the price of the advertisement. The price is used to bill the advertiser. The data (i.e., tracked user interaction e.g., query) is also used to improve subsequent (i.e., future query) performance and relevance of the advertisement (col 6, lines 38- 65). The anticipated (or projected) improvement e.g., more clicks (among other indicators) based on prior tracked indicators translates to more sale (Col 6, lines 38 – 65). The subsequent improved performance (projected improved indicators in user behaviors) is also directly related to a subsequent improved (i.e., projected) price of the advertisement (as Freeman discloses that these tracked indicators are used to bill the advertiser for the advertisements (Col 6, lines 38 – 65)). The argument is not persuasive.
Prior Applicant arguments (09/11/2025):
Examiner response to argument 2:
Applicant argues that Freeman does not disclose “configuring a memory with a database of active sponsored messages, the active sponsored messages comprising natural language trigger patterns and corresponding prices,”
Freeman discloses data repository stores sponsored messages (i.e., advertisements) with all the information (col 4, lines 17 -44; col 5, lines 10-26; col 11, lines 11-23) such as semantic indicators, context or topic concepts (same as claimed triggers) that will allow selection of suitable advertisements in response to user natural language queries (Col 1, line 65 – col 2, line 18). These information in the sponsored messages are the triggers. Like applicant’s claimed invention, Freeman’s system is also based on natural language processing. Applicant specification par [022] discloses a scenario where a user submits the query, “find me a nice Chinese restaurant within 2 miles of Lynn Way”. The prior art of Freeman also discloses an example where a user asks (or queries) "How do I get to the Space Needle?" (Freeman col 8, line 44 – col 9, line 2) or a user may request for information about restaurants (Freeman col 10, lines 30-51). In both cases, queries are of natural language format and semantic meanings, contexts and other indicators (i.e., claimed triggers) are checked for finding the appropriate advertisement.
Applicant own specification discloses that a natural language query is compared against the advertisements to find if the query triggers the pattern i.e., if they are matched or compatible. This is done by contextual, semantic or fuzzy matching (Applicant specification [0023]). The prior art similarly discloses that it’s advertisement selection (i.e., compatibility) is determined by the content of the natural language voice-based input (i.e., natural language query) and the advertisement by comparing the semantic meaning of the content, context and other criteria (Freeman summary (Col 1, line 65 – col 2, line 18)). The prior art also uses fuzzy matching (Freeman Col 2, line 60 – col 3, line 27) among other semantic matching technics. The prior art additionally discloses tracking and storing various user inputs (NL voice queries, content, context, semantic indicators/meanings etc.), interaction behaviors and accounting or billing information is stored in a long term/short term data store (Freeman col 4, lines 17 -44
and col 6, lines 38 -65). It is obvious for a person of ordinary skill to consider a database to be the data store. The examiner already established how user interaction is translated into the price (or components that contribute to the price of advertisement) of an advertisement and is the main accounting or billing information. The examiner also established how past user behaviors (i.e., input, interactions, content, semantic meaning indicators, context etc. or the claimed triggers) are used to project subsequent user behavior or the corresponding price of the advertisement. The argument is not persuasive.
Examiner response to argument 3:
Applicant argues that Freeman does not disclose “configuring a memory with a database of projected results of past natural language queries processed against the natural language trigger patterns,”
Freeman (col 5, lines 10-26) discloses data repository stores sponsored messages (i.e., advertisements) with all the information that will allow selection by user natural language queries (Freeman Col 1, line 65 – col 2, line 18). These information in the sponsored messages are the triggers. Like applicant’s claimed invention, Freeman’s system is also based on natural language processing.
Applicant own specification discloses that a natural language query is compared against the advertisements to find if the query triggers the pattern i.e., if they are matched or compatible ([0023]). This is done by contextual, semantic or fuzzy matching. The prior art also discloses that it’s advertisement selection (i.e., compatibility) is determined by the content of the natural language voice-based input (i.e., natural language query) and the advertisement by comparing the semantic meaning of the content, context and other criteria. The prior art also uses fuzzy matching among other semantic matching technics. The prior art discloses tracking (Freeman col 11, lines 11-23) and storing various user inputs (present and past NL voice queries, content, context, semantic indicators/meanings etc.), interaction behaviors and accounting or billing information is stored in a long term/short term data repository. It is obvious for a person of ordinary skill to consider a database to be the data store. The examiner already established (please see response to argument 1) how user interaction is translated into the price of an advertisement and is the main accounting or billing information. The examiner also established (please see response to argument 1) how past user behaviors (i.e., input, interactions, content, semantic meaning indicators, context etc. or the claimed triggers) are used to project subsequent user behavior or the corresponding price of the advertisement. The argument is not persuasive.
Examiner response to argument 4:
Applicant argues that Freeman does not disclose “receiving, from an advertiser, a new sponsored message comprising a new natural language trigger pattern and a new corresponding price,”
Please see response to argument 2.
Examiner response to argument 5:
Applicant argues that Freeman does not disclose "predicting future ad price distributions for the new sponsored message from the projected results of the past natural language queries processed against the natural language trigger patterns".
Please see response to argument 1 and 2.
Finally, similar to applicant’s invention, Freeman’s natural language based system stores sponsored messages or advertisements (past and present) along with all the triggers (contexts, semantic meanings, billings, accountings and various criteria) in a data repository. All natural language queries or inputs with all the triggers also are tracked and stored. A natural language queries are compared against advertisements to find the suitable advertisement by comparing their historical, contextual, semantic meanings by various technics (e.g., fuzzy matching). Freeman also uses tracked, historical data to predict (project) user behavior and improve (projected/predicted improved) future performance and projected/predicted improved sale and projected/predicted improved billing for the advertisement.
Therefore, all arguments are unpersuasive.
Double Patenting
The non-statutory 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 non-statutory obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claim 1-20 are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claims 1-20 of Patent # 12013862 in view of Zhang [US 8458065 B1, 2013-06-04]. Although the conflicting claims are not identical, they are not patentably distinct from each other because the claims recite substantially similar claim limitations as depicted in the table below.
Instant Application # 18/665264
Patent # 12013862
Claims 1, 19 and 20 - configuring a memory with a database of active sponsored messages, the active sponsored messages comprising natural language trigger patterns and corresponding prices; configuring a memory with a database of projected results of past natural language queries processed against the natural language trigger patterns; indexing data stored in the database of past natural language queries;
receiving, from an advertiser, a new sponsored message comprising a new natural language trigger pattern and a new corresponding price;
predicting future ad price distributions for the new sponsored message based, at least in part, on the projected results of the past natural language queries processed against the natural language trigger patterns; and
reporting the future ad price distributions for the new sponsored message to the advertiser.
Claims 1, 2 and 20 - configuring a memory with a database of past natural language queries, the past natural language queries being represented as deep structures; configuring the memory with a database of active sponsored messages, the active sponsored messages comprising natural language trigger patterns and corresponding prices; configuring the memory with a database of projected results of the past queries processed against the natural language trigger patterns and the corresponding prices of the active sponsored messages; indexing data stored in the database of past natural language queries, the indexing being based on the deep structures;
predicting future ad price distributions from the projected results of the past queries processed against the natural language trigger patterns and the corresponding prices of the active sponsored messages; and reporting the future ad price distributions to an advertiser.
Patent # 12013862 does not explicitly teach “receiving, a new sponsored message comprising a new natural language trigger pattern and a new corresponding price”; However, Zhang [US 8458065 B1, 2013-06-04] in col. 20, line 50- col. 21, line 6 teaches “the system and methods may automatically or on a user's command search the features of the financial objects (e.g. prices) in the indexed database to determine whether any of the indexed features contain the features of interest to the user. These determinations may be made in any suitable way, including employing signal processing and pattern analysis algorithms.”
It would have been obvious to one of ordinary skill in the art, at the time the invention was made to have modified the system of Galas as modified by Miller with automatically receive features (price) of the financial objects in real-time and indexing data stored in the database of Zhang. Such a modification would allow locally-store index database, and also indexed database to be updated from the external real-time data sources (Zhang, col. 11, lines 39-55).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Freeman et al. (US Patent. 7,818,176 B2).
Regarding claim 1, Freeman discloses
A method of predicting future ad price distributions, the method comprising:
configuring a memory with a database of active sponsored messages, the active sponsored messages comprising natural language trigger patterns and corresponding prices (col 5, lines 10-18: “Referring to FIG. 2, an exemplary advertising system 200 is illustrated according to various aspects of the invention. System 200 may include a server 230 for receiving one or more advertisements from an advertiser 220, wherein the advertisements may be stored in a data repository 260 associated with server 230. For example, advertisements may include sponsored messages or marketing communications, calls to action, purchase opportunities, trial downloads, coupons, or any other suitable marketing, advertising, campaign, or other information, as would be apparent to those skilled in the art.” [i.e., a data repository/database is created to store sponsored messages]; col 5, lines 35-42: “Advertisers may specify criteria for a campaign or targeting information for an advertisement (e.g., a start date, an end date, budget information, geo-targeting information, conceptual or contextual information, or any other suitable criteria), which may be used to facilitate selecting an advertisement in relation to a particular voice-based input.” [i.e., Advertisers can include trigger patterns/contextual information/criteria, prices/budget]) ;
configuring a memory with a database of projected results of past natural language queries processed against the natural language trigger patterns (Col 4, lines 17-35: “Conversational language processor 120 may build long-term and/or short-term shared knowledge in one or more knowledge source. For example, shared knowledge sources may include information about previous utterances, requests, and other user interactions to inform generating an appropriate response to a current utterance. The shared knowledge may include public/non-private (i.e., environmental) knowledge, as well as personal/private (i.e., historical) knowledge. For example, conversational language processor 120 may use context determination module 130 to establish a context for a current utterance by having domain agents 135 competitively generate a context-based interpretation of the utterance (e.g., by scoring possible interpretations and selecting a highest scoring interpretation). As such, agents 135 may model various domains (e.g., navigation, music, a specific user, global users, advertising, e-commerce, etc.), and conversational language processor 120 may interpret and/or respond to a voice-based input accordingly. ” [i.e., a shared knowledge/database is built to store past/previous queries/utterances/requests; Interpretation processing of the query/utterance/request requires using trigger patterns/context/criteria.]);
indexing data stored in the database of projected results of past natural language queries (Col 4, lines 17-35: [shared resources store previous queries/utterances/requests and interactions which includes results]);
Freeman does not explicitly disclose indexing data stored in the database.
However, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to include indexing feature in Freeman’s shared resource to make data retrieval faster and efficient.
receiving, from an advertiser, a new sponsored message comprising a new natural language trigger pattern and a new corresponding price (col 5, lines 10-18: “Referring to FIG. 2, an exemplary advertising system 200 is illustrated according to various aspects of the invention. System 200 may include a server 230 for receiving one or more advertisements from an advertiser 220, wherein the advertisements may be stored in a data repository 260 associated with server 230. For example, advertisements may include sponsored messages or marketing communications, calls to action, purchase opportunities, trial downloads, coupons, or any other suitable marketing, advertising, campaign, or other information, as would be apparent to those skilled in the art.” [i.e., a data repository/database is created to store sponsored messages]; col 5, lines 35-42: “Advertisers may specify criteria for a campaign or targeting information for an advertisement (e.g., a start date, an end date, budget information, geo-targeting information, conceptual or contextual information, or any other suitable criteria), which may be used to facilitate selecting an advertisement in relation to a particular voice-based input.” [i.e., Advertisers can include trigger patterns/contextual information/criteria, prices/budget]);
predicting future ad price distributions for the new sponsored message based, at least in part, on the projected results of the past natural language queries processed against the natural language trigger patterns (The Applicant specification[0081] indicates “The queries and price points database 127 records projected results of past queries replayed against active patterns and price points to predict future price distributions”. That means the past results are replayed against active/current patterns and price points to predict future advertisement price.
Prior art of Freeman (col 4, line 45 – col 5, line 9) and (col 6, lines 38- 65) disclose that various interaction attributes, user interaction or behavior with the advertisement, content of advertisement, topics are tracked and this information is used to bill or invoice advertisers. Advertiser is not billed for the product but the advertisement contrary to what the applicant tried to argue. These are the factors that decide the price of the advertisement. Also, the system can predict user behavior based on the tracked information (col 4, line 45 – col 5, line 9). Freeman (col 7, lines 32 – 57) states that the interaction of user and the system is in the form of a query and the current query is interpreted based on the previous query.
Accordingly, the tracking information may be used to bill or invoice advertisers 220, as well as to improve subsequent performance and relevance of advertisements selected using advertisement selection module 250.
it is clear from various excerpts of Freeman (Please see the cited sections presented above for reviewer’s ease) that the user’s tracked behavior i.e., user’s interaction (e.g., past and present query) with the advertisement dictates the price of the advertisement. The price is used to bill the advertiser. The data (i.e., tracked user interaction e.g., query) is also used to improve subsequent (i.e., future query) performance and relevance of the advertisement (col 6, lines 38- 65). The anticipated (or projected) improvement e.g., more clicks (among other indicators) based on prior tracked indicators translates to more sale (Col 6, lines 38 – 65). The subsequent improved performance (projected improved indicators) is also directly related to a subsequent improved (i.e., projected) sale of advertised products and also improved (projected/predicted) price of the advertisement (as Freeman discloses that these tracked indicators are used to bill the advertiser for the advertisements (Col 6, lines 38 – 65));
and reporting the future ad price distributions for the new sponsored message to the advertiser (Col 6, lines 38-67: See above explanation, [i.e., selected Advertisement for the current utterances are billed by the tracker.]).
Regarding claim 2, Freeman discloses:
The method of claim 1, additionally comprising reporting a frequency of other active sponsored messages from the database of active sponsored messages that are triggered by the new natural language trigger pattern (Col 2, lines 5-20: “A user may subsequently interact with the advertisement (e.g., via a voice-based input), and action may be taken in response to the interaction. Furthermore, the interaction may be tracked to build statistical profiles of user behavior based on affinities or clusters among advertisements, user profiles, contexts, topics, semantic indicators, concepts, or other criteria.” [i.e., statistical attributes e.g., frequency of advertisements are tracked and stored (obviously)]).
Regarding claim 3, Freeman discloses:
The method of claim 2, additionally comprising reporting a ranking of the new sponsored message among the other active sponsored messages from the database of active sponsored messages that are triggered by the new natural language trigger pattern (Col 6, lines 21-26: “Furthermore, selected advertisements may be presented according to a predetermined ordering or ranking (e.g., based on a ranking of relevance to an advertisement.”).
Regarding claim 4, Freeman discloses:
The method of claim 1, wherein at least one active sponsored message of the active sponsored messages is added to the database of active sponsored messages after the past natural language queries have been received and stored in the database of past queries (col 5, lines 10-18: “Referring to FIG. 2, an exemplary advertising system 200 is illustrated according to various aspects of the invention. System 200 may include a server 230 for receiving one or more advertisements from an advertiser 220, wherein the advertisements may be stored in a data repository 260 associated with server 230. For example, advertisements may include sponsored messages or marketing communications, calls to action, purchase opportunities, trial downloads, coupons, or any other suitable marketing, advertising, campaign, or other information, as would be apparent to those skilled in the art.” [i.e., a data repository/database is created to store/add active/new sponsored messages]; Col 4, lines 17-35: “Conversational language processor 120 may build long-term and/or short-term shared knowledge in one or more knowledge source. For example, shared knowledge sources may include information about previous utterances, requests, and other user interactions to inform generating an appropriate response to a current utterance. The shared knowledge may include public/non-private (i.e., environmental) knowledge, as well as personal/private (i.e., historical) knowledge.” [i.e., in both advertisements and requests are stored and obviously past/previous ones would be stored earlier than the new ones]).
Regarding claim 5, Freeman discloses:
The method of claim 1, additionally comprising updating the database of active sponsored messages with the new sponsored message (Col 3, lines 19-26: “In addition, as further described therein, fuzzy set possibilities or prior probabilities for the words in the dictionary and phrase tables may be dynamically updated to maximize the probability of correct recognition at each stage of the dialog (e.g., the probabilities or possibilities may be dynamically updated based on application domains, questions or commands, contexts, user profiles and preferences, user dialog histories, recognizer dictionary and phrase tables, word spellings, and/or other criteria.” Also see col 5, lines 10-18 for adding sponsored messages to the repository).
Regarding claim 6, Freeman discloses:
The method of The method of wherein the active sponsored messages include trigger pattern criteria provided by sponsors of results, wherein the projected results are determined by identifying trigger pattern criteria of an active sponsored message, of the active sponsored messages, that matches a past query of the past queries, and wherein the method further comprises providing a sponsored result associated with the identified trigger pattern criteria that matches the past query (See claim 1 explanation).
Regarding claim 7, Freeman discloses:
The method of claim 1, wherein the past natural language queries are natural language queries obtained from spoken words (Col 1, lines 65-67: “a system and method for selecting and presenting advertisements based on natural language processing of voice-based inputs is provided.”).
Regarding claim 8, Freeman discloses:
The method of claim 7, wherein the past natural language queries are represented as deep structures, the deep structures using semantic representation that populates predefined categories using words included in the natural language queries (Col 6, lines 38-67: “Furthermore, the user's subsequent interaction with an advertisement may be tracked using tracking module 255. For example, tracking module 255 may determine whether a conversion or click-through occurs for each advertisement presented to users. Further, tracking module 255 may maintain accounting and/or billing information associated with advertisers 220. For example, advertisers 220 may specify a maximum insertion cost, a cost-per-click-through, an average insertion cost, or other criteria specifying a budget constraint for an advertisement. As such, tracking module 255 may track which advertisements are selected and/or presented, which advertisements result in a conversion or click-through, whether a click-through or conversion results in a transaction or sale, associations between advertisements and users, requests, concepts, semantic indicators, and/or other criteria. For example, tracking user interaction with advertisements may be used to build user-specific and/or global statistical profiles that map or cluster advertisements to topics, semantic indicators, contexts, concepts, etc. based on user behavior, demographics, targeting constraints, content of advertisements, content of requests, actions associated with requests, or other statistically relevant information.” [i.e., the queries/requests, content of requests, their semantic indicators are tracked.]).
Regarding claim 9, Freeman discloses:
The method of claim 1, wherein the active sponsored messages are paid-for active sponsored messages (Col 2, lines 1-30: “a system and method for selecting and presenting advertisements based on natural language processing of voice-based inputs is provided. A natural language voice-based input may be received by a voice user interface. … For example, advertisements may include sponsored messages, calls to action, purchase opportunities, trial downloads, or any other marketing communication, as would be apparent to those skilled in the art.”).
Regarding claim 10, Freeman discloses:
The method of claim 9, wherein the active sponsored messages comprise sponsored ads (Col 2, lines 1-30: “a system and method for selecting and presenting advertisements based on natural language processing of voice-based inputs is provided. A natural language voice-based input may be received by a voice user interface. … For example, advertisements may include sponsored messages, calls to action, purchase opportunities, trial downloads, or any other marketing communication, as would be apparent to those skilled in the art.”).
Regarding claim 11, Freeman discloses:
The method of claim 1, wherein the corresponding prices are prices for advertising the active sponsored messages Col 6, lines 38-67: “Furthermore, the user's subsequent interaction with an advertisement may be tracked using tracking module 255. For example, tracking module 255 may determine whether a conversion or click-through occurs for each advertisement presented to users. Further, tracking module 255 may maintain accounting and/or billing information associated with advertisers 220. For example, advertisers 220 may specify a maximum insertion cost, a cost-per-click-through, an average insertion cost, or other criteria specifying a budget constraint for an advertisement. As such, tracking module 255 may track which advertisements are selected and/or presented, which advertisements result in a conversion or click-through, whether a click-through or conversion results in a transaction or sale, associations between advertisements and users, requests, concepts, semantic indicators, and/or other criteria. For example, tracking user interaction with advertisements may be used to build user-specific and/or global statistical profiles that map or cluster advertisements to topics, semantic indicators, contexts, concepts, etc. based on user behavior, demographics, targeting constraints, content of advertisements, content of requests, actions associated with requests, or other statistically relevant information. Accordingly, the tracking information may be used to bill or invoice advertisers 220, as well as to improve subsequent performance and relevance of advertisements selected using advertisement selection module 250. Other techniques and features of selecting and presenting advertisements based on voice-based inputs may suitably be employed, as would be apparent.” [i.e., selected Advertisement for the current utterances are billed by the tracker.]).
Regarding claim 12, Freeman discloses:
The method of claim 1, wherein the corresponding prices are prices related to advertised products associated with the active sponsored messages (see explanation above).
Regarding claim 13, Freeman discloses:
The method of claim 1, wherein the corresponding prices include (i) prices for advertising the active sponsored messages and (ii) prices related to advertised products associated with the active sponsored messages (Col 6, lines 8-26: “ For instance, a user may request airline reservations via voice-enabled device 210, and content/action identification module 235 may identify specific words used in the request, a category related to the request (e.g., travel, airlines, hotels, etc.), or other information. Furthermore, user profile module 240 may identify relevant characteristics of the user (e.g., user-specific demographics, location information, preferred airlines or hotels, etc.), as well as global user characteristics (e.g., most popular airlines). In various implementations, advertisements may be selected by assigning a score to each advertisement (e.g., based on click-through rates, relevance metrics, target audiences, etc.). As such, advertisement selection module 250 may correlate the information about the request to select advertisements stored in data repository 260, and server 230 may communicate the selected advertisements to voice-enabled device 210. Furthermore, selected advertisements may be presented according to a predetermined ordering or ranking (e.g., based on a ranking of relevance to an advertisement.” [i.e., advertisements are billed and related advertisements based on the requests are selected.]).
Regarding claim 14, Freeman discloses:
The method of claim 1, further comprising displaying an active sponsored message, of the active sponsored messages, on a search results page or a landing page reached from the search results page (Col 5, lines 55 – col 6, line 8: “For example, content/action identification module 235 may identify content of the voice-based input (e.g., words in the input), requested information (e.g., search results, a web page, music, video, graphics, or other information), requested actions (e.g., calculating a navigation route, placing a telephone call, playing a song, etc.), a category or topic related to the input (e.g., music, business, stocks, sports, navigation, movies, etc.), or other criteria to use in selecting an advertisement. Further, user profile module 240 may identify characteristics of a specific user (e.g., demographics, personal preferences, location-based information, etc.), global user profiles (e.g., demographic profiles, click-through rates, etc.), or other criteria to use in selecting an advertisement. Moreover, advertisement selection module 250 may account for where a request originates from. For example, advertisements may be selected based on a default user location (e.g., identified from a user profile), current geolocation information (e.g., identified from a navigation device), whether an affiliate or partner of server 230 initiated the request, or other criteria.”).
Regarding claim 15, Freeman discloses:
The method of claim 1, further comprising reporting samples of past natural language queries processed against the natural language trigger patterns (See claim 1 explanation).
Regarding claim 16, Freeman discloses:
The method of claim 1, wherein the natural language trigger patterns include at least one of: (i) geographic criteria and the database of past natural language queries includes categorical data regarding respective geographic focuses of the past natural language queries; (ii) geographic criteria and the database of past natural language queries includes data identifying respective geographic focuses of the past natural language queries; (iii) temporal criteria and the database of past natural language queries includes categorical data regarding respective temporal focuses of the past natural language queries; and (iv) temporal criteria and the database of past natural language queries includes data identifying respective temporal focuses of the past natural language queries (Col 5, lines 26-42: “ Advertiser 220 may access advertising server 230 via an advertiser interface 245. Advertisers 220 may upload targeted advertisements to server 230 via advertiser interface 245, and server 230 may store the advertisements in data repository 260. The advertisements may include graphically-based advertisements that include banners, images, audio, video, or any suitable combination thereof. Furthermore, the advertisements may include interactive or embedded information, such as links, metadata, or computer-executable instructions, or any suitable combination thereof. Advertisers may specify criteria for a campaign or targeting information for an advertisement (e.g., a start date, an end date, budget information, geo-targeting information, conceptual or contextual information, or any other suitable criteria), which may be used to facilitate selecting an advertisement in relation to a particular voice-based input.”).
Regarding claim 17, Freeman discloses:
The method of claim 1, further comprising receiving a plurality of geographic criteria and reporting a number of previous active messages that would have been delivered in response to the past natural language queries matched with the plurality of geographic criteria (Col 2, lines 5-20: “A user may subsequently interact with the advertisement (e.g., via a voice-based input), and action may be taken in response to the interaction. Furthermore, the interaction may be tracked to build statistical profiles of user behavior based on affinities or clusters among advertisements, user profiles, contexts, topics, semantic indicators, concepts, or other criteria.” [i.e., statistical attributes such as number of ads or messages in response to a request are tracked)]).
Regarding claim 18, Freeman discloses:
The method of claim 1, wherein the natural language trigger patterns include quality criteria and the database of past natural language queries includes data identifying respective quality focuses of the past natural language queries Col 6, lines 38-67: “Furthermore, the user's subsequent interaction with an advertisement may be tracked using tracking module 255. For example, tracking module 255 may determine whether a conversion or click-through occurs for each advertisement presented to users. Further, tracking module 255 may maintain accounting and/or billing information associated with advertisers 220. For example, advertisers 220 may specify a maximum insertion cost, a cost-per-click-through, an average insertion cost, or other criteria specifying a budget constraint for an advertisement. As such, tracking module 255 may track which advertisements are selected and/or presented, which advertisements result in a conversion or click-through, whether a click-through or conversion results in a transaction or sale, associations between advertisements and users, requests, concepts, semantic indicators, and/or other criteria. For example, tracking user interaction with advertisements may be used to build user-specific and/or global statistical profiles that map or cluster advertisements to topics, semantic indicators, contexts, concepts, etc. based on user behavior, demographics, targeting constraints, content of advertisements, content of requests, actions associated with requests, or other statistically relevant information. Accordingly, the tracking information may be used to bill or invoice advertisers 220, as well as to improve subsequent performance and relevance of advertisements selected using advertisement selection module 250. Other techniques and features of selecting and presenting advertisements based on voice-based inputs may suitably be employed, as would be apparent.” [i.e., current query as interpreted in view of the previous query and various trigger pattern or criteria e.g., semantic indicators]; Col 6, lines 21-26: “Furthermore, selected advertisements may be presented according to a predetermined ordering or ranking (e.g., based on a ranking of relevance to an advertisement.” [i.e., the quality of the advertisements (results) based on the degree of relevance to the request is considered]).
Regarding claim 19,
Claim 19 is a system claim variant of claim 1 and rejected on the same basis as claim 1 rejection.
Regarding claim 20,
Claim 20 is a CRM claim variant of claim 1 and rejected on the same basis as claim 1 rejection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the
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/Apu M Mofiz/Supervisory Patent Examiner, Art Unit 2161