DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 01/31/2024.
• Claims 1-25 have been canceled.
• Claims 51-80 have been withdrawn.
• Claims 26-50 are currently pending and have been examined.
Priority
The examiner acknowledges that the instant application is a divisional of Patent Application No. 12/018,511, filed 01/23/2008.
Election/Restriction
Applicant’s election without traverse of claims 26-50 in the reply filed 04/28/2026 is acknowledged.
Information Disclosure Statement
Information Disclosure Statements received 05/23/2024, 11/18/2024, 02/27/2025, 05/12/2025, 07/29/2025, 09/22/2025, 09/25/2025, and 04/28/2026 have been reviewed and considered.
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “message receiver…”, “content extraction engine…”, “recommender…”, “message sender…”, and “user profile database…” (claims 44-50) with the functional language “configured to,” which are not preceded by a structural modifier.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. It is noted that these elements are interpreted to be software running on hardware, consistent with [0022] of the Specification.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 26-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 26-36 are directed to a process, claims 37-43 are directed to a manufacture, and claims 44-50 are directed to a machine. Therefore, claims 26-50 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 26 as representative, claim 26 recites at least the following limitations that are believed to recite an abstract idea:
receiving a message entered belonging to a user;
applying natural language on the message to:
determine that the message is associated with an activity of the user; and
extract information associated with the activity in the message by performing operations comprising identifying, from a set of predetermined keywords, one or more keywords in which the user is interested;
storing the one or more keywords in an entry in a user profile;
receiving a query from the user, wherein the query requests a recommendation for the user;
identifying, based on the query and the one or more one keywords stored in the user profile, a recommendation for the shopping activity;
generating, based on the query, a list comprising a plurality of recommended activities, wherein the list includes the recommendation for the shopping activity; and
sending the recommendation for the shopping activity for display to the user.
The above limitations recite the concept of providing recommendations based on message keywords. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, shopping activity recommendation is a marketing and sales activity. This is further illustrated in paragraph [0004] of the Specification, describing the invention relating shopping recommendations. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the determinations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Independent claims 37 and 44 recite similar limitations as claim 26 and as such, claims 37 and 44 fall within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 26, 37, and 44 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 26, 37, and 44 recite additional elements, such as a computer-executed method, a server, an activity management system, a mobile device, natural language processing (NLP), a user profile database, a non-transitory computer-readable storage medium storing instructions which when executed by a computer cause the computer to perform a method, a computer system for recommending activities, the computer system comprising: a processor; a memory coupled to the processor; a message receiver, a content extraction engine, a recommender, and a message sender. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 26, 37, and 44 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 26, 37, and 44 merely recite a commonplace business method (i.e., providing recommendations for shopping activities based on keywords) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 26, 37, and 44 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 10, and 17 specifying that the abstract idea of providing recommendations for items in a second category based on themes is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 10, and 17 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 26, 37, and 44 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 26, 37, and 44 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 26, 37, and 44, these claims recite additional elements, such as a computer-executed method, a server, an activity management system, a mobile device, natural language processing (NLP), a user profile database, a non-transitory computer-readable storage medium storing instructions which when executed by a computer cause the computer to perform a method, a computer system for recommending activities, the computer system comprising: a processor; a memory coupled to the processor; a message receiver, a content extraction engine, a recommender, and a message sender. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 26, 37, and 44 are manual processes, e.g., receiving information, analyzing information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 26, 37, and 44 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of providing recommendations for shopping activities based on keywords is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 26, 37, and 44 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 26, 37, and 44 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 27-36, 38-43, and 45-50, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-9, 27-36, 38-43, and 45-50 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. These claims, under their broadest reasonable interpretation, further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 27-32, 34-36, 38-43, and 45-50 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claim 33 further identifies the additional element of a second mobile device. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 27-36, 38-43, and 45-50 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 26, 37, and 44, dependent claims 27-36, 38-43, and 45-50 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 26-50 are ineligible.
Claim Rejections - 35 USC § 102
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. 102 that forms the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 26-28, 30, 32, 35-39, 41, 43-46, 48, and 50 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Gross (US 20080077574 A1), hereinafter Gross.
In regards to claim 26, Gross discloses a computer-executed method for recommending a shopping activity, the method comprising (Gross: [0042]):
receiving, by a server of an activity management system, a message entered into a mobile device belonging to a user (Gross: [0015] – “sources of implicit data can include…posts, blogs, podcasts, articles, stories and the like which are…authored by the person”; [0042] and Fig. 1 – “applications that can run on large scale computing systems, including servers connected to a network…a member's computing system, which can include a portable machine or a computing machine at the users premises, such as a personal computer, a PDA, digital video recorder, receiver, etc.”; [0008] and Fig. 1 – “a particular message board or post, a particular blog or website, a particular RSS Feed, etc., as shown by the data received from sources”);
applying natural language processing (NLP) on the message to (Gross: [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts”; see also [0015]):
determine that the message is associated with an activity of the user (Gross: [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies…foods, restaurants, movies, etc., depending on the intended application”); and
extract information associated with the activity in the message by performing operations comprising identifying, from a set of predetermined keywords, one or more keywords in which the user is interested (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”);
storing, by the activity management system, the one or more keywords in an entry in a user profile database (Gross: [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0008] and Fig. 1 – “A user/item compiler and database 110 includes a schema in which ratings for individual items by individual users are identified”);
receiving, by the server of the activity management system, a query from the mobile device belonging to the user, wherein the query requests a recommendation for the user (Gross: [0025] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders”; see also [0042]);
identifying, based on the query from the mobile device and the one or more one keywords stored in the user profile database, a recommendation for the shopping activity (Gross: [0025-0028] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders…The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences…To map search queries to items for the above enhancements, the topic/concept classification database 140 can be consulted as needed. Again this may result in a number of item related entries being used to modify the search results”);
generating, based on the query from the mobile device, a list comprising a plurality of recommended activities, wherein the list includes the recommendation for the shopping activity (Gross: [0025-0026] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders, the result set typically includes a set of N distinct hits. The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0035-0038] – “the present invention can be used advantageously in a number of e-commerce applications, including:…Ads”; [0034] – “An advertising engine 151 is invoked and cooperates with a recommendation engine 115 so that relevant ads are presented with an output of the latter. As noted above such ads may also be presented as suitable for inclusion with a modified set of search results for a search engine”; [0024] – “recommendation outputs 170…which a particular person may want to consider for review in their perusings at such site”; see also [0003]; the examiner interprets recommendations for items to peruse in an e-commerce application to be shopping activities); and
sending, from the server of the activity management system to the mobile device, the recommendation for the shopping activity for display to the user (Gross: [0030] – “The ads can take any form suitable for presentation within an electronic interface”; [0025-0026] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders, the result set typically includes a set of N distinct hits. The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0035-0038] – “the present invention can be used advantageously in a number of e-commerce applications, including:…Ads”; [0034] – “An advertising engine 151 is invoked and cooperates with a recommendation engine 115 so that relevant ads are presented with an output of the latter. As noted above such ads may also be presented as suitable for inclusion with a modified set of search results for a search engine”; [0024] – “recommendation outputs 170…which a particular person may want to consider for review in their perusings at such site”; see also [0003]; [0042] and Fig. 1).
In regards to claim 27, Gross discloses the method of claim 26. Gross further discloses wherein identifying the one or more keywords comprises searching the message for one or more predetermined keywords or text patterns based on the application of the NLP on the message (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”).
In regards to claim 28, Gross discloses the method of claim 26. Gross further discloses identifying an indication of willingness of the user to participate in the one or more keywords comprises: determining, based on the application of the NLP on the message, that the shopping activity of the identified one or more keywords has occurred in the past, is occurring at the present time, or is going to occur at a future time; and incorporating a relative positive or negative willingness of the user to participate in the identified one or more keywords into the indication of willingness (Gross: [0035] – “the present invention can be used advantageously in a number of e-commerce applications, including: … Ads: the invention can be employed to predict/recommend other Ads”; [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc.”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”).
In regards to claim 30, Gross discloses the method of claim 28. Gross further discloses determining, based on the application of the NLP on the message, a willingness of the user to participate in the identified one or more keywords from the relative positive willingness in the indication of willingness; and promoting the activity type for generating the recommendation (Gross: [0035] – “the present invention can be used advantageously in a number of e-commerce applications, including: … Ads: the invention can be employed to predict/recommend other Ads”; [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc.”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”).
In regards to claim 32, Gross discloses the method of claim 26. Gross further discloses causing the entry to expire in the use profile database based on the activity time in the entry and a set of pre-defined expiration rules (Gross: [0020] – “The weightings again can be based on system performance requirements, objectives, and other well-known parameters. Thus with all other things being equal, older designations may receive higher scores than more recent designations, so long as the former are still designated as active in the user's day to day experience. So for example, after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, ‘stale’ endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings.”).
In regards to claim 35, Gross discloses the method of claim 26. Gross further discloses receiving a series of messages from the mobile device (Gross: [0015] – “sources of implicit data can include…posts, blogs, podcasts, articles, stories and the like which are…authored by the person”; [0042] and Fig. 1 – “applications that can run on large scale computing systems, including servers connected to a network…a member's computing system, which can include a portable machine or a computing machine at the users premises, such as a personal computer, a PDA, digital video recorder, receiver, etc.”; [0008] and Fig. 1 – “a particular message board or post, a particular blog or website, a particular RSS Feed, etc., as shown by the data received from sources”);
revising a model of plans for the user based on the series of messages based on the application of the NLP on the message (Gross: [0020-0021] – “Some designations may be rated or scaled higher than others, depending on their recency, relative use, etc. The weightings again can be based on system performance requirements, objectives, and other well-known parameters. Thus with all other things being equal, older designations may receive higher scores than more recent designations, so long as the former are still designated as active in the user's day to day experience. So for example, after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, "stale" endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings. Quantitatively, the ratings therefore can be a simple mathematical relationship of usage frequency and age of the endorsement. The ratings may also be affected by the context in which they are generated, or in which the recommendation is solicited, as noted in the Tuzhilin materials above. The ratings can be updated at any regular desired interval of time, such as on a daily, weekly, or other convenient basis. For example, one approach may use the product of (frequency of use * age of the endorsement), with some normalization applied. This will result in an increase in score for older and more frequently used items”);
reducing the probability of interest for the activity and increasing a second probability of interest for a second activity, based on the revised model; and modifying the recommendation to incorporate the second activity in response to determining that the second probability of interest is above a predetermined threshold (Gross: [0024] – “recommendation outputs 170 on specific authors, topics, posts, etc. which a particular person may want to consider for review in their perusings at such site; this data can be presented to a user in the form of…top x lists”; [0020-0021] – “Some designations may be rated or scaled higher than others, depending on their recency, relative use, etc. The weightings again can be based on system performance requirements, objectives, and other well-known parameters. Thus with all other things being equal, older designations may receive higher scores than more recent designations, so long as the former are still designated as active in the user's day to day experience. So for example, after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, "stale" endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings. Quantitatively, the ratings therefore can be a simple mathematical relationship of usage frequency and age of the endorsement. The ratings may also be affected by the context in which they are generated, or in which the recommendation is solicited, as noted in the Tuzhilin materials above. The ratings can be updated at any regular desired interval of time, such as on a daily, weekly, or other convenient basis. For example, one approach may use the product of (frequency of use * age of the endorsement), with some normalization applied. This will result in an increase in score for older and more frequently used items”).
In regards to claim 36, Gross discloses the method of claim 26. Gross further discloses recording an uncertainty variable that indicates a degree to which the activity management system is uncertain of a value of an activity time (Gross: [0020-0021] – “Some designations may be rated or scaled higher than others, depending on their recency, relative use, etc. The weightings again can be based on system performance requirements, objectives, and other well-known parameters. Thus with all other things being equal, older designations may receive higher scores than more recent designations, so long as the former are still designated as active in the user's day to day experience. So for example, after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, "stale" endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings. Quantitatively, the ratings therefore can be a simple mathematical relationship of usage frequency and age of the endorsement. The ratings may also be affected by the context in which they are generated, or in which the recommendation is solicited, as noted in the Tuzhilin materials above. The ratings can be updated at any regular desired interval of time, such as on a daily, weekly, or other convenient basis. For example, one approach may use the product of (frequency of use * age of the endorsement), with some normalization applied. This will result in an increase in score for older and more frequently used items”PDA, digital video recorder, receiver, etc.”).
In regards to claim 37, claim 37 is directed to a medium. Claim 37 recites limitations that are substantially parallel in nature to those addressed above for claim 26 which is directed towards a method. The method of Gross discloses the limitations of claim 26 as noted above. Gross further discloses a non-transitory computer-readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for recommending activities, the method comprising (Gross: [0022]; [0042-0043]). Claim 37 is therefore rejected for the reasons set forth above in claim 26 and in this paragraph.
In regards to claims 38-39, 41, and 43, all the limitations in medium claims 38-39, 41, and 43 are closely parallel to the limitations of method claims 27-28, 30, and 32 analyzed above and rejected on the same bases.
In regards to claim 44, Gross discloses a computer system for recommending activities, the computer system comprising: a processor; a memory coupled to the processor (Gross: [0026]; [0042-0043]):
a message receiver configured to receive a message entered into a mobile device belonging to a user (Gross: [0015] – “sources of implicit data can include…posts, blogs, podcasts, articles, stories and the like which are…authored by the person”; [0042] and Fig. 1 – “applications that can run on large scale computing systems, including servers connected to a network…a member's computing system, which can include a portable machine or a computing machine at the users premises, such as a personal computer, a PDA, digital video recorder, receiver, etc.”; [0008] and Fig. 1 – “a particular message board or post, a particular blog or website, a particular RSS Feed, etc., as shown by the data received from sources”);
a content extraction engine configured to applying natural language processing (NLP) on the message to (Gross: [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts”; see also [0015]):
determine that the message is associated with an activity of the user (Gross: [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies…foods, restaurants, movies, etc., depending on the intended application”); and
extract information associated with the activity in the message by performing operations comprising identifying, from a set of predetermined keywords, one or more keywords in which the user is interested (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”);
wherein the content extraction engine is further configured to store the one or more one keywords in an entry in a user profile database (Gross: [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0008] and Fig. 1 – “A user/item compiler and database 110 includes a schema in which ratings for individual items by individual users are identified”);
a recommender configured to: receive a query from the mobile device belonging to the user, wherein the query requests a recommendation for the user (Gross: [0025] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders”; see also [0042]);
generating, based on the query from the mobile device, a list comprising a plurality of recommended activities, wherein the list includes the recommendation for the shopping activity (Gross: [0025-0026] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders, the result set typically includes a set of N distinct hits. The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0035-0038] – “the present invention can be used advantageously in a number of e-commerce applications, including:…Ads”; [0034] – “An advertising engine 151 is invoked and cooperates with a recommendation engine 115 so that relevant ads are presented with an output of the latter. As noted above such ads may also be presented as suitable for inclusion with a modified set of search results for a search engine”; [0024] – “recommendation outputs 170…which a particular person may want to consider for review in their perusings at such site”; see also [0003]; the examiner interprets recommendations for items to peruse in an e-commerce application to be shopping activities); and
identify, based on the query from the mobile device and the one or more one keywords stored in the user profile database, a recommendation for the shopping activity (Gross: [0025-0028] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders…The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences…To map search queries to items for the above enhancements, the topic/concept classification database 140 can be consulted as needed. Again this may result in a number of item related entries being used to modify the search results”); and
a message sender configured to send the recommendation for the shopping activity for display to the user (Gross: [0030] – “The ads can take any form suitable for presentation within an electronic interface”; [0025-0026] – “an output to adjust, adapt or personalize search engine (not shown) results presented to a user in response to a query on a specific subject. For example if a user performed a search at a site relating to video recorders, the result set typically includes a set of N distinct hits. The information from the recommendation engine 115 may be used to tailor the results more particularly to the user…the user may have expressed a favorable interest, endorsement or inclination towards Sony. This data in turn could be used to optionally modify, bias or alter the N distinct hits to accommodate the prior experiences”; [0035-0038] – “the present invention can be used advantageously in a number of e-commerce applications, including:…Ads”; [0034] – “An advertising engine 151 is invoked and cooperates with a recommendation engine 115 so that relevant ads are presented with an output of the latter. As noted above such ads may also be presented as suitable for inclusion with a modified set of search results for a search engine”; [0024] – “recommendation outputs 170…which a particular person may want to consider for review in their perusings at such site”; see also [0003]; [0042] and Fig. 1).
In regards to claim 45, all the limitations in system claim 45 are closely parallel to the limitations of method claim 27 analyzed above and rejected on the same bases.
In regards to claim 46, Gross discloses the system of claim 44. Gross further discloses wherein the content extraction engine is configured to identify an indication of willingness of the user to participate in the one or more keywords comprising: determining, based on the application of the NLP on the message, that an activity of the identified one or more keywords has occurred in the past, is occurring at the present time, or is going to occur at a future time; and incorporating a relative positive or negative willingness of the user to participate in the identified one or more keywords into the indication of willingness (Gross: [0035] – “the present invention can be used advantageously in a number of e-commerce applications, including: … Ads: the invention can be employed to predict/recommend other Ads”; [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc.”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”).
In regards to claim 48, Gross discloses the method of claim 46. Gross further discloses wherein the recommender is further configured to: determine, based on the application of the NLP on the message, a willingness to participate in the identified one or more keywords from the relative positive willingness in the indication of willingness; and promote the one or more keywords for generating the recommendation (Gross: [0035] – “the present invention can be used advantageously in a number of e-commerce applications, including: … Ads: the invention can be employed to predict/recommend other Ads”; [0016] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc.”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”).
In regards to claim 50, Gross discloses the system of claim 44. Gross further discloses wherein the user profile database is configured to cause the entry to expire in the user profile database based on the temporal information in the entry and a set of pre-defined expiration rules (Gross: [0020] – “The weightings again can be based on system performance requirements, objectives, and other well-known parameters. Thus with all other things being equal, older designations may receive higher scores than more recent designations, so long as the former are still designated as active in the user's day to day experience. So for example, after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, ‘stale’ endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
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 29, 40, and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Gross, in view of Chakrabarti et al. (US 20080294617 A1), hereinafter Chakrabarti.
In regards to claim 29, Gross discloses the method of claim 28. Gross further discloses the identified one or more keywords (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”).
Yet Gross does not explicitly disclose determining, based on the application of the NLP on the message, a lack of willingness of the user to participate in the identified from the relative negative willingness in the indication of willingness; and demoting the activity type for generating the recommendation.
However, Chakrabarti teaches a similar recommendation system (Chakrabarti: [abstract]), including
determining, based on the application of the NLP on the message, a lack of willingness of the user to participate in the identified from the relative negative willingness in the indication of willingness; and demoting the activity type for generating the recommendation (Chakrabarti: [0059] – “if users who dislike the iPod Nano.TM. also tend to dislike the iPod Shuffle.TM. digital media player (e.g., as indicated by a similarity table), giving a low rating to the iPod Nano.TM. may be a reason to include the iPod Shuffle.TM. in the negative feedback category. Many different types of reasons may be considered for each category, such as adding items to a shopping cart or wish list, viewing items, rating items, tagging items, deselecting items or categories in an existing recommendations list, deselecting items in a shopping cart or wish list, items that the customer has returned, and the like”; [0064] – “sums the adjusted category scores and the negative feedback score for each item to achieve an overall adjusted score for each item. In an embodiment, the sum of the adjusted category scores is indicative of the number of reasons for a particular product, adjusted with probabilistic noise. The negative feedback score in one embodiment is negative-valued, or rather is subtracted from the sum of the adjusted category scores. The negative feedback score therefore reduces the strength of the overall adjusted score, taking into account that even though there may be reasons for recommending an item, there may be reasons for not recommending an item. In various embodiments, the negative feedback reasons (and hence the negative feedback score) may outweigh the reasons for recommending an item (and hence the sum of the adjusted category scores), and vice versa”; [0021] – “recommends catalog items to users of an e-commerce web site”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the lack of willingness of Chakrabarti in the method of Gross because Gross already discloses a willingness and Chakrabarti is merely demonstrating a lack of willingness. Additionally, it would have been obvious to have included determining, based on the application of the NLP on the message, a lack of willingness of the user to participate in the identified from the relative negative willingness in the indication of willingness; and demoting the activity type for generating the recommendation as taught by Chakrabarti because negative feedback is well-known and the use of it in a recommendation system would have taken into account reasons for not recommending an item (Chakrabarti: [0064]).
In regards to claim 40, all the limitations in medium claim 40 are closely parallel to the limitations of method claim 29 analyzed above and rejected on the same bases.
In regards to claim 47, Gross discloses the system of claim 46. Gross further discloses the identified one or more keywords (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”).
Yet Gross does not explicitly disclose determine, based on the application of the NLP on the message, a lack of willingness to participate in the identified from the relative negative willingness in the indication of willingness; and demote for generating the recommendation.
However, Chakrabarti teaches a similar recommendation system (Chakrabarti: [abstract]), including
determine, based on the application of the NLP on the message, a lack of willingness to participate in the identified from the relative negative willingness in the indication of willingness; and demote for generating the recommendation (Chakrabarti: [0059] – “if users who dislike the iPod Nano.TM. also tend to dislike the iPod Shuffle.TM. digital media player (e.g., as indicated by a similarity table), giving a low rating to the iPod Nano.TM. may be a reason to include the iPod Shuffle.TM. in the negative feedback category. Many different types of reasons may be considered for each category, such as adding items to a shopping cart or wish list, viewing items, rating items, tagging items, deselecting items or categories in an existing recommendations list, deselecting items in a shopping cart or wish list, items that the customer has returned, and the like”; [0064] – “sums the adjusted category scores and the negative feedback score for each item to achieve an overall adjusted score for each item. In an embodiment, the sum of the adjusted category scores is indicative of the number of reasons for a particular product, adjusted with probabilistic noise. The negative feedback score in one embodiment is negative-valued, or rather is subtracted from the sum of the adjusted category scores. The negative feedback score therefore reduces the strength of the overall adjusted score, taking into account that even though there may be reasons for recommending an item, there may be reasons for not recommending an item. In various embodiments, the negative feedback reasons (and hence the negative feedback score) may outweigh the reasons for recommending an item (and hence the sum of the adjusted category scores), and vice versa”; [0021] – “recommends catalog items to users of an e-commerce web site”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the lack of willingness of Chakrabarti in the method of Gross because Gross already discloses a willingness and Chakrabarti is merely demonstrating a lack of willingness. Additionally, it would have been obvious to have included determine, based on the application of the NLP on the message, a lack of willingness to participate in the identified from the relative negative willingness in the indication of willingness; and demote for generating the recommendation as taught by Chakrabarti because negative feedback is well-known and the use of it in a recommendation system would have taken into account reasons for not recommending an item (Chakrabarti: [0064]).
Claims 31, 42, and 49 are rejected under 35 U.S.C. 103 as being unpatentable over Gross, in view of Natsume et al. (US 20040199631 A1), hereinafter Natsume.
In regards to claim 31, Gross discloses the method of claim 26. Gross further discloses converting the identified one or more keywords, an indication of willingness to a canonical form that corresponds to the canonical form of the activity time; and storing, in the entry, the converted information in association with the activity time in the canonical form (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”; [0020-0021] – “the ratings in the above types of applications can be based on any convenient scale depending on the source of the data and the intended use. Some designations may be rated or scaled higher than others, depending on their recency, relative use, etc…after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, ‘stale’ endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings. Quantitatively, the ratings therefore can be a simple mathematical relationship of usage frequency and age of the endorsement…can be updated at any regular desired interval of time, such as on a daily, weekly, or other convenient basis”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”; the examiner interprets the rating to be a canonical form).
Yet Gross does not explicitly disclose converting location information to a canonical form.
However, Natsume teaches a similar recommendation system (Natsume: [0100]), including
converting location information to a canonical form (Natsume: [0075] – “The location of each terminal 101 is indexed with respect to the user ID. The location may be in longitude/latitude format, for example with an accuracy of plus or minus three feet or it may be two dimensional grid coordinates for a grid encompassing only the convention center. The conversion from GPS format, for example, to grid format may be handled by the location detector 202 or the location analyzing unit 205.”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the conversion of location of Natsume in the method of Gross because Gross already discloses converting information and Natsume is merely demonstrating what type of information is converted. Additionally, it would have been obvious to have included converting location information to a canonical form as taught by Natsume because converting location is well-known and the use of it in a recommendation system would have improved user efficiency (Natsume: [0001]).
In regards to claim 42, Gross discloses the medium of claim 37. Gross further discloses converting the identified activity type, an indication of willingness to a canonical form that corresponds to the canonical form of the activity time; and storing, in the entry, the converted information in association with the activity time in the canonical form (Gross: [0016-0018] – “Regardless of the source of the implicit data, the invention uses a natural language classifier/mapper module 130 to translate the raw data into one or more predefined concepts--representing the items in this instance--with reference to a topic/concept classification database 140. For example, a topic/concept may include such items as personal interests/hobbies, music bands, company names, stock symbols, brand names, foods, restaurants, movies, etc., depending on the intended application…items for the recommender database 140 can be mapped onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an N:1 basis. In other words, if an item in the recommender database 140 is designated with the label ‘Sony,’ there may be an identical entry in the topic/concept classification with such term. Semantic equivalents may also be used where appropriate. Similarly a single item ‘Sony’ may be associated with multiple topics/concepts, such as a reference to a particular product or service offered by such company (for example Vaio) a stock symbol for Sony, a reference to a key employee/officer of Sony…can recognize words/phrases within a search page, ad, post, etc., and correlate them to one or more topics/concepts. Thus if a document contains the word Dell, the NL classifier can be taught to recognize such word as corresponding to such concepts as a particular brand name, a computer company, and the like”; [0020-0021] – “the ratings in the above types of applications can be based on any convenient scale depending on the source of the data and the intended use. Some designations may be rated or scaled higher than others, depending on their recency, relative use, etc…after a predefined period, the first designated favorite author for a particular individual may receive a boosting to their rating if such author is still being read by the individual. Similarly, ‘stale’ endorsements may be reduced over time if they are not frequently used. The degree of activity may be benchmarked to cause a desired result (i.e., endorsements receiving no activity within N days may receive a maximum attenuation factor) monitored to attenuate the ratings. Quantitatively, the ratings therefore can be a simple mathematical relationship of usage frequency and age of the endorsement…can be updated at any regular desired interval of time, such as on a daily, weekly, or other convenient basis”; [0026] – “the user has a prior profile which can be determined and exploited from item/user database 110, so that the search results are modified accordingly. As an example, the user may have expressed a favorable interest, endorsement or inclination towards Sony”; the examiner interprets the rating to be a canonical form).
Yet Gross does not explicitly disclose converting location information to a canonical form.
However, Natsume teaches a similar recommendation system (Natsume: [0100]), including
converting location information to a canonical form (Natsume: [0075] – “The location of each terminal 101 is indexed with respect to the user ID. The location may be in longitude/latitude format, for example with an accuracy of plus or minus three feet or it may be two dimensional grid coordinates for a grid encompassing only the convention center. The conversion from GPS format, for example, to grid format may be handled by the location detector 202 or the location analyzing unit 205.”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the conversion of location of Natsume in the method of Gross because Gross already discloses converting information and Natsume is merely demonstrating what type of information is converted. Additionally, it would have been obvious to have included converting location information to a canonical form as taught by Natsume because converting location is well-known and the use of it in a recommendation system would have improved user efficiency (Natsume: [0001]).
In regards to claim 49, all the limitations in system claim 49 are closely parallel to the limitations of method claim 31 analyzed above and rejected on the same bases.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Gross, in view of Seagraves et al. (US 5652880 A), hereinafter Seagraves.
In regards to claim 33, Gross discloses the method of claim 26. Gross further discloses receiving a second message from a second mobile device belonging to a second user; and the second message (Gross: [0009] – “As an example of an explicit data source 120, in a typical message board application such as operated by Yahoo! (under the moniker Yahoo Message Boards) or the Motley Fool, users are permitted to designate "favorite" authors, and/or to "recommend" posts written by particular individuals. In accordance with the present invention these designations of favorite authors and recommendations for posts are monitored, tabulated, and then translated into ratings for such authors/posts and compiled in a database under control of an item/user compiler module. The ratings will be a function of the environment in which the information is collected of course, so that a recommendation by person A for a post written by person B can be scored as a simple 1 or 0”; [0042] – “a portion of the hardware and software of FIG. 1 will be contained locally to a member's computing system, which can include a portable machine or a computing machine at the users premises, such as a personal computer, a PDA, digital video recorder, receiver, etc.”).
Yet Gross does not explicitly disclose assigning a default future tense to the information.
However, Seagraves teaches a similar method of objects of interest (Seagraves: [abstract]), including
assigning a default future tense to the information (Seagraves: Col. 14, Ln. 32-37 – “The method of this invention automatically transforms to past or future tense, as required, the links that have expired, have yet to commence, or are scheduled to commence again based on user-defined temporal information stored in the codified links”; Col. 13, Ln. 1-14 – “Steps 278-279 transform to future tense each entry in O-TSB 16 that represents one or more objects whose linkage to the object of interest has yet to commence based upon the temporal information about the linkage, or whose linkage represents a discrete event that is to occur at some future point in time (for non-textual or non-English language implementations, modify the heading with the appropriate symbols, graphics or sounds to cause the same effect). For example, an entry of "Works at" is transformed to "Will work at". This transformation is done by rules incorporated into the software or that were learned by the software by way of operation of the software and/or taught or overridden by humans, machines or other software during the course of operation of the software”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the future tense of Seagraves in the method of Gross because Gross already discloses tenses and Seagraves is merely demonstrating what type of tense. Additionally, it would have been obvious to have included assigning a default future tense to the information as taught by Seagraves because default tenses are well-known and the use of it in a recommendation system would have identified events yet to commence (Seagraves: Col. 14, Ln. 32-37).
Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Gross, in view of Malik (US 20040078445 A1), hereinafter Malik.
In regards to claim 34, Gross discloses the method of claim 26. Gross further discloses collecting statistics from users to determine (Gross: [0039-0040] – “monitoring group behavior and treating any such collection of individuals as a single entity for item/rating purposes. This aggregation can be used to recommend higher order logical groupings of individuals, particularly in social networking applications, to enhance the user experience…individuals are automatically assigned to specific clusters based on a determination of a significant number of common interests/tastes. In the present invention the individual self-selected groupings within social networks can be broken down and treated as clusters so that comparisons can be made against particular user's interests, predilections, etc. Based on such comparisons groups can opt to extend invitations to new members which they would otherwise not notice or come into contact with. Conversely new members can be given some immediate insight into potentially fruitful social groups.”).
Yet Gross does not explicitly disclose a poll of users and determining a default time for an activity.
However, Malik teaches a similar method of messaging (Malik: [abstract]), including
a poll of users (Malik: [0129] – “the IM chat window 1100 may include another icon 1195 (also referred to herein as "pre-invite icon" 1195) that configures the message-handling IM client 1100 to poll for the presence of the technician. That pre-invite icon 1195 may be customized by the support staff to initiate polling for the presence of any individual that the support staff wishes to include in the IM chat session”) and
determining a default time for an activity (Malik: [0073] – “The timing logic 315 tracks the elapsed time from when Juliet's IM message is displayed for Romeo. In this regard, for some embodiments, the timing logic 315 also serves as a trigger for any auto-replying, auto-forwarding, or auto-transferring of Juliet's IM messages. As is known in the art, the default time for triggering such events may be set by the user or may be hard-coded into the message-handling IM client 115a”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the poll and default time of Malik in the method of Gross because Gross already discloses times and user activity and Malik is merely demonstrating what type of user activity and time. Additionally, it would have been obvious to have included a poll of users and determining a default time for an activity as taught by Malik because polls and default times are well-known and the use of it in a recommendation system would have improved messaging (Malik: [0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference U teaches a recommender system. Customer preferences are analyzed in order to provide recommendations. Similar customer activity may be used to provide the recommendation.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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