INTERNATIONAL BUSINESS MACHINES CORPORATION

Patent Trials and Appeals BoardMar 26, 2021

2019005805 (P.T.A.B. Mar. 26, 2021)

UNITED STATES PATENT AND TRADEMARK OFFICE UNITED STATES DEPARTMENT OF COMMERCE United States Patent and Trademark Office Address: COMMISSIONER FOR PATENTS P.O. Box 1450 Alexandria, Virginia 22313-1450 www.uspto.gov APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO. 13/799,156 03/13/2013 Jilin Chen ARC920120107US1 4911 48915 7590 03/26/2021 CANTOR COLBURN LLP-IBM YORKTOWN 20 Church Street 22nd Floor Hartford, CT 06103 EXAMINER JAHNIGE, CAROLINE H ART UNIT PAPER NUMBER 2451 NOTIFICATION DATE DELIVERY MODE 03/26/2021 ELECTRONIC Please find below and/or attached an Office communication concerning this application or proceeding. The time period for reply, if any, is set in the attached communication. Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the following e-mail address(es): usptopatentmail@cantorcolburn.com PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE ________________ BEFORE THE PATENT TRIAL AND APPEAL BOARD ________________ Ex parte JILIN CHEN, KYUMIN LEE, JALAL U. MAHMUD, JEFFREY W. NICHOLS, and MICHELLE X. ZHOU ________________ Appeal 2019-005805 Application 13/799,156 Technology Center 2400 ________________ Before JASON V. MORGAN, JAMES B. ARPIN, and MICHAEL J. ENGLE, Administrative Patent Judges. MORGAN, Administrative Patent Judge. DECISION ON APPEAL STATEMENT OF THE CASE Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the Examiner’s decision to reject claims 1, 3–10, 29, and 30, all the pending claims. Claims 2, 11–28, 31, and 32 are canceled. Appeal Br. 12, 15. We have jurisdiction under 35 U.S.C. § 6(b). We affirm. 1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant identifies the real party in interest as International Business Machines Corporation. Appeal Br. 3. Appeal 2019-005805 Application 13/799,156 2 SUMMARY OF THE DISCLOSURE Appellant’s claimed subject matter relates to: identifying users for initiating information spreading in social network. . . . [I]nformation for one or more users of a social network is collected and one or more features for each of the one or more users based on the collected information is computed. The one or more features are compared with a statistical model and calculating a probability that each of the one or more users will spread a message received from outside their social network based on the comparison. Abstract. REPRESENTATIVE CLAIM (disputed limitations emphasized and bracketing added) 1. A method for identifying users for initiating information spreading in a social network, the method comprising: collecting first information for a first group of one or more users of the social network; computing a respective set of features for each user in the first group of one or more users based on the collected first information, each respective set of features comprising a set of relatedness features including a topic-based relatedness feature, a location-based relatedness feature, and a time-based relatedness feature, wherein the set of relatedness features quantify a degree of relatedness between a user and information that is spread by the user; requesting that each user in the first group of one or more users spread a first message received from outside a respective group of established friends and followers associated with the user in the social network; monitoring the social network to identify a subset of the first group of one or more users that spread the first message; building a statistical model based at least in part on at least a subset of features of the respective set of features associated with each user in the subset, wherein the at least a subset of Appeal 2019-005805 Application 13/799,156 3 features comprises the set of relatedness features and a number of message shares per status message; collecting second information for a second group of one or more users of the social network; computing a respective set of features for each user in the second group of one or more users based on the collected second information, each respective set of features comprising the set of relatedness features; calculating, using the statistical model, a respective probability that each user in the second group of one or more users spreads a second message at least in part by comparing the set of relatedness features for each user in the second group of one or more users to the statistical model and to information contained in the second message; determining that the respective probability that a first user in the second group of one or more users spreads the second message meets or exceeds a threshold value; requesting that the first user spread the second message; [1] calculating a respective probability that each user in the second group of one or more users spreads the second message using at least one word having the positive connotation[2] by comparing the respective set of one or more features for each user in the second group of one or more users to the statistical model; [2] determining that the respective probability that a second user in the second group of one or more users spreads the second message using the at least one word having the positive connotation meets or exceeds the threshold value; and requesting that the second user spread the second message. 2 There is no antecedent basis for the limitation of “the positive connotation.” In the event of further prosecution, Appellant and the Examiner should ensure that this lack of antecedent basis is remedied. Appeal 2019-005805 Application 13/799,156 4 REFERENCES The Examiner relies on the following references: Name Reference Date Argaiz US 2006/0143081 A1 June 29, 2006 Aven et al. (“Aven”) US 2009/0171748 A1 July 2, 2009 Berkman et al. (“Berkman”) US 2011/0066613 A1 Mar. 17, 2011 Peng et al. (“Peng”) US 8,312,056 B1 Nov. 13, 2012 Lange et al. (“Lange”) US 2014/0189536 A1 July 3, 2014 REJECTIONS The Examiner rejects claims 1, 5–10, 29, and 30 under 35 U.S.C. § 103(a) as obvious over the combined teachings of Argaiz, Berkman, and Aven. Appeal Br. 3–16. The Examiner rejects claim 3 under 35 U.S.C. § 103(a) as obvious over the combined teachings of Argaiz, Berkman, Aven, and Peng. Id. at 17. The Examiner rejects claim 4 under 35 U.S.C. § 103(a) as obvious over the combined teachings of Argaiz, Berkman, Aven, Peng, and Lange. Id. at 18–19 ADOPTION OF EXAMINER’S FINDINGS AND CONCLUSIONS We agree with and adopt as our own the Examiner’s findings as set forth in the Answer and in the Final Action from which this appeal was taken, and we concur with the Examiner’s conclusions. We have considered Appellant’s arguments, but we do not find them persuasive of error. We provide the following explanation for emphasis. ANALYSIS In rejecting claim 1 as obvious, the Examiner finds that the Argaiz data mining techniques, which include calculating a probabilistic network Appeal 2019-005805 Application 13/799,156 5 value index (NVI), teach or suggest recitations [1], “calculating a respective probability that each user in the second group of one or more users spreads the second message using at least one word having the positive connotation by comparing the respective set of one or more features for each user in the second group of one or more users to the statistical model,” and [2], “determining that the respective probability that a second user in the second group of one or more users spreads the second message using the at least one word having the positive connotation meets or exceeds the threshold value.” Final Act. 5 (citing Argaiz ¶¶ 39, 50, 65, 67); Ans. 3–4 (further citing Argaiz ¶¶ 28, 51). Appellant contends the Examiner errs because Argaiz merely teaches “determining a customer’s influence on a brand or market that occurs due to (or by virtue of) their positive messaging.” Appeal Br. 8. In particular, Appellant argues “generating a customer score (customer network value) based on the aggregate goodwill provided by the user’s platform (reviews, positive messages, etc.) does not require . . . determining a probability that specifically measures the likelihood that a user spreads a particular message having at least one word having a positive connotation.” Id. at 9. That is, although Appellant acknowledges “the quantified monetary value is related to the customer’s positive messaging,” Appellant argues this value is “a function of the size and structure of the customer’s social media network” and that Argaiz does not determine “a probability that specifically measures the likelihood that the other users will spread a particular message.” Reply Br. 2. Thus, the premise of Appellant’s contention that Argaiz fails to teach or suggest recitations [1] and [2] is that Argaiz fails to teach or suggest the shared recitation of a “respective probability that each user in the second Appeal 2019-005805 Application 13/799,156 6 group of one or more users spreads the second message using the at least one word having the positive connotation.” Appellant’s arguments are not persuasive because Argaiz specifically teaches “assigning a probabilistic Customer Network Value Index to individuals whose network behavior has not been explicitly detected” (Argaiz ¶ 51), which the Examiner finds teaches or suggests the claimed respective probability that each user (i.e., the individuals) in the second group (whose network behavior has not been explicitly detected) spreads the second message using at least one word having the positive connotation (Ans. 3–4). Argaiz teaches that Customer Network Value reflects the “customer’s influence on the market on behalf of a particular product or brand by virtue of the positive message disseminated through her relationship network.” Argaiz ¶ 28 (emphasis added). For example, “a customer’s impact on the market can be achieved by . . . direct recommendation of the product to other people.” Id. That is, the information used to measure the probabilistic Customer Network Value includes the likelihood of “pass along message activity,” including direct recommendation of the product to other people. Id. ¶¶ 28, 67, 28. Given that the network behavior (message pass-along activity) of the users identified in Argaiz has not been explicitly detected, calculating a probabilistic Customer Network Value for these users suggests calculating their probable network behavior. Thus, Argaiz suggests assigning a probabilistic Customer Network Value Index using a calculated probability that a user spreads a direct recommendation of the product to other people (i.e., a message using at least one word having a positive connotation). Appeal 2019-005805 Application 13/799,156 7 Therefore, we agree with the Examiner that Argaiz suggests disputed recitations [1] and [2]. Accordingly, we sustain the Examiner’s obviousness rejection of claim 1, and claims 3–10, 29, and 30, which Appellant does not argue separately. Appeal Br. 9. CONCLUSION Claim(s) Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed 1, 5–10, 29, 30 103(a) Argaiz, Berkman, Aven 1, 5–10, 29, 30 3 103(a) Argaiz, Berkman, Aven, Peng 3 4 103(a) Argaiz, Berkman, Aven, Peng, Lange 4 Overall Outcome 1, 3–10, 29, 30 TIME PERIOD FOR RESPONSE No time period for taking subsequent action in connection with this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R. § 1.136(a)(1)(iv). AFFIRMED