Response to Amendment
This communication is in response to the amendments and arguments filed on 1/5/2026 and 10/24/2025. Claims 1-2, 4-10, and 12-20 are pending.
Claim Rejections - 35 USC § 103
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) 1-2, 4, 6-9, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haneda et al (U.S. Pub. No. 2019/0238139, cited in the Prior Art of Record section in the Non-Final Office Action dated 4/25/2025, hereinafter “Haneda”) in view of Uehara (U.S. Pub. No. 2020/0274512), and Siekmeier et al (U.S. Pub. No. 2006/0089824, hereinafter “Siekmeier”).
Regarding Claim 1, Haneda teaches a clock circuit apparatus comprising: a resonator configured to output an uncompensated clock signal (Figs. 4-8, resonator 10); a temperature sensor assembly operably coupled to the resonator, the temperature sensor assembly being configured to measure temperature and provide temperature measurements (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4 in the integrated circuit device 20 which includes a resonator 10); and compensation circuitry (digital signal processing circuit 23) configured to: receive the uncompensated clock signal (paragraphs [0025]-[0026]); receive the temperature measurements and generate temperature data based on the temperature measurements (paragraphs [0025]-[0026], first and second temperature detection data generated based on first and second temperature detection voltages); apply a frequency predicting neural network to the temperature data to determine a frequency correction (Fig. 22, S204; paragraphs [0146]-[0151], frequency control data); apply the uncompensated clock signal and the frequency correction to generate a stabilized clock output signal (paragraphs [0025]-[0026], temperature compensation process to correct temperature characteristics of the resonator); and output the stabilized clock output signal (paragraphs [0025]-[0026]), wherein the frequency predicting neural network is a multi-layer artificial neural network (Fig. 18) that receives the temperature data defined in multiple dimensions, the multiple dimensions include location and time (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4, each sensor determines temperature data over time and has an associated location in the integrated circuit device 20 that includes a resonator 10 that allows determination of temperature gradients across resonator device, see paragraph [0067]), and the frequency predicting neural network performs a weighted sum of the inputs to determine a resultant (paragraphs [0131]-[0136], weights between neurons, Equation 1), and the frequency predicting neural network applies the resultant to a non-linear activation function to determine a predicted frequency for the resonator for use in generating the frequency correction (paragraph [0137]).
Haneda does not specifically teach that the uncompensated clock signal and the frequency correction are applied to a synthesizer to generate a stabilized clock output signal. However, Uehara teaches use of a direct digital synthesizer 68 to generate a clock signal CK2 (equated to the stabilized clock output signal) having a frequency set by the frequency setting signal FSD (equated to the frequency correction) using the clock signal CK1 (equated to the uncompensated clock signal) as a reference clock signal (paragraph [0088]). It would have been obvious to one skilled in the art at the time of the invention to include the DDS of Uehara in the system of Haneda, because a synthesizer can generate a clock signal have any frequency based on a reference clock signal and an integration setting value (see Uehara, paragraph [0090]).
Haneda does not specifically teach that the input data is received as a vector of inputs. However, Siekmeier teaches, in paragraph [0073], that a neural model may sum a weighted vector of inputs to produce an output. It would have been obvious to one skilled in the art before the effective filing date of the invention to use a neural model including a vector of inputs such as is described in Siekmeier as the neural network of Haneda, because such a neural model is simple (see Siekmeier, paragraph [0073]).
Regarding Claim 2, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4).
Regarding Claim 4, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda does not specifically teach wherein the synthesizer is a direct digital synthesizer configured to receive the uncompensated clock signal and the frequency correction and generate a stabilized clock output signal based on the uncompensated clock signal and the frequency correction. However, Uehara teaches use of a direct digital synthesizer 68 to generate a clock signal CK2 (equated to the stabilized clock output signal) having a frequency set by the frequency setting signal FSD (equated to the frequency correction) using the clock signal CK1 (equated to the uncompensated clock signal) as a reference clock signal (paragraph [0088]). It would have been obvious to one skilled in the art at the time of the invention to include the DDS of Uehara in the system of Haneda, because a synthesizer can generate a clock signal have any frequency based on a reference clock signal and an integration setting value (see Uehara, paragraph [0090]).
Regarding Claim 6, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), the compensation circuitry is configured to generate the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), and the frequency predicting neural network determines the frequency correction based on the temperature gradient across the resonator indicated by the temperature data (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Regarding Claim 7, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda further teaches wherein the compensation circuitry is configured to store temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24, paragraph [0099]; paragraphs [0147]-[0148], timings in the past), and the frequency predicting neural network determines the frequency correction based on the temperature readings at the plurality of predetermined past times (paragraph [0147], change in time of temperature detection data includes past temperature readings).
Regarding Claim 8, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 7. Haneda further teaches wherein times of the plurality of predetermined past times have an exponential relationship (paragraphs [0147]-[0148], timings in the past; it is noted that the claim merely requires that data corresponding to two times in the past data have an exponential relationship, and does not specify what that exponential relationship is; it is therefore noted that the exponential relationship (i.e., multiplication factor) that exists between any pair of times taught in Haneda reads on the claim language).
Regarding Claim 9, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), wherein the compensation circuitry is configured to: store the temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24; paragraphs [0147]-[0148], timings in the past); and generate the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), the frequency predicting neural network determines the frequency correction based on the temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Regarding Claim 17, Haneda teaches a method for implementing a clock circuit, the method comprising: receiving an uncompensated clock signal from a resonator (Figs. 4-8, resonator 10; paragraphs [0025]-[0026]); receiving temperature measurements from a temperature sensor assembly and generating temperature data based on the temperature measurements, the temperature sensor assembly being operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4 in the integrated circuit device 20 which includes a resonator 10; paragraphs [0025]-[0026], first and second temperature detection data generated based on first and second temperature detection voltages); applying, by a compensation circuitry (digital signal processing circuit 23), a frequency predicting neural network to the temperature data to determine a frequency correction (Fig. 22, S204; paragraphs [0146]-[0151], frequency control data); applying the uncompensated clock signal and the frequency correction to generate a stabilized clock output signal (paragraphs [0025]-[0026], temperature compensation process to correct temperature characteristics of the resonator); and outputting the stabilized clock output signal (paragraphs [0025]-[0026]), wherein the frequency predicting neural network is a multi-layer artificial neural network (Fig. 18) that receives the temperature data defined in multiple dimensions, the multiple dimensions include location and time (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4, each sensor determines temperature data over time and has an associated location in the integrated circuit device 20 that includes a resonator 10 that allows determination of temperature gradients across resonator device, see paragraph [0067]), and the frequency predicting neural network performs a weighted sum of the inputs to determine a resultant (paragraphs [0131]-[0136], weights between neurons, Equation 1), and the frequency predicting neural network applies the resultant to a non-linear activation function to determine a predicted frequency for the resonator for use in generating the frequency correction (paragraph [0137]).
Haneda does not specifically teach that the uncompensated clock signal and the frequency correction are applied to a synthesizer to generate a stabilized clock output signal. However, Uehara teaches use of a direct digital synthesizer 68 to generate a clock signal CK2 (equated to the stabilized clock output signal) having a frequency set by the frequency setting signal FSD (equated to the frequency correction) using the clock signal CK1 (equated to the uncompensated clock signal) as a reference clock signal (paragraph [0088]). It would have been obvious to one skilled in the art at the time of the invention to include the DDS of Uehara in the system of Haneda, because a synthesizer can generate a clock signal have any frequency based on a reference clock signal and an integration setting value (see Uehara, paragraph [0090]).
Haneda does not specifically teach that the input data is received as a vector of inputs. However, Siekmeier teaches, in paragraph [0073], that a neural model may sum a weighted vector of inputs to produce an output. It would have been obvious to one skilled in the art before the effective filing date of the invention to use a neural model including a vector of inputs such as is described in Siekmeier as the neural network of Haneda, because such a neural model is simple (see Siekmeier, paragraph [0073]).
Regarding Claim 18, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 17. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), and the method further comprises generating the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), and determining the frequency correction based on the temperature gradient across the resonator indicated by the temperature data by the frequency predicting neural network (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Regarding Claim 19, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 17. Haneda further teaches storing temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24, paragraph [0099]; paragraphs [0147]-[0148], timings in the past), and determining the frequency correction based on the temperature readings at the plurality of predetermined past times by the frequency predicting neural network (paragraph [0147], change in time of temperature detection data includes past temperature readings).
Regarding Claim 20, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 17. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), and the method further comprises: storing the temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24; paragraphs [0147]-[0148], timings in the past); generating the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), determining, by the frequency predicting neural network, the frequency correction based on the temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Claim(s) 10, 12, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haneda in view of Uehara, Siekmeier, and Kobayashi et al (U.S. Pub. No. 2016/0099716, hereinafter “Kobayashi”).
Regarding Claim 10, Haneda teaches a clock circuit comprising: a resonator configured to output an uncompensated clock signal (Figs. 4-8, resonator 10); a temperature sensor assembly operably coupled to the resonator, the temperature sensor assembly being configured to measure temperature and provide temperature measurements (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4 in the integrated circuit device 20 which includes a resonator 10); and compensation circuitry (digital signal processing circuit 23) configured to: receive the uncompensated clock signal (paragraphs [0025]-[0026]); receive the temperature measurements and generate temperature data based on the temperature measurements (paragraphs [0025]-[0026], first and second temperature detection data generated based on first and second temperature detection voltages); apply a frequency predicting neural network to the temperature data to determine a frequency correction (Fig. 22, S204; paragraphs [0146]-[0151], frequency control data); apply the uncompensated clock signal and the frequency correction to generate a stabilized clock output signal (paragraphs [0025]-[0026], temperature compensation process to correct temperature characteristics of the resonator); and output the stabilized clock output signal (paragraphs [0025]-[0026]), wherein the frequency predicting neural network is a multi-layer artificial neural network (Fig. 18) that receives the temperature data defined in multiple dimensions, the multiple dimensions include location and time (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4, each sensor determines temperature data over time and has an associated location in the integrated circuit device 20 that includes a resonator 10 that allows determination of temperature gradients across resonator device, see paragraph [0067]), and the frequency predicting neural network performs a weighted sum of the inputs to determine a resultant (paragraphs [0131]-[0136], weights between neurons, Equation 1), and the frequency predicting neural network applies the resultant to a non-linear activation function to determine a predicted frequency for the resonator for use in generating the frequency correction (paragraph [0137]).
Haneda does not specifically teach that the uncompensated clock signal and the frequency correction are applied to a synthesizer to generate a stabilized clock output signal. However, Uehara teaches use of a direct digital synthesizer 68 to generate a clock signal CK2 (equated to the stabilized clock output signal) having a frequency set by the frequency setting signal FSD (equated to the frequency correction) using the clock signal CK1 (equated to the uncompensated clock signal) as a reference clock signal (paragraph [0088]). It would have been obvious to one skilled in the art at the time of the invention to include the DDS of Uehara in the system of Haneda, because a synthesizer can generate a clock signal have any frequency based on a reference clock signal and an integration setting value (see Uehara, paragraph [0090]).
Haneda does not specifically teach that the input data is received as a vector of inputs. However, Siekmeier teaches, in paragraph [0073], that a neural model may sum a weighted vector of inputs to produce an output. It would have been obvious to one skilled in the art before the effective filing date of the invention to use a neural model including a vector of inputs such as is described in Siekmeier as the neural network of Haneda, because such a neural model is simple (see Siekmeier, paragraph [0073]).
Haneda does not specifically teach a communications device comprising: an antenna; a transmitter configured to output radio signals via the antenna in association with a stabilized clock output signal; a receiver configured to receive radio signals via the antenna in association with the stabilized clock output signal, and that the stabilized clock signal is output to the transmitter and the receiver. However, Kobayashi teaches this in Fig. 2 (communication device 20 includes the clock signal generation unit 21 that is shown in Fig. 3 in communication with wireless communication unit 23, see also paragraph [0138]). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the wireless communication unit of Kobayashi in the system of Raghavan, because the clock signal can be used to synchronize the communications that are performed by the wireless communication unit (see Kobayashi, paragraphs [0050]-[0051]).
Regarding Claim 12, Haneda in view of Uehara, Siekmeier, and Kobayashi teaches everything that is claimed above with respect to Claim 10. Haneda does not specifically teach wherein the synthesizer is a direct digital synthesizer configured to receive the uncompensated clock signal and the frequency correction and generate a stabilized clock output signal based on the uncompensated clock signal and the frequency correction. However, Uehara teaches use of a direct digital synthesizer 68 to generate a clock signal CK2 (equated to the stabilized clock signal) having a frequency set by the frequency setting signal FSD (equated to the frequency correction) using the clock signal CK1 (equated to the uncompensated clock signal) as a reference clock signal (paragraph [0088]). It would have been obvious to one skilled in the art at the time of the invention to include the DDS of Uehara in the system of Haneda, because a synthesizer can generate a clock signal have any frequency based on a reference clock signal and an integration setting value (see Uehara, paragraph [0090]).
Regarding Claim 14, Haneda in view of Uehara, Siekmeier, and Kobayashi teaches everything that is claimed above with respect to Claim 10. Haneda further teaches wherein the compensation circuitry is configured to store temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24, paragraph [0099]; paragraphs [0147]-[0148], timings in the past), and the frequency predicting neural network determines the frequency correction based on the past temperature readings at the plurality of predetermined past times (paragraph [0147], change in time of temperature detection data includes past temperature readings).
Regarding Claim 15, Haneda in view of Uehara, Siekmeier, and Kobayashi teaches everything that is claimed above with respect to Claim 14. Haneda further teaches wherein times of the plurality of predetermined past times have an exponential relationship (paragraphs [0147]-[0148], timings in the past; it is noted that the claim merely requires that data corresponding to two times in the past data have an exponential relationship, and does not specify what that exponential relationship is; it is therefore noted that the exponential relationship (i.e., multiplication factor) that exists between any pair of times taught in Haneda reads on the claim language).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghavan in view of Haneda in view of Uehara, Siekmeier, and Ashley et al (U.S. Pub. No. 2002/0005765, hereinafter “Ashley”).
Regarding Claim 5, Haneda in view of Uehara and Siekmeier teaches everything that is claimed above with respect to Claim 1. Haneda does not specifically teach further comprising a temperature control assembly configured to receive the temperature measurements and control a thermal device to stabilize a temperature of the resonator. However, Uehara teaches in paragraph [0028] that oscillator 4 may be a constant temperature oven controlled crystal oscillator that includes a constant temperature oven, which is equated to the claimed temperature control assembly. It would have been obvious to one skilled in the art before the effective filing date of the invention to include the constant temperature oven of Uehara in the system of Haneda, because such a temperature compensating apparatus is normally used to stabilize a crystal’s frequency output, as evidenced by Ashley, paragraph [0007].
Claim(s) 13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haneda in view of Uehara, Siekmeier, Kobayashi, and Motz (U.S. Pub. No. 2016/0241186).
Regarding Claim 13, Haneda in view of Uehara, Siekmeier, and Kobayashi teaches everything that is claimed above with respect to Claim 10. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), the compensation circuitry is configured to generate the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), and wherein the frequency predicting neural network determines the frequency correction based on the temperature gradient across the resonator indicated by the temperature data (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Haneda does not specifically teach the plurality of temperature sensors being positioned symmetrically relative to the resonator. However, Motz teaches in paragraph [0162] and Figs. 11a-b arranging temperature sensors 104-1 to 104-4 symmetrically around an oscillator. It would have been obvious to one skilled in the art before the effective filing date of the invention to arrange the plurality of temperature sensors of Raghavan and Uehara symmetrically, as is taught in Motz, in order to allow a temperature difference measurement to be conducted between symmetrically arranged temperature sensors for determining temperature gradients (see Motz, paragraph [0162]).
Regarding Claim 16, Haneda in view of Uehara, Siekmeier, and Kobayashi teaches everything that is claimed above with respect to Claim 10. Haneda further teaches wherein the temperature sensor assembly comprises a plurality of temperature sensors operably coupled to the resonator (Fig. 11, paragraphs [0105]-[0111], temperature sensors 26-1 to 26-4), wherein the compensation circuitry is configured to: store the temperature readings at a plurality of predetermined past times relative to a current time (storage unit 24; paragraphs [0147]-[0148], timings in the past); and generate the temperature data based on the temperature measurements such that the temperature data indicates a temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; paragraphs [0147]-[0148]; it is noted that the outputs of the different temperature sensors 26-1 to 26-4 would indicate temperature gradient across the resonator), and the frequency predicting neural network determines the frequency correction based on the temperature gradient across the resonator and the temperature readings at the plurality of predetermined past times relative to the current time (paragraph [0067], gradient; neural network, paragraphs [0146]-[0150]).
Haneda does not specifically teach the plurality of temperature sensors being positioned symmetrically relative to the resonator. However, Motz teaches in paragraph [0162] and Figs. 11a-b arranging temperature sensors 104-1 to 104-4 symmetrically around an oscillator. It would have been obvious to one skilled in the art before the effective filing date of the invention to arrange the plurality of temperature sensors of Haneda symmetrically, as is taught in Motz, in order to allow a temperature difference measurement to be conducted between symmetrically arranged temperature sensors for determining temperature gradients (see Motz, paragraph [0162]).
Response to Arguments
The rejections of Claims 7-9, 14-16, and 19-20 under 35 U.S.C. 112(b) are withdrawn based on the amendments filed on 1/5/2026.
Applicant’s arguments, filed 1/5/2026 and 10/24/2025, with respect to the newly amended claim features have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection, which were necessitate by Applicant’s amendments, are made in view of the Haneda reference, which was cited in the Prior Art of Record section in the Non-Final OA dated 4/25/2026.
In response to applicant's argument filed on 10/24/2025 that the examiner has combined an excessive number of references, reliance on a large number of references in a rejection does not, without more, weigh against the obviousness of the claimed invention. See In re Gorman, 933 F.2d 982, 18 USPQ2d 1885 (Fed. Cir. 1991).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA L DAVIS whose telephone number is (571)272-1599. The examiner can normally be reached Monday-Friday, 7am to 3pm.
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/CYNTHIA L DAVIS/Examiner, Art Unit 2863
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857